Author: "Bioinformatique évolutive - Evolutionary Bioinformatics" - Searchworks@Jio Institute Digital Library Search Results

Your search keyword '"Bioinformatique évolutive - Evolutionary Bioinformatics"' showing total 40 results

Start Over Author "Bioinformatique évolutive - Evolutionary Bioinformatics"

40 results on '"Bioinformatique évolutive - Evolutionary Bioinformatics"'

1. DEEP LEARNING FROM PHYLOGENIES TO UNCOVER THE EPIDEMIOLOGICAL DYNAMICS OF OUTBREAKS

Author: Voznica, J., Zhukova, A., Boskova, V., Saulnier, E., Lemoine, F., Moslonka-Lefebvre, M., Gascuel, O., Bioinformatique évolutive - Evolutionary Bioinformatics, Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS), Institut de biologie de l'ENS Paris (IBENS), Département de Biologie - ENS Paris, École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Hub Bioinformatique et Biostatistique - Bioinformatics and Biostatistics HUB, Institut Pasteur [Paris] (IP)-Université Paris Cité (UPCité), Epidémiologie et modélisation de la résistance aux antimicrobiens - Epidemiology and modelling of bacterial escape to antimicrobials (EMAE), Centre de recherche en épidémiologie et santé des populations (CESP), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpital Paul Brousse-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris-Saclay, University of Vienna [Vienna], Institut de Systématique, Evolution, Biodiversité (ISYEB ), Muséum national d'Histoire naturelle (MNHN)-Université Pierre et Marie Curie - Paris 6 (UPMC)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS), Muséum national d'Histoire naturelle (MNHN)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université des Antilles (UA), J.V. is supported by Ecole Normale Supérieure Paris-Saclay and by ED Frontières de l’Innovation en Recherche et Education, Programme Bettencourt. V.B. would like to thank Swiss National Science Foundation for funding (Early PostDoc mobility grant P2EZP3_184543). O.G. is supported by PRAIRIE (ANR-19-P3IA-0001)., ANR-19-P3IA-0001,PRAIRIE,PaRis Artificial Intelligence Research InstitutE(2019), Gascuel, Olivier, and PaRis Artificial Intelligence Research InstitutE - - PRAIRIE2019 - ANR-19-P3IA-0001 - P3IA - VALID
Subjects: tree simulation and representation, [SDV.BID.EVO]Life Sciences [q-bio]/Biodiversity/Populations and Evolution [q-bio.PE], [SDV.BID.EVO] Life Sciences [q-bio]/Biodiversity/Populations and Evolution [q-bio.PE], HIV, Deep learning, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], phylodynamics, molecular epidemiology, [INFO.INFO-BI] Computer Science [cs]/Bioinformatics [q-bio.QM]
Abstract: International audience; Widely applicable, accurate and fast inference methods in phylodynamics are needed to fully profit from the richness of genetic data in uncovering the dynamics of epidemics. Standard methods, including maximum-likelihood and Bayesian approaches, generally rely on complex mathematical formulae and approximations, and do not scale with dataset size. We develop a likelihood-free, simulation-based approach, which combines deep learning with (1) a large set of summary statistics measured on phylogenies or (2) a complete and compact representation of trees, which avoids potential limitations of summary statistics and applies to any phylodynamics model. Our method enables both model selection and estimation of epidemiological parameters from very large phylogenies. We demonstrate its speed and accuracy on simulated data, where it performs better than the state-of-the-art methods. To illustrate its applicability, we assess the dynamics induced by superspreading individuals in an HIV dataset of men-having-sex-with-men in Zurich. Our tool PhyloDeep is available on github.com/evolbioinfo/phylodeep.
Published: 2022

2. Deep learning from phylogenies to uncover the epidemiological dynamics of outbreaks

Author: Voznica, J., Zhukova, A., Boskova, V., Saulnier, E., Lemoine, F., Moslonka-Lefebvre, M., Gascuel, O., Bioinformatique évolutive - Evolutionary Bioinformatics, Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS), Institut de biologie de l'ENS Paris (IBENS), Département de Biologie - ENS Paris, École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Hub Bioinformatique et Biostatistique - Bioinformatics and Biostatistics HUB, Institut Pasteur [Paris] (IP)-Université Paris Cité (UPCité), Epidémiologie et modélisation de la résistance aux antimicrobiens - Epidemiology and modelling of bacterial escape to antimicrobials (EMAE), Centre de recherche en épidémiologie et santé des populations (CESP), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpital Paul Brousse-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris-Saclay, University of Vienna [Vienna], Institut de Systématique, Evolution, Biodiversité (ISYEB ), Muséum national d'Histoire naturelle (MNHN)-Université Pierre et Marie Curie - Paris 6 (UPMC)-École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS), Muséum national d'Histoire naturelle (MNHN)-École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université des Antilles (UA), J.V. is supported by Ecole Normale Supérieure Paris-Saclay and by ED Frontières de l’Innovation en Recherche et Education, Programme Bettencourt. V.B. would like to thank Swiss National Science Foundation for funding (Early PostDoc mobility grant P2EZP3_184543). O.G. is supported by PRAIRIE (ANR-19-P3IA-0001)., and ANR-19-P3IA-0001,PRAIRIE,PaRis Artificial Intelligence Research InstitutE(2019)
Subjects: Male, tree simulation and representation, Multidisciplinary, Statistical methods, [SDV.BID.EVO]Life Sciences [q-bio]/Biodiversity/Populations and Evolution [q-bio.PE], HIV, General Physics and Astronomy, Bayes Theorem, General Chemistry, phylodynamics, molecular epidemiology, General Biochemistry, Genetics and Molecular Biology, Disease Outbreaks, Deep Learning, Viral infection, Machine learning, Humans, Computer Simulation, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], Phylogeny
Abstract: Widely applicable, accurate and fast inference methods in phylodynamics are needed to fully profit from the richness of genetic data in uncovering the dynamics of epidemics. Standard methods, including maximum-likelihood and Bayesian approaches, generally rely on complex mathematical formulae and approximations, and do not scale with dataset size. We develop a likelihood-free, simulation-based approach, which combines deep learning with (1) a large set of summary statistics measured on phylogenies or (2) a complete and compact representation of trees, which avoids potential limitations of summary statistics and applies to any phylodynamics model. Our method enables both model selection and estimation of epidemiological parameters from very large phylogenies. We demonstrate its speed and accuracy on simulated data, where it performs better than the state-of-the-art methods. To illustrate its applicability, we assess the dynamics induced by superspreading individuals in an HIV dataset of men-having-sex-with-men in Zurich. Our tool PhyloDeep is available on github.com/evolbioinfo/phylodeep.
Published: 2022

3. Phylogénétique : quelles mesures de support pour les branches d’un arbre ?

Author: Gascuel, Olivier, Lemoine, Frédéric, Institut de Systématique, Evolution, Biodiversité (ISYEB ), Muséum national d'Histoire naturelle (MNHN)-École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université des Antilles (UA), Bioinformatique évolutive - Evolutionary Bioinformatics, Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS), O.G. is supported by PRAIRIE (ANR-19-P3IA-0001)., Gilles Didier, Stéphane Guindon, and ANR-19-P3IA-0001,PRAIRIE,PaRis Artificial Intelligence Research InstitutE(2019)
Subjects: bootstrap de transfert, bootstrap RELL, [SDV.BID.EVO]Life Sciences [q-bio]/Biodiversity/Populations and Evolution [q-bio.PE], [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], Statistique, supports de branches, supports aLRT, bootstrap phylogénétique de Felsenstein, Phylogénétique
Abstract: International audience; Une fois une phylogénie estimée, se pose la question du support de ses branches et des inférences associées : Décrivent-elles la réalité du passé biologique ? Sontelles fiables, reproductibles, robustes ? Ou à l'inverse peut-on conclure que le signal contenu dans les données ne permet pas de trancher entre plusieurs hypothèses alternatives ? De nombreuses approches ont été proposées pour répondre à ces questions. Nous décrivons dans ce chapitre les principales méthodes utilisées aujourd'hui en phylogénétique. Certaines sont locales et demandent peu de temps calcul. D'autres comme le bootstrap requièrent à l'inverse des calculs lourds mais donnent une vision plus globale. On distingue les méthodes paramétriques qui font explicitement appel à un modèle de l'évolution des séquences et se basent sur la fonction de vraisemblance ou la distribution a posteriori dans un cadre Bayésien, et les approches non-paramétriques qui reposent sur moins d'hypothèses explicites. On trouvera quatre parties dans ce chapitre : (1) supports locaux ; (2) bootstrap phylogénétique de Felsenstein et bootstrap de transfert ; (3) supports Bayésiens basés sur les probabilités a posteriori; (4) comparaison de ces approches et discussion.
Published: 2022

4. Genome-wide diversity of Zika virus

Author: Sofia G Seabra, Pieter J K Libin, Kristof Theys, Anna Zhukova, Barney I Potter, Hanna Nebenzahl-Guimaraes, Alexander E Gorbalenya, Igor A Sidorov, Victor Pimentel, Marta Pingarilho, Ana T R de Vasconcelos, Simon Dellicour, Ricardo Khouri, Olivier Gascuel, Anne-Mieke Vandamme, Guy Baele, Lize Cuypers, Ana B Abecasis, Universidade Nova de Lisboa = NOVA University Lisbon (NOVA), Vrije Universiteit Brussel (VUB), Rega Institute for Medical Research [Leuven, België], Catholic University of Leuven - Katholieke Universiteit Leuven (KU Leuven), Hasselt University (UHasselt), Bioinformatique évolutive - Evolutionary Bioinformatics, Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS), Hub Bioinformatique et Biostatistique - Bioinformatics and Biostatistics HUB, Institut Pasteur [Paris] (IP)-Université Paris Cité (UPCité), Leiden University Medical Center (LUMC), Universiteit Leiden, Lomonosov Moscow State University (MSU), Laboratorio Nacional de Computação Cientifica [Rio de Janeiro] (LNCC / MCT), Université libre de Bruxelles (ULB), Instituto Gonçalo Moniz / Gonçalo Moniz Research Centre - Fiocruz Bahia [Salvador, Brésil] (IGM), Fundação Oswaldo Cruz / Oswaldo Cruz Foundation (FIOCRUZ), Réseau International des Instituts Pasteur (RIIP)-Réseau International des Instituts Pasteur (RIIP), Institut de Systématique, Evolution, Biodiversité (ISYEB ), Muséum national d'Histoire naturelle (MNHN)-École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université des Antilles (UA), University Hospitals Leuven [Leuven], This research was supported in part by the European Union’s Horizon 2020 research and innovation program ZIKAlliance (Agreement No 734548) and by Fundação para a Ciência e Tecnologia (FCT) through funds GHTM-UID/04413/2020. S.G.S. was funded by FCT, Portugal, through contrato-programa 1567 (CEECINST/00102/2018). K.T. was supported by a Fonds Wetenschappelijk Onderzoek post-doctoral grant. P.L. was supported by a doctoral (1S31916N) and post-doctoral grant (#1242021N) provided by the Fonds Wetenschappelijk Onderzoek and was also supported by funding from the Flemish Government under the ‘Onderzoeksprogramma Artificiele Intelligentie (AI) Vlaanderen’ programme. P.L. also received funding from the research council of the Vrije Universiteit Brussel (OZR-VUB) via grant number OZR3863BOF. S.D. acknowledges funding from the Fonds de la Recherche Scientifique (FNRS, Belgium). S.D. and G.B. acknowledge support from the Research Foundation—Flanders (Fonds voor Wetenschappelijk Onderzoek—Vlaanderen, G098321N). B.P. and G.B. acknowledge support from the Internal Fondsen KU Leuven/Internal Funds KU Leuven (Grant No. C14/18/094). G.B. also acknowledges support from the Research Foundation—Flanders (‘Fonds voor Wetenschappelijk Onderzoek—Vlaanderen,’ G0E1420N)., European Project: 734548,ZIKAlliance(2016), and Informatics and Applied Informatics
Subjects: arbovirus, Zika virus, phylogeography, molecular epidemiology, Virology, [SDV.BID.EVO]Life Sciences [q-bio]/Biodiversity/Populations and Evolution [q-bio.PE], [SDV.MP.VIR]Life Sciences [q-bio]/Microbiology and Parasitology/Virology, evolutionary biology, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], Microbiology
Abstract: International audience; The Zika virus (ZIKV) disease caused a public health emergency of international concern that started in February 2016. The overall number of ZIKV-related cases increased until November 2016, after which it declined sharply. While the evaluation of the potential risk and impact of future arbovirus epidemics remains challenging, intensified surveillance efforts along with a scale-up of ZIKV whole-genome sequencing provide an opportunity to understand the patterns of genetic diversity, evolution, and spread of ZIKV. However, a classification system that reflects the true extent of ZIKV genetic variation is lacking. Our objective was to characterize ZIKV genetic diversity and phylodynamics, identify genomic footprints of differentiation patterns, and propose a dynamic classification system that reflects its divergence levels. We analysed a curated dataset of 762 publicly available sequences spanning the full-length coding region of ZIKV from across its geographical span and collected between 1947 and 2021. The definition of genetic groups was based on comprehensive evolutionary dynamics analyses, which included recombination and phylogenetic analyses, within- and between-group pairwise genetic distances comparison, detection of selective pressure, and clustering analyses. Evidence for potential recombination events was detected in a few sequences. However, we argue that these events are likely due to sequencing errors as proposed in previous studies. There was evidence of strong purifying selection, widespread across the genome, as also detected for other arboviruses. A total of 50 sites showed evidence of positive selection, and for a few of these sites, there was amino acid (AA) differentiation between genetic clusters. Two main genetic clusters were defined, ZA and ZB, which correspond to the already characterized ‘African’ and ‘Asian’ genotypes, respectively. Within ZB, two subgroups, ZB.1 and ZB.2, represent the Asiatic and the American (and Oceania) lineages, respectively. ZB.1 is further subdivided into ZB.1.0 (a basal Malaysia sequence sampled in the 1960s and a recent Indian sequence), ZB.1.1 (South-Eastern Asia, Southern Asia, and Micronesia sequences), and ZB.1.2 (very similar sequences from the outbreak in Singapore). ZB.2 is subdivided into ZB.2.0 (basal American sequences and the sequences from French Polynesia, the putative origin of South America introduction), ZB.2.1 (Central America), and ZB.2.2 (Caribbean and North America). This classification system does not use geographical references and is flexible to accommodate potential future lineages. It will be a helpful tool for studies that involve analyses of ZIKV genomic variation and its association with pathogenicity and serve as a starting point for the public health surveillance and response to on-going and future epidemics and to outbreaks that lead to the emergence of new variants.The Zika virus (ZIKV) disease caused a public health emergency of international concern that started in February 2016. The overall number of ZIKV-related cases increased until November 2016, after which it declined sharply. While the evaluation of the potential risk and impact of future arbovirus epidemics remains challenging, intensified surveillance efforts along with a scale-up of ZIKV whole-genome sequencing provide an opportunity to understand the patterns of genetic diversity, evolution, and spread of ZIKV. However, a classification system that reflects the true extent of ZIKV genetic variation is lacking. Our objective was to characterize ZIKV genetic diversity and phylodynamics, identify genomic footprints of differentiation patterns, and propose a dynamic classification system that reflects its divergence levels. We analysed a curated dataset of 762 publicly available sequences spanning the full-length coding region of ZIKV from across its geographical span and collected between 1947 and 2021. The definition of genetic groups was based on comprehensive evolutionary dynamics analyses, which included recombination and phylogenetic analyses, within- and between-group pairwise genetic distances comparison, detection of selective pressure, and clustering analyses. Evidence for potential recombination events was detected in a few sequences. However, we argue that these events are likely due to sequencing errors as proposed in previous studies. There was evidence of strong purifying selection, widespread across the genome, as also detected for other arboviruses. A total of 50 sites showed evidence of positive selection, and for a few of these sites, there was amino acid (AA) differentiation between genetic clusters. Two main genetic clusters were defined, ZA and ZB, which correspond to the already characterized ‘African’ and ‘Asian’ genotypes, respectively. Within ZB, two subgroups, ZB.1 and ZB.2, represent the Asiatic and the American (and Oceania) lineages, respectively. ZB.1 is further subdivided into ZB.1.0 (a basal Malaysia sequence sampled in the 1960s and a recent Indian sequence), ZB.1.1 (South-Eastern Asia, Southern Asia, and Micronesia sequences), and ZB.1.2 (very similar sequences from the outbreak in Singapore). ZB.2 is subdivided into ZB.2.0 (basal American sequences and the sequences from French Polynesia, the putative origin of South America introduction), ZB.2.1 (Central America), and ZB.2.2 (Caribbean and North America). This classification system does not use geographical references and is flexible to accommodate potential future lineages. It will be a helpful tool for studies that involve analyses of ZIKV genomic variation and its association with pathogenicity and serve as a starting point for the public health surveillance and response to on-going and future epidemics and to outbreaks that lead to the emergence of new variants.
Published: 2022

