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1. A bird’s white-eye view on avian sex chromosome evolution

Author

Leroy, Thibault, Anselmetti, Yoann, Tilak, Marie-Ka, Bérard, Sèverine, Csukonyi, Laura, Gabrielli, Maëva, Scornavacca, Céline, Milá, Borja, Thébaud, Christophe, and Nabholz, Benoit

Subjects
Archaeology, CC1-960, Science
Abstract

Chromosomal organization is relatively stable among avian species, especially with regards to sex chromosomes. Members of the large Sylvioidea clade however have a pair of neo-sex chromosomes which is unique to this clade and originate from a parallel translocation of a region of the ancestral 4A chromosome on both W and Z chromosomes. Here, we took advantage of this unusual event to study the early stages of sex chromosome evolution. To do so, we sequenced a female (ZW) of two Sylvioidea species, a Zosterops borbonicus and a Z. pallidus. Then, we organized the Z. borbonicus scaffolds along chromosomes and annotated genes. Molecular phylogenetic dating under various methods and calibration sets confidently confirmed the recent diversification of the genus Zosterops (1-3.5 million years ago), thus representing one of the most exceptional rates of diversification among vertebrates. We then combined genomic coverage comparisons of five males and seven females, and homology with the zebra finch genome (Taeniopygia guttata) to identify sex chromosome scaffolds, as well as the candidate chromosome breakpoints for the two translocation events. We observed reduced levels of within-species diversity in both translocated regions and, as expected, even more so on the neoW chromosome. In order to compare the rates of molecular evolution in genomic regions of the autosomal-to-sex transitions, we then estimated the ratios of non-synonymous to synonymous polymorphisms (N/S) and substitutions (dN/dS). Based on both ratios, no or little contrast between autosomal and Z genes was observed, thus representing a very different outcome than the higher ratios observed at the neoW genes. In addition, we report significant changes in base composition content for translocated regions on the W and Z chromosomes and a large accumulation of transposable elements (TE) on the newly W region. Our results revealed contrasted signals of molecular evolution changes associated to these autosome-to-sex transitions, with congruent signals of a W chromosome degeneration yet a surprisingly weak support for a fast-Z effect.

Published
2021
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2. Median and Small Parsimony Problems on RNA trees

Author

Marchand, Bertrand, Anselmetti, Yoann, Lafond, Manuel, and Ouangraoua, Aïda

Subjects
Computer Science - Data Structures and Algorithms
Abstract

Motivation: Non-coding RNAs (ncRNAs) express their functions by adopting molecular structures. Specifically, RNA secondary structures serve as a relatively stable intermediate step before tertiary structures, offering a reliable signature of molecular function. Consequently, within an RNA functional family, secondary structures are generally more evolutionarily conserved than sequences. Conversely, homologous RNA families grouped within an RNA clan share ancestors but typically exhibit structural differences. Inferring the evolution of RNA structures within RNA families and clans is crucial for gaining insights into functional adaptations over time and providing clues about the Ancient RNA World Hypothesis. Results: We introduce the median problem and the small parsimony problem for ncRNA families, where secondary structures are represented as leaf-labelled trees. We utilize the Robinson-Foulds (RF) tree distance, which corresponds to a specific edit distance between RNA trees, and a new metric called the Internal-Leafset (IL) distance. While the RF tree distance compares sets of leaves descending from internal nodes of two RNA trees, the IL distance compares the collection of leaf-children of internal nodes. The latter is better at capturing differences in structural elements of RNAs than the RF distance, which is more focused on base pairs. We also consider a more general tree edit distance that allows the mapping of base pairs that are not perfectly aligned. We study the theoretical complexity of the median problem and the small parsimony problem under the three distance metrics and various biologically-relevant constraints, and we present polynomial-time maximum parsimony algorithms for solving some versions of the problems. Our algorithms are applied to ncRNA families from the RFAM database, illustrating their practical utility

Published
2024

3. Reconciliation with Segmental Duplication, Transfer, Loss and Gain

Author

Anselmetti, Yoann, Delabre, Mattéo, El-Mabrouk, Nadia, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Jin, Lingling, editor, and Durand, Dannie, editor

Published
2022
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4. Haplotype divergence supports long-term asexuality in the oribatid mite Oppiella nova

Author

Brandt, Alexander, Van, Patrick Tran, Bluhm, Christian, Anselmetti, Yoann, Dumas, Zoé, Figuet, Emeric, François, Clémentine M., Galtier, Nicolas, Heimburger, Bastian, Jaron, Kamil S., Labédan, Marjorie, Maraun, Mark, Parker, Darren J., Robinson-Rechavi, Marc, Schaefer, Ina, Simion, Paul, Scheu, Stefan, Schwander, Tanja, and Bast, Jens

Published
2021

6. Evolutionary superscaffolding and chromosome anchoring to improve Anopheles genome assemblies

Author

Waterhouse, Robert M., Aganezov, Sergey, Anselmetti, Yoann, Lee, Jiyoung, Ruzzante, Livio, Reijnders, Maarten J. M. F., Feron, Romain, Bérard, Sèverine, George, Phillip, Hahn, Matthew W., Howell, Paul I., Kamali, Maryam, Koren, Sergey, Lawson, Daniel, Maslen, Gareth, Peery, Ashley, Phillippy, Adam M., Sharakhova, Maria V., Tannier, Eric, Unger, Maria F., Zhang, Simo V., Alekseyev, Max A., Besansky, Nora J., Chauve, Cedric, Emrich, Scott J., and Sharakhov, Igor V.

