Orthology inference at scale with FastOMA

Orthology inference at scale with FastOMA

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 th...

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Veröffentlicht in:Nature methods 2025-01
Hauptverfasser: Majidian, Sina, Nevers, Yannis, Yazdizadeh Kharrazi, Ali, Warwick Vesztrocy, Alex, Pascarelli, Stefano, Moi, David, Glover, Natasha, Altenhoff, Adrian M, Dessimoz, Christophe
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container_title Nature methods
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creator Majidian, Sina
Nevers, Yannis
Yazdizadeh Kharrazi, Ali
Warwick Vesztrocy, Alex
Pascarelli, Stefano
Moi, David
Glover, Natasha
Altenhoff, Adrian M
Dessimoz, Christophe
description 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/ .
doi_str_mv 10.1038/s41592-024-02552-8
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title Orthology inference at scale with FastOMA
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