5. The seventh pandemic of cholera in Europe revisited by microbial genomics

Author: Radu Cojocaru, Anatoly N. Kravetz, Daniela Cristea, Jean Rauzier, Clifford G. Clark, Olivier Gascuel, Marie-Laure Quilici, François-Xavier Weill, Elisabeth Njamkepo, Adriana Simona Ciontea, Monakhova Ev, Maria Damian, Mihaela Oprea, Anna Zhukova, Cantacuzino National Medico-Military Institute for Research Development [Bucharest], Cantacuzino Institute [Romania], Réseau International des Instituts Pasteur (RIIP)-Réseau International des Instituts Pasteur (RIIP), Bactéries pathogènes entériques (BPE), Institut Pasteur [Paris] (IP), Bioinformatique évolutive - Evolutionary Bioinformatics, Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS), Hub Bioinformatique et Biostatistique - Bioinformatics and Biostatistics HUB, National Microbiology Laboratory [Winnipeg, Canada], Public Health Agency of Canada, Kiev Research Institute of Epidemiology and Infectious Diseases, Rostov-on-Don Research Institute for Plague Control, National Centre for Public Health [Chisinau, Republic of Moldova], This study was supported by the Institut Pasteur, Cantacuzino NMMIRD, Santé publiqueFrance, the Fondation Le Roch-Les Mousquetaires, the French government’s Investisse-ment d’Avenir programme, Laboratoire d’Excellence‘Integrative Biology of EmergingInfectious Diseases’(grant number ANR-10-LABX-62-IBEID), and'Institut Convergence for the study of Emergence of Pathology Through Individuals and Populations'(INCEPTION, grant number PIA/ANR-16-CONV-0005). D.C. was supported by a trai-neeship grant from the Calmette and Yersin Program of the Institut Pasteur InternationalNetwork. M.O. was supported by a mobility grant from the Romanian Ministry of Research and Innovation CNCS - UEFISCDI, project no. 495/2018, within PNCDI III.The Institut Pasteur Biomics platform is supported by France Génomique (ANR10-INBS-09-08) and IBISA. We thank I. Najjar and L. Lemée for sequencing the isolates., ANR-10-LABX-0062,IBEID,Integrative Biology of Emerging Infectious Diseases(2010), ANR-16-CONV-0005,INCEPTION,Institut Convergences pour l'étude de l'Emergence des Pathologies au Travers des Individus et des populatiONs(2016), ANR-10-INBS-0009,France-Génomique,Organisation et montée en puissance d'une Infrastructure Nationale de Génomique(2010), Institut Pasteur [Paris], and Institut Pasteur [Paris]-Centre National de la Recherche Scientifique (CNRS)
Subjects: 0301 basic medicine, Epidemiology, Black sea region, General Physics and Astronomy, MESH: Genome, Bacterial, Ribotyping, El Tor, West africa, Cholera, Pandemic, Socioeconomics, MESH: Phylogeny, lcsh:Science, Vibrio cholerae, Phylogeny, MESH: Evolution, Molecular, Multidisciplinary, Middle East, biology, MESH: Genomics, Genomics, 3. Good health, Europe, Geography, MESH: Human Migration, MESH: History, 20th Century, Pathogens, MESH: History, 21st Century, MESH: Pandemics, Science, Human Migration, 030106 microbiology, History, 21st Century, Article, General Biochemistry, Genetics and Molecular Biology, Evolution, Molecular, 03 medical and health sciences, Spatio-Temporal Analysis, MESH: Spatio-Temporal Analysis, MESH: Cholera, Drug Resistance, Bacterial, MESH: Drug Resistance, Bacterial, medicine, Humans, Clinical microbiology, Pandemics, MESH: Humans, Outbreak, MESH: Ribotyping, General Chemistry, History, 20th Century, medicine.disease, biology.organism_classification, Microbial genomics, Bacterial genes, 030104 developmental biology, lcsh:Q, MESH: Europe, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], Genome, Bacterial, MESH: Vibrio cholerae
Abstract: In 1970, the seventh pandemic of cholera (7 P) reached both Africa and Europe. Between 1970 and 2011, several European countries reported cholera outbreaks of a few to more than 2,000 cases. We report here a whole-genome analysis of 1,324 7 P V. cholerae El Tor (7 PET) isolates, including 172 from autochthonous sporadic or outbreak cholera cases occurring between 1970 and 2011 in Europe, providing insight into the spatial and temporal spread of this pathogen across Europe. In this work, we show that the 7 PET lineage was introduced at least eight times into two main regions: Eastern and Southern Europe. Greater recurrence of the disease was observed in Eastern Europe, where it persisted until 2011. It was introduced into this region from Southern Asia, often circulating regionally in the countries bordering the Black Sea, and in the Middle East before reaching Eastern Africa on several occasions. In Southern Europe, the disease was mostly seen in individual countries during the 1970s and was imported from North and West Africa, except in 1994, when cholera was imported into Albania and Italy from the Black Sea region. These results shed light on the geographic course of cholera during the seventh pandemic and highlight the role of humans in its global dissemination., Since 1970, several cholera outbreaks caused by the “seventh pandemic” (7PET) lineage have been reported in Europe. Here, the authors demonstrate that the outbreaks were caused by repeated introductions of 7PET into Europe, rather than local environmental sources.
Published: 2020

6. Deciphering RNA G-quadruplex function during the early steps of HIV-1 infection

Author: Samir Amrane, Chloé Jaubert, Amina Bedrat, Tiffany Rundstadler, Patricia Recordon-Pinson, Cindy Aknin, Aurore Guédin, Aurore De Rache, Laura Bartolucci, Ibra Diene, Frédéric Lemoine, Olivier Gascuel, Geneviève Pratviel, Jean-Louis Mergny, Marie-Line Andreola, Andreola, Marie-Line, PaRis Artificial Intelligence Research InstitutE - - PRAIRIE2019 - ANR-19-P3IA-0001 - P3IA - VALID, Acides Nucléiques : Régulations Naturelle et Artificielle (ARNA), Université de Bordeaux (UB)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS), Université de Bordeaux (UB), Ecole d'Ingénieurs de Purpan (INPT - EI Purpan), Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, Laboratoire de chimie de coordination (LCC), Institut de Chimie de Toulouse (ICT-FR 2599), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS), Microbiologie Fondamentale et Pathogénicité (MFP), Université Bordeaux Segalen - Bordeaux 2-Centre National de la Recherche Scientifique (CNRS), Université de Bordeaux Ségalen [Bordeaux 2], Bioinformatique évolutive - Evolutionary Bioinformatics, Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS), Hub Bioinformatique et Biostatistique - Bioinformatics and Biostatistics HUB, Institut Pasteur [Paris] (IP)-Université Paris Cité (UPCité), Institut de Systématique, Evolution, Biodiversité (ISYEB ), Muséum national d'Histoire naturelle (MNHN)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université des Antilles (UA), Laboratoire d'Optique et Biosciences (LOB), École polytechnique (X)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), ANR-19-P3IA-0001,PRAIRIE,PaRis Artificial Intelligence Research InstitutE(2019), Université de Toulouse (UT)-Université de Toulouse (UT), Institut de Chimie de Toulouse (ICT), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS), Muséum national d'Histoire naturelle (MNHN)-École Pratique des Hautes Études (EPHE), and Ecole d'Ingénieurs de Purpan (INP - PURPAN)
Subjects: G-Quadruplexes, [SDV.MP]Life Sciences [q-bio]/Microbiology and Parasitology, HIV-1, Genetics, Humans, RNA, Regulatory Sequences, Nucleic Acid, [SDV.MP] Life Sciences [q-bio]/Microbiology and Parasitology, Conserved Sequence
Abstract: G-quadruplexes (G4s) are four-stranded nucleic acid structures formed by the stacking of G-tetrads. Here we investigated their formation and function during HIV-1 infection. Using bioinformatics and biophysics analyses we first searched for evolutionary conserved G4-forming sequences in HIV-1 genome. We identified 10 G4s with conservation rates higher than those of HIV-1 regulatory sequences such as RRE and TAR. We then used porphyrin-based G4-binders to probe the formation of the G4s during infection of human cells by native HIV-1. The G4-binders efficiently inhibited HIV-1 infectivity, which is attributed to the formation of G4 structures during HIV-1 replication. Using a qRT-PCR approach, we showed that the formation of viral G4s occurs during the first 2 h post-infection and their stabilization by the G4-binders prevents initiation of reverse transcription. We also used a G4-RNA pull-down approach, based on a G4-specific biotinylated probe, to allow the direct detection and identification of viral G4-RNA in infected cells. Most of the detected G4-RNAs contain crucial regulatory elements such as the PPT and cPPT sequences as well as the U3 region. Hence, these G4s would function in the early stages of infection when the viral RNA genome is being processed for the reverse transcription step.
Published: 2022

7. Sensitive Detection of Site-wise Convergent Evolution in Large Protein Alignments with ConDor

Author: Morel, Marie, Lemoine, Frédéric, Gascuel, Olivier, Bioinformatique évolutive - Evolutionary Bioinformatics, Institut Pasteur [Paris]-Centre National de la Recherche Scientifique (CNRS), Université de Paris (UP), Hub Bioinformatique et Biostatistique - Bioinformatics and Biostatistics HUB, Institut de Systématique, Evolution, Biodiversité (ISYEB ), Muséum national d'Histoire naturelle (MNHN)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université des Antilles (UA), This work was supported by INCEPTION program (Convention ANR-16-CONV-0005, MM PhD grant) and by PRAIRIE program (Convention ANR-19-P3IA-0001, OG)., ANR-16-CONV-0005,INCEPTION,Institut Convergences pour l'étude de l'Emergence des Pathologies au Travers des Individus et des populatiONs(2016), ANR-19-P3IA-0001,PRAIRIE,PaRis Artificial Intelligence Research InstitutE(2019), Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS), Université Paris Cité (UPCité), Institut Pasteur [Paris] (IP)-Université Paris Cité (UPCité), and Muséum national d'Histoire naturelle (MNHN)-École Pratique des Hautes Études (EPHE)
Subjects: phylogenetics, convergence, rhodopsin, molecular evolution, [SDV.BID.EVO]Life Sciences [q-bio]/Biodiversity/Populations and Evolution [q-bio.PE], selection, HIV, sense organs, adaptation, [SDV.BID.SPT]Life Sciences [q-bio]/Biodiversity/Systematics, Phylogenetics and taxonomy, resistance to drugs
Abstract: Evolutionary convergences are observed at all levels, from phenotype to DNA and protein sequences, and the changes observed at these different levels tend to be strongly correlated. Here we propose a simulation-based method to detect positions under convergent evolution in large protein alignments, without prior knowledge on the phenotype and environmental constraints. A phylogeny is inferred from the data and used in simulations to estimate the expected number of amino-acid changes in stable evolutionary constraints (null model) for each position. Similarly, we count the number of mutations towards the same amino acid in the data and test if they are occurring more often than expected. We applied our method to two real datasets: HIV reverse transcriptase and fish rhodopsin, and to HIVlike simulated data. On the latter, with known convergent events and substitution model, we detected on average two third of these events, with a low fraction of false positives. With HIV data, one knows that drug resistance mutations (DRMs) are convergent. Even without any knowledge of patient treatment status, we retrieved more than 70% of positions corresponding to known DRMs. On the rhodopsin dataset, four substitutions are supposed to be convergent, as they change the maximum wavelength absorption of the photoreceptor and occurred several times independently during evolution. We detected three of them. These results demonstrate the potential of the method to target specific mutations to be further studied experimentally or, for example, using a nonsynonymous/synonymous rate ratio approach. Our software named ConDor is available at http://condor.pasteur.cloud.
Published: 2021

8. Measuring Phylogenetic Information of Incomplete Sequence Data

Author: Tae-Kun Seo, Jeffrey L. Thorne, Olivier Gascuel, Korea Polar Research Institute (KOPRI), Bioinformatique évolutive - Evolutionary Bioinformatics, Institut Pasteur [Paris]-Centre National de la Recherche Scientifique (CNRS), Institut de Systématique, Evolution, Biodiversité (ISYEB ), Muséum national d'Histoire naturelle (MNHN)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université des Antilles (UA), North Carolina State University [Raleigh] (NC State), University of North Carolina System (UNC), This work was supported by the Korea Polar Research Institute [PE21130 and PE21140 to T.-K.S.], PRAIRIE [ANR-19-P3IA-0001 to O.G.], N.S.F. [DEB-1754142] and N.I.H. [R01 GM118508] to J.L.T., ANR-19-P3IA-0001,PRAIRIE,PaRis Artificial Intelligence Research InstitutE(2019), Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS), and Muséum national d'Histoire naturelle (MNHN)-École Pratique des Hautes Études (EPHE)
Subjects: 0106 biological sciences, [SDV]Life Sciences [q-bio], Sequence alignment, Biology, 010603 evolutionary biology, 01 natural sciences, insertion, Set (abstract data type), Evolution, Molecular, 03 medical and health sciences, symbols.namesake, Tree (descriptive set theory), INDEL Mutation, Genetics, deletion, [MATH]Mathematics [math], Fisher information, Ecology, Evolution, Behavior and Systematics, Phylogeny, 030304 developmental biology, 0303 health sciences, Sequence, Models, Statistical, Phylogenetic tree, Models, Genetic, business.industry, gaps, Probabilistic logic, Pattern recognition, Data set, model adequacy, indel, sequence alignment, symbols, goodness-of-fit test, Artificial intelligence, business, Regular Articles
Abstract: Widely used approaches for extracting phylogenetic information from aligned sets of molecular sequences rely upon probabilistic models of nucleotide substitution or amino-acid replacement. The phylogenetic information that can be extracted depends on the number of columns in the sequence alignment and will be decreased when the alignment contains gaps due to insertion or deletion events. Motivated by the measurement of information loss, we suggest assessment of the effective sequence length (ESL) of an aligned data set. The ESL can differ from the actual number of columns in a sequence alignment because of the presence of alignment gaps. Furthermore, the estimation of phylogenetic information is affected by model misspecification. Inevitably, the actual process of molecular evolution differs from the probabilistic models employed to describe this process. This disparity means the amount of phylogenetic information in an actual sequence alignment will differ from the amount in a simulated data set of equal size, which motivated us to develop a new test for model adequacy. Via theory and empirical data analysis, we show how to disentangle the effects of gaps and model misspecification. By comparing the Fisher information of actual and simulated sequences, we identify which alignment sites and tree branches are most affected by gaps and model misspecification. [Fisher information; gaps; insertion; deletion; indel; model adequacy; goodness-of-fit test; sequence alignment.]
Published: 2021

9. Drug resistance mutations in HIV: new bioinformatics approaches and challenges

Author: Katherine E. Atkins, Christian Julian Villabona-Arenas, Olivier Gascuel, Stéphane Hué, Anna Zhukova, Luc Blassel, Bioinformatique évolutive - Evolutionary Bioinformatics, Institut Pasteur [Paris]-Centre National de la Recherche Scientifique (CNRS), Collège doctoral [Sorbonne universités], Sorbonne Université (SU), Hub Bioinformatique et Biostatistique - Bioinformatics and Biostatistics HUB, London School of Hygiene and Tropical Medicine (LSHTM), University of Edinburgh, Institut de Systématique, Evolution, Biodiversité (ISYEB ), Muséum national d'Histoire naturelle (MNHN)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université des Antilles (UA), This work was supported by: Horizon 2020 framework program (Award number 634650, VIROGENESIS project, OG recipient, LB master salary), Agence Nationale de la Recherche (Award number ANR-19-P3IA-0001, PIA3 programme, PRAIRIE project, OG recipient, LB PhD salary), European Research Council (ERC) Starting Grant (Number 757688, awarded to Katherine E. Atkins, KEA and CJVA)., ANR-19-P3IA-0001,PRAIRIE,PaRis Artificial Intelligence Research InstitutE(2019), European Project: 634650,H2020,H2020-PHC-2014-two-stage,VIROGENESIS(2015), Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS), Collège Doctoral, Institut Pasteur [Paris] (IP)-Université Paris Cité (UPCité), and Muséum national d'Histoire naturelle (MNHN)-École Pratique des Hautes Études (EPHE)
Subjects: Human immunodeficiency virus (HIV), HIV Infections, Drug resistance, [SDV.BID]Life Sciences [q-bio]/Biodiversity, Biology, Bioinformatics, medicine.disease_cause, Deep sequencing, 03 medical and health sciences, 0302 clinical medicine, Data sequences, Virology, Drug Resistance, Viral, medicine, Humans, 030212 general & internal medicine, Hiv transmission, Phylogeny, 030304 developmental biology, 0303 health sciences, Transmission (medicine), Treatment options, Computational Biology, HIV, Virological failure, 3. Good health, Mutation
Abstract: International audience; Drug resistance mutations appear in HIV under treatment pressure. Resistant variants can be transmitted to treatmentnaive individuals, which can lead to rapid virological failure and can limit treatment options. Consequently, quantifying the prevalence, emergence and transmission of drug resistance is critical to effectively treating patients and to shape health policies. We review recent bioinformatics developments and in particular describe: (1) the machine learning approaches intended to predict and explain the level of resistance of HIV variants from their sequence data; (2) the phylogenetic methods used to survey the emergence and dynamics of resistant HIV transmission clusters; (3) the impact of deep sequencing in studying within-host and between-host genetic diversity of HIV variants, notably regarding minority resistant variants.
Published: 2021

10. Gotree/Goalign: toolkit and Go API to facilitate the development of phylogenetic workflows

Author: Olivier Gascuel, Frédéric Lemoine, Bioinformatique évolutive - Evolutionary Bioinformatics, Institut Pasteur [Paris]-Centre National de la Recherche Scientifique (CNRS), Hub Bioinformatique et Biostatistique - Bioinformatics and Biostatistics HUB, Institut de Systématique, Evolution, Biodiversité (ISYEB ), Muséum national d'Histoire naturelle (MNHN)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université des Antilles (UA), PRAIRIE [ANR-19-P3IA-0001 to O.G.]. Funding for open access charge: Institute Pasteur., First, we thank the Gotree/Goalign users for their feedbacks, which allow us to add new functionnalities and improve existing ones. We also thank all the Evolutionary Bioinformatics lab: Anna Zhukova, Marie Morel, Luc Blassel and Jakub Voznica for testing and using Gotree/Goalign extensively., ANR-19-P3IA-0001,PRAIRIE,PaRis Artificial Intelligence Research InstitutE(2019), Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS), Institut Pasteur [Paris] (IP)-Université Paris Cité (UPCité), and Muséum national d'Histoire naturelle (MNHN)-École Pratique des Hautes Études (EPHE)
Subjects: 0106 biological sciences, AcademicSubjects/SCI01140, AcademicSubjects/SCI01060, Computer science, Application Notes, AcademicSubjects/SCI00030, Reuse, computer.software_genre, AcademicSubjects/SCI01180, 010603 evolutionary biology, 01 natural sciences, 03 medical and health sciences, 030304 developmental biology, 0303 health sciences, Multiple sequence alignment, Phylogenetic tree, Programming language, computer.file_format, Tree (data structure), Task (computing), Workflow, Scripting language, Executable, AcademicSubjects/SCI00980, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], Corrigendum, computer
Abstract: International audience; Phylogenetics is nowadays at the center of numerous studies in many fields, ranging from comparative genomics to molecular epidemiology. However, phylogenetic analysis workflows are usually complex and difficult to implement, as they are often composed of many small, reccuring, but important data manipulations steps. Among these, we can find file reformatting, sequence renaming, tree re-rooting, tree comparison, bootstrap support computation, etc. These are often performed by custom scripts or by several heterogeneous tools, which may be error prone, uneasy to maintain and produce results that are challenging to reproduce. For all these reasons, the development and reuse of phylogenetic workflows is often a complex task. We identified many operations that are part of most phylogenetic analyses, and implemented them in a toolkit called Gotree/Goalign. The Gotree/Goalign toolkit implements more than 120 user-friendly commands and an API dedicated to multiple sequence alignment and phylogenetic tree manipulations. It is developed in Go, which makes executables easily installable, integrable in workflow environments, and parallelizable when possible. Moreover, Go is a compiled language, which accelerates computations compared to interpreted languages. This toolkit is freely available on most platforms (Linux, MacOS and Windows) and most architectures (amd64, i386) on GitHub at https://github.com/evolbioinfo/gotree, Bioconda and DockerHub.
Published: 2021