Published
2020
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9. Convergent consequences of parthenogenesis on stick insect genomes

Author

Jaron, Kamil S., Parker, Darren J., Anselmetti, Yoann, Van, Patrick Tran, Bast, Jens, Dumas, Zoe, Figuet, Emeric, Francois, Clementine M., Hayward, Keith, Rossier, Victor, Simion, Paul, Robinson-Rechavi, Marc, Galtier, Nicolas, Schwander, Tanja, Jaron, Kamil S., Parker, Darren J., Anselmetti, Yoann, Van, Patrick Tran, Bast, Jens, Dumas, Zoe, Figuet, Emeric, Francois, Clementine M., Hayward, Keith, Rossier, Victor, Simion, Paul, Robinson-Rechavi, Marc, Galtier, Nicolas, and Schwander, Tanja

Abstract

The shift from sexual reproduction to parthenogenesis has occurred repeatedly in animals, but how the loss of sex affects genome evolution remains poorly understood. We generated reference genomes for five independently evolved parthenogenetic species in the stick insect genus Timema and their closest sexual relatives. Using these references and population genomic data, we show that parthenogenesis results in an extreme reduction of heterozygosity and often leads to genetically uniform populations. We also find evidence for less effective positive selection in parthenogenetic species, suggesting that sex is ubiquitous in natural populations because it facilitates fast rates of adaptation. Parthenogenetic species did not show increased transposable element (TE) accumulation, likely because there is little TE activity in the genus. By using replicated sexual-parthenogenetic comparisons, our study reveals how the absence of sex affects genome evolution in natural populations, providing empirical support for the negative consequences of parthenogenesis as predicted by theory.

Published
2022

10. Convergent consequences of parthenogenesis on stick insect genomes

Author

Jaron, Kamil S., primary, Parker, Darren J., additional, Anselmetti, Yoann, additional, Tran Van, Patrick, additional, Bast, Jens, additional, Dumas, Zoé, additional, Figuet, Emeric, additional, François, Clémentine M., additional, Hayward, Keith, additional, Rossier, Victor, additional, Simion, Paul, additional, Robinson-Rechavi, Marc, additional, Galtier, Nicolas, additional, and Schwander, Tanja, additional

Published
2022
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11. Supplemental Material for Tran Van et al., 2021

Author

Van, Patrick Tran, Anselmetti, Yoann, Bast, Jens, Dumas, Zoé, Galtier, Nicolas, Jaron, Kamil S., Martens, Koen, Parker, Darren J., Robinson-Rechavi, Marc, Schwander, Tanja, Simion, Paul, and Schön, Isa

Subjects
Evolutionary Biology, FOS: Biological sciences, 60204 Freshwater Ecology, 60408 Genomics, Zoology
Abstract

Supplementary Material describing more details of the genome assembly pipelines and assembly parameters (Sm1), the annotation of protein coding genes (SM2) and the estimation of genome size and heterozygosity by genome profiling analysis (SM3). The supplementary material also contains an overview of RNA sequencing reads from ostracods in GenBank (Table S1), the origin of the biological ostracod material of the study (Table S2), statistics of ostracod genome sequence data (Table S3), statistics of ostracod transcriptome sequence data (Table S4), statistics of ostracod resequencing data (Table S5), statistics of genome assemblies of ostracods (Table S6), statistics of protein coding gene annotations and other gene features in ostracod genomes (Table S7), and annotations and gene features of crustacean genomes of the last four years (Table S8). We also show results of GenomeScope analyses of two individuals of the ostracod Darwinula stevensoni and two of the ostracod Notodromas monacha (Figure S1A-D).

Published
2021
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12. Haplotype divergence supports long-term asexuality in the oribatid miteOppiella nova

Author

Brandt, Alexander, primary, Tran Van, Patrick, additional, Bluhm, Christian, additional, Anselmetti, Yoann, additional, Dumas, Zoé, additional, Figuet, Emeric, additional, François, Clémentine M., additional, Galtier, Nicolas, additional, Heimburger, Bastian, additional, Jaron, Kamil S., additional, Labédan, Marjorie, additional, Maraun, Mark, additional, Parker, Darren J., additional, Robinson-Rechavi, Marc, additional, Schaefer, Ina, additional, Simion, Paul, additional, Scheu, Stefan, additional, Schwander, Tanja, additional, and Bast, Jens, additional

Published
2021
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14. First annotated draft genomes of nonmarine ostracods (Ostracoda, Crustacea) with different reproductive modes

Author

Van, Patrick Tran, Anselmetti, Yoann, Bast, Jens, Dumas, Zoe, Galtier, Nicolas, Jaron, Kamil S., Martens, Koen, Parker, Darren J., Robinson-Rechavi, Marc, Schwander, Tanja, Simion, Paul, Schon, Isa, Van, Patrick Tran, Anselmetti, Yoann, Bast, Jens, Dumas, Zoe, Galtier, Nicolas, Jaron, Kamil S., Martens, Koen, Parker, Darren J., Robinson-Rechavi, Marc, Schwander, Tanja, Simion, Paul, and Schon, Isa

Abstract

Ostracods are one of the oldest crustacean groups with an excellent fossil record and high importance for phylogenetic analyses but genome resources for this class are still lacking. We have successfully assembled and annotated the first reference genomes for three species of nonmarine ostracods; two with obligate sexual reproduction (Cyprideis torosa and Notodromas monacha) and the putative ancient asexual Darwinula stevensoni. This kind of genomic research has so far been impeded by the small size of most ostracods and the absence of genetic resources such as linkage maps or BAC libraries that were available for other crustaceans. For genome assembly, we used an Illumina-based sequencing technology, resulting in assemblies of similar sizes for the three species (335-382 Mb) and with scaffold numbers and their N50 (19-56 kb) in the same orders of magnitude. Gene annotations were guided by transcriptome data from each species. The three assemblies are relatively complete with BUSCO scores of 92-96. The number of predicted genes (13,771-17,776) is in the same range as Branchiopoda genomes but lower than in most malacostracan genomes. These three reference genomes from nonmarine ostracods provide the urgently needed basis to further develop ostracods as models for evolutionary and ecological research.

