Search

Your search keyword '"Galtier, Nicolas"' showing total 18 results
Start Over Author "Galtier, Nicolas" Topic [sdv.bbm.gtp]life sciences [q-bio]/biochemistry, molecular biology/genomics [q-bio.gn]
18 results on '"Galtier, Nicolas"'

1. Phylogenetics in the Genomic Era

Author

Scornavacca, Celine, Delsuc, Frédéric, Galtier, Nicolas, Institut des Sciences de l'Evolution de Montpellier (UMR ISEM), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Montpellier (UM)-Institut de recherche pour le développement [IRD] : UR226-Centre National de la Recherche Scientifique (CNRS), Scornavacca, Celine, Delsuc, Frédéric, Galtier, Nicolas, Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-École Pratique des Hautes Études (EPHE), Bibal, Christine, École pratique des hautes études (EPHE), and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Institut de recherche pour le développement [IRD] : UR226

Subjects
[SDV.BIBS] Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], [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], [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], [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], ComputingMilieux_MISCELLANEOUS
Abstract

International audience

Published
2020

2. Influence of Recombination and GC-biased Gene Conversion on the Adaptive and Nonadaptive Substitution Rate in Mammals versus Birds

Author

Rousselle, Marjolaine, Laverré, Alexandre, Figuet, Emeric, Nabholz, Benoit, Galtier, Nicolas, Institut des Sciences de l'Evolution de Montpellier (UMR ISEM), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Montpellier (UM)-Institut de recherche pour le développement [IRD] : UR226-Centre National de la Recherche Scientifique (CNRS), and Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-École Pratique des Hautes Études (EPHE)

Subjects
GC-biased gene conversion, [SDV.GEN.GPO]Life Sciences [q-bio]/Genetics/Populations and Evolution [q-bio.PE], coding sequence evolution, birds, primates, [SDV.BBM.GTP]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Genomics [q-bio.GN], [SDV.BID.SPT]Life Sciences [q-bio]/Biodiversity/Systematics, Phylogenetics and taxonomy, recombination, adaptive substitution rate
Abstract

International audience; Recombination is expected to affect functional sequence evolution in several ways. On the one hand, recombination is thought to improve the efficiency of multilocus selection by dissipating linkage disequilibrium. On the other hand, natural selection can be counteracted by recombination-associated transmission distorters such as GC-biased gene conversion (gBGC), which tends to promote G and C alleles irrespective of their fitness effect in high-recombining regions. It has been suggested that gBGC might impact coding sequence evolution in vertebrates, and particularly the ratio of nonsynonymous to synonymous substitution rates (dN/dS). However, distinctive gBGC patterns have been reported in mammals and birds, maybe reflecting the documented contrasts in evolutionary dynamics of recombination rate between these two taxa. Here, we explore how recombination and gBGC affect coding sequence evolution in mammals and birds by analyzing proteome-wide data in six species of Galloanserae (fowls) and six species of catarrhine primates. We estimated the dN/dS ratio and rates of adaptive and nonadaptive evolution in bins of genes of increasing recombination rate, separately analyzing AT ! GC, GC ! AT, and G $ C/A $ T mutations. We show that in both taxa, recombination and gBGC entail a decrease in dN/dS. Our analysis indicates that recombination enhances the efficiency of purifying selection by lowering Hill-Robertson effects, whereas gBGC leads to an overestimation of the adaptive rate of AT ! GC mutations. Finally, we report a mutagenic effect of recombination, which is independent of gBGC.

