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Opfi: A Python package for identifying gene clusters in large genomics and metagenomics data sets.

Alexis M Hill1, James R Rybarski2, Kuang Hu1,2

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Summary

We developed Opfi, a new software tool to identify gene clusters in genomic data. This pipeline aids biotechnology research by efficiently extracting functional gene sets from large datasets.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Gene clusters, co-localized genes performing specific functions, are crucial in biotechnology.
  • Extracting these clusters from extensive genomic data requires efficient computational tools.

Purpose of the Study:

  • To introduce Opfi, a modular pipeline designed for the identification of arbitrary gene clusters.
  • To provide a software solution for analyzing assembled genomic and metagenomic sequences.

Main Methods:

  • Opfi employs functions for annotation, de-duplication, and visualization of gene clusters.
  • A customizable, rule-based filtering approach allows for user-defined criteria selection of candidate systems.

Main Results:

  • Opfi facilitates the identification of putative gene clusters within large genomic datasets.
  • The pipeline supports the analysis of both genomic and metagenomic sequences.

Conclusions:

  • Opfi offers a flexible and efficient solution for gene cluster identification.
  • The tool is readily available via Python Package Index and Bioconda, supporting broader research applications.