Published
2021

15. First annotated draft genomes of nonmarine ostracods (Ostracoda, Crustacea) with different reproductive modes

Author

Tran Van, Patrick, primary, Anselmetti, Yoann, additional, Bast, Jens, additional, Dumas, Zoé, additional, Galtier, Nicolas, additional, Jaron, Kamil S, additional, Martens, Koen, additional, Parker, Darren J, additional, Robinson-Rechavi, Marc, additional, Schwander, Tanja, additional, Simion, Paul, additional, and Schön, Isa, additional

Published
2021
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16. First annotated draft genomes of non-marine ostracods (Ostracoda, Crustacea) with different reproductive modes

Author

Van, Patrick Tran, primary, Anselmetti, Yoann, additional, Bast, Jens, additional, Dumas, Zoé, additional, Galtier, Nicolas, additional, Jaron, Kamil S., additional, Martens, Koen, additional, Parker, Darren J., additional, Robinson-Rechavi, Marc, additional, Schwander, Tanja, additional, Simion, Paul, additional, and Schön, Isa, additional

Published
2020
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17. Convergent consequences of parthenogenesis on stick insect genomes

Author

Jaron, Kamil S., primary, Parker, Darren J., additional, Anselmetti, Yoann, additional, Tran Van, Patrick, additional, Bast, Jens, additional, Dumas, Zoé, additional, Figuet, Emeric, additional, François, Clémentine M., additional, Hayward, Keith, additional, Rossier, Victor, additional, Simion, Paul, additional, Robinson-Rechavi, Marc, additional, Galtier, Nicolas, additional, and Schwander, Tanja, additional

Published
2020
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19. MOESM1 of Evolutionary superscaffolding and chromosome anchoring to improve Anopheles genome assemblies

Author

Waterhouse, Robert, Aganezov, Sergey, Anselmetti, Yoann, Jiyoung Lee, Ruzzante, Livio, Reijnders, Maarten, Feron, Romain, Sèverine Bérard, George, Phillip, Hahn, Matthew, Howell, Paul, Kamali, Maryam, Koren, Sergey, Lawson, Daniel, Maslen, Gareth, Peery, Ashley, Phillippy, Adam, Sharakhova, Maria, Tannier, Eric, Unger, Maria, Zhang, Simo, Alekseyev, Max, Besansky, Nora, Chauve, Cedric, Emrich, Scott, and Sharakhov, Igor

Subjects
ComputingMethodologies_DOCUMENTANDTEXTPROCESSING, ComputingMilieux_COMPUTERSANDEDUCATION, Data_FILES, ComputerApplications_COMPUTERSINOTHERSYSTEMS
Abstract

Additional file 1. Supplementary online material

Published
2020
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20. Evolutionary superscaffolding and chromosome anchoring to improve Anopheles genome assemblies

Author

Entomology, Waterhouse, Robert M., Aganezov, Sergey, Anselmetti, Yoann, Lee, Jiyoung, Ruzzante, Livio, Reijnders, Maarten J. M. F., Feron, Romain, Bérard, Sèverine, George, Phillip, Hahn, Matthew W., Howell, Paul I., Kamali, Maryam, Koren, Sergey, Lawson, Daniel, Maslen, Gareth, Peery, Ashley, Phillippy, Adam M., Sharakhova, Maria V., Tannier, Eric, Unger, Maria F., Zhang, Simo V., Alekseyev, Max A., Besansky, Nora J., Chauve, Cedric, Emrich, Scott J., Sharakhov, Igor V., Entomology, Waterhouse, Robert M., Aganezov, Sergey, Anselmetti, Yoann, Lee, Jiyoung, Ruzzante, Livio, Reijnders, Maarten J. M. F., Feron, Romain, Bérard, Sèverine, George, Phillip, Hahn, Matthew W., Howell, Paul I., Kamali, Maryam, Koren, Sergey, Lawson, Daniel, Maslen, Gareth, Peery, Ashley, Phillippy, Adam M., Sharakhova, Maria V., Tannier, Eric, Unger, Maria F., Zhang, Simo V., Alekseyev, Max A., Besansky, Nora J., Chauve, Cedric, Emrich, Scott J., and Sharakhov, Igor V.

Abstract

Background New sequencing technologies have lowered financial barriers to whole genome sequencing, but resulting assemblies are often fragmented and far from ‘finished’. Updating multi-scaffold drafts to chromosome-level status can be achieved through experimental mapping or re-sequencing efforts. Avoiding the costs associated with such approaches, comparative genomic analysis of gene order conservation (synteny) to predict scaffold neighbours (adjacencies) offers a potentially useful complementary method for improving draft assemblies. Results We evaluated and employed 3 gene synteny-based methods applied to 21 Anopheles mosquito assemblies to produce consensus sets of scaffold adjacencies. For subsets of the assemblies, we integrated these with additional supporting data to confirm and complement the synteny-based adjacencies: 6 with physical mapping data that anchor scaffolds to chromosome locations, 13 with paired-end RNA sequencing (RNAseq) data, and 3 with new assemblies based on re-scaffolding or long-read data. Our combined analyses produced 20 new superscaffolded assemblies with improved contiguities: 7 for which assignments of non-anchored scaffolds to chromosome arms span more than 75% of the assemblies, and a further 7 with chromosome anchoring including an 88% anchored Anopheles arabiensis assembly and, respectively, 73% and 84% anchored assemblies with comprehensively updated cytogenetic photomaps for Anopheles funestus and Anopheles stephensi. Conclusions Experimental data from probe mapping, RNAseq, or long-read technologies, where available, all contribute to successful upgrading of draft assemblies. Our evaluations show that gene synteny-based computational methods represent a valuable alternative or complementary approach. Our improved Anopheles reference assemblies highlight the utility of applying comparative genomics approaches to improve community genomic resources.

