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Kirilenko BM, Munegowda C, Osipova E, Jebb D, Sharma V, Blumer M, Morales AE, Ahmed AW, Kontopoulos DG, Hilgers L, Lindblad-Toh K, Karlsson EK, Hiller M, Andrews G, Armstrong JC, Bianchi M, Birren BW, Bredemeyer KR, Breit AM, Christmas MJ, Clawson H, Damas J, Di Palma F, Diekhans M, Dong MX, Eizirik E, Fan K, Fanter C, Foley NM, Forsberg-Nilsson K, Garcia CJ, Gatesy J, Gazal S, Genereux DP, Goodman L, Grimshaw J, Halsey MK, Harris AJ, Hickey G, Hiller M, Hindle AG, Hubley RM, Hughes GM, Johnson J, Juan D, Kaplow IM, Karlsson EK, Keough KC, Kirilenko B, Koepfli KP, Korstian JM, Kowalczyk A, Kozyrev SV, Lawler AJ, Lawless C, Lehmann T, Levesque DL, Lewin HA, Li X, Lind A, Lindblad-Toh K, Mackay-Smith A, Marinescu VD, Marques-Bonet T, Mason VC, Meadows JRS, Meyer WK, Moore JE, Moreira LR, Moreno-Santillan DD, Morrill KM, Muntané G, Murphy WJ, Navarro A, Nweeia M, Ortmann S, Osmanski A, Paten B, Paulat NS, Pfenning AR, Phan BN, Pollard KS, Pratt HE, Ray DA, Reilly SK, Rosen JR, Ruf I, Ryan L, Ryder OA, Sabeti PC, Schäffer DE, Serres A, Shapiro B, Smit AFA, Springer M, Srinivasan C, Steiner C, Storer JM, Sullivan KAM, Sullivan PF, Sundström E, Supple MA, Swofford R, Talbot JE, Teeling E, Turner-Maier J, Valenzuela A, Wagner F, Wallerman O, Wang C, Wang J, Weng Z, Wilder AP, Wirthlin ME, Xue JR, Zhang X. Integrating gene annotation with orthology inference at scale. Science 2023; 380:eabn3107. [PMID: 37104600 DOI: 10.1126/science.abn3107] [Citation(s) in RCA: 31] [Impact Index Per Article: 31.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/29/2023]
Abstract
Annotating coding genes and inferring orthologs are two classical challenges in genomics and evolutionary biology that have traditionally been approached separately, limiting scalability. We present TOGA (Tool to infer Orthologs from Genome Alignments), a method that integrates structural gene annotation and orthology inference. TOGA implements a different paradigm to infer orthologous loci, improves ortholog detection and annotation of conserved genes compared with state-of-the-art methods, and handles even highly fragmented assemblies. TOGA scales to hundreds of genomes, which we demonstrate by applying it to 488 placental mammal and 501 bird assemblies, creating the largest comparative gene resources so far. Additionally, TOGA detects gene losses, enables selection screens, and automatically provides a superior measure of mammalian genome quality. TOGA is a powerful and scalable method to annotate and compare genes in the genomic era.
