Orthology inference at scale with FastOMA
Journal article, 2025
The surge in genome data, with ongoing efforts aiming to sequence 1.5 M eukaryotes in a decade, could revolutionize genomics, revealing the origins, evolution and genetic innovations of biological processes. Yet, traditional genomics methods scale poorly with such large datasets. Here, addressing this, ‘FastOMA’ provides linear scalability for orthology inference, enabling the processing of thousands of eukaryotic genomes within a day. FastOMA maintains the high accuracy and resolution of the well-established Orthologous Matrix (OMA) approach in benchmarks. FastOMA is available via GitHub at https://github.com/DessimozLab/FastOMA/.
Author
Sina Majidian
University of Lausanne
Swiss Institute of Bioinformatics
Yannis Nevers
University of Lausanne
Swiss Institute of Bioinformatics
Ali Yazdizadeh Kharrazi
University of Lausanne
Alex Warwick Vesztrocy
University of Lausanne
Swiss Institute of Bioinformatics
Stefano Pascarelli
Swiss Institute of Bioinformatics
University of Lausanne
David Moi
Swiss Institute of Bioinformatics
University of Lausanne
Natasha Glover
University of Lausanne
Swiss Institute of Bioinformatics
Adrian M. Altenhoff
Swiss Institute of Bioinformatics
University of Lausanne
Christophe Dessimoz
Swiss Institute of Bioinformatics
University of Lausanne
Nature Methods
1548-7091 (ISSN) 1548-7105 (eISSN)
Vol. 22 269-272Subject Categories (SSIF 2025)
Bioinformatics (Computational Biology)
DOI
10.1038/s41592-024-02552-8