Published
2020

21. Evolutionary superscaffolding and chromosome anchoring to improve Anopheles genome assemblies.

Author

Waterhouse, Robert M., Aganezov, Sergey, Anselmetti, Yoann, Lee, Jiyoung, Ruzzante, Livio, Reijnders, Maarten J.M.F., Feron, Romain, Bérard, Sèverine, George, Phillip, Hahn, Matthew W., Howell, Paul I., Kamali, Maryam, Koren, Sergey, Lawson, Daniel, Maslen, Gareth, Peery, Ashley, Phillippy, Adam M., Sharakhova, Maria V., Tannier, Eric, Unger, Maria F., Zhang, Simo V., Alekseyev, Max A., Besansky, Nora J., Chauve, Cedric, Emrich, Scott J., Sharakhov, Igor V., Waterhouse, Robert M., Aganezov, Sergey, Anselmetti, Yoann, Lee, Jiyoung, Ruzzante, Livio, Reijnders, Maarten J.M.F., Feron, Romain, Bérard, Sèverine, George, Phillip, Hahn, Matthew W., Howell, Paul I., Kamali, Maryam, Koren, Sergey, Lawson, Daniel, Maslen, Gareth, Peery, Ashley, Phillippy, Adam M., Sharakhova, Maria V., Tannier, Eric, Unger, Maria F., Zhang, Simo V., Alekseyev, Max A., Besansky, Nora J., Chauve, Cedric, Emrich, Scott J., and Sharakhov, Igor V.

Published
2019

23. Additional file 4 of Phylogenetic signal from rearrangements in 18 Anopheles species by joint scaffolding extant and ancestral genomes

Author

Anselmetti, Yoann, Duchemin, Wandrille, Tannier, Eric, Chauve, Cedric, and Sèverine Bérard

Abstract

Figure S3. Distribution of gene families number (red bars) and number of genes (blue bars) per families containing x species. Left graph: distribution of the 17,780 raw input gene trees corresponding to 212,800 genes. Middle graph: distribution of the 14,981 gene families, containing 184,719 genes, after discarding families containing included genes (after step 2 of Additional file 3: Figure S2). Right graph: distribution of the 14,940 gene trees, composed of 183,680 genes, after gene trees inference pipeline (after steps 3 and 4 of Additional file 5: Figure S4). (PDF 83 kb)

Published
2018
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24. Comparative Methods for Reconstructing Ancient GenomeOrganization

Author

Anselmetti, Yoann, Luhmann, Nina, Bérard, Sèverine, Tannier, Eric, 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), Universität Bielefeld = Bielefeld University, 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), Bioinformatique, phylogénie et génomique évolutive (BPGE), Département PEGASE [LBBE] (PEGASE), 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), Artificial Evolution and Computational Biology (BEAGLE), Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-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-Université Lumière - Lyon 2 (UL2)-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), Department of Mathematics [Burnaby] (SFU), and Simon Fraser University (SFU.ca)

Subjects
[INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], ComputingMilieux_MISCELLANEOUS
Abstract

International audience

Published
2018

25. Additional file 7 of Phylogenetic signal from rearrangements in 18 Anopheles species by joint scaffolding extant and ancestral genomes

Author

Anselmetti, Yoann, Duchemin, Wandrille, Tannier, Eric, Chauve, Cedric, and SèVerine BÊrard

Abstract

Figure S6. Distributions of scaffolding adjacencies link scores computed by BESST for scaffolding adjacencies supported by at least 3 paired reads, for each of the 18 Anopheles species. Upper graphs: distribution of scores all 405,939 potential scaffolding adjacencies. Lower graphs: distribution of scores for all 68,876 scaffolding gene adjacencies used as input by DeCoSTAR. Left graphs: distribution of link dispersity scores. Middle graphs: distribution of link variation scores. Right graphs: distribution of the mean of link variation and dispersity scores. Each color corresponds to one species and the number between parenthesis in the legend indicates the number of scaffolding adjacencies inferred by BESST for each species. (PDF 64 kb)

Published
2018
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26. Additional file 6 of Phylogenetic signal from rearrangements in 18 Anopheles species by joint scaffolding extant and ancestral genomes

Author

Anselmetti, Yoann, Duchemin, Wandrille, Tannier, Eric, Chauve, Cedric, and SèVerine BÊrard

Abstract

Figure S5. Distributions of scaffolding adjacencies scores computed by BESST for scaffolding adjacencies supported by at least 3 paired reads. Left graph: adjacency scores distribution between all contigs or scaffolds, over 405,939 scaffolding adjacencies. Right graph: adjacency scores distribution for contigs and scaffolds with gene corresponding to the 68,876 scaffolding gene adjacencies considered by DeCoSTAR. Blue bars represent the link variation score, red bars the link dispersity score and purple bars the mean of the two link scores. For more information on the link scores see SI text and [62]. (PDF 37 kb)

Published
2018
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27. Additional file 3 of Phylogenetic signal from rearrangements in 18 Anopheles species by joint scaffolding extant and ancestral genomes

Author

Anselmetti, Yoann, Duchemin, Wandrille, Tannier, Eric, Chauve, Cedric, and Sèverine Bérard

Abstract

Figure S2. The pipeline is split into two parts: The first part (Blue one) processes genome content data to obtain extant genome adjacencies. Step 1 detects genes that are included in other genes. In Step 2, gene families containing these genes are filtered out to avoid ambiguity in defining observed extant gene adjacencies. In Step 3, gene trees are inferred from the gene sequences, one gene tree per gene family (see Additional file 5: Figure S4 for more information on gene trees inference pipeline). Finally, in Step 4 genes contained in gene families for which gene trees have not been inferred are discarded from the analysis (41 gene families containing representing 1,039 genes). The second part of the pipeline (Green one) processes sequencing data to obtain scaffolding adjacencies that will be used to improve extant genome assembly with the ADseq algorithm. Step A trims reads with Trimmomatic to remove low qualities reads and remaining adapters. In Step B, trimmed reads are mapped onto their respective genome with Bowtie2 considering all multiple mappings. In Step C, pairs of contigs for which paired-end reads suggest a possible contiguity along their chromosome are linked with the scaffolding software BESST, and the resulting potential scaffolding adjacencies are scored according to the BESST model. Then, in Step 5 scaffolding gene adjacencies are determined from contigs adjacencies obtained from sequencing data processing part with genes present in gene trees. This results in scaffolds with observed scored scaffolding adjacencies that are used as input of DeCoSTAR (Step 6). See Additional file 18: Table S2 for a description per species on dataset used for DeCoSTAR. Pipeline to produce input data for ADseq on 18 Anopheles dataset. The pipeline takes as input a species tree for the 18 Anopheles species, the whole set of gene families and gene trees for these species and genomic data (contigs, scaffolds and chromosomes). The goal of the pipeline is to produce input data for the ADseq algorithm to reconstruct ancestral genome structure and evolution and to improve extant genome scaffolding. (PDF 103 kb)