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Affiliation(s)
- Bogdan M Kirilenko
- Max Planck Institute of Molecular Cell Biology and Genetics, 01307 Dresden, Germany
- Max Planck Institute for the Physics of Complex Systems, 01187 Dresden, Germany
- Center for Systems Biology Dresden, 01307 Dresden, Germany
- LOEWE Centre for Translational Biodiversity Genomics, 60325 Frankfurt, Germany
- Senckenberg Research Institute, 60325 Frankfurt, Germany
- Goethe University Frankfurt, Faculty of Biosciences, 60438 Frankfurt, Germany
| | - Chetan Munegowda
- Max Planck Institute of Molecular Cell Biology and Genetics, 01307 Dresden, Germany
- Max Planck Institute for the Physics of Complex Systems, 01187 Dresden, Germany
- Center for Systems Biology Dresden, 01307 Dresden, Germany
- LOEWE Centre for Translational Biodiversity Genomics, 60325 Frankfurt, Germany
- Senckenberg Research Institute, 60325 Frankfurt, Germany
- Goethe University Frankfurt, Faculty of Biosciences, 60438 Frankfurt, Germany
| | - Ekaterina Osipova
- Max Planck Institute of Molecular Cell Biology and Genetics, 01307 Dresden, Germany
- Max Planck Institute for the Physics of Complex Systems, 01187 Dresden, Germany
- Center for Systems Biology Dresden, 01307 Dresden, Germany
- LOEWE Centre for Translational Biodiversity Genomics, 60325 Frankfurt, Germany
- Senckenberg Research Institute, 60325 Frankfurt, Germany
- Goethe University Frankfurt, Faculty of Biosciences, 60438 Frankfurt, Germany
| | - David Jebb
- Max Planck Institute of Molecular Cell Biology and Genetics, 01307 Dresden, Germany
- Max Planck Institute for the Physics of Complex Systems, 01187 Dresden, Germany
- Center for Systems Biology Dresden, 01307 Dresden, Germany
| | - Virag Sharma
- Max Planck Institute of Molecular Cell Biology and Genetics, 01307 Dresden, Germany
- Max Planck Institute for the Physics of Complex Systems, 01187 Dresden, Germany
- Center for Systems Biology Dresden, 01307 Dresden, Germany
| | - Moritz Blumer
- Max Planck Institute of Molecular Cell Biology and Genetics, 01307 Dresden, Germany
- Max Planck Institute for the Physics of Complex Systems, 01187 Dresden, Germany
- Center for Systems Biology Dresden, 01307 Dresden, Germany
| | - Ariadna E Morales
- LOEWE Centre for Translational Biodiversity Genomics, 60325 Frankfurt, Germany
- Senckenberg Research Institute, 60325 Frankfurt, Germany
- Goethe University Frankfurt, Faculty of Biosciences, 60438 Frankfurt, Germany
| | - Alexis-Walid Ahmed
- LOEWE Centre for Translational Biodiversity Genomics, 60325 Frankfurt, Germany
- Senckenberg Research Institute, 60325 Frankfurt, Germany
- Goethe University Frankfurt, Faculty of Biosciences, 60438 Frankfurt, Germany
| | - Dimitrios-Georgios Kontopoulos
- LOEWE Centre for Translational Biodiversity Genomics, 60325 Frankfurt, Germany
- Senckenberg Research Institute, 60325 Frankfurt, Germany
- Goethe University Frankfurt, Faculty of Biosciences, 60438 Frankfurt, Germany
| | - Leon Hilgers
- LOEWE Centre for Translational Biodiversity Genomics, 60325 Frankfurt, Germany
- Senckenberg Research Institute, 60325 Frankfurt, Germany
- Goethe University Frankfurt, Faculty of Biosciences, 60438 Frankfurt, Germany
| | - Kerstin Lindblad-Toh
- Science for Life Laboratory, Department of Medical Biochemistry and Microbiology, Uppsala University, 751 32 Uppsala, Sweden
- Broad Institute of MIT and Harvard, Cambridge, MA 02139, USA
| | - Elinor K Karlsson
- Broad Institute of MIT and Harvard, Cambridge, MA 02139, USA
- Program in Bioinformatics and Integrative Biology, UMass Chan Medical School, Worcester, MA 01605, USA
- Program in Molecular Medicine, UMass Chan Medical School, Worcester, MA 01605, USA
| | - Michael Hiller
- Max Planck Institute of Molecular Cell Biology and Genetics, 01307 Dresden, Germany
- Max Planck Institute for the Physics of Complex Systems, 01187 Dresden, Germany
- Center for Systems Biology Dresden, 01307 Dresden, Germany
- LOEWE Centre for Translational Biodiversity Genomics, 60325 Frankfurt, Germany
