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ChIP-seq 核心分析 下游分析

發布時間:2023/12/13 综合教程 29 生活家
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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 datathenrun ChIPseeqer
Core Analysis:Quality Control:QC analysisfor the raw reads (afterSplit raw data)
Core Analysis:Gene-level annotationof peaks (Exons/introns/promoters/downstream extremities) and genomic distribution usingChIPseeqerAnnotate
Core Analysis:Quick promoters summaryof peaks usingChIPseeqerSummaryPromoters
Core Analysis: Create datatracks for the UCSC Genome Browser
Visualize peak locations in UCSC Genome Browser usingChIPseeqerPeaksTrack
Create a read density track for the UCSC Genome Browser usingChIPseeqerMakeReadDensityTrack
Core Analysis: UseChIPseeqerRunif 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 annotationusingChIPseeqerNongenicAnnotate
Extended Analysis:RNAGenes annotationusingChIPseeqerRNAGenes
Extended Analysis:Motif discovery
De novo regulatory element discovery usingChIPseeqerFIREandFIRE
Find peak matches to known transcription factor binding sites usingChIPseeqerMotifMatch
Extended Analysis:Pathways analysis
Look for enriched pathways usingChIPseeqeriPAGEandiPAGE
Find pathway matches to peaks/genes usingChIPseeqerPathwayMatch
Extended Analysis: Evaluateconservationof peaks usingChIPseeqerCons
Extended Analysis: Estimateaverage read density profiles in genes or peak regionsusingChIPseeqerDensityMatrix
Extended Analysis:Extract (maximum/average) reads countfor peak regions across multiple ChIP-seq datasets usingChIPseeqerReadCountMatrix
Extended Analysis:Clusterandvisualizethe detected peak regions usingChIPseeqerCluster
Extended Analysis-Compare datasets:Comparetwo lists ofpeaks; (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:Comparetwo lists ofRefSeq genes(e.g., Which genes are common in the two lists?)
Use CompareGenes
Extended Analysis-Compare datasets: Make asimilarity coefficient matrix(based onJaccard index) to see which TFs are similar in terms of peaks overlapping, usingChIPseeqerComputeJaccardIndex
Extended Analysis-Compare datasets: Makeone matrix for each genepart (promoters/exons/introns/distal etc) from multiple peak filesin order to find e.g.,genes promoters where most of the TFs bind.
ChIPseeqerMakeGenepartsMatrix

Othersupplementary toolscan be foundhere

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