Published
2018
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28. Additional file 5 of Phylogenetic signal from rearrangements in 18 Anopheles species by joint scaffolding extant and ancestral genomes

Author

Anselmetti, Yoann, Duchemin, Wandrille, Tannier, Eric, Chauve, Cedric, and Sèverine Bérard

Abstract

Figure S4. Pipeline to improve gene trees inference from homologous gene family. CDS sequences of genes in gene trees have been obtained from VectorBase database and homologous gene families deduced from the 14,981 gene trees resulting of step 2 of Additional file 5: Figure S4. Step A consists to multiple align homologous genes with Muscle with parameter “-maxiters 2” if a gene sequence have a size upper than 32,000 bp. In Step B, Gblocks was applied on alignments to select high confidence alignment sites. At this step, 41 gene families have been discarded due to sequences that were not present in a selected blocks. For Step C, RAxML have been used to infer maximum likelihood gene trees with the GTR-GAMMA model and 100 bootstrap iterations. Finally in Step D, the maximum likelihood gene trees are processed with ProfileNJ to potentially changing branches with bootstrap support lower than 100% in a DL reconciliation model (min(Duplication,Loss)) with the species tree [73]. (PDF 35 kb)

Published
2018
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29. Additional file 2 of Phylogenetic signal from rearrangements in 18 Anopheles species by joint scaffolding extant and ancestral genomes

Author

Anselmetti, Yoann, Duchemin, Wandrille, Tannier, Eric, Chauve, Cedric, and SèVerine BÊrard

Abstract

Figure S1. Extant and ancestral genome gene content (left) and ancestral gene degree (right). Left: Number of genes of extant species (left), ancestral species using the reconciled VectorBase gene trees (middle), and ancestral species using the reconciled ProfileNJ gene trees (right). Right: Gene degree distribution of ancestral genes after applying ADseq with the RAW gene trees (blue graph) and the ProfileNJ gene trees (red graph), compared to the expected gene degree distribution for theoretical perfectly assembled genomes (black graph). The degree of a gene is defined as the sum of the ADseq posterior scores of adjacencies involving this gene. Here the value at coordinate x is the sums of all degrees in the interval [x,x+1]. (PDF 92 kb)

Published
2018
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30. Comparative Methods for Reconstructing Ancient Genome Organization

Author

Anselmetti, Yoann, Luhmann, Nina, Bérard, Sèverine, Tannier, Eric, Chauve, Cedric, Setubal, João C., Stoye, Jens, Stadler, Peter, 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), Universität Bielefeld = Bielefeld University, 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), 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), Bioinformatique, phylogénie et génomique évolutive (BPGE), Département PEGASE [LBBE] (PEGASE), 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), Department of Mathematics [Burnaby] (SFU), Simon Fraser University (SFU.ca), 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), and Université de Lyon-Université Lumière - Lyon 2 (UL2)-Inria Grenoble - Rhône-Alpes

Subjects
0301 basic medicine, Comparative genomics, Ancestral genomes, Ancient genomes, Sequence assembly, Genome, 03 medical and health sciences, 030104 developmental biology, Geography, Extant taxon, Paleogenomics, Evolutionary biology, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], Genomic organization
Abstract

International audience; Comparative genomics considers the detection of similarities and differences between extant genomes, and, based on more or less formalized hypotheses regarding the involved evolutionary processes, inferring ancestral states explaining the similarities and an evolutionary history explaining the differences. In this chapter, we focus on the reconstruction of the organization of ancient genomes into chromosomes. We review different methodological approaches and software, applied to a wide range of datasets from different kingdoms of life and at different evolutionary depths. We discuss relations with genome assembly, and potential approaches to validate computational predictions on ancient genomes that are almost always only accessible through these predictions.

Published
2017
Full Text
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31. Reconstruction conjointe de l’ordre des gènes de génomes actuels et ancestraux et de leur évolution structurale dans un cadre phylogénétique

Author

Anselmetti, Yoann, 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 de Lyon, Eric Tannier, Sèverine Bérard, Laboratoire de Biométrie et Biologie Evolutive ( LBBE ), Université Claude Bernard Lyon 1 ( UCBL ), and Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique ( Inria ) -Centre National de la Recherche Scientifique ( CNRS )

Subjects
Ancestral genome reconstruction, Phylogénies de gènes, Adjacences de gènes, [ INFO.INFO-BI ] Computer Science [cs]/Bioinformatics [q-bio.QM], Reconstruction de génomes ancestraux, Gene adjacencies, Gene phylogenies, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], Scaffolding
Abstract

The early 2000s saw the emergence of high-throughput sequencing technologies that would bring down the time and cost of sequencing the entire genome and opening the perspective to whole genome-scale species phylogeny. In this perspective, methods for the inference of evolutionary history of the order of genomic markers along a phylogeny have been developed. However, assemblies of a majority of the large eukaryotic genomes remain incompletely resolved and therefore do not, as such, allow their exploitation for the reconstruction of evolutionary history of the order of the genes of these species. It is in this context that we have developed the algorithm ADseq which allows to jointly reconstruct the evolutionary history of the order of genes by considering the fragmentation of the extant genomes and improve the assembly of these by comparative genomics; Les années 2000 ont vu l'apparition des technologies de séquençage haut-débit permettant de faire chuter le coût en temps et argent du séquençage du génome complet et ouvrant la perspective à des analyses de la phylogénie des espèces à l'échelle de génome entiers. Dans cette optique des méthodes pour l'inférence de l'histoire évolutive de l'ordre de marqueurs génomiques le long d'un phylogénie ont été développées. Cependant, les assemblages d'une majorité des grands génomes d'eucaryotes demeurent incomplètement résolues et ne permettent donc pas, en tant que tel, leur exploitation pour la reconstruction de l'histoire évolutive de l'ordre des gènes de ces espèces. C'est dans ce contexte que nous avons développé l'algorithme adseq qui permet de conjointement reconstruire l'histoire évolutive de l'ordre de gènes en considérant la fragmentation des génomes actuels et améliorant l'assemblage de ceux-ci par génomique comparative