- Senckenberg Research Institute, 60325 Frankfurt, Germany
- Goethe University Frankfurt, Faculty of Biosciences, 60438 Frankfurt, Germany
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Lacroix T, Loux V, Gendrault A, Hoebeke M, Gibrat JF. Insyght: navigating amongst abundant homologues, syntenies and gene functional annotations in bacteria, it's that symbol! Nucleic Acids Res 2014; 42:gku867. [PMID: 25249626 PMCID: PMC4245967 DOI: 10.1093/nar/gku867] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2013] [Revised: 08/28/2014] [Accepted: 09/10/2014] [Indexed: 11/14/2022] Open
Abstract
High-throughput techniques have considerably increased the potential of comparative genomics whilst simultaneously posing many new challenges. One of those challenges involves efficiently mining the large amount of data produced and exploring the landscape of both conserved and idiosyncratic genomic regions across multiple genomes. Domains of application of these analyses are diverse: identification of evolutionary events, inference of gene functions, detection of niche-specific genes or phylogenetic profiling. Insyght is a comparative genomic visualization tool that combines three complementary displays: (i) a table for thoroughly browsing amongst homologues, (ii) a comparator of orthologue functional annotations and (iii) a genomic organization view designed to improve the legibility of rearrangements and distinctive loci. The latter display combines symbolic and proportional graphical paradigms. Synchronized navigation across multiple species and interoperability between the views are core features of Insyght. A gene filter mechanism is provided that helps the user to build a biologically relevant gene set according to multiple criteria such as presence/absence of homologues and/or various annotations. We illustrate the use of Insyght with scenarios. Currently, only Bacteria and Archaea are supported. A public instance is available at http://genome.jouy.inra.fr/Insyght. The tool is freely downloadable for private data set analysis.
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Affiliation(s)
- Thomas Lacroix
- INRA, UR 1077 Mathématique Informatique et Génome, 78352 Jouy-en-Josas, France
| | - Valentin Loux
- INRA, UR 1077 Mathématique Informatique et Génome, 78352 Jouy-en-Josas, France
| | - Annie Gendrault
- INRA, UR 1077 Mathématique Informatique et Génome, 78352 Jouy-en-Josas, France
| | - Mark Hoebeke
- CNRS, UPMC, FR2424, ABiMS, Station Biologique, 29680 Roscoff, France
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Lechner M, Hernandez-Rosales M, Doerr D, Wieseke N, Thévenin A, Stoye J, Hartmann RK, Prohaska SJ, Stadler PF. Orthology detection combining clustering and synteny for very large datasets. PLoS One 2014; 9:e105015. [PMID: 25137074 PMCID: PMC4138177 DOI: 10.1371/journal.pone.0105015] [Citation(s) in RCA: 73] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2014] [Accepted: 07/14/2014] [Indexed: 11/18/2022] Open
Abstract
The elucidation of orthology relationships is an important step both in gene function prediction as well as towards understanding patterns of sequence evolution. Orthology assignments are usually derived directly from sequence similarities for large data because more exact approaches exhibit too high computational costs. Here we present PoFF, an extension for the standalone tool Proteinortho, which enhances orthology detection by combining clustering, sequence similarity, and synteny. In the course of this work, FFAdj-MCS, a heuristic that assesses pairwise gene order using adjacencies (a similarity measure related to the breakpoint distance) was adapted to support multiple linear chromosomes and extended to detect duplicated regions. PoFF largely reduces the number of false positives and enables more fine-grained predictions than purely similarity-based approaches. The extension maintains the low memory requirements and the efficient concurrency options of its basis Proteinortho, making the software applicable to very large datasets.