11. Using machine learning and big data to explore the drug resistance landscape in HIV

Author: Blassel, Luc, Tostevin, Anna, Villabona-Arenas, Christian, Peeters, Martine, Hué, Stéphane, Gascuel, Olivier, Bioinformatique évolutive - Evolutionary Bioinformatics, Institut Pasteur [Paris]-Centre National de la Recherche Scientifique (CNRS), University College of London [London] (UCL), London School of Hygiene and Tropical Medicine (LSHTM), Recherches Translationnelles sur le VIH et les maladies infectieuses endémiques er émergentes (TransVIHMI), Université Cheikh Anta Diop [Dakar, Sénégal] (UCAD)-Institut de Recherche pour le Développement (IRD)-Université de Yaoundé I-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Montpellier (UM)-Université Montpellier 1 (UM1), Institut de Systématique, Evolution, Biodiversité (ISYEB ), Muséum national d'Histoire naturelle (MNHN)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université des Antilles (UA), This work was supported by: Horizon 2020 framework programme (Award number 634650, VIROGENESIS project, OG recipient, LB master salary), Agence Nationale de la Recherche (Award number ANR-19-P3IA-0001, PIA3 programme, PRAIRIE project, OG recipient, LB PhD salary), UK Medical Research Council (Award number 164587, AT recipient), European Research Council (ERC) Starting Grant (Award number 757688, awarded to Katherine E. Atkins, CJVA)., ANR-19-P3IA-0001,PRAIRIE,PaRis Artificial Intelligence Research InstitutE(2019), European Project: 634650,H2020,H2020-PHC-2014-two-stage,VIROGENESIS(2015), Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS), Recherches Translationnelles sur le VIH et les maladies infectieuses endémiques et émergentes (TransVIHMI), Institut de Recherche pour le Développement (IRD)-Université de Yaoundé I-Université Cheikh Anta Diop [Dakar, Sénégal] (UCAD)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Montpellier (UM), Muséum national d'Histoire naturelle (MNHN)-École Pratique des Hautes Études (EPHE), HAL-SU, Gestionnaire, PaRis Artificial Intelligence Research InstitutE - - PRAIRIE2019 - ANR-19-P3IA-0001 - P3IA - VALID, and Virus discovery and epidemic tracing from high throughput metagenomic sequencing - VIROGENESIS - - H20202015-06-01 - 2018-05-31 - 634650 - VALID
Subjects: Big Data, RNA viruses, Heredity, Genes, Viral, HIV Infections, Pathology and Laboratory Medicine, Immunodeficiency Viruses, [SDV.MHEP.MI]Life Sciences [q-bio]/Human health and pathology/Infectious diseases, Databases, Genetic, Medicine and Health Sciences, Public and Occupational Health, Drug Interactions, [SDV.MP.VIR] Life Sciences [q-bio]/Microbiology and Parasitology/Virology, [SDV.BIBS] Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], Pharmaceutics, Antimicrobials, Drugs, Antiretrovirals, Antivirals, Vaccination and Immunization, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], HIV Reverse Transcriptase, Antiretroviral therapy, Medical Microbiology, Viral Pathogens, [SDV.SP.PHARMA] Life Sciences [q-bio]/Pharmaceutical sciences/Pharmacology, Viruses, [SDV.MP.VIR]Life Sciences [q-bio]/Microbiology and Parasitology/Virology, [SDV.MHEP.MI] Life Sciences [q-bio]/Human health and pathology/Infectious diseases, Supervised Machine Learning, Drug therapy, Pathogens, Research Article, Computer and Information Sciences, Anti-HIV Agents, Immunology, Microbiology, Antiviral Therapy, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], Artificial Intelligence, Microbial Control, Virology, Drug Resistance, Viral, Retroviruses, Machine learning, Genetics, Humans, Protein sequencing, Microbial Pathogens, Pharmacology, Models, Genetic, Decision Trees, Lentivirus, Organisms, Computational Biology, Biology and Life Sciences, HIV, Bayes Theorem, Epistasis, Genetic, [INFO.INFO-LG] Computer Science [cs]/Machine Learning [cs.LG], United Kingdom, Logistic Models, Africa, Mutation, Epistasis, HIV-1, [SDV.SP.PHARMA]Life Sciences [q-bio]/Pharmaceutical sciences/Pharmacology, Preventive Medicine
Abstract: Drug resistance mutations (DRMs) appear in HIV under treatment pressure. DRMs are commonly transmitted to naive patients. The standard approach to reveal new DRMs is to test for significant frequency differences of mutations between treated and naive patients. However, we then consider each mutation individually and cannot hope to study interactions between several mutations. Here, we aim to leverage the ever-growing quantity of high-quality sequence data and machine learning methods to study such interactions (i.e. epistasis), as well as try to find new DRMs. We trained classifiers to discriminate between Reverse Transcriptase Inhibitor (RTI)-experienced and RTI-naive samples on a large HIV-1 reverse transcriptase (RT) sequence dataset from the UK (n ≈ 55, 000), using all observed mutations as binary representation features. To assess the robustness of our findings, our classifiers were evaluated on independent data sets, both from the UK and Africa. Important representation features for each classifier were then extracted as potential DRMs. To find novel DRMs, we repeated this process by removing either features or samples associated to known DRMs. When keeping all known resistance signal, we detected sufficiently prevalent known DRMs, thus validating the approach. When removing features corresponding to known DRMs, our classifiers retained some prediction accuracy, and six new mutations significantly associated with resistance were identified. These six mutations have a low genetic barrier, are correlated to known DRMs, and are spatially close to either the RT active site or the regulatory binding pocket. When removing both known DRM features and sequences containing at least one known DRM, our classifiers lose all prediction accuracy. These results likely indicate that all mutations directly conferring resistance have been found, and that our newly discovered DRMs are accessory or compensatory mutations. Moreover, apart from the accessory nature of the relationships we found, we did not find any significant signal of further, more subtle epistasis combining several mutations which individually do not seem to confer any resistance., Author summary Almost all drugs to treat HIV target the Reverse Transcriptase (RT) and Drug resistance mutations (DRMs) appear in HIV under treatment pressure. Resistant strains can be transmitted and limit treatment options at the population level. Classically, multiple statistical testing is used to find DRMs, by comparing virus sequences of treated and naive populations. However, with this method, each mutation is considered individually and we cannot hope to reveal any interaction (epistasis) between them. Here, we used machine learning to discover new DRMs and study potential epistasis effects. We applied this approach to a very large UK dataset comprising ≈ 55, 000 RT sequences. Results robustness was checked on different UK and African datasets. Six new mutations associated to resistance were found. All six have a low genetic barrier and show high correlations with known DRMs. Moreover, all these mutations are close to either the active site or the regulatory binding pocket of RT. Thus, they are good candidates for further wet experiments to establish their role in drug resistance. Importantly, our results indicate that epistasis seems to be limited to the classical scheme where primary DRMs confer resistance and associated mutations modulate the strength of the resistance and/or compensate for the fitness cost induced by DRMs.
Published: 2021

12. Origin, evolution and global spread of SARS-CoV-2

Author: Zhukova, Anna, Blassel, Luc, Lemoine, Frédéric, Morel, Marie, Voznica, Jakub, Gascuel, Olivier, Biologie Computationnelle (ex C3BI), Institut Pasteur [Paris]-Centre National de la Recherche Scientifique (CNRS), Bioinformatique évolutive - Evolutionary Bioinformatics, Hub Bioinformatique et Biostatistique - Bioinformatics and Biostatistics HUB, Ecole Doctorale Complexité du Vivant (ED515), Sorbonne Université (SU), Université de Paris (UP), Centre National de la Recherche Scientifique (CNRS), Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS), and Université Paris Cité (UPCité)
Subjects: Foyers épidémiques, [SDV.GEN]Life Sciences [q-bio]/Genetics, COVID19, Evolution, SARS-CoV-2, [SDV]Life Sciences [q-bio], [SDV.BID.EVO]Life Sciences [q-bio]/Biodiversity/Populations and Evolution [q-bio.PE], viruses, Origine, Évolution, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], Clusters, Origin, Transmission, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM]
Abstract: International audience; SARS-CoV-2 is the virus responsible for the global COVID19 pandemic. We review what is known about the origin of this virus, detected in China at the end of December 2019. The genome of this virus mainly evolves under the effect of point mutations. These are generally neutral and have no impact on virulence and severity, but some appear to influence infectivity, notably the D614G mutation of the Spike protein. To date (30/09/2020) no recombination of the virus has been documented in the human host, and very few insertions and deletions. The worldwide spread of the virus was the subject of controversies that we summarize, before proposing a new approach free from the limitations of previous methods. The results show a complex scenario with, for example, numerous introductions to the USA and returns of the virus from the USA to certain countries including France.; Le SARS-CoV-2 est le virus responsable de la pandémie mondiale de COVID19. On dresse ici un bilan de ce qui est connu sur l’origine de ce virus, détecté en Chine fin décembre 2019. Le génome de ce virus évolue sous l’effet de mutations ponctuelles. Celles-ci sont généralement neutres et sans impact sur la virulence et la sévérité, mais certaines semblent influer sur l’infectiosité, notamment la mutation D614G de la protéine Spike. A l’inverse, on n’a à ce jour (30/09/2020) documenté aucune recombinaison du virus chez l’hôte humain, et très peu d’insertions et de délétions. La propagation mondiale du virus a fait l’objet de polémiques sur lesquelles nous revenons, avant de proposer une nouvelle approche débarrassée des limites des méthodes précédentes. Les résultats montrent une propagation complexe avec, par exemple, de très nombreuses introductions aux USA et des retours du virus depuis les USA vers certains pays dont la France.
Published: 2020

13. COVID-Align: Accurate online alignment of hCoV-19 genomes using a profile HMM

Author: Olivier Gascuel, Luc Blassel, Frédéric Lemoine, Jakub Voznica, Bioinformatique évolutive - Evolutionary Bioinformatics, Institut Pasteur [Paris]-Centre National de la Recherche Scientifique (CNRS), Hub Bioinformatique et Biostatistique - Bioinformatics and Biostatistics HUB, Ecole Doctorale Complexité du Vivant (ED515), Sorbonne Université (SU), Université de Paris (UP), LB PhD Grant: PRAIRIE (ANR-19-P3IA-0001), JV PhD Grant: École Normale Supérieure Paris-Saclay and ED Frontières de l'Innovation en Recherche et Education., Sincere thanks to Amandine Perrin and Fabien Mareuil (Institut Pasteur) for help, and the GISAID Team and all its Data Contributors for sharing their genome data., ANR-19-P3IA-0001,PRAIRIE,PaRis Artificial Intelligence Research InstitutE(2019), Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS), and Université Paris Cité (UPCité)
Subjects: 0106 biological sciences, Statistics and Probability, AcademicSubjects/SCI01060, Computer science, Interface (Java), Computational biology, 01 natural sciences, Biochemistry, Genome, 03 medical and health sciences, Software, Humans, 1000 Genomes Project, Hidden Markov model, Indel, Pandemics, Molecular Biology, 030304 developmental biology, 0303 health sciences, Multiple sequence alignment, SARS-CoV-2, business.industry, [SDV.BID.EVO]Life Sciences [q-bio]/Biodiversity/Populations and Evolution [q-bio.PE], COVID-19, Computer Science Applications, Applications Note, Computational Mathematics, Computational Theory and Mathematics, Filter (video), [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], business, 010606 plant biology & botany
Abstract: Motivation The first cases of the COVID-19 pandemic emerged in December 2019. Until the end of February 2020, the number of available genomes was below 1000 and their multiple alignment was easily achieved using standard approaches. Subsequently, the availability of genomes has grown dramatically. Moreover, some genomes are of low quality with sequencing/assembly errors, making accurate re-alignment of all genomes nearly impossible on a daily basis. A more efficient, yet accurate approach was clearly required to pursue all subsequent bioinformatics analyses of this crucial data. Results hCoV-19 genomes are highly conserved, with very few indels and no recombination. This makes the profile HMM approach particularly well suited to align new genomes, add them to an existing alignment and filter problematic ones. Using a core of ∼2500 high quality genomes, we estimated a profile using HMMER, and implemented this profile in COVID-Align, a user-friendly interface to be used online or as standalone via Docker. The alignment of 1000 genomes requires ∼50 minutes on our cluster. Moreover, COVID-Align provides summary statistics, which can be used to determine the sequencing quality and evolutionary novelty of input genomes (e.g. number of new mutations and indels). Availability and implementation https://covalign.pasteur.cloud, hub.docker.com/r/evolbioinfo/covid-align. Supplementary information Supplementary data are available at Bioinformatics online.
Published: 2020

14. Ratiometric quorum sensing governs the trade-off between bacterial vertical and horizontal antibiotic resistance propagation

Author: Alvaro, Banderas, Arthur, Carcano, Elisa, Sia, Shuang, Li, Ariel B, Lindner, Evolution et ingénierie de systèmes dynamiques (SEED (UMR-S 1284/U 1284)), Centre de Recherche Interdisciplinaire / Center for Research and Interdisciplinarity [Paris, France] (CRI), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Paris (UP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Paris (UP), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Paris (UP), Bioinformatique évolutive - Evolutionary Bioinformatics, Institut Pasteur [Paris]-Centre National de la Recherche Scientifique (CNRS), Méthodes Expérimentales et Computationnelles pour la Modélisation des Processus Cellulaires / Experimental and Computational Methods for Modeling Cellular Processes (InBio ), Centre National de la Recherche Scientifique (CNRS)-Institut Pasteur [Paris]-Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), LabEx -Who am I?- Postdoctoral fellowship to AB (ANR-11-LABX-0071 SEXASYM) (https://www.enseignementsup-recherche.gouv.fr/cid51355/laboratoiresd-excellence.html) Permanent support. Fondation Bettencourt Schueller (https://www.fondationbs.org/) to ABL., ANR-11-LABX-0071,WHO AM I,Determinants de l'Identité : de la molécule à l'individu(2011), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Cité (UPCité), Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS), InBio - Méthodes Expérimentales et Computationnelles pour la Modélisation des Processus Cellulaires / Experimental and Computational Methods for Modeling Cellular Processes, Institut Pasteur [Paris] (IP)-Inria de Paris, Bodescot, Myriam, and Determinants de l'Identité : de la molécule à l'individu - - WHO AM I2011 - ANR-11-LABX-0071 - LABX - VALID
Subjects: Gene Transfer, Gene Expression, Social Sciences, Pathology and Laboratory Medicine, Biochemistry, Pheromones, Short Reports, Mobile Genetic Elements, Enterococcus faecalis, Medicine and Health Sciences, Psychology, Biology (General), Horizontal Gene Transfer, Virulence, [SDV.BID.EVO]Life Sciences [q-bio]/Biodiversity/Populations and Evolution [q-bio.PE], Quorum Sensing, Drug Resistance, Microbial, Genomics, Anti-Bacterial Agents, Bacterial Pathogens, Nucleic acids, Medical Microbiology, Conjugation, Genetic, Sensory Perception, Pathogens, Plasmids, Evolutionary Processes, Gene Transfer, Horizontal, Forms of DNA, QH301-705.5, Green Fluorescent Protein, Microbiology, Genetic Elements, Bacterial Proteins, Population Metrics, Microbial Control, Genetics, [SDV.BID.EVO] Life Sciences [q-bio]/Biodiversity/Populations and Evolution [q-bio.PE], Microbial Pathogens, Population Density, Pharmacology, Evolutionary Biology, Models, Statistical, Bacteria, Population Biology, Organisms, Cognitive Psychology, Biology and Life Sciences, Proteins, DNA, [SDV.MP.BAC]Life Sciences [q-bio]/Microbiology and Parasitology/Bacteriology, Bacterial Load, Luminescent Proteins, Antibiotic Resistance, Cognitive Science, Perception, Genetic Fitness, Antimicrobial Resistance, [SDV.MP.BAC] Life Sciences [q-bio]/Microbiology and Parasitology/Bacteriology, Enterococcus, Neuroscience
Abstract: Plasmid-mediated horizontal gene transfer of antibiotic resistance and virulence in pathogenic bacteria underlies a major public health issue. Understanding how, in the absence of antibiotic-mediated selection, plasmid-bearing cells avoid being outnumbered by plasmid-free cells is key to developing counterstrategies. Here, we quantified the induction of the plasmidial sex pheromone pathway of Enterococcus faecalis to show that the integration of the stimulatory (mate-sensing) and inhibitory (self-sensing) signaling modules from the pCF10 conjugative plasmid provides a precise measure of the recipient-to-donor ratio, agnostic to variations in population size. Such ratiometric control of conjugation favors vertical plasmid transfer under low mating likelihood and allows activation of conjugation functions only under high mating likelihood. We further show that this strategy constitutes a cost-effective investment into mating effort because overstimulation produces unproductive self-aggregation and growth rate reduction. A mathematical model suggests that ratiometric control of conjugation increases plasmid fitness and predicts a robust long-term, stable coexistence of donors and recipients. Our results demonstrate how population-level parameters can control transfer of antibiotic resistance in bacteria, opening the door for biotic control strategies., The pathogenic bacterium Enterococcus faecalis controls the horizontal gene transfer of antibiotic resistance according to the ratio of non-resistant to resistant bacteria, while remaining agnostic to variation in the total culture density. This ratiometric twist on the well-known phenomenon of quorum-sensing allows investment in expensive conjugative transfer of plasmids only when chances are high, akin to sexual systems in more complex organisms.
Published: 2020