Published
2017

32. DeCoSTAR: Reconstructing the Ancestral Organization of Genes or Genomes Using Reconciled Phylogenies

Author

Duchemin, Wandrille, Anselmetti, Yoann, Patterson, Murray, Ponty, Yann, Bérard, Sèverine, Chauve, Cedric, Scornavacca, Celine, Daubin, Vincent, Tannier, Eric, 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), 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), 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), Dipartimento di Informatica Sistemistica e Comunicazione (DISCo), Università degli Studi di Milano-Bicocca = University of Milano-Bicocca (UNIMIB), Laboratoire d'informatique de l'École polytechnique [Palaiseau] (LIX), École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS), Algorithms and Models for Integrative Biology (AMIB ), École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire de Recherche en Informatique (LRI), Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Algorithms and Models for Integrative BIOlogy (AMIBIO), École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Department of Mathematics [Burnaby] (SFU), Simon Fraser University (SFU.ca), ANR-10-BINF-0001,ANCESTROME,Approche de phylogénie intégrative pour la reconstruction de génomes ancestraux(2010), 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-Université Lumière - Lyon 2 (UL2)-Inria Grenoble - Rhône-Alpes, Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-École pratique des hautes études (EPHE), Università degli Studi di Milano-Bicocca [Milano] (UNIMIB), Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X), Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X)-Laboratoire de Recherche en Informatique (LRI), Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Duchemin, W, Anselmetti, Y, Patterson, M, Ponty, Y, Bérard, S, Chauve, C, Scornavacca, C, Daubin, V, Tannier, E, Tannier, Eric, and Bio-informatique - Approche de phylogénie intégrative pour la reconstruction de génomes ancestraux - - ANCESTROME2010 - ANR-10-BINF-0001 - BINF - VALID

Subjects
Genome Resources, gene order, Rearrangement, Gene, Evolution, Molecular, Anopheles, evolution, Genetics, Animals, rearrangements, Phylogeny, [INFO.INFO-BI] Computer Science [cs]/Bioinformatics [q-bio.QM], Genome, Animal, software, protein domain, gene fusion/fission, Ecology, Evolution, Behavior and Systematic, Algorithm, Actinobacteria, Genes, reconciliation, Drosophila, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], Anophele, Algorithms
Abstract

DeCoSTAR is a software that aims at reconstructing the organization of ancestral genes or genomes in the form of sets of neighborhood relations (adjacencies) between pairs of ancestral genes or gene domains. It can also improve the assembly of fragmented genomes by proposing evolutionary-induced adjacencies between scaffolding fragments. Ancestral genes or domains are deduced from reconciled phylogenetic trees under an evolutionary model that considers gains, losses, speciations, duplications, and transfers as possible events for gene evolution. Reconciliations are either given as input or computed with the ecceTERA package, into which DeCoSTAR is integrated. DeCoSTAR computes adjacency evolutionary scenarios using a scoring scheme based on a weighted sum of adjacency gains and breakages. Solutions, both optimal and near-optimal, are sampled according to the Boltzmann–Gibbs distribution centered around parsimonious solutions, and statistical supports on ancestral and extant adjacencies are provided. DeCoSTAR supports the features of previously contributed tools that reconstruct ancestral adjacencies, namely DeCo, DeCoLT, ART-DeCo, and DeClone. In a few minutes, DeCoSTAR can reconstruct the evolutionary history of domains inside genes, of gene fusion and fission events, or of gene order along chromosomes, for large data sets including dozens of whole genomes from all kingdoms of life. We illustrate the potential of DeCoSTAR with several applications: ancestral reconstruction of gene orders for Anopheles mosquito genomes, multidomain proteins in Drosophila, and gene fusion and fission detection in Actinobacteria. Availability: http://pbil.univ-lyon1.fr/software/DeCoSTAR (Last accessed April 24, 2017).

Published
2017
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33. A bird’s white-eye view on avian sex chromosome evolution

Author

Leroy, Thibault, primary, Anselmetti, Yoann, additional, Tilak, Marie-Ka, additional, Bérard, Sèverine, additional, Csukonyi, Laura, additional, Gabrielli, Maëva, additional, Scornavacca, Céline, additional, Milá, Borja, additional, Thébaud, Christophe, additional, and Nabholz, Benoit, additional

Published
2018
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34. Evolutionary superscaffolding and chromosome anchoring to improve Anopheles genome assemblies

Author

Waterhouse, Robert M., primary, Aganezov, Sergey, additional, Anselmetti, Yoann, additional, Lee, Jiyoung, additional, Ruzzante, Livio, additional, Reijnders, Maarten J.M.F., additional, Feron, Romain, additional, Bérard, Sèverine, additional, George, Phillip, additional, Hahn, Matthew W., additional, Howell, Paul I., additional, Kamali, Maryam, additional, Koren, Sergey, additional, Lawson, Daniel, additional, Maslen, Gareth, additional, Peery, Ashley, additional, Phillippy, Adam M., additional, Sharakhova, Maria V., additional, Tannier, Eric, additional, Unger, Maria F., additional, Zhang, Simo V., additional, Alekseyev, Max A., additional, Besansky, Nora J., additional, Chauve, Cedric, additional, Emrich, Scott J., additional, and Sharakhov, Igor V., additional

Published
2018
Full Text
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35. Comment la reconstruction de génomes ancestraux peut aider à l'assemblage de génomes actuels