Published
2019
Full Text
View/download PDF

3. Lacking conservation genomics in the giant Galápagos tortoise

Author

Loire, Etienne, Galtier, Nicolas, Université de Montpellier (UM), Institut des Sciences de l'Evolution de Montpellier (UMR ISEM), École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Montpellier (UM)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre National de la Recherche Scientifique (CNRS)-Institut de recherche pour le développement [IRD] : UR226, Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Animal, Santé, Territoires, Risques et Ecosystèmes (UMR ASTRE), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-École Pratique des Hautes Études (EPHE), and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Montpellier (UM)-Institut de recherche pour le développement [IRD] : UR226-Centre National de la Recherche Scientifique (CNRS)

Subjects
0106 biological sciences, Galapagos tortoise, [SDV.GEN]Life Sciences [q-bio]/Genetics, 0303 health sciences, education.field_of_study, Genetic diversity, [SDV.GEN.GPO]Life Sciences [q-bio]/Genetics/Populations and Evolution [q-bio.PE], [SDV]Life Sciences [q-bio], Population, Endangered species, Genomics, [SDV.BID]Life Sciences [q-bio]/Biodiversity, Biology, biology.organism_classification, [SDV.BID.SPT]Life Sciences [q-bio]/Biodiversity/Systematics, Phylogenetics and taxonomy, 010603 evolutionary biology, 01 natural sciences, Population genomics, 03 medical and health sciences, Evolutionary biology, Genetic marker, [SDV.BBM.GTP]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Genomics [q-bio.GN], Microsatellite, education, 030304 developmental biology
Abstract

This preprint has been reviewed and recommended by Peer Community In Evolutionary Biology (http://dx.doi.org/10.24072/pci.evolbiol.100031).Conservation policy in the giant Galápagos tortoise, an iconic endangered animal, has been assisted by genetic markers for ∼15 years: a dozen loci have been used to delineate thirteen (sub)species, between which hybridization is prevented. Here, comparative reanalysis of a previously published NGS data set reveals a conflict with traditional markers. Genetic diversity and population substructure in the giant Galápagos tortoise are found to be particularly low, questioning the genetic relevance of current conservation practices. Further examination of giant Galapagos tortoise population genomics is critically needed.

Published
2018
Full Text
View/download PDF

4. BEAGLE 3 high-performance computational library for phylogenetic inference

Author

Ayres, Daniel, Lemey, Philippe, Baele, Guy, Suchard, Marc A, Scornavacca, Celine, Delsuc, Frédéric, Galtier, Nicolas, 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), University of California [Los Angeles] (UCLA), and University of California

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], [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM]
Abstract

International audience; Maximum-likelihood and Bayesian inference approaches to statistical phylogenetics require repeatedly computing of the observed sequence data likelihood. As increasingly cheaper sequencing technology provides phylogenomic studies with larger data set sizes, there stands a critical need to efficiently evaluate this likelihood function in order for phylogenetic computations to complete in reasonable time. The adoption of powerful computing architectures, in the form of multi-core central processing units and many-core graphics cards dedicated to scientific computing, offers unprecedented opportunity to perform massively parallel computation in many research fields, including likelihood evaluation in phylogenetics. In this chapter, we provide insight into the inner workings of BEAGLE, a high-performance likelihood-calculation platform for use on multi-core and many-core computer systems (ubiquitous nowadays in standard desktop computers and laptops) and available in several phylogenetic inference applications to improve computational performance.

Published
2020

5. To What Extent Current Limits of Phylogenomics Can Be Overcome?

Author

Simion, Paul, Delsuc, Frédéric, Philippe, Herve, Laboratoire d’Ecologie et Génétique Evolutive (LEGE), URBE University of Namur, Namur, Belgium, Institut des Sciences de l'Evolution de Montpellier (UMR ISEM), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Montpellier (UM)-Institut de recherche pour le développement [IRD] : UR226-Centre National de la Recherche Scientifique (CNRS), Station d'écologie théorique et expérimentale (SETE), Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Observatoire Midi-Pyrénées (OMP), Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD), Scornavacca, Celine, Delsuc, Frédéric, Galtier, Nicolas, Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-École Pratique des Hautes Études (EPHE), Université de Toulouse (UT)-Université de Toulouse (UT)-Observatoire Midi-Pyrénées (OMP), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), and Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -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], [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM]
Abstract

