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GPSmatch: an R package for comparing Genomic-binding Profile Similarity among transcriptional regulators using customizable databases. | LitMetric

AI Article Synopsis

  • Eukaryotic gene expression relies on many transcriptional regulators, and understanding their interactions at genomic-binding sites is crucial for insights into gene regulation.
  • The R package GPSmatch has been created to compare ChIP-Seq data by calculating the Jaccard index, helping to identify shared binding among regulators and assess statistical significance with a Monte Carlo method.
  • GPSmatch is freely accessible on GitHub and can aid researchers in ranking transcriptional regulators based on their genomic-binding profiles, offering potential clues about gene regulatory mechanisms.

Article Abstract

Summary: Eukaryotic gene expression requires coordination among hundreds of transcriptional regulators. To characterize a specific transcriptional regulator, identifying how it shares genomic-binding sites with other regulators can generate important insights into its action. As genomic data such as chromatin immunoprecipitation assays with sequencing (ChIP-Seq) are being continously generated from individual labs, there is a demand for timely integration and analysis of these new data. We have developed an R package, GPSmatch (Genomic-binding Profile Similarity match), for calculating the Jaccard index to compare the ChIP-Seq peaks from one experiment to other experiments stored in a user-supplied customizable database. GPSmatch also evaluates the statistical significance of the calculated Jaccard index using a nonparametric Monte Carlo procedure. We show that GPSmatch is suitable for identifying and ranking transcriptional regulators with shared genomic-binding profiles, which may unravel potential mechanistic actions of gene regulation.

Availability And Implementation: The software is freely available at https://github.com/Bao-Lab/GPSmatch.

Supplementary Information: Supplementary data are available at Bioinformatics online.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8756198PMC
http://dx.doi.org/10.1093/bioinformatics/btab728DOI Listing

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