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1. AI Competitions and Benchmarks, Practical issues: Proposals, grant money, sponsors, prizes, dissemination, publicity

Author

Richard, Magali, Blum, Yuna, Guinney, Justin, Stolovitzky, Gustavo, and Pavão, Adrien

Subjects
Computer Science - Machine Learning
Abstract

This chapter provides a comprehensive overview of the pragmatic aspects involved in organizing AI competitions. We begin by discussing strategies to incentivize participation, touching upon effective communication techniques, aligning with trending topics in the field, structuring awards, potential recruitment opportunities, and more. We then shift to the essence of community engagement, and into organizational best practices and effective means of disseminating challenge outputs. Lastly, the chapter addresses the logistics, exposing on costs, required manpower, and resource allocation for effectively managing and executing a challenge. By examining these practical problems, readers will gain actionable insights to navigate the multifaceted landscape of AI competition organization, from inception to completion.

Published
2024

2. Codabench: Flexible, Easy-to-Use and Reproducible Benchmarking Platform

Author

Xu, Zhen, Escalera, Sergio, Guyon, Isabelle, Pavão, Adrien, Richard, Magali, Tu, Wei-Wei, Yao, Quanming, and Zhao, Huan

Subjects
Computer Science - Machine Learning, Computer Science - Distributed, Parallel, and Cluster Computing
Abstract

Obtaining standardized crowdsourced benchmark of computational methods is a major issue in data science communities. Dedicated frameworks enabling fair benchmarking in a unified environment are yet to be developed. Here we introduce Codabench, an open-source, community-driven platform for benchmarking algorithms or software agents versus datasets or tasks. A public instance of Codabench (https://www.codabench.org/) is open to everyone, free of charge, and allows benchmark organizers to compare fairly submissions, under the same setting (software, hardware, data, algorithms), with custom protocols and data formats. Codabench has unique features facilitating the organization of benchmarks flexibly, easily and reproducibly, such as the possibility of re-using templates of benchmarks, and supplying compute resources on-demand. Codabench has been used internally and externally on various applications, receiving more than 130 users and 2500 submissions. As illustrative use cases, we introduce 4 diverse benchmarks covering Graph Machine Learning, Cancer Heterogeneity, Clinical Diagnosis and Reinforcement Learning.

Published
2021
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3. Pacpaint: a histology-based deep learning model uncovers the extensive intratumor molecular heterogeneity of pancreatic adenocarcinoma

Author

Saillard, Charlie, Delecourt, Flore, Schmauch, Benoit, Moindrot, Olivier, Svrcek, Magali, Bardier-Dupas, Armelle, Emile, Jean Francois, Ayadi, Mira, Rebours, Vinciane, de Mestier, Louis, Hammel, Pascal, Neuzillet, Cindy, Bachet, Jean Baptiste, Iovanna, Juan, Dusetti, Nelson, Blum, Yuna, Richard, Magali, Kermezli, Yasmina, Paradis, Valerie, Zaslavskiy, Mikhail, Courtiol, Pierre, Kamoun, Aurelie, Nicolle, Remy, and Cros, Jerome

Published
2023
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6. Abstract B064: Redefining phenotypic heterogeneity of pancreatic ductal adenocarcinoma: A bottom-up approach

Author

Hilmi, Marc, primary, Delecourt, Flore, additional, Rafenne, Jerôme, additional, Bourega, Taib, additional, Dusetti, Nelson, additional, Iovanna, Juan, additional, Blum, Yuna, additional, Richard, Magali, additional, Neuzillet, Cindy, additional, Couvelard, Anne, additional, De Mestier, Louis, additional, Rebours, Vinciane, additional, Nicolle, Rémy, additional, and Cros, Jérôme, additional

Published
2024
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7. Redefining phenotypic intratumor heterogeneity of pancreatic ductal adenocarcinoma: a bottom-up approach