15. SBML Level 3: an extensible format for the exchange and reuse of biological models

Author: Edda Klipp, Marco Antoniotti, Frank Bergmann, James C. Schaff, Peter D. Karp, Daniel Lucio, Kedar Nath Natarajan, Thomas M. Hamm, Leandro Watanabe, Henning Hermjakob, David Tolnay, John Wagner, Joerg Stelling, Alida Palmisano, Falk Schreiber, Yukiko Matsuoka, Harold F. Gómez, Huaiyu Mi, Carole J. Proctor, Ulrike Wittig, Neil Swainston, Jan Červený, Denis Thieffry, Piero Dalle Pezze, Julio Saez-Rodriguez, Maciej J. Swat, Bin Hu, Martina Kutmon, Thomas Pfau, Bas Teusink, Sarah M. Keating, Fedor A. Kolpakov, Andreas Dräger, Pedro Mendes, Martin Scharm, Emek Demir, Ioannis Xenarios, Christoph Flamm, Axel von Kamp, Darren J. Wilkinson, Nick Juty, Fengkai Zhang, Leonard A. Harris, Michael Schubert, Dagmar Waltemath, Lucian P. Smith, Steffen Klamt, Herbert M. Sauro, Ali Ebrahim, Wolfram Liebermeister, Christian Knüpfer, Nicolas Rodriguez, Tramy Nguyen, Naoki Tanimura, Christopher Cox, Stuart C. Sealfon, Nicholas Alexander Allen, Clemens Wrzodek, Bastian R. Angermann, Martin Meier-Schellersheim, Anna Zhukova, Jean-Baptiste Pettit, Hovakim Grabski, Devin P. Sullivan, Claudine Chaouiya, Michael L. Blinov, John Doyle, Ilya Kiselev, Roman Schulte, Alex Gutteridge, Mélanie Courtot, Eric Mjolsness, Finja Wrzodek, Rahuman S Malik-Sheriff, Ronan M. T. Fleming, Bruce E. Shapiro, Kimberly Begley, Leslie M. Loew, Colin S. Gillespie, Ibrahim Vazirabad, Michael Hucka, Akira Funahashi, Bernhard O. Palsson, Hamid Bolouri, Tomáš Helikar, Camille Laibe, William S. Denney, Chris T. Evelo, Florian Mittag, William S. Hlavacek, Ron Henkel, Harish Dharuri, Julien Dorier, Karthik Raman, Martina Fröhlich, Conor Lawless, Rainer Machné, Falko Krause, Damon Hachmeister, Matthias König, Clifford A. Shaffer, Benjamin D. Heavner, Douglas B. Kell, Jonathan R. Karr, Mihai Glont, Lukas Endler, Melanie I. Stefan, Robert Phair, Lu Li, Henning Schmidt, Dirk Drasdo, Johan Elf, Allyson L. Lister, Hiroaki Kitano, Richard R. Adams, Oliver A. Ruebenacker, Roland Keller, Sven Sahle, Ion I. Moraru, Gary D. Bader, Poul M. F. Nielsen, Johann M. Rohwer, Johannes Eichner, Daniel R. Hyduke, James R. Faeder, Stefan Hoops, Emanuel Gonçalves, Yuichiro Inagaki, Aurélien Naldi, Koichi Takahashi, Sylvain Soliman, Brett G. Olivier, Kieran Smallbone, Stuart L. Moodie, Pedro T. Monteiro, Chris J. Myers, Martin Golebiewski, Tomas Radivoyevitch, Jeremy Zucker, Hidde de Jong, Andrew Finney, Keating, S, Waltemath, D, König, M, Zhang, F, Dräger, A, Chaouiya, C, Bergmann, F, Finney, A, Gillespie, C, Helikar, T, Hoops, S, Malik-Sheriff, R, Moodie, S, Moraru, I, Myers, C, Naldi, A, Olivier, B, Sahle, S, Schaff, J, Smith, L, Swat, M, Thieffry, D, Watanabe, L, Wilkinson, D, Blinov, M, Begley, K, Faeder, J, Gómez, H, Hamm, T, Inagaki, Y, Liebermeister, W, Lister, A, Lucio, D, Mjolsness, E, Proctor, C, Raman, K, Rodriguez, N, Shaffer, C, Shapiro, B, Stelling, J, Swainston, N, Tanimura, N, Wagner, J, Meier-Schellersheim, M, Sauro, H, Palsson, B, Bolouri, H, Kitano, H, Funahashi, A, Hermjakob, H, Doyle, J, Hucka, M, Adams, R, Allen, N, Angermann, B, Antoniotti, M, Bader, G, Červený, J, Courtot, M, Cox, C, Dalle Pezze, P, Demir, E, Denney, W, Dharuri, H, Dorier, J, Drasdo, D, Ebrahim, A, Eichner, J, Elf, J, Endler, L, Evelo, C, Flamm, C, Fleming, R, Fröhlich, M, Glont, M, Gonçalves, E, Golebiewski, M, Grabski, H, Gutteridge, A, Hachmeister, D, Harris, L, Heavner, B, Henkel, R, Hlavacek, W, Hu, B, Hyduke, D, Jong, H, Juty, N, Karp, P, Karr, J, Kell, D, Keller, R, Kiselev, I, Klamt, S, Klipp, E, Knüpfer, C, Kolpakov, F, Krause, F, Kutmon, M, Laibe, C, Lawless, C, Li, L, Loew, L, Machne, R, Matsuoka, Y, Mendes, P, Mi, H, Mittag, F, Monteiro, P, Natarajan, K, Nielsen, P, Nguyen, T, Palmisano, A, Jean-Baptiste, P, Pfau, T, Phair, R, Radivoyevitch, T, Rohwer, J, Ruebenacker, O, Saez-Rodriguez, J, Scharm, M, Schmidt, H, Schreiber, F, Schubert, M, Schulte, R, Sealfon, S, Smallbone, K, Soliman, S, Stefan, M, Sullivan, D, Takahashi, K, Teusink, B, Tolnay, D, Vazirabad, I, Kamp, A, Wittig, U, Wrzodek, C, Wrzodek, F, Xenarios, I, Zhukova, A, Zucker, J, European Bioinformatics Institute [Hinxton] (EMBL-EBI), EMBL Heidelberg, Heidelberg University Hospital [Heidelberg], Swiss Institute of Bioinformatics [Lausanne] (SIB), Université de Lausanne = University of Lausanne (UNIL), European Molecular Biology Laboratory (EMBL), University of Connecticut (UCONN), National Institutes of Health [Bethesda] (NIH), Chercheur indépendant, Amazon Web Services [Seattle] (AWS), Università degli Studi di Milano-Bicocca = University of Milano-Bicocca (UNIMIB), University of Toronto, Masaryk University [Brno] (MUNI), Terry Fox Laboratory, BC Cancer Agency (BCCRC)-British Columbia Cancer Agency Research Centre, The University of Tennessee [Knoxville], The Babraham Institute [Cambridge, UK], Oregon Health and Science University [Portland] (OHSU), Human Predictions LLC, Illumina, Swiss-Prot Group, Swiss Institute of Bioinformatics [Genève] (SIB), Modelling and Analysis for Medical and Biological Applications (MAMBA), Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Jacques-Louis Lions (LJLL (UMR_7598)), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité), University of California [San Diego] (UC San Diego), University of California (UC), Center for Bioinformatics (ZBIT), Eberhard Karls Universität Tübingen = Eberhard Karls University of Tuebingen, Uppsala University, Institut für Populationsgenetik [Vienna], Veterinärmedizinische Universität Wien, Maastricht University [Maastricht], Alpen-Adria-Universität Klagenfurt [Klagenfurt, Austria], Medizinische Universität Wien = Medical University of Vienna, German Cancer Research Center - Deutsches Krebsforschungszentrum [Heidelberg] (DKFZ), Heidelberg Institute for Theoretical Studies (HITS ), Russian-Armenian University (RAU), GlaxoSmithKline [Stevenage, UK] (GSK), GlaxoSmithKline [Headquarters, London, UK] (GSK), Microsoft Technology Licensing (MTL), Microsoft Corporation [Redmond, Wash.], Vanderbilt University School of Medicine [Nashville], University of Washington [Seattle], University of Rostock, Los Alamos National Laboratory (LANL), Lorentz Institute, Universiteit Leiden, Tegmine Therapeutics, Modeling, simulation, measurement, and control of bacterial regulatory networks (IBIS), Laboratoire Adaptation et pathogénie des micro-organismes [Grenoble] (LAPM), Université Joseph Fourier - Grenoble 1 (UJF)-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Centre National de la Recherche Scientifique (CNRS)-Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut Jean Roget, SRI International [Menlo Park] (SRI), Icahn School of Medicine at Mount Sinai [New York] (MSSM), University of Liverpool, Universitätsklinikum Tübingen - University Hospital of Tübingen, Institute of Information and Computational Technologies (IICT), Max Planck Institute for Dynamics of Complex Technical Systems, Max-Planck-Gesellschaft, Max-Planck-Institut für Molekulare Genetik (MPIMG), Friedrich-Schiller-Universität = Friedrich Schiller University Jena [Jena, Germany], Humboldt University Of Berlin, Newcastle University [Newcastle], École polytechnique (X), Heinrich Heine Universität Düsseldorf = Heinrich Heine University [Düsseldorf], The Systems Biology Institute [Tokyo] (SBI), Centro de Quimica Estrutural (CQE), Instituto Superior Técnico, Universidade Técnica de Lisboa (IST), University of Southern California (USC), Instituto Gulbenkian de Ciência [Oeiras] (IGC), Fundação Calouste Gulbenkian, University of Southern Denmark (SDU), University of Auckland [Auckland], University of Utah, Virginia Tech [Blacksburg], University of Luxembourg [Luxembourg], Integrative Bioinformatics Inc [Mountain View], Cleveland Clinic, Stellenbosch University, Broad Institute of MIT and Harvard (BROAD INSTITUTE), Harvard Medical School [Boston] (HMS)-Massachusetts Institute of Technology (MIT)-Massachusetts General Hospital [Boston], Universität Heidelberg [Heidelberg] = Heidelberg University, Leibniz Institute of Plant Genetics and Crop Plant Research [Gatersleben] (IPK-Gatersleben), Laboratoire de Biologie du Développement de Villefranche sur mer (LBDV), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut de la Mer de Villefranche (IMEV), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Mount Sinai School of Medicine, Department of Psychiatry-Icahn School of Medicine at Mount Sinai [New York] (MSSM), University of Manchester [Manchester], Computational systems biology and optimization (Lifeware), Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), California Institute of Technology (CALTECH), Encodia Inc [San Diego], Shinshu University [Nagano], University of Amsterdam [Amsterdam] (UvA), Versiti Blood Center of Wisconsin, Greifswald University Hospital, Bioinformatique évolutive - Evolutionary Bioinformatics, Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS), Pacific Northwest National Laboratory (PNNL), National Institute of Allergy and Infectious Diseases [Bethesda] (NIAID-NIH), Department of Bioengineering, University of California (UC)-University of California (UC), ANSYS, Virginia Polytechnic Institute and State University [Blacksburg], Eight Pillars Ltd, Center for Integrative Genomics - Institute of Bioinformatics, Génopode (CIG), Université de Lausanne = University of Lausanne (UNIL)-Université de Lausanne = University of Lausanne (UNIL), Universität Heidelberg, Bioquant, Applied Biomathematics [New York], SimCYP Ltd, Institut de biologie de l'ENS Paris (IBENS), Département de Biologie - ENS Paris, École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), University of Utah School of Medicine [Salt Lake City], University of Pittsburgh School of Medicine, Pennsylvania Commonwealth System of Higher Education (PCSHE), Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology [Zürich] (ETH Zürich), Mizuho Information and Research Institute, Mathématiques et Informatique Appliquées du Génome à l'Environnement [Jouy-En-Josas] (MaIAGE), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), University of Oxford, Computer Science (North Carolina State University), North Carolina State University [Raleigh] (NC State), University of North Carolina System (UNC)-University of North Carolina System (UNC), University of California [Irvine] (UC Irvine), Indian Institute of Technology Madras (IIT Madras), California State University [Northridge] (CSUN), Biotechnology and Biological Sciences Research Council (BBSRC), IBM Research [Melbourne], Benaroya Research Institute [Seattle] (BRI), Okinawa Institute of Science and Technology Graduate University, Keio University, Department of Computing and Mathematical sciences, members, SBML Level 3 Community, Université de Lausanne (UNIL), Università degli Studi di Milano-Bicocca [Milano] (UNIMIB), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP), University of California, Universiteit Leiden [Leiden], Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Inria Grenoble - Rhône-Alpes, Humboldt University of Berlin, Universität Heidelberg [Heidelberg], Institut Pasteur [Paris]-Centre National de la Recherche Scientifique (CNRS), Humboldt-Universität zu Berlin, University of California-University of California, Université de Lausanne (UNIL)-Université de Lausanne (UNIL), Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Département de Biologie - ENS Paris, École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM), University of Oxford [Oxford], University of California [Irvine] (UCI), Biotechnology and Biological Sciences Research Council, Computer Science, Institut de biologie de l'ENS Paris (UMR 8197/1024) (IBENS), and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS Paris)
Subjects: computational modeling, Medicine (General), Markup language, [SDV.BIO]Life Sciences [q-bio]/Biotechnology, INFORMATION, Interoperability, interoperability, Review, [SDV.BC.BC]Life Sciences [q-bio]/Cellular Biology/Subcellular Processes [q-bio.SC], ANNOTATION, 0302 clinical medicine, Software, file forma, Models, Biology (General), 0303 health sciences, Computational model, Applied Mathematics, Systems Biology, systems biology, File format, 3. Good health, Networking and Information Technology R&D, Networking and Information Technology R&D (NITRD), Computational Theory and Mathematics, SIMULATION, General Agricultural and Biological Sciences, STANDARDS, REPOSITORY, Information Systems, QH301-705.5, Bioinformatics, Systems biology, Software ecosystem, Reviews, Bioengineering, Methods & Resources, Biology, MARKUP LANGUAGE, Models, Biological, SBML Level 3 Community members, General Biochemistry, Genetics and Molecular Biology, 03 medical and health sciences, R5-920, Animals, Humans, SBML, reproducibility, 030304 developmental biology, ENVIRONMENT, General Immunology and Microbiology, file format, business.industry, Computational Biology, Biological, ONTOLOGY, Metabolism, Logistic Models, Biochemistry and Cell Biology, Other Biological Sciences, Software engineering, business, 030217 neurology & neurosurgery
Abstract: Systems biology has experienced dramatic growth in the number, size, and complexity of computational models. To reproduce simulation results and reuse models, researchers must exchange unambiguous model descriptions. We review the latest edition of the Systems Biology Markup Language (SBML), a format designed for this purpose. A community of modelers and software authors developed SBML Level 3 over the past decade. Its modular form consists of a core suited to representing reaction‐based models and packages that extend the core with features suited to other model types including constraint‐based models, reaction‐diffusion models, logical network models, and rule‐based models. The format leverages two decades of SBML and a rich software ecosystem that transformed how systems biologists build and interact with models. More recently, the rise of multiscale models of whole cells and organs, and new data sources such as single‐cell measurements and live imaging, has precipitated new ways of integrating data with models. We provide our perspectives on the challenges presented by these developments and how SBML Level 3 provides the foundation needed to support this evolution., Over the past two decades, scientists from different fields have been developing SBML, a standard format for encoding computational models in biology and medicine. This article summarizes recent progress and gives perspectives on emerging challenges.
Published: 2020

16. Deep learning from phylogenies to understand the dynamics of epidemics

Author: Voznica, Jakub, Zhukova, Anna, Dot, Tristan, Ocaña, Kary, Lemoine, Frédéric, Moslonka-Lefebvre, Mathieu, Gascuel, Olivier, Bioinformatique évolutive - Evolutionary Bioinformatics, Institut Pasteur [Paris]-Centre National de la Recherche Scientifique (CNRS), Université Sorbonne Paris Cité (USPC), and Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS)
Subjects: [SDV.BID.EVO]Life Sciences [q-bio]/Biodiversity/Populations and Evolution [q-bio.PE], [SDV.BID.SPT]Life Sciences [q-bio]/Biodiversity/Systematics, Phylogenetics and taxonomy, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], ComputingMilieux_MISCELLANEOUS
Abstract: International audience
Published: 2019

17. Accounting for ambiguity in ancestral sequence reconstruction

Author: Laurent Brehelin, Olivier Gascuel, Adrien Oliva, Sylvain Pulicani, Stéphane Guindon, Vincent Lefort, Méthodes et Algorithmes pour la Bioinformatique (MAB), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Australian Centre for Ancient DNA, University of Adelaide, Bioinformatique évolutive - Evolutionary Bioinformatics, Institut Pasteur [Paris]-Centre National de la Recherche Scientifique (CNRS), This research was supported by the Institut Français de Bioinformatique (RENABI-IFB, Investissements d’Avenir, ANR-11-INBS-0013) and the Agence Nationale pour la Recherche through the project GENOSPACE., ANR-11-INBS-0013,IFB (ex Renabi-IFB),Institut français de bioinformatique(2011), ANR-16-CE02-0008,GenoSpace,Nouveaux outils statistiques pour l'analyse spatiale des données génétiques(2016), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), and Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS)
Subjects: 0106 biological sciences, Statistics and Probability, Biometry, Computer science, media_common.quotation_subject, Inference, Scale (descriptive set theory), [SDV.BID.SPT]Life Sciences [q-bio]/Biodiversity/Systematics, Phylogenetics and taxonomy, 010603 evolutionary biology, 01 natural sciences, Biochemistry, Set (abstract data type), Evolution, Molecular, 03 medical and health sciences, Molecular evolution, Position (vector), Maximum a posteriori estimation, Nucleotide, Amino Acid Sequence, Molecular Biology, Phylogeny, 030304 developmental biology, media_common, chemistry.chemical_classification, 0303 health sciences, Sequence, Likelihood Functions, Phylogenetic tree, [SDV.BID.EVO]Life Sciences [q-bio]/Biodiversity/Populations and Evolution [q-bio.PE], Ambiguity, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], Computer Science Applications, Computational Mathematics, Tree (data structure), Computational Theory and Mathematics, chemistry, Algorithm, Sequence Analysis
Abstract: MotivationThe reconstruction of ancestral genetic sequences from the analysis of contemporaneous data is a powerful tool to improve our understanding of molecular evolution. Various statistical criteria defined in a phylogenetic framework can be used to infer nucleotide, amino-acid or codon states at internal nodes of the tree, for every position along the sequence. These criteria generally select the state that maximizes (or minimizes) a given criterion. Although it is perfectly sensible from a statistical perspective, that strategy fails to convey useful information about the level of uncertainty associated to the inference.ResultsThe present study introduces a new criterion for ancestral sequence reconstruction, the minimum posterior expected error (MPEE), that selects a single state whenever the signal conveyed by the data is strong, and a combination of multiple states otherwise. We also assess the performance of a criterion based on the Brier scoring scheme which, like MPEE, does not rely on any tuning parameters. The precision and accuracy of several other criteria that involve arbitrarily set tuning parameters are also evaluated. Large scale simulations demonstrate the benefits of using the MPEE and Brier-based criteria with a substantial increase in the accuracy of the inference of past sequences compared to the standard approach and realistic compromises on the precision of the solutions returned.Availability and implementationThe software package PhyML (https://github.com/stephaneguindon/phyml) provides an implementation of the Maximum A Posteriori (MAP) and MPEE criteria for reconstructing ancestral nucleotide and amino-acid sequences.
Published: 2019

