Elementolab/ChIPseeqer Tutorial

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Elementolab/ChIPseeqer_Install

ChIPseeqer TUTORIAL

This is a comprehensive step-by-step tutorial on how to use the ChIPseeqer tools to analyze ChIP-seq data.

To run the tools directly from any folder, you need to add the $CHIPSEEQERDIR and $CHIPSEEQERDIR/SCRIPTS to your $PATH variable. Read How to set the CHIPSEEQERDIR variable.

• Core Analysis : Quality Control: QC analysis for the raw reads

• Core Analysis : Peak detection: Split raw data then run ChIPseeqer

• 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

• Extended Analysis : Nongenic annotation using ChIPseeqerNongenicAnnotate

• 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 using ChIPseeqeriPAGE and iPAGE

• Extended Analysis : Evaluate conservation of peaks using ChIPseeqerCons

• Extended Analysis : Cluster and visualize the detected peak regions (Using the Cluster and Java TreeView programs)
  • Use ChIPseeqer2DensityMatrix-ChIPseeqerCluster

• Extended Analysis : Plot average read density profile in peak regions using ChIPseeqerGetReadDensityProfiles

• Extended Analysis : Plot average read density profile in gene parts: promoters, first exon, first intron, all other exons, all other introns, etc. using ChIPseeqerPlotAverageReadDensityInGenes

• Extended Analysis : Extract (maximum/average) reads count for peak regions using ChIPseeqerGetReadCountInPeakRegions

• 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