Author

Hilmi, Marc, primary, Delecourt, Flore, additional, Raffenne, Jérôme, additional, Bourega, Taib, additional, Dusetti, Nelson, additional, Iovanna, Juan, additional, Blum, Yuna, additional, Richard, Magali, additional, Neuzillet, Cindy, additional, Couvelard, Anne, additional, de Mestier, Louis, additional, Rebours, Vinciane, additional, Nicolle, Rémy, additional, and Cros, Jérôme, additional

Published
2023
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10. Additional file 1 of DECONbench: a benchmarking platform dedicated to deconvolution methods for tumor heterogeneity quantification

Author

Decamps, Clémentine, Arnaud, Alexis, Petitprez, Florent, Ayadi, Mira, Baurès, Aurélia, Armenoult, Lucile, Escalera, Sergio, Guyon, Isabelle, Nicolle, Rémy, Tomasini, Richard, de Reyniès, Aurélien, Cros, Jérôme, Blum, Yuna, and Richard, Magali

Subjects
FOS: Computer and information sciences, FOS: Biological sciences, Genetics, FOS: Mathematics, 19999 Mathematical Sciences not elsewhere classified, 80699 Information Systems not elsewhere classified, 69999 Biological Sciences not elsewhere classified, Cancer
Abstract

Additional file 1: Figure S1: DECONbench benchmark of OLS and RLR methods: an example of graphical outputs of new contributions to the benchmark. Source code.

Published
2021
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12. MOESM2 of Guidelines for cell-type heterogeneity quantification based on a comparative analysis of reference-free DNA methylation deconvolution software

Author

Decamps, Clémentine, Privé, Florian, Bacher, Raphael, Jost, Daniel, Waguet, Arthur, Houseman, Eugene, Lurie, Eugene, Lutsik, Pavlo, Milosavljevic, Aleksandar, Scherer, Michael, Blum, Michael, and Richard, Magali

Abstract

Additional file 2: Table S1. Number of remaining probes in Fig. 3. Table S2. Number of remaining probes in Additional file 1: Figure S8. Table S3. Mean execution time in Fig. 7 (minutes). Table S4. Table of cells used for simulations. Table S5. Informative loci.

Published
2020
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15. First contribution of the excavation and the chronostratigraphic study of Ruways 1 Neolithic shell midden (Oman), in terms of Neolithization, palaeoeconomy, social-environmental interactions and site formation processes

Author

Berger, Jean-Francois, Guilbert-Berger, Raphaelle, Anaïs, Marrast, Munoz, Olivia, Guy, Hervé, Barra, Adrien, López Sáez, José Antonio, Pérez-Díaz, S., Mashkour, Marian, Mougne, Caroline, Lefevre, Christine, Stéphanie, Thorin, Oberlin, Christine, Mercier, Norbert, Debue, K., Richard, Magali, Depreux, Bruno, Béarez, Philippe, Charpentier, Vincent, Environnement Ville Société (EVS), École normale supérieure - Lyon (ENS Lyon)-École des Mines de Saint-Étienne (Mines Saint-Étienne MSE), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université Lumière - Lyon 2 (UL2)-Université Jean Moulin - Lyon 3 (UJML), Université de Lyon-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)-Université Jean Monnet - Saint-Étienne (UJM)-École Nationale des Travaux Publics de l'État (ENTPE)-École nationale supérieure d'architecture de Lyon (ENSAL)-Centre National de la Recherche Scientifique (CNRS), Institut national de recherches archéologiques préventives (Inrap), Archéozoologie, archéobotanique : sociétés, pratiques et environnements (AASPE), Muséum national d'Histoire naturelle (MNHN)-Centre National de la Recherche Scientifique (CNRS), Trajectoires - UMR 8215, Université Paris 1 Panthéon-Sorbonne (UP1)-Centre National de la Recherche Scientifique (CNRS), Archéologies et Sciences de l'Antiquité (ArScAn), Université Paris 1 Panthéon-Sorbonne (UP1)-Université Paris 8 Vincennes-Saint-Denis (UP8)-Université Paris Nanterre (UPN)-Ministère de la Culture et de la Communication (MCC)-Institut national de recherches archéologiques préventives (Inrap)-Centre National de la Recherche Scientifique (CNRS), Ministère de la Culture et de la Communication, Centre d'Études Préhistoire, Antiquité, Moyen-Age (CEPAM), Université Nice Sophia Antipolis (1965 - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS), Consejo Superior de Investigaciones Científicas [Madrid] (CSIC), Archéologie et Archéométrie (ArAr), Université Lumière - Lyon 2 (UL2)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS), Institut de Recherches sur les Archéomatériaux (IRAMAT), Université de Technologie de Belfort-Montbeliard (UTBM)-Université d'Orléans (UO)-Université Bordeaux Montaigne-Centre National de la Recherche Scientifique (CNRS), IRAMAT-Centre de recherche en physique appliquée à l’archéologie (IRAMAT-CRP2A), Université de Technologie de Belfort-Montbeliard (UTBM)-Université d'Orléans (UO)-Université Bordeaux Montaigne-Centre National de la Recherche Scientifique (CNRS)-Université de Technologie de Belfort-Montbeliard (UTBM)-Université d'Orléans (UO)-Université Bordeaux Montaigne-Centre National de la Recherche Scientifique (CNRS), and ANR-16-CE03-0007,NeoArabia,Analyse de la durabilité et des réorganisations des systèmes socio-environnementaux du Néolithique côtier arabique à l'Holocène moyen (6.2-2.8 ka BCE)(2016)

