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A Fast and Quantitative Method for Post-translational Modification and Variant Enabled Mapping of Peptides to Genomes
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ePIANNO: ePIgenomics ANNOtation tool.

Chia-Hsin Liu1,2,3, Bing-Ching Ho4,5, Chun-Ling Chen1

  • 1Institute of Statistical Science, Academia Sinica, Nangang, Taipei, Taiwan.

Plos One
|February 10, 2016
PubMed
Summary
This summary is machine-generated.

ePIANNO integrates ChIP-seq data with genomic variations and gene-disease associations. This webtool simplifies exploring transcription factor binding sites and their functional relevance for hypothesis generation.

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Area of Science:

  • Genomics and Bioinformatics
  • Epigenetics and Gene Regulation

Background:

  • Next-generation sequencing (NGS) combined with chromatin immunoprecipitation (ChIP-seq) identifies genomic binding sites for regulatory factors, histone modifications, and chromatin accessibility.
  • Researchers require integrated data on genomic variations, allele frequencies, disease associations, and neighboring transcription factor (TF) binding sites for robust hypothesis generation.
  • Existing public ChIP-seq datasets are vast and complex, posing challenges for data integration and analysis.

Purpose of the Study:

  • To develop a webtool, ePIgenomic ANNOtation tool (ePIANNO), that integrates ChIP-seq data with population-specific SNP information and genome-wide association study (GWAS) disease associations.
  • To provide researchers with a user-friendly platform for exploring, navigating, and extracting combined ChIP-seq and variation data.
  • To facilitate the exploration of transcription factor-related genomic variants and their potential biological functions.

Main Methods:

  • Developed the ePIANNO web server, integrating ChIP-seq data (hmChIP, ENCODE, ROADMAP epigenomics) with SNP data (1000 Genomes Project) and GWAS data (NHGRI).
  • Implemented a user-friendly website interface for data exploration, navigation, and extraction.
  • Utilized query functions for searching target regions, transcription factors, and annotations.

Main Results:

  • Successfully integrated diverse datasets including ChIP-seq, SNP, and GWAS information into a cohesive web tool.
  • Demonstrated the utility of ePIANNO through examples showcasing the exploration of TF-related genomic variants.
  • Provided efficient query functionalities for accessing specific genomic regions, TFs, and annotations.

Conclusions:

  • ePIANNO offers a valuable resource for researchers by simplifying the integration and analysis of complex genomic and epigenomic data.
  • The webtool aids in generating hypotheses and exploring potential biological functions by linking TF binding sites with genetic variations and disease associations.
  • ePIANNO enhances the utility of public ChIP-seq data, making it more accessible and actionable for biological discovery.