Orthology inference at scale with FastOMA
Artikel i vetenskaplig tidskrift, 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/.
Författare
Sina Majidian
Université de Lausanne
Swiss Institute of Bioinformatics
Yannis Nevers
Université de Lausanne
Swiss Institute of Bioinformatics
Ali Yazdizadeh Kharrazi
Université de Lausanne
Alex Warwick Vesztrocy
Université de Lausanne
Swiss Institute of Bioinformatics
Stefano Pascarelli
Swiss Institute of Bioinformatics
Université de Lausanne
David Moi
Swiss Institute of Bioinformatics
Université de Lausanne
Natasha Glover
Université de Lausanne
Swiss Institute of Bioinformatics
Adrian M. Altenhoff
Swiss Institute of Bioinformatics
Université de Lausanne
Christophe Dessimoz
Swiss Institute of Bioinformatics
Université de Lausanne
Nature Methods
1548-7091 (ISSN) 1548-7105 (eISSN)
Vol. 22 269-272Ämneskategorier (SSIF 2025)
Bioinformatik (beräkningsbiologi)
DOI
10.1038/s41592-024-02552-8