Subjects
[SHS.ARCHEO]Humanities and Social Sciences/Archaeology and Prehistory, [SHS.ANTHRO-BIO]Humanities and Social Sciences/Biological anthropology
Abstract

International audience

Published
2018

34. DECONbench: a benchmarking platform dedicated to deconvolution methods for tumor heterogeneity quantification

Author

Decamps, Clémentine, Arnaud, Alexis, Petitprez, Florent, Ayadi, Mira, Baurès, Aurélia, Armenoult, Lucile, Alcala, N., Arnaud, A., Avila Cobos, Francisco, Batista, Luciana, Batto, A.-F., Blum, Y., Chuffart, F., Cros, J., Decamps, C., Dirian, L., Doncevic, D., Durif, G., Bahena Hernandez, S. Y., Jakobi, M., Jardillier, R., Jeanmougin, M., Jedynak, P., Jumentier, B., Kakoichankava, A., Kondili, Maria, Liu, J., Maie, T., Marécaille, J., Merlevede, J., Meylan, M., Nazarov, P., Newar, K., Nyrén, K., Petitprez, F., Novella Rausell, C., Richard, M., Scherer, M., Sompairac, N., Waury, K., Xie, T., Zacharouli, M.-A., Escalera, Sergio, Guyon, Isabelle, Nicolle, Rémy, Tomasini, Richard, de Reyniès, Aurélien, Cros, Jérôme, Blum, Yuna, Richard, Magali, HADACA consortium, [missing], Translational Innovation in Medicine and Complexity / Recherche Translationnelle et Innovation en Médecine et Complexité - UMR 5525 (TIMC ), VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA), Ligue Nationale Contre le Cancer - Paris, Ligue Nationnale Contre le Cancer, University of Barcelona, Laboratoire Interdisciplinaire des Sciences du Numérique (LISN), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), A&O (Apprentissage et Optimisation) (A&O), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Science des Données (SDD), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Algorithmes, Apprentissage et Calcul (AAC), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Centre de Recherche en Cancérologie de Marseille (CRCM), Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut Paoli-Calmettes, Fédération nationale des Centres de lutte contre le Cancer (FNCLCC)-Fédération nationale des Centres de lutte contre le Cancer (FNCLCC)-Aix Marseille Université (AMU), Institut de Génétique et Développement de Rennes (IGDR), Structure Fédérative de Recherche en Biologie et Santé de Rennes ( Biosit : Biologie - Santé - Innovation Technologique )-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES), The research leading to these results was supported by Univ. Grenoble-Alpes via the Grenoble Alpes Data Institute [MR, AA] (ANR-15-IDEX-02), EIT Health Campus HADACA and COMETH programs [MR, YB], activities 19359 and 20377 and the Ligue Nationale Contre le Cancer. Other fundings: South-Eastern Norway Regional Health Authority (project number 2019030 [MJ]), European IMI IMMUCAN project [NS], European Union's Horizon 2020 program (Grant 826121, iPC project, [JM, FAC]). This article did not receive specific sponsorship in the design of the study, analysis, interpretation of data and in writing the manuscript., ANR-15-IDEX-0002,UGA,IDEX UGA(2015), European Project: 826121,iPaediatricCurie, Biologie Computationnelle et Modélisation (TIMC-BCM ), Université Grenoble Alpes (UGA)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Ligue Nationale Contre le Cancer (LNCC), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Science des Données (SDD), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Algorithmes, Apprentissage et Calcul (AAC), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Aix Marseille Université (AMU)-Institut Paoli-Calmettes, Fédération nationale des Centres de lutte contre le Cancer (FNCLCC)-Fédération nationale des Centres de lutte contre le Cancer (FNCLCC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Université de Rennes (UR)-Centre National de la Recherche Scientifique (CNRS)-Structure Fédérative de Recherche en Biologie et Santé de Rennes ( Biosit : Biologie - Santé - Innovation Technologique ), Richard, Magali, IDEX UGA - - UGA2015 - ANR-15-IDEX-0002 - IDEX - VALID, and European Union’s Horizon 2020 program (grant No. 826121, iPC project) - iPaediatricCurie - 826121 - INCOMING