Author

Anselmetti, Yoann, Berry, Vincent, Chauve, Cedric, Chateau, Annie, Tannier, Eric, Bérard, Sèverine, 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, 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), Méthodes et Algorithmes pour la Bioinformatique (MAB), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Department of Mathematics [Burnaby] (SFU), Simon Fraser University (SFU.ca), Bioinformatique, phylogénie et génomique évolutive (BPGE), Département PEGASE [LBBE] (PEGASE), 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), Artificial Evolution and Computational Biology (BEAGLE), Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-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-Université Lumière - Lyon 2 (UL2)-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), 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), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), 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, Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-École pratique des hautes études (EPHE), and Tannier, Eric

Subjects
[INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], [INFO.INFO-BI] Computer Science [cs]/Bioinformatics [q-bio.QM]
Abstract

National audience; Le document est un résumé étendu

Published
2016

36. DeCoSTAR: Reconstructing the ancestral organization of genes or genomes using reconciled phylogenies

Author

Duchemin, W, Anselmetti, Y, Patterson, M, Ponty, Y, Bérard, S, Chauve, C, Scornavacca, C, Daubin, V, Tannier, E, Duchemin, Wandrille, Anselmetti, Yoann, Patterson, Murray, Ponty, Yann, Bérard, Sèverine, Chauve, Cedric, Scornavacca, Celine, Daubin, Vincent, Tannier, Eric, Duchemin, W, Anselmetti, Y, Patterson, M, Ponty, Y, Bérard, S, Chauve, C, Scornavacca, C, Daubin, V, Tannier, E, Duchemin, Wandrille, Anselmetti, Yoann, Patterson, Murray, Ponty, Yann, Bérard, Sèverine, Chauve, Cedric, Scornavacca, Celine, Daubin, Vincent, and Tannier, Eric

Abstract

DeCoSTAR is a software that aims at reconstructing the organization of ancestral genes or genomes in the form of sets of neighborhood relations (adjacencies) between pairs of ancestral genes or gene domains. It can also improve the assembly of fragmented genomes by proposing evolutionary-induced adjacencies between scaffolding fragments. Ancestral genes or domains are deduced from reconciled phylogenetic trees under an evolutionarymodel that considers gains, losses, speciations, duplications, and transfers as possible events for gene evolution. Reconciliations are either given as input or computed with the ecceTERA package, into which DeCoSTAR is integrated. DeCoSTAR computes adjacency evolutionary scenarios using a scoring scheme based on aweighted sum of adjacency gains and breakages. Solutions, both optimal and near-optimal, are sampled according to the Boltzmann-Gibbs distribution centered around parsimonious solutions, and statistical supports on ancestral and extant adjacencies are provided.DeCoSTAR supports the features of previously contributed tools that reconstruct ancestral adjacencies, namely DeCo, DeCoLT, ART-DeCo, and DeClone. In a fewminutes, DeCoSTAR can reconstruct the evolutionary history of domains inside genes, of gene fusion and fission events, or of gene order along chromosomes, for large data sets including dozens of whole genomes from all kingdoms of life. We illustrate the potential of DeCoSTAR with several applications: ancestral reconstruction of gene orders for Anopheles mosquito genomes, multidomain proteins in Drosophila, and gene fusion and fission detection in Actinobacteria.

Published
2017

39. Additional file 20 of Phylogenetic signal from rearrangements in 18 Anopheles species by joint scaffolding extant and ancestral genomes

Author

Anselmetti, Yoann, Duchemin, Wandrille, Tannier, Eric, Chauve, Cedric, and SèVerine BÊrard

Subjects
3. Good health
Abstract

Table S4. Scaffolding statistics on the 18 Anopheles genomes before and after ADseq (with the X (upper table) and WG (lower table) species phylogenies). The columns 2-5 correspond to assemblies statistics before running the ADseq algorithm. Column 2 corresponds to the number of contigs in reference assemblies. The N50 statistic corresponding to the contig size where 50% of the total assembly length is comprised in contigs with size superior or equal to this value. This metric is computed with size considerd both in bp (in column 3) and in gene number (in column 4). Column 5 gives the number of genes in genome assemblies give as input to ADseq. Columns 6-9 and 10-13 represent scaffolding statistics of ADseq respectively for X chromosome species tree topology and Whole-Genome topology. Columns 6 & 10 represent scaffolds number after ADseq. Columns 7 & 11, and 8 & 12 represent N50 statistics respectively for size in bp and size in gene number. Columns 9 & 13 represent new adjacencies inferred by ADseq (#scaff adj) represent the number of new adjacencies that are scaffolding adjacencies (i.e. adjacencies with sequence signal proposed by BESST and inferred by ADseq). (PDF 56 kb)

40. Additional file 14 of Phylogenetic signal from rearrangements in 18 Anopheles species by joint scaffolding extant and ancestral genomes

Author

Anselmetti, Yoann, Duchemin, Wandrille, Tannier, Eric, Chauve, Cedric, and Sèverine Bérard

Subjects
3. Good health
Abstract

Figure S13. Scatter plot exhibiting scaffolding improvement of the 18 Anopheles genomes by ADseq with X species tree phylogeny. Right plot is a zoom of a small part of the left graph. Each color corresponds to one species. For each species, upper part of vertical line corresponds to number of segments in initial genome assembly and lower part the number of segments after scaffolding improvement by ADseq. Circle diameter is proportional to the % of scaffolding improvement of the genome where scale is displayed in lower right part of the graphs. The X axis represent the sum of a posteriori scores of discarded adjacencies during linearization step representing the degree of syntenic conflicts in adjacencies prediction of ADseq (see paragraph “Conflict” in section “Results”). (PDF 56 kb)

44. Additional file 12 of Phylogenetic signal from rearrangements in 18 Anopheles species by joint scaffolding extant and ancestral genomes

Author

Anselmetti, Yoann, Duchemin, Wandrille, Tannier, Eric, Chauve, Cedric, and SèVerine BÊrard

