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1. 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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2. 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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3. Identification of regulators of the three-dimensional polycomb organization by a microscopy-based genome-wide RNAi screen

Author

Thierry Cheutin, Aubin Thomas, Inma González, Giacomo Cavalli, Julio Mateos-Langerak, Institut de génétique humaine (IGH), and Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)

Subjects
genetic structures, SUMO protein, Polycomb-Group Proteins, macromolecular substances, Biology, Cell Line, 03 medical and health sciences, 0302 clinical medicine, Gene silencing, Animals, Cluster Analysis, Drosophila Proteins, Molecular Biology, Gene, Psychological repression, ComputingMilieux_MISCELLANEOUS, Loss function, 030304 developmental biology, Genetics, Cell Nucleus, [SDV.GEN]Life Sciences [q-bio]/Genetics, 0303 health sciences, fungi, Sumoylation, Cell Biology, Sumoylation Pathway, Chromatin, Cell biology, Protein Transport, Drosophila melanogaster, Gene Ontology, Phenotype, Imaginal Discs, Epigenetic Repression, Gene Knockdown Techniques, RNA Interference, 030217 neurology & neurosurgery, Protein Binding
Abstract

Polycomb group (PcG) proteins dynamically define cellular identities through epigenetic repression of key developmental genes. PcG target gene repression can be stabilized through the interaction in the nucleus at PcG foci. Here, we report the results of a high-resolution microscopy genome-wide RNAi screen that identifies 129 genes that regulate the nuclear organization of Pc foci. Candidate genes include PcG components and chromatin factors, as well as many protein-modifying enzymes, including components of the SUMOylation pathway. In the absence of SUMO, Pc foci coagulate into larger aggregates. Conversely, loss of function of the SUMO peptidase Velo disperses Pc foci. Moreover, SUMO and Velo colocalize with PcG proteins at PREs, and Pc SUMOylation affects its chromatin targeting, suggesting that the dynamic regulation of Pc SUMOylation regulates PcG-mediated silencing by modulating the kinetics of Pc binding to chromatin as well as its ability to form Polycomb foci.

Published
2013
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4. 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

5. 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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6. Erratum: Topoisomerase I suppresses genomic instability by preventing interference between replication and transcription

Author

Sandie Tuduri, Laure Crabbé, Chiara Conti, Hélène Tourrière, Heidi Holtgreve-Grez, Anna Jauch, Véronique Pantesco, John De Vos, Aubin Thomas, Charles Theillet, Yves Pommier, Jamal Tazi, Arnaud Coquelle, Philippe Pasero, 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), 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), 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), and Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)

Subjects
[SDV.GEN]Life Sciences [q-bio]/Genetics, 0303 health sciences, 03 medical and health sciences, 0302 clinical medicine, 030220 oncology & carcinogenesis, Cell Biology, ComputingMilieux_MISCELLANEOUS, 030304 developmental biology
Abstract

International audience

Published
2010
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