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Affiliation(s)
- Marcus Lechner
- Institut für Pharmazeutische Chemie, Philipps-Universität Marburg, Marburg, Germany
- * E-mail:
| | - Maribel Hernandez-Rosales
- Bioinformatics Group, Department of Computer Science, Universität Leipzig, Leipzig, Germany
- Interdisciplinary Center for Bioinformatics, Universität Leipzig, Leipzig, Germany
- Max Planck Institute for Mathematics in the Sciences, Leipzig, Germany
- Departamento de Ciência da Computação, Instituto de Ciências Exatas, Universidade de Brasília, Brasília, Brasil
| | - Daniel Doerr
- Genome Informatics, Faculty of Technology, Bielefeld University, Bielefeld, Germany
- Institute for Bioinformatics, Center for Biotechnology, Bielefeld University, Bielefeld, Germany
| | - Nicolas Wieseke
- Faculty of Mathematics and Computer Science University of Leipzig, Leipzig, Germany
| | - Annelyse Thévenin
- Genome Informatics, Faculty of Technology, Bielefeld University, Bielefeld, Germany
- Institute for Bioinformatics, Center for Biotechnology, Bielefeld University, Bielefeld, Germany
| | - Jens Stoye
- Genome Informatics, Faculty of Technology, Bielefeld University, Bielefeld, Germany
- Institute for Bioinformatics, Center for Biotechnology, Bielefeld University, Bielefeld, Germany
| | - Roland K. Hartmann
- Institut für Pharmazeutische Chemie, Philipps-Universität Marburg, Marburg, Germany
| | - Sonja J. Prohaska
- Computational EvoDevo Group, Department of Computer Science, Universität Leipzig, Leipzig, Germany
| | - Peter F. Stadler
- Bioinformatics Group, Department of Computer Science, Universität Leipzig, Leipzig, Germany
- Interdisciplinary Center for Bioinformatics, Universität Leipzig, Leipzig, Germany
- Max Planck Institute for Mathematics in the Sciences, Leipzig, Germany
- Institute for Theoretical Chemistry, University of Vienna, Vienna, Austria
- Center for non-coding RNA in Technology and Health, University of Copenhagen, Frederiksberg, Denmark
- The Santa Fe Institute, Santa Fe, New Mexico, United States of America
- RNomics Group, Fraunhofer Institut for Cell Therapy and Immunology, Leipzig, Germany
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Ali RH, Muhammad S, Khan M, Arvestad L. Quantitative synteny scoring improves homology inference and partitioning of gene families. BMC Bioinformatics 2014; 14 Suppl 15:S12. [PMID: 24564516 PMCID: PMC3852004 DOI: 10.1186/1471-2105-14-s15-s12] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
Background Clustering sequences into families has long been an important step in characterization of genes and proteins. There are many algorithms developed for this purpose, most of which are based on either direct similarity between gene pairs or some sort of network structure, where weights on edges of constructed graphs are based on similarity. However, conserved synteny is an important signal that can help distinguish homology and it has not been utilized to its fullest potential. Results Here, we present GenFamClust, a pipeline that combines the network properties of sequence similarity and synteny to assess homology relationship and merge known homologs into groups of gene families. GenFamClust identifies homologs in a more informed and accurate manner as compared to similarity based approaches. We tested our method against the Neighborhood Correlation method on two diverse datasets consisting of fully sequenced genomes of eukaryotes and synthetic data. Conclusions The results obtained from both datasets confirm that synteny helps determine homology and GenFamClust improves on Neighborhood Correlation method. The accuracy as well as the definition of synteny scores is the most valuable contribution of GenFamClust.