International audience; Current phylogenomic methods are still a long way from implementing a realistic genome evolution model. An ideal approach would require a general joint analysis of genomic sequences, while including coding sequence annotation, protein evolution or gene transfer, among other mechanisms , to infer the complete evolutionary history of the studied genomes. Such an approach is computationally intractable and currently approximated by phylogenomic pipelines that implement a series of independent steps ranging from gene annotation to species tree inference or positive selection detection. Here we review the virtues and limits of current phylogenomic methods compared to what could be expected from an ideal method. We present five case studies to illustrate various issues and limits in current phylogenomic practices, while assessing their relative importance. We argue that data error is pervasive in modern datasets and models are still too simplistic compared to the complexity of biological and evolutionary processes. Importantly , joint analyses should be a research focus as the many steps of phylogenomic pipelines are not mutually independent. It is essential to recognize the hidden assumptions of the many types of analysis available to our community so as to circumvent model misspecifications and critically evaluate the relevance of their results. In conclusion, the quality of datasets should be enhanced via numerous, rigorous checkpoints, while also boosting the capability of models to handle biological complexity by the development of better models, particularly through joint analyses.

Published
2020

6. The Bayesian Approach to Molecular Phylogeny

Author

Lartillot, Nicolas, Bioinformatique, phylogénie et génomique évolutive (BPGE), Département PEGASE [LBBE] (PEGASE), Laboratoire de Biométrie et Biologie Evolutive - UMR 5558 (LBBE), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-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 Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-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)-Laboratoire de Biométrie et Biologie Evolutive - UMR 5558 (LBBE), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-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), Scornavacca, Celine, Delsuc, Frédéric, and Galtier, Nicolas

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], [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM]
Abstract

International audience; Bayesian inference is now routinely used in phylogenomics and, more generally, in macro-evolutionary studies. Beyond the philosophical debates it has raised concerning the choice of the prior and the meaning of posterior probabilities, Bayesian inference, combined with generic Monte Carlo algorithms, offers a flexible framework for introducing subjective or context information through the prior, but also, for designing hierarchical models formalizing complex patterns of variation (across sites or branches) or the integration of multiple levels of evolutionary processes. In this chapter, the principles of Bayesian inference, such as applied to phylogenetic reconstruction , are first introduced, with an emphasis on the key features of the Bayesian paradigm that explain its flexibility in terms of model design and its robustness in inferring complex patterns and processes. A more specific focus is then put on the question of modeling pattern-heterogeneity across sites, using both parametric and non-parametric random-effect models. Finally, the current computational challenges are discussed.

Published
2020

7. Reconciling Gene trees with Species Trees

Author

Boussau, Bastien, Scornavacca, Celine, Bioinformatique, phylogénie et génomique évolutive (BPGE), Département PEGASE [LBBE] (PEGASE), Laboratoire de Biométrie et Biologie Evolutive - UMR 5558 (LBBE), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-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 Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-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)-Laboratoire de Biométrie et Biologie Evolutive - UMR 5558 (LBBE), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-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), Institut des Sciences de l'Evolution de Montpellier (UMR ISEM), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Montpellier (UM)-Institut de recherche pour le développement [IRD] : UR226-Centre National de la Recherche Scientifique (CNRS), Scornavacca, Celine, Delsuc, Frédéric, Galtier, Nicolas, and Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-École Pratique des Hautes Études (EPHE)

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], [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM]
Abstract

International audience; In the last decade, we witnessed the ascent of reconciliations as an important tool to model and study the evolution of gene families. Reconciliations model discordance between gene trees and species trees caused by gene-level processes: duplications, losses and transfers of genes, Incomplete Lineage Sorting among others can be combined to generate a panoply of different models. In this review article, we give an overview of this vast topic by skimming over the different models and methods that have been proposed, and presenting some of their applications in phylogenomics. We also present the pros and cons of these methods and give some directions for future research that we are convinced will enhance their efficiency and use.