18. Scientific workflows for computational reproducibility in the life sciences: Status, challenges and opportunities

Author: Olivier Collin, Sarah Cohen-Boulakia, Konrad Hinsen, Alban Gaignard, Khalid Belhajjame, Pierre Larmande, Fabien Mareuil, Christine Froidevaux, Frédéric Lemoine, Christophe Blanchet, Jérôme Chopard, Christophe Pradal, Hervé Ménager, Yvan Le Bras, Scientific Data Management (ZENITH), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Laboratoire de Recherche en Informatique (LRI), Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), Institut de Biologie Computationnelle (IBC), Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision (LAMSADE), Université Paris Dauphine-PSL, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS), Plateforme bioinformatique GenOuest [Rennes], Université de Rennes (UR)-Plateforme Génomique Santé Biogenouest®-Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-GESTION DES DONNÉES ET DE LA CONNAISSANCE (IRISA-D7), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Mathématiques, Informatique et STatistique pour l'Environnement et l'Agronomie (MISTEA), Institut National de la Recherche Agronomique (INRA)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Centre hospitalier universitaire de Nantes (CHU Nantes), Synchrotron SOLEIL (SSOLEIL), Centre National de la Recherche Scientifique (CNRS), Centre de biophysique moléculaire (CBM), Université d'Orléans (UO)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS), Diversité, adaptation, développement des plantes (UMR DIADE), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud]), Bioinformatique évolutive - Evolutionary Bioinformatics, Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS), Centre d'Informatique pour la Biologie, Institut Pasteur [Paris] (IP), Centre de Bioinformatique, Biostatistique et Biologie Intégrative (C3BI), Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Modeling plant morphogenesis at different scales, from genes to phenotype (VIRTUAL PLANTS), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de la Recherche Agronomique (INRA)-Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut Français de Bioinformatique - UMS CNRS 3601 (IFB-CORE), Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), The authors acknowledge the support of GDR CNRS MaDICS, pro-gramme CPER Region Bretagne 'CeSGO', and programme Region Paysde la Loire 'Connect Talent' (SyMeTRIC). We acknowledge funding by thecall Infrastructures in Biology and Health' in the framework of the FrenchInvestments for the Future' (ANR-11-INBS-0012 and ANR-11-INBS-0013).This work was conducted in part at the IBC (Institute of Computational Bi-ology) in Montpellier, France., ANR-11-INBS-0013,IFB (ex Renabi-IFB),Institut français de bioinformatique(2011), ANR-11-INBS-0011,NeurATRIS,Infrastructure de Recherche Translationnelle pour les Biothérapies en Neurosciences(2011), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Inria Sophia Antipolis - Méditerranée (CRISAM), CentraleSupélec-Université Paris-Sud - Paris 11 (UP11)-Centre National de la Recherche Scientifique (CNRS), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Plateforme Génomique Santé Biogenouest®-Inria Rennes – Bretagne Atlantique, Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes 1 (UR1), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de la Recherche Agronomique (INRA), Université d'Orléans (UO)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Chimie du CNRS (INC), Institut Pasteur [Paris]-Centre National de la Recherche Scientifique (CNRS), Institut Pasteur [Paris], Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-CentraleSupélec-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Institut de Recherche pour le Développement (IRD [France-Sud])-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Université Paris Dauphine-PSL-Centre National de la Recherche Scientifique (CNRS), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Inria Sophia Antipolis - Méditerranée (CRISAM), Université d'Orléans (UO)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de la Recherche Agronomique (INRA), ANR-11-INBS-0013/11-INBS-0013,ReNaBi-IFB,Institut français de bioinformatique(2011), ANR-11-INBS-0011/11-INBS-0011,NeurATRIS,Infrastructure de Recherche Translationnelle pour les Biothérapies en Neurosciences(2011), Cohen-Boulakia, Sarah, Infrastructures - Institut français de bioinformatique - - IFB (ex Renabi-IFB)2011 - ANR-11-INBS-0013 - INBS - VALID, and Infrastructures - Infrastructure de Recherche Translationnelle pour les Biothérapies en Neurosciences - - NeurATRIS2011 - ANR-11-INBS-0011 - INBS - VALID
Subjects: 0301 basic medicine, Scientific Workflows, Computer Networks and Communications, Computer science, [INFO.INFO-DS]Computer Science [cs]/Data Structures and Algorithms [cs.DS], [INFO.INFO-DS] Computer Science [cs]/Data Structures and Algorithms [cs.DS], Context (language use), 02 engineering and technology, Domain (software engineering), 03 medical and health sciences, 0202 electrical engineering, electronic engineering, information engineering, [INFO.INFO-DB] Computer Science [cs]/Databases [cs.DB], Packaging environments, Use case, Set (psychology), environments, [INFO.INFO-BI] Computer Science [cs]/Bioinformatics [q-bio.QM], Reproducibility, [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], Data science, 030104 developmental biology, Workflow, C30 - Documentation et information, Packaging, 13. Climate action, Hardware and Architecture, Scientific workflows, Provenance, [INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR], 020201 artificial intelligence & image processing, [INFO.INFO-IR] Computer Science [cs]/Information Retrieval [cs.IR], U30 - Méthodes de recherche, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], Software
Abstract: International audience; With the development of new experimental technologies, biologists are faced with an avalanche of data to be computationally analyzed for scientific advancements and discoveries to emerge. Faced with the complexity of analysis pipelines, the large number of computational tools, and the enormous amount of data to manage, there is compelling evidence that many if not most scientific discoveries will not stand the test of time: increasing the reproducibility of computed results is of paramount importance. The objective we set out in this paper is to place scientific workflows in the context of reproducibility. To do so, we define several kinds of repro-ducibility that can be reached when scientific workflows are used to perform experiments. We characterize and define the criteria that need to be catered for by reproducibility-friendly scientific workflow systems, and use such criteria to place several representative and widely used workflow systems and companion tools within such a framework. We also discuss the remaining challenges posed by reproducible scientific workflows in the life sciences. Our study was guided by three use cases from the life science domain involving in silico experiments.
Published: 2017

19. Phylogenetic models and algorithms

Author: Gascuel, Olivier, Bioinformatique évolutive - Evolutionary Bioinformatics, Institut Pasteur [Paris]-Centre National de la Recherche Scientifique (CNRS), and Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS)
Subjects: [SDV.BID.EVO]Life Sciences [q-bio]/Biodiversity/Populations and Evolution [q-bio.PE], [SDV.BID.SPT]Life Sciences [q-bio]/Biodiversity/Systematics, Phylogenetics and taxonomy, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], ComputingMilieux_MISCELLANEOUS
Abstract: International audience
Published: 2019

20. NGPhylogeny.fr: new generation phylogenetic services for non-specialists

Author: Damien Correia, Frédéric Lemoine, Sarah Cohen-Boulakia, Olivia Doppelt-Azeroual, Vincent Lefort, Olivier Gascuel, Fabien Mareuil, Bioinformatique évolutive - Evolutionary Bioinformatics, Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS), Hub Bioinformatique et Biostatistique - Bioinformatics and Biostatistics HUB, Méthodes et Algorithmes pour la Bioinformatique (MAB), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Laboratoire de Recherche en Informatique (LRI), Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), Bioinformatique (LRI) (BioInfo - LRI), Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), Institut Français de Bioinformatique [ANR-11-INBS-0013], INCEPTION project [PIA/ANR-16-CONV-0005]. Funding for open access charge: Institut Pasteur., ANR-11-INBS-0013,IFB (ex Renabi-IFB),Institut français de bioinformatique(2011), ANR-16-CONV-0005,INCEPTION,Institut Convergences pour l'étude de l'Emergence des Pathologies au Travers des Individus et des populatiONs(2016), Institut Pasteur [Paris]-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), CentraleSupélec-Université Paris-Sud - Paris 11 (UP11)-Centre National de la Recherche Scientifique (CNRS), and CentraleSupélec-Université Paris-Sud - Paris 11 (UP11)-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Sud - Paris 11 (UP11)-Centre National de la Recherche Scientifique (CNRS)
Subjects: 0106 biological sciences, Databases, Factual, [INFO.INFO-DS]Computer Science [cs]/Data Structures and Algorithms [cs.DS], Biology, computer.software_genre, [SDV.BID.SPT]Life Sciences [q-bio]/Biodiversity/Systematics, Phylogenetics and taxonomy, 01 natural sciences, 03 medical and health sciences, Software, Genetics, Phylogeny, 030304 developmental biology, 0303 health sciences, Internet, [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], business.industry, Model selection, Tree (data structure), Workflow, Code refactoring, [INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR], Scalability, Personal computer, Web Server Issue, The Internet, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], business, Software engineering, computer, 010606 plant biology & botany
Abstract: Phylogeny.fr, created in 2008, has been designed to facilitate the execution of phylogenetic workflows, and is nowadays widely used. However, since its development, user needs have evolved, new tools and workflows have been published, and the number of jobs has increased dramatically, thus promoting new practices, which motivated its refactoring. We developed NGPhylogeny.fr to be more flexible in terms of tools and workflows, easily installable, and more scalable. It integrates numerous tools in their latest version (e.g. TNT, FastME, MrBayes, etc.) as well as new ones designed in the last ten years (e.g. PhyML, SMS, FastTree, trimAl, BOOSTER, etc.). These tools cover a large range of usage (sequence searching, multiple sequence alignment, model selection, tree inference and tree drawing) and a large panel of standard methods (distance, parsimony, maximum likelihood and Bayesian). They are integrated in workflows, which have been already configured (‘One click’), can be customized (‘Advanced’), or are built from scratch (‘A la carte’). Workflows are managed and run by an underlying Galaxy workflow system, which makes workflows more scalable in terms of number of jobs and size of data. NGPhylogeny.fr is deployable on any server or personal computer, and is freely accessible at https://ngphylogeny.fr.
Published: 2019

21. Distribution and asymptotic behavior of the phylogenetic transfer distance

Author: Frédéric Lemoine, Miraine Dávila Felipe, Olivier Gascuel, Jean-Baka Domelevo Entfellner, Jakub Truszkowski, Bioinformatique évolutive - Evolutionary Bioinformatics, Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS), International Livestock Research Institute [CGIAR, Nairobi] (ILRI), International Livestock Research Institute [CGIAR, Ethiopie] (ILRI), Consultative Group on International Agricultural Research [CGIAR] (CGIAR)-Consultative Group on International Agricultural Research [CGIAR] (CGIAR), Hub Bioinformatique et Biostatistique - Bioinformatics and Biostatistics HUB, Méthodes et Algorithmes pour la Bioinformatique (MAB), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), This work was supported by the EU-H2020 Virogenesis project (Grant No. 634650 –JT, OG), and by the INCEPTION project (PIA/ANR-16-CONV-0005 – MDF, FL, OG), ANR-16-CONV-0005,INCEPTION,Institut Convergences pour l'étude de l'Emergence des Pathologies au Travers des Individus et des populatiONs(2016), European Project: 634650,H2020,H2020-PHC-2014-two-stage,VIROGENESIS(2015), Institut Pasteur [Paris]-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Laboratoire de Mathématiques Appliquées de Compiègne (LMAC), Université de Technologie de Compiègne (UTC), and International Livestock Research Institute [Nairobi] (ILRI)
Subjects: 0106 biological sciences, [SDV]Life Sciences [q-bio], Caterpillar tree, [SDV.BID.SPT]Life Sciences [q-bio]/Biodiversity/Systematics, Phylogenetics and taxonomy, 01 natural sciences, 010305 fluids & plasmas, 05C05, Tree (descriptive set theory), 62F05, Beta (velocity), [MATH]Mathematics [math], Phylogeny, Mathematics, Distances between bipartitions and phylogenies, 0303 health sciences, Phylogenetic tree, Degree (graph theory), Random phylogenies, Applied Mathematics, Agricultural and Biological Sciences (miscellaneous), 62P10, Modeling and Simulation, 60E15, Algorithms, Gene Transfer, Horizontal, Matching (graph theory), Context (language use), 010603 evolutionary biology, Measure (mathematics), Article, Combinatorics, 03 medical and health sciences, Phylogenetic trees, 0103 physical sciences, Order (group theory), Computer Simulation, [INFO]Computer Science [cs], 030304 developmental biology, Models, Genetic, 62F40, R-distance, Order (ring theory), Primary 92-08, Transfer (group theory), Lattice paths, Distribution (mathematics), Secondary 05A05, Tree (set theory), Concentration inequalities, 62F35
Abstract: The transfer distance (TD) was introduced in the classification framework and studied in the context of phylogenetic tree matching. Recently, Lemoine et al. (Nature 556(7702):452–456, 2018. 10.1038/s41586-018-0043-0) showed that TD can be a powerful tool to assess the branch support on large phylogenies, thus providing a relevant alternative to Felsenstein’s bootstrap. This distance allows a reference branch\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\beta $$\end{document}β in a reference tree \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathcal {T}}$$\end{document}T to be compared to a branch b from another tree T (typically a bootstrap tree), both on the same set of n taxa. The TD between these branches is the number of taxa that must be transferred from one side of b to the other in order to obtain \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\beta $$\end{document}β. By taking the minimum TD from \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\beta $$\end{document}β to all branches in T we define the transfer index, denoted by \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\phi (\beta ,T)$$\end{document}ϕ(β,T), measuring the degree of agreement of T with \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\beta $$\end{document}β. Let us consider a reference branch \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\beta $$\end{document}β having p tips on its light side and define the transfer support (TS) as \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$1 - \phi (\beta ,T)/(p-1)$$\end{document}1-ϕ(β,T)/(p-1). Lemoine et al. (2018) used computer simulations to show that the TS defined in this manner is close to 0 for random “bootstrap” trees. In this paper, we demonstrate that result mathematically: when T is randomly drawn, TS converges in probability to 0 when n tends to \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\infty $$\end{document}∞. Moreover, we fully characterize the distribution of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\phi (\beta ,T)$$\end{document}ϕ(β,T) on caterpillar trees, indicating that the convergence is fast, and that even when n is small, moderate levels of branch support cannot appear by chance.
Published: 2019

22. The dream of Darwin: reconstructing evolution from DNA: Invited conference for a broad audience (>1000 attendees)

Author: Gascuel, Olivier, Bioinformatique évolutive - Evolutionary Bioinformatics, Institut Pasteur [Paris]-Centre National de la Recherche Scientifique (CNRS), and Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS)
Subjects: [SDV.BID.EVO]Life Sciences [q-bio]/Biodiversity/Populations and Evolution [q-bio.PE], [SDV.BID.SPT]Life Sciences [q-bio]/Biodiversity/Systematics, Phylogenetics and taxonomy, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], ComputingMilieux_MISCELLANEOUS
Abstract: International audience
Published: 2019

23. Close proximity interactions support transmission of ESBL-K. pneumoniae but not ESBL-E. coli in healthcare settings

Author: Jean-Louis Herrmann, Thomas Obadia, Laura Temime, Audrey Duval, Pierre-Yves Boëlle, Lulla Opatowski, Eric Fleury, Didier Guillemot, Bodescot, Myriam, Integrative Biology of Emerging Infectious Diseases - - IBEID2010 - ANR-10-LABX-0062 - LABX - VALID, Biostatistique, Biomathématique, Pharmacoépidémiologie et Maladies Infectieuses (B2PHI), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Institut Pasteur [Paris] (IP)-Institut National de la Santé et de la Recherche Médicale (INSERM), Malaria : parasites et hôtes - Malaria : parasites and hosts, Institut Pasteur [Paris] (IP), Bioinformatique évolutive - Evolutionary Bioinformatics, Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS), Institut Pierre Louis d'Epidémiologie et de Santé Publique (iPLESP), Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU), Laboratoire de l'Informatique du Parallélisme (LIP), École normale supérieure - Lyon (ENS Lyon)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS), Dynamic Networks : Temporal and Structural Capture Approach (DANTE), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire de l'Informatique du Parallélisme (LIP), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Lyon (ENS Lyon)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut Rhône-Alpin des systèmes complexes (IXXI), École normale supérieure - Lyon (ENS Lyon)-Université Lumière - Lyon 2 (UL2)-Université Jean Moulin - Lyon 3 (UJML), Université de Lyon-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Université Lumière - Lyon 2 (UL2)-Université Jean Moulin - Lyon 3 (UJML), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Service de Microbiologie [Garches], Hôpital Raymond Poincaré [AP-HP], Infection et inflammation (2I), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Institut National de la Santé et de la Recherche Médicale (INSERM), Laboratoire Modélisation, épidémiologie et surveillance des risques sanitaires (MESuRS), Conservatoire National des Arts et Métiers [CNAM] (CNAM), HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM), Pasteur-Cnam Risques infectieux et émergents (PACRI), Institut Pasteur [Paris] (IP)-Conservatoire National des Arts et Métiers [CNAM] (CNAM), This study was supported by the European Commission under the Life Science Health Priority of the 6th Framework Program (MOSAR network contract LSHP-CT-2007-037941), funding was also received from the French Government through the National Clinical Research Program and the Investissement d’Avenir program, Laboratoire d’Excellence 'Integrative Biology of Emerging Infectious Diseases' (grant no. ANR-10-LABX-62-IBEID, http://www.agencenationale-recherche.fr/ProjetIA-10-LABX-0062) to DG and from the Ecole des Hautes Etudes en Santé Publique (EHESP, https://www.ehesp.fr/) to AD., ANR-10-LABX-0062,IBEID,Integrative Biology of Emerging Infectious Diseases(2010), École normale supérieure de Lyon (ENS de Lyon)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure de Lyon (ENS de Lyon)-Université Claude Bernard Lyon 1 (UCBL), École normale supérieure de Lyon (ENS de Lyon)-Université Lumière - Lyon 2 (UL2)-Université Jean Moulin - Lyon 3 (UJML), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Institut Pasteur [Paris]-Institut National de la Santé et de la Recherche Médicale (INSERM), Institut Pasteur [Paris], Institut Pasteur [Paris]-Centre National de la Recherche Scientifique (CNRS), Sorbonne Université (SU)-Institut National de la Santé et de la Recherche Médicale (INSERM), Institut Pasteur [Paris]-Conservatoire National des Arts et Métiers [CNAM] (CNAM), Centre National de la Recherche Scientifique (CNRS)-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-École normale supérieure - Lyon (ENS Lyon), Université de Lyon-École normale supérieure - Lyon (ENS Lyon)-Centre National de la Recherche Scientifique (CNRS)-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), and Université de Lyon-École normale supérieure - Lyon (ENS Lyon)-Institut Rhône-Alpin des systèmes complexes (IXXI)
Subjects: 0301 basic medicine, Male, Species Delimitation, Klebsiella pneumoniae, Nosocomial Infections, Speciation, Health Care Providers, Nurses, Pathology and Laboratory Medicine, law.invention, Klebsiella Pneumoniae, 0302 clinical medicine, law, Antibiotics, [SDV.MHEP.MI]Life Sciences [q-bio]/Human health and pathology/Infectious diseases, Klebsiella, Epidemiology, Medicine and Health Sciences, polycyclic compounds, Infection control, Public and Occupational Health, 030212 general & internal medicine, Medical Personnel, Biology (General), Escherichia coli Infections, 0303 health sciences, Cross Infection, Ecology, biology, Antimicrobials, K pneumoniae, Drugs, Drug Resistance, Microbial, Hygiene, Middle Aged, Hospitals, 3. Good health, Care facility, Anti-Bacterial Agents, Bacterial Pathogens, Professions, Transmission (mechanics), Infectious Diseases, Computational Theory and Mathematics, Medical Microbiology, Modeling and Simulation, [SDV.MHEP.MI] Life Sciences [q-bio]/Human health and pathology/Infectious diseases, Female, Pathogens, Wireless Technology, Research Article, Adult, medicine.medical_specialty, Evolutionary Processes, QH301-705.5, Microbiology, beta-Lactamases, Cellular and Molecular Neuroscience, 03 medical and health sciences, Antibiotic resistance, Enterobacteriaceae, Environmental health, Microbial Control, Drug Resistance, Bacterial, Genetics, medicine, Disease Transmission, Infectious, Escherichia coli, Humans, Molecular Biology, Microbial Pathogens, Ecology, Evolution, Behavior and Systematics, Aged, Pharmacology, Infection Control, Evolutionary Biology, Bacteria, 030306 microbiology, Organisms, Biology and Life Sciences, biochemical phenomena, metabolism, and nutrition, biology.organism_classification, bacterial infections and mycoses, Health Care, 030104 developmental biology, [SDV.SPEE] Life Sciences [q-bio]/Santé publique et épidémiologie, Antibiotic Resistance, Healthcare settings, People and Places, bacteria, Population Groupings, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, Antimicrobial Resistance, 030217 neurology & neurosurgery
Abstract: Antibiotic-resistance of hospital-acquired infections is a major public health issue. The worldwide emergence and diffusion of extended-spectrum β-lactamase (ESBL)-producing Enterobacteriaceae, including Escherichia coli (ESBL-EC) and Klebsiella pneumoniae (ESBL-KP), is of particular concern. Preventing their nosocomial spread requires understanding their transmission. Using Close Proximity Interactions (CPIs), measured by wearable sensors, and weekly ESBL-EC–and ESBL-KP–carriage data, we traced their possible transmission paths among 329 patients in a 200-bed long-term care facility over 4 months. Based on phenotypically defined resistance profiles to 12 antibiotics only, new bacterial acquisitions were tracked. Extending a previously proposed statistical method, the CPI network’s ability to support observed incident-colonization episodes of ESBL-EC and ESBL-KP was tested. Finally, mathematical modeling based on our findings assessed the effect of several infection-control measures. A potential infector was identified in the CPI network for 80% (16/20) of ESBL-KP acquisition episodes. The lengths of CPI paths between ESBL-KP incident cases and their potential infectors were shorter than predicted by chance (P = 0.02), indicating that CPI-network relationships were consistent with dissemination. Potential ESBL-EC infectors were identified for 54% (19/35) of the acquisitions, with longer-than-expected lengths of CPI paths. These contrasting results yielded differing impacts of infection control scenarios, with contact reduction interventions proving less effective for ESBL-EC than for ESBL-KP. These results highlight the widely variable transmission patterns among ESBL-producing Enterobacteriaceae species. CPI networks supported ESBL-KP, but not ESBL-EC spread. These outcomes could help design more specific surveillance and control strategies to prevent in-hospital Enterobacteriaceae dissemination., Author summary Tracing extended-spectrum β-lactamase (ESBL) dissemination in hospitals is an important step in the fight against the spread of multi-drug resistant bacteria. Indeed, understanding ESBL spreading dynamics will help identify efficient control interventions. In the i-Bird study, patients and hospital staff from a French long-term care facility carried a wearable sensor to capture their interactions at less than 1.5 meters, every 30 seconds over a 4-month period. Every week, patients were also swabbed to detect carriage of ESBL-producing Enterobacteriaceae. Based on the analysis of these longitudinal data, this study shows that ESBL-producing Klebsiella pneumoniae (ESBL-KP) mostly spreads during close-proximity interactions between individuals, while this is not the case for ESBL-producing Escherichia coli (ESBL-EC), suggesting that ESBL-KP but not ESBL-EC may be controlled by contact reduction interventions.
Published: 2019