Subjects
3. Good health
Abstract

Figure S11. Venn diagrams showing adjacencies shared by the three scaffolding methods ADseq, AD and BESST with a sample of 50% of the reads. Upper Venn diagrams: results for Anopheles albimanus. Middle Venn diagrams: results for Anopheles arabiensis. Lower Venn diagrams: results for Anopheles dirus. Left diagrams: False Negative (FN) adjacencies, corresponding to adjacencies created by the fragmentation process and that have not been recovered. Center diagrams: results for True Positive (TP) adjacencies. Here, an adjacency is considered TP if the pair of genes is adjacent in the reference assembly and the orientation of genes involved in the adjacency is properly recovered. Right diagrams: results for Certain False Positive (CFP) adjacencies. An adjacency is determined as CFP when the pair of gene does not belong to the reference assemblies and one of the two genes is not located at a contig extremity in reference genome, or if the recovered orientation of genes is incorrect. If we consider method individually, these results show that ADseq outperforms AD and BESST with the lowest number of FN adjacencies, the largest number of TP adjacencies and the lowest number of CFP adjacencies (except for An. albimanus where AD has the lowest number of CFP (224 vs. 231 for ADseq). However, if we combine a posterioriAD and BESST, this performs better than ADseq in terms of recall (higher number of TP adjacencies) but at the expense of a strong decreases of precision (much higher number of CFP adjacencies). (PDF 20 kb)

45. Additional file 9 of Phylogenetic signal from rearrangements in 18 Anopheles species by joint scaffolding extant and ancestral genomes

Author

Anselmetti, Yoann, Duchemin, Wandrille, Tannier, Eric, Chauve, Cedric, and SèVerine BÊrard

Subjects
3. Good health
Abstract

Figure S8. Precision and recall statistics for scaffolding adjacencies on three artificially fragmented genomes (A.alb: Anopheles albimanus, A.ara: Anopheles arabiensis and A.dir: Anopheles dirus), when gene orientations are not accounted for. Left graph: results with 50% of reads. Right graph: results with all reads. The different methods results are plotted with the precision on the Y axis and the recall on the X axis. For ADseq and AD, results for three different adjacency support threshold (0.1, 0.5 and 0.8) before genome linearization are plotted and represented with a color gradient. These results show similar results to Fig. 2 showing that for most of the predicted adjacencies the three methods infer the correct gene orientation. (PDF 106 kb)

47. Additional file 20 of Phylogenetic signal from rearrangements in 18 Anopheles species by joint scaffolding extant and ancestral genomes

Author

Anselmetti, Yoann, Duchemin, Wandrille, Tannier, Eric, Chauve, Cedric, and SèVerine BÊrard

Subjects
3. Good health
Abstract

Table S4. Scaffolding statistics on the 18 Anopheles genomes before and after ADseq (with the X (upper table) and WG (lower table) species phylogenies). The columns 2-5 correspond to assemblies statistics before running the ADseq algorithm. Column 2 corresponds to the number of contigs in reference assemblies. The N50 statistic corresponding to the contig size where 50% of the total assembly length is comprised in contigs with size superior or equal to this value. This metric is computed with size considerd both in bp (in column 3) and in gene number (in column 4). Column 5 gives the number of genes in genome assemblies give as input to ADseq. Columns 6-9 and 10-13 represent scaffolding statistics of ADseq respectively for X chromosome species tree topology and Whole-Genome topology. Columns 6 & 10 represent scaffolds number after ADseq. Columns 7 & 11, and 8 & 12 represent N50 statistics respectively for size in bp and size in gene number. Columns 9 & 13 represent new adjacencies inferred by ADseq (#scaff adj) represent the number of new adjacencies that are scaffolding adjacencies (i.e. adjacencies with sequence signal proposed by BESST and inferred by ADseq). (PDF 56 kb)

48. Additional file 18 of Phylogenetic signal from rearrangements in 18 Anopheles species by joint scaffolding extant and ancestral genomes

Author

Anselmetti, Yoann, Duchemin, Wandrille, Tannier, Eric, Chauve, Cedric, and Sèverine Bérard

Subjects
3. Good health
Abstract

Table S2. Assembly statistics on the 18 Anopheles genomes. Statistics before before processing are displayed in columns 2-7 and after the pipeline to produce input data for the DeCoSTAR algorithm in columns 8-11 (see Additional file 3: Figure S2 for illustration of the data preprocessing step). For initial dataset assembly statistics, columns 2 and 3 present contigs number and N50 statistic in bp for all contigs in genome assemblies. In columns 4-7, only contigs with at least one gene are considered. Column 4 corresponds to contigs number with gene in reference assemblies. Columns 5 & 6 represent N50 statistics respectively in bp and in gene number. Column 7 represent the number of gene in reference genome assemblies. For genome assemblies used as input of DeCoSTAR, all contigs contains at least one gene. Column 8 gives the number of contigs after step 4 of Additional file 3: Figure S2. Columns 9 & 10 represent N50 statistics respectively in bp and in gene number. And column 11 represents the number of gene in genomes taken as input of DeCoSTAR. The input dataset of DeCoSTAR is composed of 14,940 gene trees (see Additional file 4: Figure S3 and Additional file 5: Figure S4 for more information on gene trees) and 68,876 gene adjacencies with sequence support (scaffolding gene adjacencies) (see Additional file 6: Figure S5 and Additional file 7: Figure S6 for more information on scaffolding adjacencies). (PDF 33 kb)

49. Additional file 9 of Phylogenetic signal from rearrangements in 18 Anopheles species by joint scaffolding extant and ancestral genomes

Author

Anselmetti, Yoann, Duchemin, Wandrille, Tannier, Eric, Chauve, Cedric, and SèVerine BÊrard

Subjects
3. Good health
Abstract

Figure S8. Precision and recall statistics for scaffolding adjacencies on three artificially fragmented genomes (A.alb: Anopheles albimanus, A.ara: Anopheles arabiensis and A.dir: Anopheles dirus), when gene orientations are not accounted for. Left graph: results with 50% of reads. Right graph: results with all reads. The different methods results are plotted with the precision on the Y axis and the recall on the X axis. For ADseq and AD, results for three different adjacency support threshold (0.1, 0.5 and 0.8) before genome linearization are plotted and represented with a color gradient. These results show similar results to Fig. 2 showing that for most of the predicted adjacencies the three methods infer the correct gene orientation. (PDF 106 kb)

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