Published
2020

8. Species Delimitation

Author

Rannala, Bruce, Yang, Ziheng, Department of Evolution and Ecology, UC Davis, Davis, United States of America, Department of Genetics, Evolution and Environment, University College of London [London] (UCL), Scornavacca, Celine, Delsuc, Frédéric, and Galtier, Nicolas

Subjects
urogenital system, [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], food and beverages, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM]
Abstract

International audience; Species delimitation is the process of determining which groups of individual organisms constitute different populations of a single species and which constitute different species. The problem goes back to the earliest days of taxonomy and formalized processes for describing new species exist and are widely used, although the methods are time-intensive and problematic for some species. Genomic data carries extensive information about the degree of genetic isolation among species and about ancient and recent introgression. For this reason, genomic data can play an important role in species delimitation under many existing species concepts. Here we review the history of molecular species delimitation leading up to the current genomic era. We then describe the most widely used computational methods for species delimitation using single-and multi-locus genomic data. Relative strengths and weaknesses of the approaches are discussed and a new method for delimiting species based on empirical criteria is proposed.

Published
2020

9. Efficient Maximum Likelihood Tree Building Methods

Author

Stamatakis, Alexandros, Kozlov, Alexey M., Kozlov, Alexey, Bibal, Christine, Scornavacca, Celine, Delsuc, Frédéric, Galtier, Nicolas, Institute of Theoretical Informatics, Karlsruhe Institute of Technology (KIT), and Heidelberg Institute for Theoretical Studies (HITS )

Subjects
[SDV.BIBS] Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], [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], [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], [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM]
Abstract

International audience; The number of possible unrooted binary trees (phylogenies) increases super-exponentially with the number of taxa. To find the Maximum Likelihood (ML) tree one has to enumerate and evaluate all these trees. As we will see, this is computationally not feasible. Therefore, one predominantly deploys ad hoc tree search methods that strive to find a "good" ML tree in the hope that it will be close, either with respect to the likelihood score or the topological structure, to the globally optimal ML tree. In this chapter we provide an overview over the most popular and efficient ML tree search techniques. How to cite: Alexandros Stamatakis and Alexey M.

Published
2020

10. Detecting Adaptation from Multi-species Protein-coding DNA Sequence Alignments

Author

Lowe, Christine, Rodrigue, Nicolas, Bibal, Christine, Scornavacca, Celine, Delsuc, Frédéric, Galtier, Nicolas, Biological Informatics Centre of Excellence, Agriculture and Agri-Food [Ottawa] (AAFC), Carleton University, and School of Mathematics and Statistics [Ottawa]

Subjects
[SDV.BIBS] Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], [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], [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], [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM]
Abstract

International audience; Modern methods to detecting adaptive evolution from interspecific protein-coding gene alignments rely on statistical models of sequence evolution formulated at the level of codons. By performing model comparisons, one measures the evidence for signals of adaptive substitution processes, relative to a null model that disallows any adaptive regime. In this chapter, we present the detailed form of these models of sequence evolution, and how they are applied to real data sets. The classical codon substitution models are based on evaluating the relative nonsynonymous to synonymous substitution rates, and the main focus has traditionally been placed on devising models allowing for increasingly more subtle manifestations of adaptive substitution processes. We also overview a contrasting modeling direction that has emerged in the last decade-although with roots two decades back-in which the emphasis is placed on devising a richer modeling of purifying selection. Using simulations, we expand the characterization of this latter approach, followed by a contrasting of its conclusions on real data with those of classical codon models. Finally , we discuss the numerous model violations that can lead to erroneous inferences on various tests, and potential future directions meriting attention.

Published
2020

11. Orthology: Definitions, Prediction, and Impact on Species Phylogeny Inference

Author

Fernández, Rosa, Gabaldon, Toni, Dessimoz, Christophe, Bibal, Christine, Scornavacca, Celine, Delsuc, Frédéric, Galtier, Nicolas, Institut de Biologia Evolutiva [Barcelona] (IBE / UPF - CSIC), Universitat Pompeu Fabra [Barcelona] (UPF), Barcelona Supercomputing Center - Centro Nacional de Supercomputacion (BSC - CNS), Institute for research in Biomedicine (IRB), Catalan Institution for Research and Advanced Studies, Center for Integrative Genomics - Institute of Bioinformatics, Génopode (CIG), Swiss Institute of Bioinformatics [Lausanne] (SIB), Université de Lausanne (UNIL)-Université de Lausanne (UNIL), Centre for Life’s Origins and Evolution, Department of Computational Biology, Université de Lausanne (UNIL), Department of Computer Science, University College of London [London] (UCL), and Swiss Institute of Bioinformatics (SIB/ISB)