24. Numerical Optimization Techniques in Maximum Likelihood Tree Inference

Author: Stéphane Guindon, Olivier Gascuel, Méthodes et Algorithmes pour la Bioinformatique (MAB), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Bioinformatique évolutive - Evolutionary Bioinformatics, Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS), Tandy Warnow, Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), and Institut Pasteur [Paris]-Centre National de la Recherche Scientifique (CNRS)
Subjects: 0106 biological sciences, Optimization, 0303 health sciences, Phylogenetic tree, Computer science, Markov processes, [SDV.BID.EVO]Life Sciences [q-bio]/Biodiversity/Populations and Evolution [q-bio.PE], Inference, Markov process, Variation (game tree), Mixture model, [SDV.BID.SPT]Life Sciences [q-bio]/Biodiversity/Systematics, Phylogenetics and taxonomy, 010603 evolutionary biology, 01 natural sciences, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], 03 medical and health sciences, symbols.namesake, Core (graph theory), symbols, Enhanced Data Rates for GSM Evolution, Likelihood function, Mixture models, Algorithm, 030304 developmental biology, Maximum likelihood
Abstract: International audience; In this chapter, we present recent computational and algorithmic advances for improving the inference of phylogenetic trees from the analysis of homologous genetic sequences under the maximum likelihood criterion. In particular, we detail how the use of matrix algebra at the core of Felsenstein’s pruning algorithm, combined with the architecture of modern day computer processors, leads to efficient techniques for optimizing edge lengths. We also discuss some properties of the likelihood function when considering the optimization of the parameters of mixture models that are used to describe the variation of rates-across sites .
Published: 2019

25. Non-specific interference of cobalt with siderophore-dependent iron uptake pathways

Author: Bénédicte Pesset, Pamela Saint Auguste, Olivier Cunrath, Dirk Bumann, Odile Sismeiro, Julien Pérard, Quentin Perraud, Isabelle Michaud-Soret, Isabelle J. Schalk, Rachel Legendre, Anne Forster, Pierre Fechter, Ana Carballido Lopez, Jean Yves Coppée, Gaëtan L. A. Mislin, Gwenaëlle Graulier, Hugo Varet, Biotechnologie et signalisation cellulaire (BSC), Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS)-Institut de recherche de l'Ecole de biotechnologie de Strasbourg (IREBS), Laboratoire de Chimie et Biologie des Métaux (LCBM - UMR 5249), Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche Interdisciplinaire de Grenoble (IRIG), Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Transcriptome et Epigénome (PF2), Institut Pasteur [Paris], Hub Bioinformatique et Biostatistique - Bioinformatics and Biostatistics HUB, Institut Pasteur [Paris]-Centre National de la Recherche Scientifique (CNRS), Focal Area Infection Biology, Biozentrum, University of Basel (Unibas), This work was partly funded by the Centre National de la Recherche Scientifique. O. Cunrath held a fellowship from the French Ministère de la Recherche et de la Technologie (3 years) and the FRM (Fondation pour la Recherche Médicale, 6 months). A. Y. Carballido Lopez held a fellowship from the University of Strasbourg (IDEX) and B. Pesset from the DGA (Direction Générale des l’Armement). J. P. and I. M. S. thank the Laboratory of Excellence GRAL (grant ANR-11-LABX-49-01) and the Labex ARCANE (grant ANR-11-LABX-0003-01). The Transcriptome and Epigenome Platform is a member of the France Génomique consortium (ANR10-NBS-09-08)., We thank Dr A. Boos and P. Ronot (Institut Pluridisciplinaire Hubert Curien, Strasbourg) for their help in the ICP-AES measurements., ANR-11-LABX-0003,ARCANE,Grenoble, une chimie bio-motivée(2011), Biologie des Métaux (BioMet), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut de Recherche Interdisciplinaire de Grenoble (IRIG), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut de Recherche Interdisciplinaire de Grenoble (IRIG), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Bioinformatique évolutive - Evolutionary Bioinformatics, Centre National de la Recherche Scientifique (CNRS)-Institut Pasteur [Paris], Université de Strasbourg (UNISTRA)-Institut de recherche de l'Ecole de biotechnologie de Strasbourg (IREBS)-Centre National de la Recherche Scientifique (CNRS), Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut de Recherche Interdisciplinaire de Grenoble (IRIG), Institut Pasteur [Paris] (IP), and Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS)
Subjects: 0301 basic medicine, inorganic chemicals, Siderophore, Iron, Biophysics, Down-Regulation, Siderophores, Biochemistry, Models, Biological, Biomaterials, 03 medical and health sciences, chemistry.chemical_compound, Biosynthesis, Bacterial Proteins, Phenols, Operon, medicine, Transcriptional regulation, [SDV.BBM]Life Sciences [q-bio]/Biochemistry, Molecular Biology, Psychological repression, 030102 biochemistry & molecular biology, biology, Chemistry, Metals and Alloys, Transporter, Cobalt, Gene Expression Regulation, Bacterial, biology.organism_classification, Sciences du Vivant [q-bio]/Microbiologie et Parasitologie, Up-Regulation, Thiazoles, 030104 developmental biology, [SDV.MP]Life Sciences [q-bio]/Microbiology and Parasitology, Chemistry (miscellaneous), Pseudomonas aeruginosa, Ferric, Bacterial outer membrane, Oligopeptides, Bacteria, medicine.drug
Abstract: International audience; Much data shows that biological metals other than Fe3+ can interfere with Fe3+ acquisition by siderophores in bacteria. Siderophores are small Fe3+ chelators produced by the microorganisms to obtain access to Fe3+. Here, we show that Co2+ is imported into Pseudomonas aeruginosa cells in a complex with the siderophore pyochelin (PCH) by the ferri-PCH outer membrane transporter FptA. Moreover, the presence of Co2+ in the bacterial environment strongly affects the production of PCH. Proteomic and transcriptomic approaches showed that a decrease of PCH production is associated with repression of the expression of the genes involved in PCH biosynthesis. We used various molecular biology approaches to show that this repression is not Fur-(ferric uptake transcriptional regulator) dependent but due to competition of PCH-Co with PCH-Fe for PchR (transcriptional activator), thus inhibiting the formation of PchR-PCH-Fe and consequently the expression of the PCH genes. We observed a similar mechanism of repression of PCH production, but to a lesser extent, by Ni2+, but not for Zn2+, Cu2+, or Mn2+. Here, we show, for the first time at a molecular level, how the presence of a contaminant metal can interfere with Fe3+ acquisition by the siderophores PCH and PVD.
Published: 2019

26. Incorporating alignment uncertainty into Felsenstein's phylogenetic bootstrap to improve its reliability

Author: Olivier Gascuel, Jia-Ming Chang, Evan Floden, Paolo Di Tommaso, Cedric Notredame, Javier Herrero, European Bioinformatics Institute [Hinxton] (EMBL-EBI), EMBL Heidelberg, Centre for Genomic Regulation [Barcelona] (CRG), Universitat Pompeu Fabra [Barcelona] (UPF)-Centro Nacional de Analisis Genomico [Barcelona] (CNAG), Bioinformatique évolutive - Evolutionary Bioinformatics, Institut Pasteur [Paris]-Centre National de la Recherche Scientifique (CNRS), Universitat Pompeu Fabra [Barcelona] (UPF), This work was supported by the Spanish Ministry of Science Plan Nacional [BFU2008-00419 to P.D.T. and C.N.], the Wellcome Trust [WT095908 to P.F.], the INCEPTION project [PIA/ANR-16-CONV-0005 to O.G.], the Taiwan Ministry of Science and Technology [106-2221-E-004-011-MY2 to J.-M.C.]. We acknowledge support of the European Molecular Biology Laboratory, the Spanish Ministry of Economy and Competitiveness, 'Centro de Excelencia Severo Ochoa 2013-2017' and 'The Human Project from Mind, Brain and Learning' of NCCU from the Higher Education Sprout Project by the Ministry of Education in Taiwan., ANR-16-CONV-0005,INCEPTION,Institut Convergences pour l'étude de l'Emergence des Pathologies au Travers des Individus et des populatiONs(2016), and Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS)
Subjects: 0106 biological sciences, Statistics and Probability, AcademicSubjects/SCI01060, Concatenation, [SDV.BID.SPT]Life Sciences [q-bio]/Biodiversity/Systematics, Phylogenetics and taxonomy, 010603 evolutionary biology, 01 natural sciences, Biochemistry, Filogènia, 03 medical and health sciences, Clade, Molecular Biology, Gene, Bootstrapping (statistics), 030304 developmental biology, Mathematics, 0303 health sciences, Sequence, Multiple sequence alignment, Phylogenetic tree, [SDV.BID.EVO]Life Sciences [q-bio]/Biodiversity/Populations and Evolution [q-bio.PE], Original Papers, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], Computer Science Applications, Phylogenetics, Computational Mathematics, Computational Theory and Mathematics, Benchmark (computing), Algorithm, Genètica, Type I and type II errors
Abstract: Motivation: Most evolutionary analyses are based on pre-estimated multiple sequence alignment. Wong et al. established the existence of an uncertainty induced by multiple sequence alignment when reconstructing phylogenies. They were able to show that in many cases different aligners produce different phylogenies, with no simple objective criterion sufficient to distinguish among these alternatives. Results: We demonstrate that incorporating MSA induced uncertainty into bootstrap sampling can significantly increase correlation between clade correctness and its corresponding bootstrap value. Our procedure involves concatenating several alternative multiple sequence alignments of the same sequences, produced using different commonly used aligners. We then draw bootstrap replicates while favoring columns of the more unique aligner among the concatenated aligners. We named this concatenation and bootstrapping method, Weighted Partial Super Bootstrap (wpSBOOT). We show on three simulated datasets of 16, 32 and 64 tips that our method improves the predictive power of bootstrap values. We also used as a benchmark an empirical collection of 853 1-to-1 orthologous genes from seven yeast species and found wpSBOOT to significantly improve discrimination capacity between topologically correct and incorrect trees. Bootstrap values of wpSBOOT are comparable to similar readouts estimated using a single method. However, for reduced trees by 50% and 95% bootstrap thresholds, wpSBOOT comes out the lowest Type I error (less FP). Availability: The automated generation of replicates has been implemented in the T-Coffee package, which is available as open source freeware available from www.tcoffee.org. Supplementary information: Supplementary data are available at Bioinformatics online. This work was supported by the Spanish Ministry of Science Plan Nacional [BFU2008-00419 to P.D.T. and C.N.]; the Wellcome Trust [WT095908 to P.F.]; the INCEPTION project [PIA/ANR-16-CONV-0005 to O.G.]; the Taiwan Ministry of Science and Technology [106-2221-E-004-011-MY2 to J.-M.C.]. We acknowledge support of the European Molecular Biology Laboratory, the Spanish Ministry of Economy and Competitiveness, “Centro de Excelencia Severo Ochoa 2013-2017” and “The Human Project from Mind, Brain and Learning” of NCCU from the Higher Education Sprout Project by the Ministry of Education in Taiwan
Published: 2019

27. Reconstructing and predicting the evolution of drug resistances in HIV: Invited conference

Author: Gascuel, Olivier, Bioinformatique évolutive - Evolutionary Bioinformatics, Institut Pasteur [Paris]-Centre National de la Recherche Scientifique (CNRS), Institut Pasteur, and Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS)
Subjects: [SDV.BID.EVO]Life Sciences [q-bio]/Biodiversity/Populations and Evolution [q-bio.PE], [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], ComputingMilieux_MISCELLANEOUS
Abstract: International audience
Published: 2018

28. Phylogenetic Models

Author: Gascuel, Olivier, Bioinformatique évolutive - Evolutionary Bioinformatics, Institut Pasteur [Paris]-Centre National de la Recherche Scientifique (CNRS), and Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS)
Subjects: [SDV.BID.EVO]Life Sciences [q-bio]/Biodiversity/Populations and Evolution [q-bio.PE], [SDV.BID.SPT]Life Sciences [q-bio]/Biodiversity/Systematics, Phylogenetics and taxonomy, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], ComputingMilieux_MISCELLANEOUS
Abstract: International audience
Published: 2018

29. A Fast Likelihood Method to Reconstruct and Visualize Ancestral Scenarios

Author: Anna Zhukova, Wataru Iwasaki, Olivier Gascuel, Sohta A. Ishikawa, Bioinformatique évolutive - Evolutionary Bioinformatics, Institut Pasteur [Paris]-Centre National de la Recherche Scientifique (CNRS), Department of Biological Sciences, The University of Tokyo, Hongo 7-3-1, Bunkyoku, Tokyo 113-0033, Japan, Génomique évolutive des virus à ARN - Evolutionary genomics of RNA viruses, Institut Pasteur [Paris], This work was supported by the EU-H2020 Virogenesis project (Grant No. 634650, O.G.), by the INCEPTION project (PIA/ANR-16-CONV-0005, A.Z. and O.G.), and by Postdoctoral Fellowship and KAKENHI 282725 (S.A.I.), 16H06279 (W.I.), and 16H06154 (W.I.) from Japan Society for the Promotion of Science, ANR-16-CONV-0005,INCEPTION,Institut Convergences pour l'étude de l'Emergence des Pathologies au Travers des Individus et des populatiONs(2016), European Project: 634650,H2020,H2020-PHC-2014-two-stage,VIROGENESIS(2015), Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS), and Institut Pasteur [Paris] (IP)
Subjects: 0106 biological sciences, maximum a posteriori, Computer science, Posterior probability, phylogeography, Biology, [SDV.BID.SPT]Life Sciences [q-bio]/Biodiversity/Systematics, Phylogenetics and taxonomy, 010603 evolutionary biology, 01 natural sciences, Dengue, Set (abstract data type), 03 medical and health sciences, Decision Theory, Robustness (computer science), Graph drawing, Node (computer science), Methods, Genetics, Maximum a posteriori estimation, maximum likelihood, drug resistance mutations, Molecular Biology, Phylogeny, Ecology, Evolution, Behavior and Systematics, 030304 developmental biology, 0303 health sciences, Phylogenetic tree, business.industry, [SDV.BID.EVO]Life Sciences [q-bio]/Biodiversity/Populations and Evolution [q-bio.PE], Computational Biology, HIV, Brier scoring rule, Pattern recognition, Dengue Virus, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], ancestral character reconstruction, phylogenetics, Data set, Tree (data structure), Brier score, simulations, Artificial intelligence, business, marginal and joint posterior probabilities, Software
Abstract: The reconstruction of ancestral scenarios is widely used to study the evolution of characters along phylogenetic trees. One commonly uses the marginal posterior probabilities of the character states, or the joint reconstruction of the most likely scenario. However, marginal reconstructions provide users with state probabilities, which are difficult to interpret and visualize, whereas joint reconstructions select a unique state for every tree node and thus do not reflect the uncertainty of inferences. We propose a simple and fast approach, which is in between these two extremes. We use decision-theory concepts (namely, the Brier score) to associate each node in the tree to a set of likely states. A unique state is predicted in tree regions with low uncertainty, whereas several states are predicted in uncertain regions, typically around the tree root. To visualize the results, we cluster the neighboring nodes associated with the same states and use graph visualization tools. The method is implemented in the PastML program and web server. The results on simulated data demonstrate the accuracy and robustness of the approach. PastML was applied to the phylogeography of Dengue serotype 2 (DENV2), and the evolution of drug resistances in a large HIV data set. These analyses took a few minutes and provided convincing results. PastML retrieved the main transmission routes of human DENV2 and showed the uncertainty of the human-sylvatic DENV2 geographic origin. With HIV, the results show that resistance mutations mostly emerge independently under treatment pressure, but resistance clusters are found, corresponding to transmissions among untreated patients.
Published: 2018
Full Text: View/download PDF

