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Related Experiment Video

Updated: Aug 23, 2025

Pattern-based Search of Epigenomic Data Using GeNemo

Pattern-based Search of Epigenomic Data Using GeNemo

Published on: October 8, 2017

genomeSidekick: A user-friendly epigenomics data analysis tool.

Junjie Chen¹, Ashley J Zhu², René R S Packard^1,3,4,5

¹Division of Cardiology, Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States.

Frontiers in Bioinformatics

|October 28, 2022

Summary

This summary is machine-generated.

GenomeSidekick is a new R-based tool that simplifies the analysis of large epigenomic datasets. It empowers researchers without bioinformatics expertise to explore transcriptomic and chromatin data for biological discovery.

Keywords:

Shiny app bioinformatics chromatin data visualization epigenomics

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Area of Science:

Genomics
Bioinformatics
Molecular Biology

Background:

Epigenomic measurements generate vast datasets requiring specialized analysis.
Integrating multiple epigenomic experiments increases analytical complexity.
A need exists for user-friendly tools to analyze genomic sequencing data.

Purpose of the Study:

To develop an accessible tool for analyzing transcriptomic and chromatin accessibility/immunoprecipitation data.
To bridge the gap between wet lab scientists and bioinformaticians in genomic data analysis.
To facilitate mechanistic discovery from large omics datasets.

Main Methods:

Development of genomeSidekick, a graphical user interface (GUI) in R.
GUI enables bespoke analyses without command-line interface.
Integrates visualization, gene ontology analysis, and PubMed querying.

Main Results:

genomeSidekick provides lists of differentially expressed genes and chromatin features.
Interactive volcano plots visualize omics data.
Facilitates local Gene Ontology analysis and PubMed searches for gene candidates.

Conclusions:

genomeSidekick enhances the ability of researchers to analyze complex omics data.
The tool promotes shared understanding between biologists and bioinformaticians.
Aims to accelerate mechanistic discovery in biological processes.