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1. Cooperativity, Specificity, and Evolutionary Stability of Polycomb Targeting in Drosophila

Author

Bernd Schuettengruber, Noa Oded Elkayam, Tom Sexton, Marianne Entrevan, Shani Stern, Aubin Thomas, Eitan Yaffe, Hugues Parrinello, Amos Tanay, and Giacomo Cavalli

Subjects
Biology (General), QH301-705.5
Abstract

Summary: Metazoan genomes are partitioned into modular chromosomal domains containing active or repressive chromatin. In flies, Polycomb group (PcG) response elements (PREs) recruit PHO and other DNA-binding factors and act as nucleation sites for the formation of Polycomb repressive domains. The sequence specificity of PREs is not well understood. Here, we use comparative epigenomics and transgenic assays to show that Drosophila domain organization and PRE specification are evolutionarily conserved despite significant cis-element divergence within Polycomb domains, whereas cis-element evolution is strongly correlated with transcription factor binding divergence outside of Polycomb domains. Cooperative interactions of PcG complexes and their recruiting factor PHO stabilize PHO recruitment to low-specificity sequences. Consistently, PHO recruitment to sites within Polycomb domains is stabilized by PRC1. These data suggest that cooperative rather than hierarchical interactions among low-affinity sequences, DNA-binding factors, and the Polycomb machinery are giving rise to specific and strongly conserved 3D structures in Drosophila. : Schuettengruber et al. present an extensive comparative epigenomics data set, providing new insights into cis-driven versus buffered evolution of Polycomb recruitment and Polycomb domain specificity. Using chromatin immunoprecipitation sequencing and transgenic assays, they demonstrate an extremely high conservation of Polycomb repressive domains in five Drosophila species. Using Hi-C and knockout experiments, they challenge the standard hierarchical Polycomb recruitment model and demonstrate that cooperative rather than hierarchical interactions among DNA motifs, transcription factors, and Polycomb group complexes define Polycomb domains.

Published
2014
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2. TALC: Transcript-level Aware Long-read Correction

Author

Dany Severac, Andrew J. Oldfield, Aubin Thomas, William Ritchie, Lucile Broseus, Emeric Dubois, Institut de génétique humaine (IGH), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Institut de Génomique Fonctionnelle - Montpellier GenomiX (IGF MGX), Institut de Génomique Fonctionnelle (IGF), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-BioCampus (BCM), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Montpellier (UM)-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), Oldfield, Andrew, and Université de Montpellier (UM)-Université Montpellier 1 (UM1)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Montpellier 2 - Sciences et Techniques (UM2)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Université Montpellier 1 (UM1)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Montpellier 2 - Sciences et Techniques (UM2)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Statistics and Probability, Gene isoform, [INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], Computer science, Transcript level, computer.software_genre, Biochemistry, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Transcriptome, 03 medical and health sciences, 0302 clinical medicine, [SDV.BBM.GTP]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Genomics [q-bio.GN], Molecular Biology, 030304 developmental biology, [INFO.INFO-BI] Computer Science [cs]/Bioinformatics [q-bio.QM], 0303 health sciences, RNA, High-Throughput Nucleotide Sequencing, Genomics, Sequence Analysis, DNA, Computer Science Applications, Transcriptome Sequencing, Computational Mathematics, Rna expression, Computational Theory and Mathematics, [SDV.BBM.GTP] Life Sciences [q-bio]/Biochemistry, Molecular Biology/Genomics [q-bio.GN], Data mining, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], computer, 030217 neurology & neurosurgery, Algorithms, Software
Abstract

Motivation Long-read sequencing technologies are invaluable for determining complex RNA transcript architectures but are error-prone. Numerous ‘hybrid correction’ algorithms have been developed for genomic data that correct long reads by exploiting the accuracy and depth of short reads sequenced from the same sample. These algorithms are not suited for correcting more complex transcriptome sequencing data. Results We have created a novel reference-free algorithm called Transcript-level Aware Long-Read Correction (TALC) which models changes in RNA expression and isoform representation in a weighted De Bruijn graph to correct long reads from transcriptome studies. We show that transcript-level aware correction by TALC improves the accuracy of the whole spectrum of downstream RNA-seq applications and is thus necessary for transcriptome analyses that use long read technology. Availability and implementation TALC is implemented in C++ and available at https://github.com/lbroseus/TALC. Supplementary information Supplementary data are available at Bioinformatics online.