30. Measuring dynamic social contacts in a rehabilitation hospital: effect of wards, patient and staff characteristics

Author: Audrey Duval, Thomas Obadia, Lucie Martinet, Pierre-Yves Boëlle, Eric Fleury, Didier Guillemot, Lulla Opatowski, Laura Temime, I-Bird study group, Biostatistique, Biomathématique, Pharmacoépidémiologie et Maladies Infectieuses (B2PHI), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Institut Pasteur [Paris]-Institut National de la Santé et de la Recherche Médicale (INSERM), Bioinformatique évolutive - Evolutionary Bioinformatics, Institut Pasteur [Paris]-Centre National de la Recherche Scientifique (CNRS), Institut Pasteur [Paris], Dynamic Networks : Temporal and Structural Capture Approach (DANTE), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire de l'Informatique du Parallélisme (LIP), Centre National de la Recherche Scientifique (CNRS)-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-École normale supérieure - Lyon (ENS Lyon)-Centre National de la Recherche Scientifique (CNRS)-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-École normale supérieure - Lyon (ENS Lyon)-Institut Rhône-Alpin des systèmes complexes (IXXI), École normale supérieure - Lyon (ENS Lyon)-Université Lumière - Lyon 2 (UL2)-Université Jean Moulin - Lyon 3 (UJML), Université de Lyon-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Université Lumière - Lyon 2 (UL2)-Université Jean Moulin - Lyon 3 (UJML), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Epidémiologie, Systèmes d'Information, Modélisation, Université Pierre et Marie Curie - Paris 6 (UPMC)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU), Laboratoire Modélisation, épidémiologie et surveillance des risques sanitaires (MESuRS), Conservatoire National des Arts et Métiers [CNAM] (CNAM), Pasteur-Cnam Risques infectieux et émergents (PACRI), Institut Pasteur [Paris]-Conservatoire National des Arts et Métiers [CNAM] (CNAM), This study was supported by the European Commission under the Life Science Health Priority of the 6th Framework Program (MOSAR network contract LSHP-CT-2007-037941). Funding was also received from the French Government through the National Clinical Research Program and the Investissement d’Avenir program, Laboratoire d’Excellence 'Integrative Biology of Emerging Infectious Diseases' (grant no. ANR-10-LABX-62-IBEID)., We thank Thomas Sorel for his work on the database and Judith Legrand for statistical advice, Consortia: I-Bird study group, Anne Sophie Alvarez, Audrey Baraffe, Mariano Beiró, Inga Bertucci, Camille Cyncynatus, Florence Dannet, Marie Laure Delaby, Pierre Denys, Matthieu Domenech de Cellès, Antoine Fraboulet, Jean-Louis Gaillard, Jean-Louis Herrmann, Boris Labrador, Jennifer Lasley, Christine Lawrence, Judith Legrand, Odile Le Minor, Caroline Ligier, Karine Mignon, Catherine Sacleux, Jérôme Salomon, Marie Perard, Laure Petit, Laeticia Remy, Anne Thiebaut, Damien Thomas, Philippe Tronchet & Isabelle Villain, ANR-10-LABX-0062,IBEID,Integrative Biology of Emerging Infectious Diseases(2010), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Institut Pasteur [Paris] (IP)-Institut National de la Santé et de la Recherche Médicale (INSERM), Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS), Institut Pasteur [Paris] (IP), École normale supérieure de Lyon (ENS de Lyon)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure de Lyon (ENS de Lyon)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut Rhône-Alpin des systèmes complexes (IXXI), École normale supérieure de Lyon (ENS de Lyon)-Université Lumière - Lyon 2 (UL2)-Université Jean Moulin - Lyon 3 (UJML), HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM), Institut Pasteur [Paris] (IP)-Conservatoire National des Arts et Métiers [CNAM] (CNAM), École normale supérieure - Lyon (ENS Lyon)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Lyon (ENS Lyon)-Université Claude Bernard Lyon 1 (UCBL), Fleury, Eric, and Integrative Biology of Emerging Infectious Diseases - - IBEID2010 - ANR-10-LABX-0062 - LABX - VALID
Subjects: 0301 basic medicine, Rehabilitation hospital, medicine.medical_specialty, Statistical methods, Epidemiology, lcsh:Medicine, Context (language use), Article, Care setting, 03 medical and health sciences, [INFO.INFO-MC]Computer Science [cs]/Mobile Computing, 0302 clinical medicine, [INFO.INFO-MC] Computer Science [cs]/Mobile Computing, medicine, 030212 general & internal medicine, Contact duration, Duration (project management), lcsh:Science, Health policy, Multidisciplinary, [SDV.BIBS] Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], Network topology, business.industry, lcsh:R, medicine.disease, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], 3. Good health, Care facility, Experimental models of disease, 030104 developmental biology, lcsh:Q, Medical emergency, business
Abstract: Understanding transmission routes of hospital-acquired infections (HAI) is key to improve their control. In this context, describing and analyzing dynamic inter-individual contact patterns in hospitals is essential. In this study, we used wearable sensors to detect Close Proximity Interactions (CPIs) among patients and hospital staff in a 200-bed long-term care facility over 4 months. First, the dynamic CPI data was described in terms of contact frequency and duration per individual status or activity and per ward. Second, we investigated the individual factors associated with high contact frequency or duration using generalized linear mixed-effect models to account for inter-ward heterogeneity. Hospital porters and physicians had the highest daily number of distinct contacts, making them more likely to disseminate HAI among individuals. Conversely, contact duration was highest between patients, with potential implications in terms of HAI acquisition risk. Contact patterns differed among hospital wards, reflecting varying care patterns depending on reason for hospitalization, with more frequent contacts in neurologic wards and fewer, longer contacts in geriatric wards. This study is the first to report proximity-sensing data informing on inter-individual contacts in long-term care settings. Our results should help better understand HAI spread, parameterize future mathematical models, and propose efficient control strategies.
Published: 2018

31. Improving pairwise comparison of protein sequences with domain co-occurrence

Author: Laurent Brehelin, Olivier Gascuel, Christophe Menichelli, Méthodes et Algorithmes pour la Bioinformatique (MAB), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Institut de Biologie Computationnelle (IBC), Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Bioinformatique évolutive - Evolutionary Bioinformatics, Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS), Institut Pasteur [Paris]-Centre National de la Recherche Scientifique (CNRS), and Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)
Subjects: Plasmodium, Computer science, Markov models, Protozoan Proteins, Biochemistry, 0302 clinical medicine, Sequence Analysis, Protein, False positive paradox, Feature (machine learning), Limit (mathematics), Hidden Markov models, Databases, Protein, lcsh:QH301-705.5, 0303 health sciences, Database and informatics methods, Sequence analysis, Physical sciences, Algorithms, Research Article, Multiple Alignment Calculation, Bioinformatics, Protein domain, Plasmodium falciparum, Sequence Databases, Computational biology, Domain (software engineering), 03 medical and health sciences, Protein Domains, Sequence Motif Analysis, Computational Techniques, Parasite Groups, Amino Acid Sequence, BLAST algorithm, 030304 developmental biology, Smith–Waterman algorithm, Co-occurrence, Biology and Life Sciences, Proteins, Computational Biology, Probability theory, Split-Decomposition Method, Research and analysis methods, Biological Databases, lcsh:Biology (General), Pairwise comparison, Parasitology, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], Sequence Alignment, Apicomplexa, 030217 neurology & neurosurgery, Mathematics
Abstract: Comparing and aligning protein sequences is an essential task in bioinformatics. More specifically, local alignment tools like BLAST are widely used for identifying conserved protein sub-sequences, which likely correspond to protein domains or functional motifs. However, to limit the number of false positives, these tools are used with stringent sequence-similarity thresholds and hence can miss several hits, especially for species that are phylogenetically distant from reference organisms. A solution to this problem is then to integrate additional contextual information to the procedure. Here, we propose to use domain co-occurrence to increase the sensitivity of pairwise sequence comparisons. Domain co-occurrence is a strong feature of proteins, since most protein domains tend to appear with a limited number of other domains on the same protein. We propose a method to take this information into account in a typical BLAST analysis and to construct new domain families on the basis of these results. We used Plasmodium falciparum as a case study to evaluate our method. The experimental findings showed an increase of 14% of the number of significant BLAST hits and an increase of 25% of the proteome area that can be covered with a domain. Our method identified 2240 new domains for which, in most cases, no model of the Pfam database could be linked. Moreover, our study of the quality of the new domains in terms of alignment and physicochemical properties show that they are close to that of standard Pfam domains. Source code of the proposed approach and supplementary data are available at: https://gite.lirmm.fr/menichelli/pairwise-comparison-with-cooccurrence, Author summary Deciphering the functions of the different proteins of an organism constitutes a first step toward the understanding of its biology. Because they provide strong clues regarding protein functions, domains occupy a key position among the relevant annotations that can be assigned to a protein. Protein domains are sequential motifs that are conserved along evolution and are found in different proteins and in different combinations. One common approach for identifying the domains of a protein is to run sequence-sequence comparisons with local alignment tools as BLAST. However these approaches sometimes miss several hits, especially for species that are phylogenetically distant from reference organisms. We propose here an approach to increase the sensitivity of pairwise sequence comparisons. This approach makes use of the fact that protein domains tend to appear with a limited number of other domains on the same protein (the domain co-occurrence property). On P. falciparum, our approach allows identifying 2240 new domains for which, in most cases, no domain of the Pfam database could be linked.
Published: 2018

32. Branching processes seen from their extinction time via path decompositions of reflected Lévy processes

Author: Miraine Dávila Felipe, Amaury Lambert, Laboratoire de Probabilités, Statistiques et Modélisations (LPSM (UMR_8001)), Université Paris Diderot - Paris 7 (UPD7)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Centre interdisciplinaire de recherche en biologie (CIRB), Labex MemoLife, École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Collège de France (CdF (institution))-Ecole Superieure de Physique et de Chimie Industrielles de la Ville de Paris (ESPCI Paris), Université Paris sciences et lettres (PSL)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Bioinformatique évolutive - Evolutionary Bioinformatics, Institut Pasteur [Paris]-Centre National de la Recherche Scientifique (CNRS), Laboratoire de Probabilités, Statistique et Modélisation (LPSM (UMR_8001)), École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-École normale supérieure - Paris (ENS-PSL), Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS), and Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Collège de France (CdF)-PSL Research University (PSL)
Subjects: Statistics and Probability, Pure mathematics, Lévy process conditioned to stay positive, Binary number, 01 natural sciences, Lévy process, 010104 statistics & probability, Esty transform, Mathematics::Probability, Random tree, 0101 mathematics, Invariant (mathematics), MSC (2000): Primary 60G51. Secondary 60G17, 60J55, 60J80, 60G51 (Primary), 60G17, 60J55, 60J80 (Secondary), Branching process, Mathematics, 60J80, 010102 general mathematics, Excursion, excursion measure, Williams decomposition, Infimum and supremum, [STAT]Statistics [stat], 60G17, Local time, duality, 60J55, Statistics, Probability and Uncertainty, Ray-Knight theorem, Mathematics - Probability, space-time reversal, 60G51
Abstract: We consider a spectrally positive Levy process $X$ that does not drift to $+\infty $, viewed as coding for the genealogical structure of a (sub)critical branching process, in the sense of a contour or exploration process [34, 29]. We denote by $I$ the past infimum process defined for each $t\geq 0$ by $I_t:= \inf _{[0,t]} X$ and we let $\gamma $ be the unique time at which the excursion of the reflected process $X-I$ away from 0 attains its supremum. We prove that the pre-$\gamma $ and the post-$\gamma $ subpaths of this excursion are invariant under space-time reversal, which has several other consequences in terms of duality for excursions of Levy processes. It implies in particular that the local time process of this excursion is also invariant when seen backward from its height. As a corollary, we obtain that some (sub)critical branching processes such as the binary, homogeneous (sub)critical Crump-Mode-Jagers (CMJ) processes and the excursion away from 0 of the critical Feller diffusion, which is the width process of the continuum random tree, are invariant under time reversal from their extinction time.
Published: 2018

33. Cohesins and condensins orchestrate the 4D dynamics of yeast chromosomes during the cell cycle

Author: Lazar‐Stefanita, Luciana, Scolari, Vittore F, Mercy, Guillaume, Muller, Héloise, Guérin, Thomas M, Thierry, Agnès, Mozziconacci, Julien, Koszul, Romain, Dynamique du Génome - Dynamics of the genome, Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS), Bioinformatique évolutive - Evolutionary Bioinformatics, ED 515 - Complexité du vivant, Université Pierre et Marie Curie - Paris 6 (UPMC), Institut de Radiobiologie Cellulaire et Moléculaire (IRCM), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay, Laboratoire de Physique Théorique de la Matière Condensée (LPTMC), Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS), This research was supported by funding to R.K. from the European Research Council under the 7th Framework Program (FP7/2007‐2013, ERC grant agreement 260822), from Agence Nationale pour la Recherche (MeioRec ANR‐13‐BSV6‐0012‐02), and from ERASynBio and Agence Nationale pour la Recherche (IESY ANR‐14‐SYNB‐0001‐03)., ANR-13-BSV6-0012,MeioRec,Dynamique chromosomique et recombinaison au cours de la méiose(2013), ANR-14-SYNB-0001,IESY,Induced Evolution of Synthetic Yeast genomes(2014), European Project: 260822,EC:FP7:ERC,ERC-2010-StG_20091118,DICIG(2011), Institut Pasteur [Paris]-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS)-Institut Pasteur [Paris], and Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)
Subjects: DNA Replication, [SDV.GEN]Life Sciences [q-bio]/Genetics, loop extrusion, SMC, Hi‐C, Cell Cycle, chromosome segregation, DNA Replication, Repair & Recombination, Articles, Chromatin, Epigenetics, Genomics & Functional Genomics, Article, Chromatin, Genomics & Functional Genomics, Hi-C, Repair & Recombination, Epigenetics, SMC Subject Categories Cell Cycle, replication profile
Abstract: International audience; Duplication and segregation of chromosomes involves dynamic reorganization of their internal structure by conserved architectural proteins, including the structural maintenance of chromosomes (SMC) complexes cohesin and condensin. Despite active investigation of the roles of these factors, a genome-wide view of dynamic chromosome architecture at both small and large scale during cell division is still missing. Here, we report the first comprehensive 4D analysis of the higher-order organization of the Saccharomyces cerevisiae genome throughout the cell cycle and investigate the roles of SMC complexes in controlling structural transitions. During replication, cohesion establishment promotes numerous long-range intra-chromosomal contacts and correlates with the individualization of chromosomes, which culminates at metaphase. In anaphase, mitotic chromosomes are abruptly reorganized depending on mechanical forces exerted by the mitotic spindle. Formation of a condensin-dependent loop bridging the centromere cluster with the rDNA loci suggests that condensin-mediated forces may also directly facilitate segregation. This work therefore comprehensively recapitulates cell cycle-dependent chromosome dynamics in a unicellular eukaryote, but also unveils new features of chromosome structural reorganization during highly conserved stages of cell division.
Published: 2017

34. The combinatorics of overlapping genes

Author: Olivier Gascuel, Sophie Lèbre, Institut Montpelliérain Alexander Grothendieck (IMAG), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Centre d’études d’Agents Pathogènes et Biotechologies pour la Santé (CPBS), Institut de Biologie Computationnelle (IBC), Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Université Paul-Valéry - Montpellier 3 (UPVM), Centre de Bioinformatique, Biostatistique et Biologie Intégrative (C3BI), Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS), Bioinformatique évolutive - Evolutionary Bioinformatics, Méthodes et Algorithmes pour la Bioinformatique (MAB), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Université Paul-Valéry - Montpellier 3 (UM3), Institut Pasteur [Paris]-Centre National de la Recherche Scientifique (CNRS), Université de Montpellier (UM)-Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS)-Institut Pasteur [Paris], and Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)
Subjects: 0106 biological sciences, 0301 basic medicine, Statistics and Probability, Stop codons, Reading frame, 010603 evolutionary biology, 01 natural sciences, General Biochemistry, Genetics and Molecular Biology, Combinatorics, 03 medical and health sciences, Open Reading Frames, double-coding sequences, Genes, Overlapping, Animals, Humans, Quantitative Biology - Genomics, Amino Acid Sequence, linear and logical constraints, Mathematics, Genomics (q-bio.GN), General Immunology and Microbiology, codon usage, Applied Mathematics, Null (mathematics), Frame (networking), Proteins, General Medicine, Genetic code, Biological Evolution, amino-acid composition, genetic code, Open reading frame, 030104 developmental biology, Sense strand, Modeling and Simulation, FOS: Biological sciences, overlapping gene detection, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], General Agricultural and Biological Sciences, Overlapping gene, Algorithms, Reference frame
Abstract: International audience; Overlapping genes exist in all domains of life and are much more abundant than expected upon their first discovery in the late 1970s. Assuming that the reference gene is read in frame +0, an overlapping gene can be encoded in two reading frames in the sense strand, denoted by +1 and +2, and in three reading frames in the opposite strand, denoted by-0,-1, and-2. This motivated numerous researchers to study the constraints induced by the genetic code on the various overlapping frames, mostly based on information theory. Our focus in this paper is on the constraints induced on two overlapping genes in terms of amino acids, as well as polypeptides. We show that simple linear constraints bind the amino-acid composition of two proteins encoded by overlapping genes. Novel constraints are revealed when polypeptides are considered, and not just single amino acids. For example, in double-coding sequences with an overlapping reading frame-2, each Tyrosine (denoted as Tyr or Y) in the overlapping frame overlaps a Tyrosine in the reference frame +0 (and reciprocally), whereas specific words (e.g. YY) never occur. We thus distinguish between null constraints (YY = 0 in frame-2) and non-null constraints (Y in frame +0 ó Y in frame-2). Our equivalence-based constraints are symmetrical and thus enable the characterization of the joint composition of overlapping proteins. We describe several formal frameworks and a graph algorithm to characterize and compute these constraints. As expected, the degrees of freedom left by these constraints vary drastically among the different overlapping frames. Interestingly, the biological meaning of constraints induced on two overlapping proteins (hydropathy, forbidden di-peptides, expected overlap length …) is also specific to the reading frame. We study the combinatorics of these constraints for overlapping polypeptides of length í µí± , pointing out that, (i) except for frame-2, non-null constraints are deduced from the amino-acid (length = 1) constraints and (ii) null constraints are deduced from the di-peptide (length = 2) constraints. These results yield support for understanding the mechanisms and evolution of overlapping genes, and for developing novel overlapping gene detection methods.
Published: 2017

