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Integrating Bacterial ChIP-seq and RNA-seq Data With SnakeChunks.

Claire Rioualen1,2, Lucie Charbonnier-Khamvongsa1, Julio Collado-Vides2,3

  • 1Aix-Marseille University, INSERM, Laboratory of Theory and Approaches of Genome Complexity (TAGC), Marseille, France.

Current Protocols in Bioinformatics
|February 21, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a modular system for automating next-generation sequencing (NGS) data analysis. It empowers biologists to independently process and explore large datasets, enhancing reproducibility in life science research.

Keywords:
ChIP-seqEscherichia coli K-12FAIR Guiding PrinciplesRNA-seqreproducible scienceworkflow

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Area of Science:

  • Bioinformatics
  • Genomics
  • Molecular Biology

Background:

  • Next-generation sequencing (NGS) is a standard technique in life sciences.
  • Reproducibility and handling large datasets in NGS require robust automation.
  • Experimental biologists need accessible tools for autonomous data analysis.

Purpose of the Study:

  • To present a modular system for automating NGS data processing.
  • To enable flexible and autonomous analysis of large biological datasets.
  • To facilitate the integration of experimental results with existing biological knowledge.

Main Methods:

  • Development of a modular workflow system using SnakeChunks and the Snakemake engine.
  • Illustration with a combined ChIP-seq and RNA-seq experiment in Escherichia coli.
  • Integration of results with the RegulonDB database for transcriptional regulation insights.

Main Results:

  • A flexible and user-friendly system for composing and fine-tuning NGS analysis workflows.
  • Successful application in identifying target genes of the FNR transcription factor.
  • Demonstration of enhanced data analysis reproducibility and exploration capabilities.

Conclusions:

  • The described modular system enhances the automation and accessibility of NGS data analysis.
  • It empowers experimental biologists to conduct autonomous and flexible data exploration.
  • This approach supports the integration of experimental findings with curated biological databases.