Published
2020
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3. Digital Law Commons

Author

Saint-Aubin, Thomas, Institut de recherche juridique de la Sorbonne (Paris), and Saint-Aubin, Thomas

Subjects
[SHS.DROIT]Humanities and Social Sciences/Law, [SHS.DROIT] Humanities and Social Sciences/Law
Abstract

International audience; L'ouverture des données publiques juridiques en Europe transforme progressivement le marché du droit. Plus qu’une transition numérique du monde du droit, c’est une transformation du droit qui est en marche: l’avocat doit repenser sa raison d’être et sa plus-value dans ce nouveau paradigme.

Published
2018

4. GECKO is a genetic algorithm to classify and explore high throughput sequencing data

Author

Grégory Beurier, Julie Brooke, Christelle Reynes, William Ritchie, Sylvain Barriere, Robert Sabatier, Alban Mancheron, Jean-Philippe Villemin, Lucile Broseus, Aubin Thomas, Claudio Lorenzi, Institut de génétique humaine (IGH), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), 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), Institut de Génomique Fonctionnelle (IGF), Institut National de la Santé et de la Recherche Médicale (INSERM)-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), 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), French National Research Agency (ANR), Labex EpiGenMed, MUSE initiative, Institut National de la Recherche Agronomique (INRA)-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)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Université de Montpellier (UM)-Université Montpellier 1 (UM1)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Montpellier 2 - Sciences et Techniques (UM2)-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)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), and 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)

Subjects
Computer science, Medicine (miscellaneous), Genome, F30 - Génétique et amélioration des plantes, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], 0302 clinical medicine, Software, Séquence d'ADN, apprentissage machine, lcsh:QH301-705.5, 0303 health sciences, U10 - Informatique, mathématiques et statistiques, High-Throughput Nucleotide Sequencing, 030220 oncology & carcinogenesis, Bio-informatique, General Agricultural and Biological Sciences, Algorithms, Data Structures and Algorithms, Bioinformatics, Sequencing data, Algorithme et structure de données, [INFO.INFO-DS]Computer Science [cs]/Data Structures and Algorithms [cs.DS], Predictive medicine, Breast Neoplasms, Feature selection, Computational biology, Data loss, Article, General Biochemistry, Genetics and Molecular Biology, DNA sequencing, 03 medical and health sciences, Bioinformatique, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], Machine learning, Genetic algorithm, Humans, RNA, Messenger, 030304 developmental biology, Blood Cells, business.industry, Computational Biology, DNA Methylation, MicroRNAs, lcsh:Biology (General), Sciences médicales, Mutation, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], business
Abstract

Comparative analysis of high throughput sequencing data between multiple conditions often involves mapping of sequencing reads to a reference and downstream bioinformatics analyses. Both of these steps may introduce heavy bias and potential data loss. This is especially true in studies where patient transcriptomes or genomes may vary from their references, such as in cancer. Here we describe a novel approach and associated software that makes use of advances in genetic algorithms and feature selection to comprehensively explore massive volumes of sequencing data to classify and discover new sequences of interest without a mapping step and without intensive use of specialized bioinformatics pipelines. We demonstrate that our approach called GECKO for GEnetic Classification using k-mer Optimization is effective at classifying and extracting meaningful sequences from multiple types of sequencing approaches including mRNA, microRNA, and DNA methylome data., Aubin Thomas, Sylvain Barriere et al. present a computational method for classifying and extracting meaningful sequences from high-throughput sequencing data. The method, called GECKO, uses k-mer counts that are able to classify the input data with high accuracy.

Published
2019
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6. Cooperativity, specificity, and evolutionary stability of polycomb targeting in Drosophila

Author

Shani Stern, Marianne Entrevan, Noa Oded Elkayam, Aubin Thomas, Giacomo Cavalli, Hugues Parrinello, Tom Sexton, Bernd Schuettengruber, Amos Tanay, Eitan Yaffe, Institut de génétique humaine (IGH), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Institut de Génomique Fonctionnelle - Montpellier GenomiX (IGF MGX), Institut de Génomique Fonctionnelle (IGF), and Université de Montpellier (UM)-Université Montpellier 1 (UM1)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Montpellier 2 - Sciences et Techniques (UM2)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Université Montpellier 1 (UM1)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Montpellier 2 - Sciences et Techniques (UM2)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Genetics, [SDV.GEN]Life Sciences [q-bio]/Genetics, Transgene, fungi, Polycomb-Group Proteins, Cooperativity, macromolecular substances, Biology, biology.organism_classification, Genome, General Biochemistry, Genetics and Molecular Biology, Chromatin, Cell biology, lcsh:Biology (General), [SDV.GEN.GH]Life Sciences [q-bio]/Genetics/Human genetics, Animals, Drosophila Proteins, Drosophila, PRC1, Drosophila (subgenus), lcsh:QH301-705.5, Transcription factor, Protein Binding, Transcription Factors, Epigenomics
Abstract

