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Protocol for comparing gene-level selection on coding mutations between two groups of samples with Coselens.

Jaime Iranzo1, George Gruenhagen2, Jorge Calle-Espinosa3

  • 1Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid (UPM) - Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA), Madrid, Spain; Institute for Biocomputation and Physics of Complex Systems (BIFI), University of Zaragoza, Zaragoza, Spain.

STAR Protocols
|February 28, 2023
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Summary
This summary is machine-generated.

Coselens software enables rapid comparison of gene selection between sample groups, aiding the study of adaptation and genetic underpinnings. This tool is ideal for analyzing somatic mutations and experimental evolution data.

Keywords:
BioinformaticsCancerEvolutionary biologyGenomics

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Area of Science:

  • Evolutionary Biology
  • Genomics
  • Computational Biology

Background:

  • Understanding adaptation requires studying genes under conditional selection.
  • Gene-level selection analysis reveals insights into epistasis and phenotypic plasticity.
  • Existing methods may not efficiently handle specific datasets like somatic mutations.

Purpose of the Study:

  • To introduce and detail the Coselens package for comparative gene-level selection analysis.
  • To provide a user-friendly protocol for analyzing genomic data.
  • To facilitate the study of adaptation using computational tools.

Main Methods:

  • The protocol utilizes the Coselens R package.
  • It involves installing Coselens and preparing datasets.
  • Analysis involves comparing gene selection between two distinct sample groups.

Main Results:

  • Coselens analysis is computationally efficient, typically running in under 10 minutes on a laptop.
  • The package is well-suited for analyzing somatic mutations.
  • It is also effective for data from experimental evolution studies with independently evolved samples.

Conclusions:

  • Coselens offers a fast and effective method for comparative gene-level selection analysis.
  • The package supports the investigation of genomic adaptation.
  • It is a valuable tool for researchers working with somatic mutations and experimental evolution data.