CoChat
Sign Up
Cited by 244

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

OMA orthology in 2021: website overhaul, conserved isoforms, ancestral gene order and more

Altenhoff, Adrian M, Train, Clément-Marie, Gilbert, Kimberly J, Mediratta, Ishita, Mendes de Farias, Tarcisio, Moi, David, Nevers, Yannis, Radoykova, Hale-Seda, Rossier, Victor, Warwick Vesztrocy, Alex, Glover, Natasha M, Dessimoz, Christophe·2021·Nucleic Acids Research
DOI 10.1093/nar/gkaa1007PMC PMC7779010
Open this paper in CoChat — free account

Abstract

OMA is an established resource to elucidate evolutionary relationships among genes from currently 2326 genomes covering all domains of life. OMA provides pairwise and groupwise orthologs, functional annotations, local and global gene order conservation (synteny) information, among many other functions. This update paper describes the reorganisation of the database into gene-, group- and genome-centric pages. Other new and improved features are detailed, such as reporting of the evolutionarily best conserved isoforms of alternatively spliced genes, the inferred local order of ancestral genes, phylogenetic profiling, better cross-references, fast genome mapping, semantic data sharing via RDF, as well as a special coronavirus OMA with 119 viruses from the Nidovirales order, including SARS-CoV-2, the agent of the COVID-19 pandemic. We conclude with improvements to the documentation of the resource through primers, tutorials and short videos. OMA is accessible at https://omabrowser.org.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1093/nar/gkaa1007

Cites

2 of 2 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • One major difference between Ensembl and UniProt proteomes is that UniProt selects only one of the isoforms provided by Ensembl as their canonical isoform.

    “One major difference between Ensembl and UniProt proteomes is that UniProt selects only one of the isoforms provided by Ensembl as their canonical isoform, and one major difference between OrthoFinder and OMA is their isoform handling approach.”

    From Annotation matters: the effect of structural gene annotation on orthology inference (2025) · cites this paper as Altenhoff et al. 2021

  • One major difference between OrthoFinder and OMA is their isoform handling approach.

    “One major difference between Ensembl and UniProt proteomes is that UniProt selects only one of the isoforms provided by Ensembl as their canonical isoform, and one major difference between OrthoFinder and OMA is their isoform handling approach.”

    From Annotation matters: the effect of structural gene annotation on orthology inference (2025) · cites this paper as Altenhoff et al. 2021

  • OrthoFinder selects the longest isoforms, and OMA opts for the "evolutionarily best conserved isoforms".

    “OrthoFinder selects the longest isoforms, and OMA opts for the “evolutionarily best conserved isoforms” (Altenhoff et al. 2021).”

    From Annotation matters: the effect of structural gene annotation on orthology inference (2025) · cites this paper as Altenhoff et al. 2021

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Flagged first, then by citations