Summary: Metazoan genomes are partitioned into modular chromosomal domains containing active or repressive chromatin. In flies, Polycomb group (PcG) response elements (PREs) recruit PHO and other DNA-binding factors and act as nucleation sites for the formation of Polycomb repressive domains. The sequence specificity of PREs is not well understood. Here, we use comparative epigenomics and transgenic assays to show that Drosophila domain organization and PRE specification are evolutionarily conserved despite significant cis-element divergence within Polycomb domains, whereas cis-element evolution is strongly correlated with transcription factor binding divergence outside of Polycomb domains. Cooperative interactions of PcG complexes and their recruiting factor PHO stabilize PHO recruitment to low-specificity sequences. Consistently, PHO recruitment to sites within Polycomb domains is stabilized by PRC1. These data suggest that cooperative rather than hierarchical interactions among low-affinity sequences, DNA-binding factors, and the Polycomb machinery are giving rise to specific and strongly conserved 3D structures in Drosophila. : Schuettengruber et al. present an extensive comparative epigenomics data set, providing new insights into cis-driven versus buffered evolution of Polycomb recruitment and Polycomb domain specificity. Using chromatin immunoprecipitation sequencing and transgenic assays, they demonstrate an extremely high conservation of Polycomb repressive domains in five Drosophila species. Using Hi-C and knockout experiments, they challenge the standard hierarchical Polycomb recruitment model and demonstrate that cooperative rather than hierarchical interactions among DNA motifs, transcription factors, and Polycomb group complexes define Polycomb domains.

Published
2014
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7. Topoisomerase I suppresses genomic instability by preventing interference between replication and transcription

Author

John De Vos, Laure Crabbe, Anna Jauch, Véronique Pantesco, Yves Pommier, Aubin Thomas, Heidi Holtgreve-Grez, Charles Theillet, Philippe Pasero, Sandie Tuduri, Arnaud Coquelle, Jamal Tazi, Hélène Tourrière, Chiara Conti, Institut de génétique humaine (IGH), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Institut de recherche en cancérologie de Montpellier (IRCM - U896 Inserm - UM1), CRLCC Val d'Aurelle - Paul Lamarque-Université de Montpellier (UM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Montpellier 1 (UM1), Institut de recherche en biothérapie (IRB), Université Montpellier 1 (UM1)-Centre Hospitalier Régional Universitaire [Montpellier] (CHRU Montpellier)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Montpellier (UM), Université Montpellier 1 - UFR de Médecine (UM1 Médecine), Université Montpellier 1 (UM1), Unité de thérapie cellulaire, Centre Hospitalier Régional Universitaire [Montpellier] (CHRU Montpellier)-CHU Saint-Eloi, Institut de Génétique Moléculaire de Montpellier (IGMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Institut de génétique humaine ( IGH ), Université de Montpellier ( UM ) -Centre National de la Recherche Scientifique ( CNRS ), Institut de recherche en cancérologie de Montpellier ( IRCM ), Université Montpellier 1 ( UM1 ) -CRLCC Val d'Aurelle - Paul Lamarque-Institut National de la Santé et de la Recherche Médicale ( INSERM ) -Université de Montpellier ( UM ), Institut de recherche en biothérapie ( IRB ), Université Montpellier 1 ( UM1 ) -Centre Hospitalier Régional Universitaire [Montpellier] ( CHRU Montpellier ) -Université de Montpellier ( UM ), Montpellier 1 - UFR de Médecine ( UM1 Médecine ), Université Montpellier 1 ( UM1 ), Centre Hospitalier Régional Universitaire [Montpellier] ( CHRU Montpellier ) -CHU Saint-Eloi, and Institut de Génétique Moléculaire de Montpellier ( IGMM )

Subjects
DNA re-replication, DNA Replication, Chromatin Immunoprecipitation, Transcription, Genetic, Eukaryotic DNA replication, Biology, Pre-replication complex, Article, Genomic Instability, S Phase, DNA replication factor CDT1, 03 medical and health sciences, 0302 clinical medicine, Replication factor C, Minichromosome maintenance, Control of chromosome duplication, Animals, ComputingMilieux_MISCELLANEOUS, 030304 developmental biology, 0303 health sciences, [SDV.GEN]Life Sciences [q-bio]/Genetics, Cell Biology, Molecular biology, Cell biology, DNA Topoisomerases, Type I, 030220 oncology & carcinogenesis, biology.protein, Origin recognition complex, [ SDV.GEN ] Life Sciences [q-bio]/Genetics
Abstract