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Jiang Y, Gao X, Liu S, Zhang Y, Liu H, Sun F, Bao L, Waldbieser G, Liu Z. Whole genome comparative analysis of channel catfish (Ictalurus punctatus) with four model fish species. BMC Genomics 2013; 14:780. [PMID: 24215161 PMCID: PMC3840565 DOI: 10.1186/1471-2164-14-780] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2013] [Accepted: 10/28/2013] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Comparative mapping is a powerful tool to study evolution of genomes. It allows transfer of genome information from the well-studied model species to non-model species. Catfish is an economically important aquaculture species in United States. A large amount of genome resources have been developed from catfish including genetic linkage maps, physical maps, BAC end sequences (BES), integrated linkage and physical maps using BES-derived markers, physical map contig-specific sequences, and draft genome sequences. Application of such genome resources should allow comparative analysis at the genome scale with several other model fish species. RESULTS In this study, we conducted whole genome comparative analysis between channel catfish and four model fish species with fully sequenced genomes, zebrafish, medaka, stickleback and Tetraodon. A total of 517 Mb draft genome sequences of catfish were anchored to its genetic linkage map, which accounted for 62% of the total draft genome sequences. Based on the location of homologous genes, homologous chromosomes were determined among catfish and the four model fish species. A large number of conserved syntenic blocks were identified. Analysis of the syntenic relationships between catfish and the four model fishes supported that the catfish genome is most similar to the genome of zebrafish. CONCLUSION The organization of the catfish genome is similar to that of the four teleost species, zebrafish, medaka, stickleback, and Tetraodon such that homologous chromosomes can be identified. Within each chromosome, extended syntenic blocks were evident, but the conserved syntenies at the chromosome level involve extensive inter-chromosomal and intra-chromosomal rearrangements. This whole genome comparative map should facilitate the whole genome assembly and annotation in catfish, and will be useful for genomic studies of various other fish species.
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Affiliation(s)
| | | | | | | | | | | | | | | | - Zhanjiang Liu
- The Fish Molecular Genetics and Biotechnology Laboratory, Department of Fisheries and Allied Aquacultures, Program of Cell and Molecular Biosciences, Aquatic Genomics Unit, 203 Swingle Hall, Auburn University, Auburn, AL 36849, USA.
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Zhang YB, Liu TK, Jiang J, Shi J, Liu Y, Li S, Gui JF. Identification of a novel Gig2 gene family specific to non-amniote vertebrates. PLoS One 2013; 8:e60588. [PMID: 23593256 PMCID: PMC3617106 DOI: 10.1371/journal.pone.0060588] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2012] [Accepted: 02/28/2013] [Indexed: 12/15/2022] Open
Abstract
Gig2 (grass carp reovirus (GCRV)-induced gene 2) is first identified as a novel fish interferon (IFN)-stimulated gene (ISG). Overexpression of a zebrafish Gig2 gene can protect cultured fish cells from virus infection. In the present study, we identify a novel gene family that is comprised of genes homologous to the previously characterized Gig2. EST/GSS search and in silico cloning identify 190 Gig2 homologous genes in 51 vertebrate species ranged from lampreys to amphibians. Further large-scale search of vertebrate and invertebrate genome databases indicate that Gig2 gene family is specific to non-amniotes including lampreys, sharks/rays, ray-finned fishes and amphibians. Phylogenetic analysis and synteny analysis reveal lineage-specific expansion of Gig2 gene family and also provide valuable evidence for the fish-specific genome duplication (FSGD) hypothesis. Although Gig2 family proteins exhibit no significant sequence similarity to any known proteins, a typical Gig2 protein appears to consist of two conserved parts: an N-terminus that bears very low homology to the catalytic domains of poly(ADP-ribose) polymerases (PARPs), and a novel C-terminal domain that is unique to this gene family. Expression profiling of zebrafish Gig2 family genes shows that some duplicate pairs have diverged in function via acquisition of novel spatial and/or temporal expression under stresses. The specificity of this gene family to non-amniotes might contribute to a large extent to distinct physiology in non-amniote vertebrates.