Subjects
Classificació de tumors, Source code, Computer science, Deconvolution, computer.software_genre, Biochemistry, Omics integration, 0302 clinical medicine, Resource (project management), Structural Biology, Medicine and Health Sciences, Biology (General), Càncer, media_common, Cancer, 0303 health sciences, [SDV.BIBS] Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], DNA methylation, Applied Mathematics, Benchmarking, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], Computer algorithms, 3. Good health, Computer Science Applications, Benchmarking platform, 030220 oncology & carcinogenesis, Benchmark (computing), Algorithms, EXPRESSION, Cellular heterogeneity, QH301-705.5, media_common.quotation_subject, Computer applications to medicine. Medical informatics, R858-859.7, Adenocarcinoma, Machine learning, Set (abstract data type), 03 medical and health sciences, Computational Deconvolution, Humans, Leverage (statistics), Algorismes computacionals, CELL, Molecular Biology, 030304 developmental biology, business.industry, Tumors classification, Computational Biology, Gold standard (test), Pancreatic Neoplasms, Artificial intelligence, business, Transcriptome, computer, Software
Abstract

Background Quantification of tumor heterogeneity is essential to better understand cancer progression and to adapt therapeutic treatments to patient specificities. Bioinformatic tools to assess the different cell populations from single-omic datasets as bulk transcriptome or methylome samples have been recently developed, including reference-based and reference-free methods. Improved methods using multi-omic datasets are yet to be developed in the future and the community would need systematic tools to perform a comparative evaluation of these algorithms on controlled data. Results We present DECONbench, a standardized unbiased benchmarking resource, applied to the evaluation of computational methods quantifying cell-type heterogeneity in cancer. DECONbench includes gold standard simulated benchmark datasets, consisting of transcriptome and methylome profiles mimicking pancreatic adenocarcinoma molecular heterogeneity, and a set of baseline deconvolution methods (reference-free algorithms inferring cell-type proportions). DECONbench performs a systematic performance evaluation of each new methodological contribution and provides the possibility to publicly share source code and scoring. Conclusion DECONbench allows continuous submission of new methods in a user-friendly fashion, each novel contribution being automatically compared to the reference baseline methods, which enables crowdsourced benchmarking. DECONbench is designed to serve as a reference platform for the benchmarking of deconvolution methods in the evaluation of cancer heterogeneity. We believe it will contribute to leverage the benchmarking practices in the biomedical and life science communities. DECONbench is hosted on the open source Codalab competition platform. It is freely available at: https://competitions.codalab.org/competitions/27453.

