8
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Ten Years of Collaborative Progress in the Quest for Orthologs

      review-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Accurate determination of the evolutionary relationships between genes is a foundational challenge in biology. Homology—evolutionary relatedness—is in many cases readily determined based on sequence similarity analysis. By contrast, whether or not two genes directly descended from a common ancestor by a speciation event (orthologs) or duplication event (paralogs) is more challenging, yet provides critical information on the history of a gene. Since 2009, this task has been the focus of the Quest for Orthologs (QFO) Consortium. The sixth QFO meeting took place in Okazaki, Japan in conjunction with the 67th National Institute for Basic Biology conference. Here, we report recent advances, applications, and oncoming challenges that were discussed during the conference. Steady progress has been made toward standardization and scalability of new and existing tools. A feature of the conference was the presentation of a panel of accessible tools for phylogenetic profiling and several developments to bring orthology beyond the gene unit—from domains to networks. This meeting brought into light several challenges to come: leveraging orthology computations to get the most of the incoming avalanche of genomic data, integrating orthology from domain to biological network levels, building better gene models, and adapting orthology approaches to the broad evolutionary and genomic diversity recognized in different forms of life and viruses.

          Related collections

          Most cited references121

          • Record: found
          • Abstract: found
          • Article: found
          Is Open Access

          STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets

          Abstract Proteins and their functional interactions form the backbone of the cellular machinery. Their connectivity network needs to be considered for the full understanding of biological phenomena, but the available information on protein–protein associations is incomplete and exhibits varying levels of annotation granularity and reliability. The STRING database aims to collect, score and integrate all publicly available sources of protein–protein interaction information, and to complement these with computational predictions. Its goal is to achieve a comprehensive and objective global network, including direct (physical) as well as indirect (functional) interactions. The latest version of STRING (11.0) more than doubles the number of organisms it covers, to 5090. The most important new feature is an option to upload entire, genome-wide datasets as input, allowing users to visualize subsets as interaction networks and to perform gene-set enrichment analysis on the entire input. For the enrichment analysis, STRING implements well-known classification systems such as Gene Ontology and KEGG, but also offers additional, new classification systems based on high-throughput text-mining as well as on a hierarchical clustering of the association network itself. The STRING resource is available online at https://string-db.org/.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            BUSCO: assessing genome assembly and annotation completeness with single-copy orthologs.

            Genomics has revolutionized biological research, but quality assessment of the resulting assembled sequences is complicated and remains mostly limited to technical measures like N50.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Fast and sensitive protein alignment using DIAMOND.

              The alignment of sequencing reads against a protein reference database is a major computational bottleneck in metagenomics and data-intensive evolutionary projects. Although recent tools offer improved performance over the gold standard BLASTX, they exhibit only a modest speedup or low sensitivity. We introduce DIAMOND, an open-source algorithm based on double indexing that is 20,000 times faster than BLASTX on short reads and has a similar degree of sensitivity.
                Bookmark

                Author and article information

                Journal
                Mol Biol Evol
                Mol Biol Evol
                molbev
                Molecular Biology and Evolution
                Oxford University Press
                0737-4038
                1537-1719
                August 2021
                02 April 2021
                02 April 2021
                : 38
                : 8
                : 3033-3045
                Affiliations
                [1 ] LIRMM, University of Montpellier, CNRS , Montpellier, France
                [2 ] SPYGEN, Le Bourget-du-Lac , France
                [3 ] Institute of Cell Biology and Neuroscience, Goethe University Frankfurt , Frankfurt, Germany
                [4 ] Senckenberg Biodiversity and Climate Research Centre (S-BIKF) , Frankfurt, Germany
                [5 ] LOEWE Center for Translational Biodiversity Genomics (TBG) , Frankfurt, Germany
                [6 ] Earth-Life Science Institute, Tokyo Institute of Technology , Meguro, Tokyo, Japan
                [7 ] Blue Marble Space Institute of Science , Seattle, WA, USA
                [8 ] Swiss Institute of Bioinformatics , Lausanne, Switzerland
                [9 ] Center for Integrative Genomics, University of Lausanne , Lausanne, Switzerland
                [10 ] Department of Computational Biology, University of Lausanne , Lausanne, Switzerland
                [11 ] European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus , Hinxton, Cambridge, United Kingdom
                [12 ] Department of Computer Science, ICube, UMR 7357, University of Strasbourg, CNRS, Fédération de Médecine Translationnelle de Strasbourg , Strasbourg, France
                [13 ] Division of Bioinformatics, Department of Preventive Medicine, University of Southern California , Los Angeles, CA, USA
                [14 ] Barcelona Supercomputing Centre (BCS-CNS), Jordi Girona , Barcelona, Spain
                [15 ] Institute for Research in Biomedicine (IRB), The Barcelona Institute of Science and Technology (BIST) , Barcelona, Spain
                [16 ] Institució Catalana de Recerca i Estudis Avançats (ICREA) , Barcelona, Spain
                [17 ] Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University , Solna, Sweden
                [18 ] Department of Computer Science, University College London , London, United Kingdom
                [19 ] Department of Genetics, Evolution and Environment, University College London , London, United Kingdom
                [20 ] Department of Theoretical Biology, National Institute for Basic Biology, National Institutes of Natural Sciences , Okazaki, Aichi, Japan
                Author notes

                Members of the QFO Consortium are listed in the Acknowledgments section.

                Author information
                https://orcid.org/0000-0002-5555-898X
                https://orcid.org/0000-0002-8199-7011
                Article
                msab098
                10.1093/molbev/msab098
                8321534
                33822172
                b99be5e0-a238-45fa-8d28-b4f13c1997f2
                © The Author(s) 2021. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 27 October 2020
                : 07 February 2021
                : 01 April 2021
                : 29 March 2021
                Page count
                Pages: 13
                Funding
                Funded by: Swiss Institute of Bioinformatics;
                Funded by: Swiss National Science Foundation, DOI 10.13039/501100001711;
                Award ID: 183723
                Funded by: French government;
                Funded by: NSF, DOI 10.13039/100000001;
                Award ID: 1724300
                Funded by: KAKENHI, DOI 10.13039/501100001691;
                Award ID: JP18H01325
                Funded by: Wellcome Trust, DOI 10.13039/100010269;
                Award ID: 108749/Z/15/Z
                Funded by: European Molecular Biology Laboratory, DOI 10.13039/100013060;
                Funded by: National Human Genome Research Institute, DOI 10.13039/100000051;
                Funded by: National Institutes of Health, DOI 10.13039/100000002;
                Award ID: U41HG002273
                Funded by: National Science Foundation, DOI 10.13039/100000001;
                Award ID: 1917302
                Funded by: Japan Society for the Promotion of Science, DOI 10.13039/501100001691;
                Award ID: 16H06279
                Award ID: 19F19089
                Categories
                Review
                AcademicSubjects/SCI01130
                AcademicSubjects/SCI01180

                Molecular biology
                orthology,viruses,phylogenetic profiling,paralogy,xenology,gene models
                Molecular biology
                orthology, viruses, phylogenetic profiling, paralogy, xenology, gene models

                Comments

                Comment on this article