Research works printed but not linked: 41

  1. Fitch, 1970, Distinguishing homologous from analogous proteins, Syst. Zool., 19, 99, 10.2307/2412448 · DOI · Paper page
  2. Glover, 2019, Advances and Applications in the Quest for Orthologs, Mol. Biol. Evol., 36, 2157, 10.1093/molbev/msz150 · DOI · Paper page
  3. Dessimoz, 2005, OMA, a comprehensive, automated project for the identification of orthologs from complete genome data: introduction and first achievements, 61
  4. Altenhoff, 2011, OMA 2011: orthology inference among 1000 complete genomes, Nucleic Acids Res., 39, D289, 10.1093/nar/gkq1238 · DOI · Paper page
  5. Altenhoff, 2015, The OMA orthology database in 2015: function predictions, better plant support, synteny view and other improvements, Nucleic Acids Res., 43, D240, 10.1093/nar/gku1158 · DOI
  6. Altenhoff, 2018, The OMA orthology database in 2018: retrieving evolutionary relationships among all domains of life through richer web and programmatic interfaces, Nucleic Acids Res, 46, D477, 10.1093/nar/gkx1019 · DOI · Paper page
  7. Glover, 2016, Homoeologs: what are they and how do we infer them, Trends Plant Sci., 21, 609, 10.1016/j.tplants.2016.02.005 · DOI
  8. Zahn-Zabal, 2020, Identifying orthologs with OMA: A primer, F1000Res., 9, 27, 10.12688/f1000research.21508.1 · DOI
  9. Yates, 2020, Ensembl 2020, Nucleic Acids Res, 48, D682
  10. The UniProt Consortium, 2019, UniProt: a worldwide hub of protein knowledge, Nucleic Acids Research, 47, D506, 10.1093/nar/gky1049 · DOI · Paper page
  11. Pruitt, 2013, RefSeq: an update on mammalian reference sequences, Nucleic Acids Res., 42, D756, 10.1093/nar/gkt1114 · DOI
  12. Lam, 2016, Gene3D: expanding the utility of domain assignments, Nucleic Acids Res., 44, D404, 10.1093/nar/gkv1231 · DOI
  13. Braschi, 2019, Genenames.org: the HGNC and VGNC resources in 2019, Nucleic Acids Res., 47, D786, 10.1093/nar/gky930 · DOI
  14. Sayers, 2020, GenBank, Nucleic Acids Res., 48, D84
  15. Alliance of Genome Resources Consortium, 2020, Alliance of genome resources portal: unified model organism research platform, Nucleic Acids Res, 48, D650, 10.1093/nar/gkz813 · DOI
  16. Bastian, 2008, Bgee: integrating and comparing heterogeneous transcriptome data among species, 124, 10.1007/978-3-540-69828-9_12 · DOI
  17. Waterhouse, 2018, SWISS-MODEL: homology modelling of protein structures and complexes, Nucleic Acids Res., 46, W296, 10.1093/nar/gky427 · DOI
  18. Bourque, 2002, Genome-scale evolution: reconstructing gene orders in the ancestral species, Genome Res., 12, 26
  19. Ma, 2006, Reconstructing contiguous regions of an ancestral genome, Genome Res., 16, 1557, 10.1101/gr.5383506 · DOI
  20. Chauve, 2008, A methodological framework for the reconstruction of contiguous regions of ancestral genomes and its application to mammalian genomes, PLoS Comput. Biol., 4, e1000234, 10.1371/journal.pcbi.1000234 · DOI
  21. Illa, 2011, Comparative analysis of rosaceous genomes and the reconstruction of a putative ancestral genome for the family, BMC Evol. Biol., 11, 9, 10.1186/1471-2148-11-9 · DOI
  22. Nakatani, 2007, Reconstruction of the vertebrate ancestral genome reveals dynamic genome reorganization in early vertebrates, Genome Res., 17, 1254, 10.1101/gr.6316407 · DOI · Paper page
  23. Train, 2019, iHam and pyHam: visualizing and processing hierarchical orthologous groups, Bioinformatics, 35, 2504, 10.1093/bioinformatics/bty994 · DOI
  24. Broder, 1997, On the resemblance and containment of documents, 21
  25. Dessimoz, 2017, The Gene Ontology Handbook, 10.1007/978-1-4939-3743-1 · DOI
  26. Pellegrini, 1999, Assigning protein functions by comparative genome analysis: protein phylogenetic profiles, Proc. Natl. Acad. Sci. U.S.A., 96, 4285, 10.1073/pnas.96.8.4285 · DOI
  27. Moi, 2020, Scalable phylogenetic profiling using MinHash uncovers likely eukaryotic sexual reproduction genes, PLoS Comput. Biol., 16, e1007553, 10.1371/journal.pcbi.1007553 · DOI
  28. Nevers, 2017, Insights into ciliary genes and evolution from multi-level phylogenetic profiling, Mol. Biol. Evol., 34, 2016, 10.1093/molbev/msx146 · DOI
  29. Altenhoff, 2019, OMA standalone: orthology inference among public and custom genomes and transcriptomes, Genome Res., 29, 1152, 10.1101/gr.243212.118 · DOI
  30. Manber, 1993, Suffix arrays: a new method for on-line string searches, SIAM J. Comput., 22, 935, 10.1137/0222058 · DOI
  31. Yamamoto, 2018, YummyData: providing high-quality open life science data, Database, 2018, bay022, 10.1093/database/bay022 · DOI
  32. Whetzel, 2011, BioPortal: enhanced functionality via new Web services from the National Center for Biomedical Ontology to access and use ontologies in software applications, Nucleic Acids Res., 39, W541, 10.1093/nar/gkr469 · DOI
  33. Kaleb, 2019, Expanding the Orthologous Matrix (OMA) programmatic interfaces: REST API and the OmaDB packages for R and Python, F1000Res., 8, 42, 10.12688/f1000research.17548.2 · DOI
  34. de Farias, 2017, Leveraging logical rules for efficacious representation of large orthology datasets
  35. Cunningham, 2016, Nine principles of semantic harmonization, AMIA Annu. Symp. Proc., 2016, 451
  36. Sima, 2019, Enabling semantic queries across federated bioinformatics databases, Database, 2019, baz106, 10.1093/database/baz106 · DOI
  37. Zdobnov, 2017, OrthoDB v9.1: cataloging evolutionary and functional annotations for animal, fungal, plant, archaeal, bacterial and viral orthologs, Nucleic Acids Res, 45, D744, 10.1093/nar/gkw1119 · DOI · Paper page
  38. Sima, 2019, A hands-on introduction to querying evolutionary relationships across multiple data sources using SPARQL, F1000Res, 8, 1822, 10.12688/f1000research.21027.1 · DOI
  39. Amid, 2020, The European Nucleotide Archive in 2019, Nucleic Acids Res, 48, D70
  40. Dylus, 2020, How to build phylogenetic species trees with OMA, F1000Res., 9, 511, 10.12688/f1000research.23790.1 · DOI
  41. Glover, 2020, Making the most of genomic data with OMA, F1000Res., 9, 665, 10.12688/f1000research.24904.1 · DOI

Cited by

Sorted by citations

Metadata

Authors
Altenhoff, A. M., Train, C.-M., Gilbert, K. J., Mediratta, I., Mendes de Farias, T., Moi, D., Nevers, Y., Radoykova, H.-S., Rossier, V., Warwick Vesztrocy, A., Glover, N. M., & Dessimoz, C.
Affiliations
  • SIB Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
  • Department of Computational Biology, University of Lausanne, 1015 Lausanne, Switzerland
  • Centre for Life's Origins and Evolution, Department of Genetics, Evolution and Environment, University College London, Gower St, London WC1E 6BT, United Kingdom
ORCID
Dessimoz, Christophe
Venue
Nucleic Acids Research
Publisher
Oxford University Press (OUP)
Published
2020-11-11
Volume
49
Issue
D1
Pages
D373-D379
ISSN
  • 0305-1048
  • 1362-4962
DOI
10.1093/nar/gkaa1007
PMC
PMC7779010
Cited by
244
References
2
Language
en
Licence
creativecommons.org/licenses/by/4.0/ (vor)
Funders
  • Swiss Institute of Bioinformatics
  • Swiss National Science Foundation (183723, 186397)

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review