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35. Rewriting The Book Of Life.

Author

Boglioli, Elsy and Richard, Magali

Subjects
GENOME editing, PHARMACEUTICAL industry, GENETIC engineering, NUCLEASE genetics, BUSINESS partnerships, COMMERCIALIZATION
Abstract

The article reports on the advancements in gene-editing technology and its impact on the pharmaceutical industry in 2015. Topics discussed include the introduction of nuclease-based gene editing and its engineering possibilities, the partnership of drug companies with gene-editing technology providers and the factors to be considered, and key developments related to commercialization.

Published
2015

36. CRELD1 is an evolutionarily-conserved maturational enhancer of ionotropic acetylcholine receptors

Author

Manuela D'Alessandro, Magali Richard, Christian Stigloher, Vincent Gache, Thomas Boulin, Janet E Richmond, Jean-Louis Bessereau, Institut NeuroMyoGène (INMG), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), University of Illinois [Chicago] (UIC), University of Illinois System, Boulin, Thomas, Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM), and Richard, Magali

Subjects
Heart Defects, Congenital, QH301-705.5, [SDV]Life Sciences [q-bio], Science, Protein Disulfide-Isomerases, Cys-loop receptor, [SDV.BC]Life Sciences [q-bio]/Cellular Biology, Cell Line, neuroscience, acetylcholine receptor (AChR), Mice, cell biology, Animals, Receptors, Cholinergic, Biology (General), Caenorhabditis elegans, Caenorhabditis elegans Proteins, protein disulphide isomerase (PDI), [SDV.BC] Life Sciences [q-bio]/Cellular Biology, mouse, Extracellular Matrix Proteins, Muscle Cells, neuromuscular junction (NMJ), Genetic Complementation Test, endoplasmic reticulum (ER), [SDV] Life Sciences [q-bio], Gene Knockdown Techniques, Synapses, C. elegans, Medicine, Protein Multimerization, Cell Adhesion Molecules, Protein Binding, Research Article
Abstract

International audience; The assembly of neurotransmitter receptors in the endoplasmic reticulum limits the number of receptors delivered to the plasma membrane, ultimately controlling neurotransmitter sensitivity and synaptic transfer function. In a forward genetic screen conducted in the nematode C. elegans, we identified crld-1 as a gene required for the synaptic expression of ionotropic acetylcholine receptors (AChR). We demonstrated that the CRLD-1A isoform is a membrane-associated ER-resident protein disulfide isomerase (PDI). It physically interacts with AChRs and promotes the assembly of AChR subunits in the ER. Mutations of Creld1, the human ortholog of crld-1a, are responsible for developmental cardiac defects. We showed that Creld1 knockdown in mouse muscle cells decreased surface expression of AChRs and that expression of mouse Creld1 in C. elegans rescued crld-1a mutant phenotypes. Altogether these results identify a novel and evolutionarily-conserved maturational enhancer of AChR biogenesis, which controls the abundance of functional receptors at the cell surface.

Published
2018

37. Genomics of cellular proliferation in periodic environmental fluctuations

Author

Jérôme Salignon, Magali Richard, Etienne Fulcrand, Hélène Duplus‐bottin, Gaël Yvert, Richard, Magali, Laboratoire de biologie et modélisation de la cellule (LBMC UMR 5239), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-École normale supérieure - Lyon (ENS Lyon)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM), Laboratoire de Biologie Moléculaire de la Cellule (LBMC), Institut National de la Santé et de la Recherche Médicale (INSERM)-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, Biologie Computationnelle et Mathématique (TIMC-IMAG-BCM), Techniques de l'Ingénierie Médicale et de la Complexité - Informatique, Mathématiques et Applications, Grenoble - UMR 5525 (TIMC-IMAG), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), École normale supérieure de Lyon (ENS de Lyon)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), and École normale supérieure - Lyon (ENS Lyon)-Université Claude Bernard Lyon 1 (UCBL)

