OMA standalone: orthology inference among public and custom genomes and transcriptomes

By Adrian M. Altenhoff, Jeremy Levy, M. Zarowiecki, Bartlomiej Tomiczek, Alex Warwick Vesztrocy, Daniel A. Dalquen, Steven Müller, Maximilian J. Telford, Natasha M. Glover, Christophe Dessimoz. 2018

155 citations recorded by the source corpus.

Genomes and transcriptomes are now typically sequenced by individual labs, but analysing them often remains challenging. One essential step in many analyses lies in identifying orthologs—corresponding genes across multiple species—but this is far from trivial. The OMA (Orthologous MAtrix) database is a leading resource for identifying orthologs among publicly available, complete genomes. Here, we describe the OMA pipeline available as a standalone program for Linux and Mac. When run on a cluster, it has native support for the LSF, SGE, PBS Pro, and Slurm job schedulers and can scale up to thousands of parallel processes. Another key feature of OMA standalone is that users can combine their own data with existing public data by exporting genomes and pre-computed alignments from the OMA database, which currently contains over 2100 complete genomes. We compare OMA standalone to other methods in the context of phylogenetic tree inference, by inferring a phylogeny of the Lophotrochozoa, a challenging clade within the Protostomes. We also discuss other potential applications of OMA standalone, including identifying gene families having undergone duplications/losses in specific clades, and identifying potential drug targets in non-model organisms. OMA Standalone is available at http://omabrowser.org/standalone under the permissible open source Mozilla Public License Version 2.0.

Subfields: Cancer Research, Immunology, Molecular Biology

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Citations in the corpus

OMA orthology in 2021: website overhaul, conserved isoforms, ancestral gene order and more (2020, methodology): “One solution is given by OMA standalone (31), which can efficiently combine OMA and custom data.”
Mutation severity spectrum of rare alleles in the human genome is predictive of disease type (2019, methodology): “ones retrieved from the OMA database [95] that are usually much smaller and thus exhibit better alignment quality.”
COG database update: focus on microbial diversity, model organisms, and widespread pathogens (2020, background): “Although there are several other databases of orthologous proteins in bacteria and archaea, such as eggNOG (49), KEGG Orthology (50,51), OMA (52,53), OrthoDB (54) and MBGD (55), which cover a wider selection of organisms than the COGs and provide comprehensive, automated functional annotation of the”
How to build phylogenetic species trees with OMA (2020, unknown): “This type of OG is provided by a few orthology databases such as BUSCO 8 and OMA 9,10 .”