Imetagene

Imetagene performs metagene analysis to integrate, summarize, and compare ChIP-Seq enrichment signals from complex experimental designs.


Key Features:

  • Integration with metagene and similaRpeak: Interoperates with the metagene package to aggregate ChIP-Seq enrichment into metagene plots and with similaRpeak to assess similarities and dissimilarities among ChIP-Seq profiles.
  • Aggregation into metagene plots: Aggregates ChIP-Seq enrichment signals across genomic regions to produce metagene summaries.
  • Profile comparison: Identifies similarities and dissimilarities across large numbers of ChIP-Seq profiles using similaRpeak.
  • Support for complex experimental designs: Integrates and summarizes ChIP-Seq data from multifactorial experimental layouts for comparative analysis.

Scientific Applications:

  • Regulatory factor occupancy at promoters and enhancers: Investigates differential occupancy of regulatory factors at noncoding regulatory regions such as promoters and enhancers.
  • Correlation with transcriptional activity: Explores correlations between transcriptional activity and regulatory factor occupancy, exemplified in GM12878 B-lymphocytes.
  • Detection of gradient and threshold effects: Reveals gradient effects where occupancy follows transcription patterns and threshold effects where occupancy saturates before maximal transcription.

Methodology:

Implemented in R; aggregates ChIP-Seq enrichment signals into metagene plots via the metagene package; assesses similarities and dissimilarities among ChIP-Seq profiles using similaRpeak; integrates, summarizes, and compares ChIP-Seq enrichment from complex experimental designs.

Topics

Collections

Details

License:
Artistic-2.0
Tool Type:
command-line tool, library
Operating Systems:
Linux, Windows, Mac
Programming Languages:
R
Added:
1/17/2017
Last Updated:
12/10/2018

Operations

Publications

Joly Beauparlant C, Lamaze FC, Deschênes A, Samb R, Lemaçon A, Belleau P, Bilodeau S, Droit A. metagene Profiles Analyses Reveal Regulatory Element’s Factor-Specific Recruitment Patterns. PLOS Computational Biology. 2016;12(8):e1004751. doi:10.1371/journal.pcbi.1004751. PMID:27538250. PMCID:PMC4990179.

Funding: - Natural Sciences and Engineering Research Council of Canada: 436266-2013 - Institute of Nutrition, Metabolism and Diabetes: IC513823

Documentation

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