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.