Subjects
animal structures, [SDV.BIBS] Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], [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], education, [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], [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM]
Abstract

International audience; Orthology is a central concept in evolutionary and comparative genomics, used to relate corresponding genes in different species. In particular, orthologs are needed to infer species trees. In this chapter, we introduce the fundamental concepts of orthology relationships and orthologous groups, including some non-trivial (and thus commonly misunderstood) implications. Next, we review some of the main methods and resources used to identify orthologs. The final part of the chapter discusses the impact of orthology methods on species phylogeny inference, drawing lessons from several recent comparative studies.

Published
2020

12. Phylogenomics and Genome Annotation

Author

Necsulea, Anamaria, Bioinformatique, phylogénie et génomique évolutive (BPGE), Département PEGASE [LBBE] (PEGASE), Laboratoire de Biométrie et Biologie Evolutive - UMR 5558 (LBBE), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-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 Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-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)-Laboratoire de Biométrie et Biologie Evolutive - UMR 5558 (LBBE), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-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), Scornavacca, Celine, Delsuc, Frédéric, and Galtier, Nicolas

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], [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], ComputingMilieux_MISCELLANEOUS
Abstract

International audience

Published
2020

13. Molecular Evolution and Gene Function

Author

Robinson-Rechavi, Marc, Department of Ecology and Evolution [Lausanne], Université de Lausanne (UNIL), Swiss Institute of Bioinformatics [Lausanne] (SIB), Scornavacca, Celine, Delsuc, Frédéric, Galtier, Nicolas, and Bibal, Christine

Subjects
Genomics (q-bio.GN), [SDV.BIBS] Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], FOS: Biological sciences, [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], [SDV.BID.EVO] Life Sciences [q-bio]/Biodiversity/Populations and Evolution [q-bio.PE], Populations and Evolution (q-bio.PE), [SDV.BBM.GTP] Life Sciences [q-bio]/Biochemistry, Molecular Biology/Genomics [q-bio.GN], Quantitative Biology - Genomics, Quantitative Biology - Populations and Evolution, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM]
Abstract

One of the basic questions of phylogenomics is how gene function evolves, whether among species or inside gene families. In this chapter, we provide a brief overview of the problems associated with defining gene function in a manner which allows comparisons which are both large scale and evolutionarily relevant. The main source of functional data, despite its limitations, is transcriptomics. Functional data provides information on evolutionary mechanisms primarily by showing which functional classes of genes evolve under stronger or weaker purifying or adaptive selection, and on which classes of mutations (e.g., substitutions or duplications). However, the example of the "ortholog conjecture" shows that we are still not at a point where we can confidently study phylogenomically the evolution of gene function at a precise scale., Comment: To be published in book "Phylogenomics" (Nicolas Galtier, C\'eline Scornavacca, Fr\'ed\'eric Delsuc, Eds.)

Published
2020

14. Strengths and Limits of Multiple Sequence Alignment and Filtering Methods

Author

Ranwez, Vincent, Chantret, Nathalie, Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), 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 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 Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Scornavacca, Celine, Delsuc, Frédéric, and Galtier, Nicolas

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], [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM]
Abstract