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Affiliation(s)
- Yi-Bing Zhang
- State Key Laboratory of Freshwater Ecology and Biotechnology, Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan, China
- * E-mail: (YZ) (YZ); (JG) (JG)
| | - Ting-Kai Liu
- State Key Laboratory of Freshwater Ecology and Biotechnology, Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan, China
| | - Jun Jiang
- State Key Laboratory of Freshwater Ecology and Biotechnology, Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan, China
| | - Jun Shi
- State Key Laboratory of Freshwater Ecology and Biotechnology, Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan, China
| | - Ying Liu
- State Key Laboratory of Freshwater Ecology and Biotechnology, Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan, China
| | - Shun Li
- State Key Laboratory of Freshwater Ecology and Biotechnology, Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan, China
| | - Jian-Fang Gui
- State Key Laboratory of Freshwater Ecology and Biotechnology, Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan, China
- * E-mail: (YZ) (YZ); (JG) (JG)
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Medema MH, Takano E, Breitling R. Detecting sequence homology at the gene cluster level with MultiGeneBlast. Mol Biol Evol 2013; 30:1218-23. [PMID: 23412913 PMCID: PMC3670737 DOI: 10.1093/molbev/mst025] [Citation(s) in RCA: 245] [Impact Index Per Article: 22.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Abstract
The genes encoding many biomolecular systems and pathways are genomically organized in operons or gene clusters. With MultiGeneBlast, we provide a user-friendly and effective tool to perform homology searches with operons or gene clusters as basic units, instead of single genes. The contextualization offered by MultiGeneBlast allows users to get a better understanding of the function, evolutionary history, and practical applications of such genomic regions. The tool is fully equipped with applications to generate search databases from GenBank or from the user’s own sequence data. Finally, an architecture search mode allows searching for gene clusters with novel configurations, by detecting genomic regions with any user-specified combination of genes. Sources, precompiled binaries, and a graphical tutorial of MultiGeneBlast are freely available from http://multigeneblast.sourceforge.net/.
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Affiliation(s)
- Marnix H Medema
- Department of Microbial Physiology, Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, Groningen, The Netherlands
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Dewey CN. Positional orthology: putting genomic evolutionary relationships into context. Brief Bioinform 2011; 12:401-12. [PMID: 21705766 PMCID: PMC3178058 DOI: 10.1093/bib/bbr040] [Citation(s) in RCA: 66] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
Abstract
Orthology is a powerful refinement of homology that allows us to describe more precisely the evolution of genomes and understand the function of the genes they contain. However, because orthology is not concerned with genomic position, it is limited in its ability to describe genes that are likely to have equivalent roles in different genomes. Because of this limitation, the concept of ‘positional orthology’ has emerged, which describes the relation between orthologous genes that retain their ancestral genomic positions. In this review, we formally define this concept, for which we introduce the shorter term ‘toporthology’, with respect to the evolutionary events experienced by a gene’s ancestors. Through a discussion of recent studies on the role of genomic context in gene evolution, we show that the distinction between orthology and toporthology is biologically significant. We then review a number of orthology prediction methods that take genomic context into account and thus that may be used to infer the important relation of toporthology.
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Affiliation(s)
- Colin N Dewey
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, 5785 Medical Sciences Center, 1300 University Ave, Madison, WI 53706, USA.
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Salse J, Abrouk M, Murat F, Quraishi UM, Feuillet C. Improved criteria and comparative genomics tool provide new insights into grass paleogenomics. Brief Bioinform 2009; 10:619-30. [DOI: 10.1093/bib/bbp037] [Citation(s) in RCA: 53] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
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Dong X, Fredman D, Lenhard B. Synorth: exploring the evolution of synteny and long-range regulatory interactions in vertebrate genomes. Genome Biol 2009; 10:R86. [PMID: 19698106 PMCID: PMC2745767 DOI: 10.1186/gb-2009-10-8-r86] [Citation(s) in RCA: 24] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2009] [Revised: 06/22/2009] [Accepted: 08/21/2009] [Indexed: 12/17/2022] Open
Abstract
Synorth is a web resource for exploring and categorizing the syntenic relationships in gene regulatory blocks across multiple genomes. Genomic regulatory blocks are chromosomal regions spanned by long clusters of highly conserved noncoding elements devoted to long-range regulation of developmental genes, often immobilizing other, unrelated genes into long-lasting syntenic arrangements. Synorth is a web resource for exploring and categorizing the syntenic relationships in genomic regulatory blocks across multiple genomes, tracing their evolutionary fate after teleost whole genome duplication at the level of genomic regulatory block loci, individual genes, and their phylogenetic context.
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Affiliation(s)
- Xianjun Dong
- Computational Biology Unit, Bergen Center for Computational Science, University of Bergen, Thormøhlensgate 55, N-5008 Bergen, Norway.
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