35. Special Issue: Mathematical and Computational Evolutionary Biology-2015

Author: David Bryant, Olivier Gascuel, University of Otago [Dunedin, Nouvelle-Zélande], Bioinformatique évolutive - Evolutionary Bioinformatics, Institut Pasteur [Paris]-Centre National de la Recherche Scientifique (CNRS), Institut de Biologie Computationnelle (IBC), Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS), and Centre National de la Recherche Scientifique (CNRS)-Institut Pasteur [Paris]
Subjects: 0106 biological sciences, 0301 basic medicine, MEDLINE, Library science, MESH: Biological Evolution, Biological evolution, Biology, 010603 evolutionary biology, 01 natural sciences, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], 03 medical and health sciences, MESH: Classification, Editorial, 030104 developmental biology, Genetics, Ecology, Evolution, Behavior and Systematics, MESH: Congresses as Topic, Introductory Journal Article, MESH: Computational Biology
Abstract: International audience; This special issue of Systematic Biology contains review articles contributed by keynote speakers at the “Mathematical and Computational EvolutionaryBiology” conference (MCEB; see web sites below), held in 2015. This is the seventh conference in this series, the first being held in Paris in 2003 with keynote addresses from such notables as Joe Felsenstein, Ziheng Yang, and Walter Fitch. Oxford University Press published two books based on the 2003 and 2005 Paris meetings (Gascuel 2005; Gascuel and Steel 2007).
Published: 2017

36. Gradients of Rac1 Nanoclusters Support Spatial Patterns of Rac1 Signaling

Author: Jean-Baptiste Masson, Fanny Cayrac, Mathieu Coppey, Amanda Remorino, Alexis Gautreau, Fahima Di Federico, Gaetan Cornilleau, Maxime Dahan, Maria Carla Parrini, Simon de Beco, Physico-Chimie-Curie (PCC), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut Curie [Paris]-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS), Laboratoire de Biochimie de l'Ecole polytechnique (BIOC), École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS), Institut Curie [Paris], Décision et processus Bayesiens / Decision and Bayesian Computation, Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS), Bioinformatique évolutive - Evolutionary Bioinformatics, Centre National de la Recherche Scientifique (CNRS)-Institut Curie [Paris]-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut de Chimie du CNRS (INC), Institut Pasteur [Paris]-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS)-Institut Pasteur [Paris], and Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X)
Subjects: 0301 basic medicine, Phosphatidylinositol 4,5-Diphosphate, rac1 GTP-Binding Protein, Cell, Supramolecular chemistry, nanoclusters, RAC1, single molecule, [SDV.BC]Life Sciences [q-bio]/Cellular Biology, Biology, General Biochemistry, Genetics and Molecular Biology, Nanoclusters, Mice, 03 medical and health sciences, Membrane Microdomains, Phosphatidylinositol Phosphates, Cell polarity, Chlorocebus aethiops, medicine, Animals, Cytoskeleton, optogenetics, lcsh:QH301-705.5, Actin, 030304 developmental biology, Molecular switch, 0303 health sciences, Effector, Chemistry, GTPase-Activating Proteins, 030302 biochemistry & molecular biology, 3T3 Cells, Single Molecule Imaging, Cell biology, DNA-Binding Proteins, cell polarity, 030104 developmental biology, medicine.anatomical_structure, lcsh:Biology (General), COS Cells, Biophysics, Lamellipodium, signaling, Rac1, Signal Transduction, Transcription Factors, Micropatterning
Abstract: The dynamics of the cytoskeleton and cell shape relies on the coordinated activation of RhoGTPase molecular switches. Among them, Rac1 participates to the orchestration in space and time of actin branching and protrusion/retraction cycles of the lamellipodia at the cell front during mesenchymal migration. Biosensor imaging has revealed a graded concentration of active GTP-loaded Rac1 in protruding regions of the cell. Here, using single molecule imaging and super-resolution microscopy, we reveal an additional supramolecular organization of Rac1. We find that, similarly to H-Ras, Rac1 partitions and is immobilized into nanoclusters of 50-100 molecules each. These nanoclusters assemble due to the interaction of the polybasic tail of Rac1 with the phosphoinositide lipids PIP2 and PIP3. The additional interactions with GEFs, GAPs, downstream effectors, and possibly other partners are responsible for an enrichment of Rac1 nanoclusters in protruding regions of the cell. Using optogenetics and micropatterning tools, we find that activation of Rac1 leads to its immobilization in nanoclusters and that the local level of Rac1 activity matches the local density of nanoclusters. Altogether, our results show that subcellular patterns of Rac1 activity are supported by gradients of signaling nanodomains of heterogeneous molecular composition, which presumably act as discrete signaling platforms. This finding implies that graded distributions of nanoclusters might encode spatial information.Significance statementThe plasma membrane of eukaryotic cells is a highly organized surface where hundreds of incoming signals are transduced to the intracellular space. How cells encode faithfully this myriad of signals is a fundamental question. Here we show that Rac1, a critical membrane-bound protein involved in the regulation of cytoskeletal dynamics, forms small aggregates together with other regulating proteins. These supramolecular assemblies, called nanoclusters, are the “quantal” units of signaling. By increasing the local concentration, nanoclusters set thresholds for downstream signaling and ensure the fidelity of information transduction. We found that Rac1 nanoclusters are distributed as spatial gradients matching the patterns of Rac1 activity. We propose that cells can encode positional information through distributed signaling quanta, hereby ensuring spatial fidelity.
Published: 2017

37. Inferring epidemiological parameters from phylogenies using regression-ABC: a comparative study

Author: Saulnier, Emma, Gascuel, Olivier, Alizon, Samuel, Maladies infectieuses et vecteurs : écologie, génétique, évolution et contrôle (MIVEGEC), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud]), Institut de Biologie Computationnelle (IBC), Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Méthodes et Algorithmes pour la Bioinformatique (MAB), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Bioinformatique évolutive - Evolutionary Bioinformatics, Institut Pasteur [Paris]-Centre National de la Recherche Scientifique (CNRS), Evolution Théorique et Expérimentale (MIVEGEC-ETE), Perturbations, Evolution, Virulence (PEV), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud])-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud])-Maladies infectieuses et vecteurs : écologie, génétique, évolution et contrôle (MIVEGEC), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud])-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud]), Centre National de la Recherche Scientifique (CNRS)-Institut Pasteur [Paris], Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS), Centre interdisciplinaire de recherche en biologie (CIRB), Labex MemoLife, École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Collège de France (CdF (institution))-Ecole Superieure de Physique et de Chimie Industrielles de la Ville de Paris (ESPCI Paris), Université Paris sciences et lettres (PSL)-École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), and Institut de Recherche pour le Développement (IRD [France-Sud])-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)
Subjects: Computer and Information Sciences, Leaves, Epidemiology, Plant Science, Research and Analysis Methods, [SDV.BID.SPT]Life Sciences [q-bio]/Biodiversity/Systematics, Phylogenetics and taxonomy, Disease Outbreaks, Sierra Leone, Kernel Methods, Risk Factors, Epidemiological Statistics, Medicine and Health Sciences, Humans, Computer Simulation, Evolutionary Systematics, lcsh:QH301-705.5, Phylogeny, Taxonomy, Data Management, Evolutionary Biology, Models, Statistical, Incidence, Plant Anatomy, Simulation and Modeling, Applied Mathematics, [SDV.BID.EVO]Life Sciences [q-bio]/Biodiversity/Populations and Evolution [q-bio.PE], Biology and Life Sciences, Bayes Theorem, Phylogenetic Analysis, Hemorrhagic Fever, Ebola, Ebolavirus, Virus Latency, Phylogenetics, [SDV.MP]Life Sciences [q-bio]/Microbiology and Parasitology, lcsh:Biology (General), Physical Sciences, Regression Analysis, Epidemiological Methods and Statistics, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], Algorithms, Mathematics, Research Article
Abstract: Inferring epidemiological parameters such as the R0 from time-scaled phylogenies is a timely challenge. Most current approaches rely on likelihood functions, which raise specific issues that range from computing these functions to finding their maxima numerically. Here, we present a new regression-based Approximate Bayesian Computation (ABC) approach, which we base on a large variety of summary statistics intended to capture the information contained in the phylogeny and its corresponding lineage-through-time plot. The regression step involves the Least Absolute Shrinkage and Selection Operator (LASSO) method, which is a robust machine learning technique. It allows us to readily deal with the large number of summary statistics, while avoiding resorting to Markov Chain Monte Carlo (MCMC) techniques. To compare our approach to existing ones, we simulated target trees under a variety of epidemiological models and settings, and inferred parameters of interest using the same priors. We found that, for large phylogenies, the accuracy of our regression-ABC is comparable to that of likelihood-based approaches involving birth-death processes implemented in BEAST2. Our approach even outperformed these when inferring the host population size with a Susceptible-Infected-Removed epidemiological model. It also clearly outperformed a recent kernel-ABC approach when assuming a Susceptible-Infected epidemiological model with two host types. Lastly, by re-analyzing data from the early stages of the recent Ebola epidemic in Sierra Leone, we showed that regression-ABC provides more realistic estimates for the duration parameters (latency and infectiousness) than the likelihood-based method. Overall, ABC based on a large variety of summary statistics and a regression method able to perform variable selection and avoid overfitting is a promising approach to analyze large phylogenies., Author summary Given the rapid evolution of many pathogens, analysing their genomes by means of phylogenies can inform us about how they spread. This is the focus of the field known as “phylodynamics”. Most existing methods inferring epidemiological parameters from virus phylogenies are limited by the difficulty of handling complex likelihood functions, which commonly incorporate latent variables. Here, we use an alternative method known as regression-based Approximate Bayesian Computation (ABC), which circumvents this problem by using simulations and dataset comparisons. Since phylogenies are difficult to compare to one another, we introduce many summary statistics to describe them and take advantage of current machine learning techniques able to perform variable selection. We show that the accuracy we reach is comparable to that of existing methods. This accuracy increases with phylogeny size and can even be higher than that of existing methods for some parameters. Overall, regression-based ABC opens new perspectives to infer epidemiological parameters from large phylogenies.
Published: 2017

38. Inferring the physical properties of yeast chromatin through Bayesian analysis of whole nucleus simulations

Author: Arbona, Jean-Michel, Herbert, Sébastien, Fabre, Emmanuelle, Zimmer, Christophe, Imagerie et Modélisation - Imaging and Modeling, Institut Pasteur [Paris]-Centre National de la Recherche Scientifique (CNRS), Centre de Bioinformatique, Biostatistique et Biologie Intégrative (C3BI), Bioinformatique évolutive - Evolutionary Bioinformatics, Cellule Pasteur, Université Paris Diderot - Paris 7 (UPD7)-PRES Sorbonne Paris Cité, Biologie et Dynamique des Chromosomes [Groupe Hospitalier Saint-Louis-Lariboisière-Fernand-Widal] (Equipe), Pathologie cellulaire : aspects moléculaires et viraux / Pathologie et Virologie Moléculaire, Institut Universitaire d'Hématologie (IUH), Université Paris Diderot - Paris 7 (UPD7)-Université Paris Diderot - Paris 7 (UPD7)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Groupe Hospitalier Saint Louis - Lariboisière - Fernand Widal [Paris], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Centre National de la Recherche Scientifique (CNRS)-Institut Universitaire d'Hématologie (IUH), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Centre National de la Recherche Scientifique (CNRS)-Université Sorbonne Paris Cité (USPC), This work was funded by Institut Pasteur, Agence Nationale de la Recherche (grant ANR-11-MONU-020–02 to C.Z.), Fondation pour la Recherche Médicale (Equipe FRM DEQ20150331762 grant to C.Z.) and a Labex Who am I grant (EE-2013 to E.F). J-M.A. was recipient of a Pasteur-Roux fellowship from Institut Pasteur., ANR-11-MONU-0020,ProbAlg,Algorithmes efficients pour modèles réalistes à grand échelle : développements fondamentaux et applications(2011), Université Paris Diderot - Paris 7 (UPD7)-Université Paris Diderot - Paris 7 (UPD7)-Groupe Hospitalier Saint Louis - Lariboisière - Fernand Widal [Paris], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut Universitaire d'Hématologie (IUH), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Sorbonne Paris Cité (USPC), Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS), BMC, BMC, and Modèles Numériques - Algorithmes efficients pour modèles réalistes à grand échelle : développements fondamentaux et applications - - ProbAlg2011 - ANR-11-MONU-0020 - MN - VALID
Subjects: Cell Nucleus, lcsh:QH426-470, Research, [SDV]Life Sciences [q-bio], Nuclear architecture, Bayes Theorem, Saccharomyces cerevisiae, Chromatin Assembly and Disassembly, Yeast, Chromatin, Chromosomes, [SDV] Life Sciences [q-bio], lcsh:Genetics, lcsh:Biology (General), Computer Simulation, lcsh:QH301-705.5, Polymer models
Abstract: Background The structure and mechanical properties of chromatin impact DNA functions and nuclear architecture but remain poorly understood. In budding yeast, a simple polymer model with minimal sequence-specific constraints and a small number of structural parameters can explain diverse experimental data on nuclear architecture. However, how assumed chromatin properties affect model predictions was not previously systematically investigated. Results We used hundreds of dynamic chromosome simulations and Bayesian inference to determine chromatin properties consistent with an extensive dataset that includes hundreds of measurements from imaging in fixed and live cells and two Hi-C studies. We place new constraints on average chromatin fiber properties, narrowing down the chromatin compaction to ~53–65 bp/nm and persistence length to ~52–85 nm. These constraints argue against a 20–30 nm fiber as the exclusive chromatin structure in the genome. Our best model provides a much better match to experimental measurements of nuclear architecture and also recapitulates chromatin dynamics measured on multiple loci over long timescales. Conclusion This work substantially improves our understanding of yeast chromatin mechanics and chromosome architecture and provides a new analytic framework to infer chromosome properties in other organisms. Electronic supplementary material The online version of this article (doi:10.1186/s13059-017-1199-x) contains supplementary material, which is available to authorized users.
Published: 2016

39. Generalized Bootstrap Supports for Phylogenetic Analyses of Protein Sequences Incorporating Alignment Uncertainty

Author: Olivier Gascuel, Maria Chatzou, Paolo Di Tommaso, Cedric Notredame, Evan Floden, Centre for Genomic Regulation [Barcelona] (CRG), Universitat Pompeu Fabra [Barcelona] (UPF)-Centro Nacional de Analisis Genomico [Barcelona] (CNAG), Méthodes et Algorithmes pour la Bioinformatique (MAB), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Bioinformatique évolutive - Evolutionary Bioinformatics, Institut Pasteur [Paris]-Centre National de la Recherche Scientifique (CNRS), We acknowledge support of the Spanish Ministry of Economy and Competitiveness, ‘Centro de Excelencia Severo Ochoa 2013–2017. We acknowledge the support of the CERCA Programme/Generalitat de Catalunya, Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), and Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS)
Subjects: 0106 biological sciences, 0301 basic medicine, Sequence analysis, Sequence alignment, Biology, [SDV.BID.SPT]Life Sciences [q-bio]/Biodiversity/Systematics, Phylogenetics and taxonomy, 010603 evolutionary biology, 01 natural sciences, 03 medical and health sciences, Genetics, Fraction (mathematics), Ecology, Evolution, Behavior and Systematics, Phylogeny, Sequence, Multiple sequence alignment, Phylogenetic tree, Models, Genetic, [SDV.BID.EVO]Life Sciences [q-bio]/Biodiversity/Populations and Evolution [q-bio.PE], Uncertainty, Sampling (statistics), Proteins, Classification, Tree (data structure), 030104 developmental biology, Bootstrap analysis, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], Algorithm, Sequence Alignment, Software
Abstract: International audience; Phylogenetic reconstructions are essential in genomics data analyses and depend on accurate multiple sequence alignment (MSA) models. We show that all currently available large-scale progressive multiple alignment methods are numerically unstable when dealing with amino-acid sequences. They produce significantly different output when changing sequence input order. We used the HOMFAM protein sequences dataset to show that on datasets larger than 100 sequences, this instability affects on average 21.5% of the aligned residues. The resulting Maximum Likelihood (ML) trees estimated from these MSAs are equally unstable with over 38% of the branches being sensitive to the sequence input order. We established that about two-thirds of this uncertainty stems from the unordered nature of children nodes within the guide trees used to estimate MSAs. To quantify this uncertainty we developed unistrap, a novel approach that estimates the combined effect of alignment uncertainty and site sampling on phylogenetic tree branch supports. Compared with the regular bootstrap procedure, unistrap provides branch support estimates that take into account a larger fraction of the parameters impacting tree instability when processing datasets containing a large number of sequences.
Published: 2016

40. Efficiently Analysing Large Viral Data Sets in Computational Phylogenomics

Author: Anna Zhukova, Olivier Gascuel, Sebastian Duchene, Daniel Ayres, Philippe Lemey, Guy Baele, Bioinformatique évolutive - Evolutionary Bioinformatics, Institut Pasteur [Paris]-Centre National de la Recherche Scientifique (CNRS), University of Melbourne, The Peter Doherty Institute for Infection and Immunity [Melbourne], The Royal Melbourne Hospital-University of Melbourne, Center for Bioinformatics and Computational Biology [Maryland] (CBCB), University of Maryland [College Park], University of Maryland System-University of Maryland System, Rega Institute for Medical Research [Leuven, België], Catholic University of Leuven - Katholieke Universiteit Leuven (KU Leuven), Scornavacca, Celine, Delsuc, Frédéric, Galtier, Nicolas, University of Melbourne-The Royal Melbourne Hospital, and Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS)
Subjects: [SDV.BID.EVO]Life Sciences [q-bio]/Biodiversity/Populations and Evolution [q-bio.PE], [SDV.BBM.GTP]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Genomics [q-bio.GN], Quantitative Biology::Populations and Evolution, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM]
Abstract: International audience; Viral evolutionary analyses are confronted with increasingly large sequence data sets, both in terms of sequence length and number of sequences. This can result in considerable computational burden, not only to infer phylogenies but also to obtain associated estimates such as their time scales and phylogeographic patterns. Here, we illustrate two frequently-used approaches to obtain phylogenomic estimates of time-measured trees and spatial dispersal patterns for fast-evolving viruses. First, we discuss computationally efficient procedures that employ a fixed tree topology obtained through maximum likelihood inference to estimate molecular clock rates and phylogeographic spread for Dengue virus genomes. Using the same viral example, we also illustrate Bayesian phylodynamic inference that jointly infers time-measured trees and phylogeo-graphy, including covariates of spatial dispersal, from sequence and trait data. We highlight state-of-the-art efforts to perform such computations more efficiently. Finally, we compare the estimates obtained by both approaches and discuss their strengths and potential pitfalls.

Catalog

Books, media, physical & digital resources

See catalog results

Searchworks

Select search scope, currently: Articles Catalog books, media & more in Jio Institute collections Articles journal articles & other e-resources

Search

Search Constraints

Refine your results

Search Limiters

Topic

Publication Year Range

Language

Journal

Database

Publisher

40 results on '"Bioinformatique évolutive - Evolutionary Bioinformatics"'

Search Results

Catalog

Select search scope, currently: Articles

Catalog

books, media & more in Jio Institute collections

Articles

journal articles & other e-resources