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 |
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Hauptverfasser: | , , , , , , , , |
Format: | Artikel |
Sprache: | eng |
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Zusammenfassung: | 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/ . |
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ISSN: | 1548-7091 1548-7105 1548-7105 |
DOI: | 10.1038/s41592-024-02552-8 |