Multiple sequence alignment (MSA) is a prerequisite for most phylogenetic analyses. Aligning sequences to unravel residue homology is a challenging task that has been the focus of much attention in recent decades. Research in this field has been extremely active from both theoretical and practical standpoints. Numerous tools have been developed to align sequences and, more recently, to post-process those alignments and filter out their most dubious parts. Whether or not the inclusion of alignment filtering in a phylogenetic pipeline improves the quality of the inferred phylogenies is still debatable. The goal of this chapter is not to provide an exhaustive list of all tools available to produce or filter an MSA, but rather to cover the limitations of current alignment methods and their causes, to highlight key differences among MSA filtering methods and provide some practical MSA filtering guidelines. We consider that filtering methods can be subdivided into two main categories. The first one includes methods that filter MSA by entirely removing some sites or sequences from the MSA. The second category contains MSA filtering methods that mask residues and are able to extract some pieces of information from a site or sequence, while disregarding the remaining information-we called these picky-filtering methods. In our benchmark, the filtering methods that perform best are, as expected, in the picky category. When inferring phylogenies, MSA filtering impacts the inferred tree topology but it also seems to significantly improve branch length estimations, especially when a picky-filtering method is used. How to cite: Vincent Ranwez and Nathalie Chantret (2020).

Published
2020

15. Ancestral Genome Organization as a Diagnosis Tool for Phylogenomics

Author

Tannier, Eric, Bazin, Adelme, Davín, Adrián, Guéguen, Laurent, Bérard, Sèverine, Chauve, Cedric, Laboratoire de Biométrie et Biologie Evolutive - UMR 5558 (LBBE), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-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), Artificial Evolution and Computational Biology (BEAGLE), Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS), Université Lumière - Lyon 2 (UL2)-École Centrale de Lyon (ECL), 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)-Centre National de la Recherche Scientifique (CNRS)-Université Lumière - Lyon 2 (UL2)-École Centrale de Lyon (ECL), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-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)-Laboratoire de Biométrie et Biologie Evolutive - UMR 5558 (LBBE), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-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)-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), Génomique métabolique (UMR 8030), Genoscope - Centre national de séquençage [Evry] (GENOSCOPE), Université Paris-Saclay-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 Paris-Saclay-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 d'Évry-Val-d'Essonne (UEVE)-Centre National de la Recherche Scientifique (CNRS), RIKEN Center for Advanced Intelligence Project [Tokyo] (RIKEN AIP), RIKEN - Institute of Physical and Chemical Research [Japon] (RIKEN), Institut des Sciences de l'Evolution de Montpellier (UMR ISEM), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Montpellier (UM)-Institut de recherche pour le développement [IRD] : UR226-Centre National de la Recherche Scientifique (CNRS), Scornavacca, Celine, Delsuc, Frédéric, Galtier, Nicolas, Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-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 Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-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)-Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-École Centrale de Lyon (ECL), Université de Lyon-Université Lumière - Lyon 2 (UL2)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-École Centrale de Lyon (ECL), Université de Lyon-Université Lumière - Lyon 2 (UL2), 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 d'Évry-Val-d'Essonne (UEVE), École pratique des hautes études (EPHE), and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Montpellier (UM)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre National de la Recherche Scientifique (CNRS)-Institut de recherche pour le développement [IRD] : UR226

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], [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM]
Abstract

International audience; The reconstruction of the chromosomal organization of ancient genomes has many applications in comparative and evolutionary genomics. Here we propose a novel, methodological, use for these predicted ancestral syntenies, directly focused on phylogenomics. It is a way to assess the accuracy of gene trees and species trees. We use a method that reconstructs, from gene trees and extant gene orders, ancestral adjacencies, i.e. the immediate neighborhood between pairs of genes, independently for each pair. This independence allows to split the computations into many independent problems that can each be solved exactly using efficient algorithms, but might result in sets of ancestral adjacencies that are incompatible with the expected linear or circular structure of chromosomes. We show here that this drawback can actually be turned into a useful 1 This work benefited from the support of ComputeCanada.

