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<p>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 <a href="https://github.com/DessimozLab/FastOMA/">https://github.com/DessimozLab/FastOMA/</a>.</p>

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Orthology inference at scale with FastOMA

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年1月3日

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  • Sina Majidian,
  • Yannis Nevers,
  • Ali Yazdizadeh Kharrazi,
  • Alex Warwick Vesztrocy,
  • Stefano Pascarelli,
  • David Moi,
  • Natasha Glover,
  • Adrian M. Altenhoff,
  • Christophe Dessimoz

摘要

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/.