ChIP-seq 核心分析 下游分析

时间：2023-03-08 22:44:58

http://icb.med.cornell.edu/wiki/index.php/Elementolab/ChIPseeqer_Tutorial

【怪毛匠子 整理】

ChIP-seq【核心分析 下游分析】

  • Core Analysis : Peak detection: Split raw data then run ChIPseeqer
  • Core Analysis : Quality Control: QC analysis for the raw reads (after Split raw data)
  • Core Analysis : Gene-level annotation of peaks (Exons/introns/promoters/downstream extremities) and genomic distribution using ChIPseeqerAnnotate
  • Core Analysis : Quick promoters summary of peaks using ChIPseeqerSummaryPromoters
  • Core Analysis : Create data tracks for the UCSC Genome Browser
  • Visualize peak locations in UCSC Genome Browser using ChIPseeqerPeaksTrack
  • Create a read density track for the UCSC Genome Browser using ChIPseeqerMakeReadDensityTrack
  • Core Analysis : Use ChIPseeqerRun if you want to run the 3 first steps of the Core Analysis (QC, Split in reads-Peak detection, Gene annotation) fast with a single command.
  • Extended Analysis : Nongenic annotation using ChIPseeqerNongenicAnnotate
  • Extended Analysis : RNAGenes annotation using ChIPseeqerRNAGenes
  • Extended Analysis : Motif discovery
  • De novo regulatory element discovery using ChIPseeqerFIRE and FIRE
  • Find peak matches to known transcription factor binding sites using ChIPseeqerMotifMatch
  • Extended Analysis : Pathways analysis
  • Look for enriched pathways using ChIPseeqeriPAGE and iPAGE
  • Find pathway matches to peaks/genes using ChIPseeqerPathwayMatch
  • Extended Analysis : Evaluate conservation of peaks using ChIPseeqerCons
  • Extended Analysis : Estimate average read density profiles in genes or peak regions using ChIPseeqerDensityMatrix
  • Extended Analysis : Extract (maximum/average) reads count for peak regions across multiple ChIP-seq datasets using ChIPseeqerReadCountMatrix
  • Extended Analysis : Cluster and visualize the detected peak regions using ChIPseeqerCluster
  • Extended Analysis - Compare datasets : Compare two lists of peaks; (e.g., Which peaks overlap ? Are there any peaks in the first list with no overlap in the second one?)
  • Use CompareIntervals
  • Extended Analysis - Compare datasets : Compare two lists of RefSeq genes (e.g., Which genes are common in the two lists?)
  • Use CompareGenes
  • Extended Analysis - Compare datasets : Make a similarity coefficient matrix (based on Jaccard index) to see which TFs are similar in terms of peaks overlapping, using ChIPseeqerComputeJaccardIndex
  • Extended Analysis - Compare datasets : Make one matrix for each genepart (promoters/exons/introns/distal etc) from multiple peak files in order to find e.g., genes promoters where most of the TFs bind. 
  • ChIPseeqerMakeGenepartsMatrix

Other supplementary tools can be found here

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