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Orthology inference at scale with FastOMA
Sina Majidian1,2, Yannis Nevers1,2, Ali Yazdizadeh Kharrazi1
1Department of Computational Biology, University of Lausanne, Lausanne, Switzerland.
Abstract:
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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