Published
2020

16. Accurate alignment of (meta)barcoding data sets using MACSE

Author

Delsuc, Frédéric, Ranwez, Vincent, Institut des Sciences de l'Evolution de Montpellier (UMR ISEM), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Montpellier (UM)-Institut de recherche pour le développement [IRD] : UR226-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)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-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 Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Scornavacca, Celine, Delsuc, Frédéric, Galtier, Nicolas, Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-École Pratique des Hautes Études (EPHE), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro - 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), and Delsuc, Frederic

Subjects
[SDE.BE] Environmental Sciences/Biodiversity and Ecology, [SDE.BE.EVO] Environmental Sciences/Biodiversity and Ecology/domain_sde.be.evo, [SDV.BIBS] Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], [SDE.BE.BIOS]Environmental Sciences/Biodiversity and Ecology/domain_sde.be.bios, [SDV.BBM.GTP]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Genomics [q-bio.GN], [SDE.BE.ECO] Environmental Sciences/Biodiversity and Ecology/domain_sde.be.eco, [SDE.BE.EVO]Environmental Sciences/Biodiversity and Ecology/domain_sde.be.evo, [SDV.BBM.GTP] Life Sciences [q-bio]/Biochemistry, Molecular Biology/Genomics [q-bio.GN], [SDE.BE.BIOS] Environmental Sciences/Biodiversity and Ecology/domain_sde.be.bios, [SDE.BE]Environmental Sciences/Biodiversity and Ecology, [SDE.BE.ECO]Environmental Sciences/Biodiversity and Ecology/domain_sde.be.eco, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM]
Abstract

International audience; Twenty years of standardized DNA barcoding practice have resulted in millions of sequences being produced for a handful of molecular markers in a wide range of fungi, animal and plant species. Despite some basic quality controls, reference barcoding data sets deposited in the Bar-code of Life Datasystem (BOLD) database are not immune to sequencing errors and undetected pseudogenes. Such database inaccuracies can significantly bias subsequent species delimitation and biodiversity estimation based on DNA barcoding. These potential problems are amplified in metabarcoding studies containing thousands of sequences produced using high throughput se-quencing technologies. Here, we propose a pipeline based on MACSE v2, an extended version of our codon-aware multiple sequence alignment software accounting for frameshifts and stop codons. The MACSE_BARCODE pipeline allows the accurate alignment of hundreds of thousands of protein-coding barcode sequences. Re-analyses of published data sets confirm that MACSE v2 is able to automatically detect most sequencing errors previously identified manually. The proposed alignment strategy hence alleviates the risk of incorrect species delimitation due the incorporation of sequencing errors or undetected pseudogenes. By applying the MACSE_BARCODE pipeline to mammal, ant, and flowering plant barcode sequences available in BOLD, we highlight several cases of database errors and provide curated reference alignments for the main protein-coding barcode genes. We anticipate our approach to be particularly useful for metabarcoding studies in which thousands of new sequences need to be compared to a reference database for subsequent taxonomic assignment. This might prove particularly helpful for diet characterization studies and large-scale biodiversity assessments through environmental DNA metabarcoding. The new MACSE_BARCODE pipeline is distributed as Nextflow workflows that are available from the MACSE project webpage (https://bioweb.supagro.inra.fr/macse/).

Published
2020

17. PhyloBayes: Bayesian Phylogenetics Using Site-heterogeneous Models

Author

Lartillot, Nicolas, Bioinformatique, phylogénie et génomique évolutive (BPGE), Département PEGASE [LBBE] (PEGASE), Laboratoire de Biométrie et Biologie Evolutive - UMR 5558 (LBBE), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-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 Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-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)-Laboratoire de Biométrie et Biologie Evolutive - UMR 5558 (LBBE), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-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), Scornavacca, Celine, Delsuc, Frédéric, and Galtier, Nicolas

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], [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM]
Abstract

International audience; PhyloBayes is a software program for Bayesian phylogenetic reconstruction. Compared to other programs, its main distinguishing feature is the implementation of the CAT model, which accounts for fine-grained variation across sites in amino acid preferences using a Bayesian non-parametric approach. This chapter provides a detailed step-by-step practical introduction to phylogenetic analyses using PhyloBayes, using as an example a previously published dataset addressing the phylogenetic position of Microsporidia within eukaryotes. Through this historically emblematic case of a long-branch attraction artifact, a complete analysis under site-homogeneous and site-heterogeneous models is conducted and interpreted, thus providing an illustration of why modeling pattern variation is so fundamental for reconstructing deep phylogenies.

Published
2020

18. 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