Topoisomerase I (Top1) is a key enzyme in functioning at the interface between DNA replication, transcription and mRNA maturation. Here, we show that Top1 suppresses genomic instability in mammalian cells by preventing a conflict between transcription and DNA replication. Using DNA combing and ChIP (chromatin immunoprecipitation)-on-chip, we found that Top1-deficient cells accumulate stalled replication forks and chromosome breaks in S phase, and that breaks occur preferentially at gene-rich regions of the genome. Notably, these phenotypes were suppressed by preventing the formation of RNA-DNA hybrids (R-loops) during transcription. Moreover, these defects could be mimicked by depletion of the splicing factor ASF/SF2 (alternative splicing factor/splicing factor 2), which interacts functionally with Top1. Taken together, these data indicate that Top1 prevents replication fork collapse by suppressing the formation of R-loops in an ASF/SF2-dependent manner. We propose that interference between replication and transcription represents a major source of spontaneous replication stress, which could drive genomic instability during the early stages of tumorigenesis.

Published
2010

8. Analysis of replication profiles reveals key role of RFC-Ctf18 in yeast replication stress response

Author

Véronique Pantesco, Armelle Lengronne, Philippe Pasero, Aubin Thomas, John De Vos, Laure Crabbe, Institut de génétique humaine (IGH), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Institut de recherche en biothérapie (IRB), and Centre Hospitalier Régional Universitaire [Montpellier] (CHRU Montpellier)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Montpellier 1 (UM1)-Université de Montpellier (UM)

Subjects
DNA Replication, Saccharomyces cerevisiae Proteins, Mutant, DNA, Single-Stranded, Cell Cycle Proteins, Saccharomyces cerevisiae, Biology, 03 medical and health sciences, 0302 clinical medicine, Control of chromosome duplication, Stress, Physiological, Structural Biology, Replication (statistics), Hydroxyurea, Molecular Biology, ComputingMilieux_MISCELLANEOUS, 030304 developmental biology, Genetics, 0303 health sciences, [SDV.GEN]Life Sciences [q-bio]/Genetics, Replication stress, Intracellular Signaling Peptides and Proteins, Budding yeast, Yeast, 3. Good health, DNA replication checkpoint, Phosphorylation, Carrier Proteins, 030217 neurology & neurosurgery, DNA Damage
Abstract

Maintenance of genome integrity relies on surveillance mechanisms that detect and signal arrested replication forks. Although evidence from budding yeast indicates that the DNA replication checkpoint (DRC) is primarily activated by single-stranded DNA (ssDNA), studies in higher eukaryotes have implicated primer ends in this process. To identify factors that signal primed ssDNA in Saccharomyces cerevisiae, we have screened a collection of checkpoint mutants for their ability to activate the DRC, using the repression of late origins as readout for checkpoint activity. This quantitative analysis reveals that neither RFC(Rad24) and the 9-1-1 clamp nor the alternative clamp loader RFC(Elg1) is required to signal paused forks. In contrast, we found that RFC(Ctf18) is essential for the Mrc1-dependent activation of Rad53 and for the maintenance of paused forks. These data identify RFC(Ctf18) as a key DRC mediator, potentially bridging Mrc1 and primed ssDNA to signal paused forks.

Published
2010
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9. Lack of Clinical Benefit of Thromboprophylaxis in Patients Hospitalized in a Medical Unit Over a 10-year Span.

Author

Migner-Laurin G, St-Aubin T, Lapointe J, Van Nguyen P, Wistaff R, Laskin M, Kolan C, and Lamarre-Cliche M

Abstract

Background: Thromboprophylaxis for hospitalized patients with a high risk of venous thromboembolic events (VTEs) is strongly recommended but is not universally applied on medical units. Outside of randomized trials, there is minimal evidence that the usual medications reduce the incidence of clinically significant VTE., Methods: We conducted a retrospective cohort study including all patients admitted into a teaching medical unit during years 2001-2002, 2003-2004, 2005-2006, 2007-2008 and 2009-2010. Inclusion criteria for the analysis were having one or more risk factors for a VTE and no contraindication to thromboprophylaxis., Results: Of 2,369 patients reviewed, 1,302 satisfied the inclusion criteria. Between years 2001-2002 and 2009-2010, the proportion of patients receiving thromboprophylaxis increased from 29.2% to 76.4% (P < 0.0001) and the duration of thromboprophylaxis increased from 63% of hospital stay to 84% (P = 0.004). There was no statistically significant association between the number of risk factors and the rate of thromboprophylaxis. Overall, only 32 patients suffered from a VTE with no decrease in VTE incidence between years 2001-2002 and 2009-2010. A total of 107 patients had a bleeding event, and there was no statistically significant change in the incidence of bleeding during our study period., Conclusions: In our medical units, we found a statistically significant increase in the use of the thromboprophylaxis practice. However, this was not associated with any statistically significant impact on the VTE incidence. This suggests that patients given thromboprophylaxis could be better selected.

Published
2014
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