Subjects
Salinity, Saccharomyces cerevisiae Proteins, [SDV]Life Sciences [q-bio], selection, Saccharomyces cerevisiae, [SDV.GEN] Life Sciences [q-bio]/Genetics, yeast, Article, stress, Methionine, Stress, Physiological, Gene Expression Regulation, Fungal, Selection, Genetic, Quantitative Biology & Dynamical Systems, ComputingMilieux_MISCELLANEOUS, Cell Proliferation, [SDV.GEN]Life Sciences [q-bio]/Genetics, Models, Genetic, fluctuating environment, Gene Expression Profiling, Articles, fitness, [SDV] Life Sciences [q-bio], Mutation, Genome-Scale & Integrative Biology, Genetic Fitness, Gene Deletion, Signal Transduction
Abstract

Living systems control cell growth dynamically by processing information from their environment. Although responses to a single environmental change have been intensively studied, little is known about how cells react to fluctuating conditions. Here, we address this question at the genomic scale by measuring the relative proliferation rate (fitness) of 3,568 yeast gene deletion mutants in out‐of‐equilibrium conditions: periodic oscillations between two environmental conditions. In periodic salt stress, fitness and its genetic variance largely depended on the oscillating period. Surprisingly, dozens of mutants displayed pronounced hyperproliferation under short stress periods, revealing unexpected controllers of growth under fast dynamics. We validated the implication of the high‐affinity cAMP phosphodiesterase and of a regulator of protein translocation to mitochondria in this group. Periodic oscillations of extracellular methionine, a factor unrelated to salinity, also altered fitness but to a lesser extent and for different genes. The results illustrate how natural selection acts on mutations in a dynamic environment, highlighting unsuspected genetic vulnerabilities to periodic stress in molecular processes that are conserved across all eukaryotes.

Published
2018

50. DECONbench: a benchmarking platform dedicated to deconvolution methods for tumor heterogeneity quantification.

Author

Decamps C, Arnaud A, Petitprez F, Ayadi M, Baurès A, Armenoult L, Escalera S, Guyon I, Nicolle R, Tomasini R, de Reyniès A, Cros J, Blum Y, and Richard M

Subjects
Algorithms, Benchmarking, Computational Biology, Humans, Adenocarcinoma, Pancreatic Neoplasms genetics
Abstract

Background: Quantification of tumor heterogeneity is essential to better understand cancer progression and to adapt therapeutic treatments to patient specificities. Bioinformatic tools to assess the different cell populations from single-omic datasets as bulk transcriptome or methylome samples have been recently developed, including reference-based and reference-free methods. Improved methods using multi-omic datasets are yet to be developed in the future and the community would need systematic tools to perform a comparative evaluation of these algorithms on controlled data., Results: We present DECONbench, a standardized unbiased benchmarking resource, applied to the evaluation of computational methods quantifying cell-type heterogeneity in cancer. DECONbench includes gold standard simulated benchmark datasets, consisting of transcriptome and methylome profiles mimicking pancreatic adenocarcinoma molecular heterogeneity, and a set of baseline deconvolution methods (reference-free algorithms inferring cell-type proportions). DECONbench performs a systematic performance evaluation of each new methodological contribution and provides the possibility to publicly share source code and scoring., Conclusion: DECONbench allows continuous submission of new methods in a user-friendly fashion, each novel contribution being automatically compared to the reference baseline methods, which enables crowdsourced benchmarking. DECONbench is designed to serve as a reference platform for the benchmarking of deconvolution methods in the evaluation of cancer heterogeneity. We believe it will contribute to leverage the benchmarking practices in the biomedical and life science communities. DECONbench is hosted on the open source Codalab competition platform. It is freely available at: https://competitions.codalab.org/competitions/27453 ., (© 2021. The Author(s).)

Published
2021
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