Elementolab/ChIPseeqer Tutorial

From Icbwiki

Revision as of 19:28, 4 February 2011 Eug2002 (Talk | contribs) ← Previous diff		Revision as of 19:36, 4 February 2011 Eug2002 (Talk | contribs) Next diff →
Line 37:		Line 37:
	* <span style="color:#347C17"> '''Extended Analysis''' </span>: Estimate '''average read density profiles in genes or peak regions''' using [http://icb.med.cornell.edu/wiki/index.php/Elementolab/ChIPseeqerDensityMatrix ChIPseeqerDensityMatrix]		* <span style="color:#347C17"> '''Extended Analysis''' </span>: Estimate '''average read density profiles in genes or peak regions''' using [http://icb.med.cornell.edu/wiki/index.php/Elementolab/ChIPseeqerDensityMatrix ChIPseeqerDensityMatrix]
		+
		+	* <span style="color:#347C17"> '''Extended Analysis''' </span>: '''Extract (maximum/average) reads count''' for peak regions across multiple ChIP-seq datasets using [http://icb.med.cornell.edu/wiki/index.php/Elementolab/ChIPseeqerGetReadCountInPeaksMatrix ChIPseeqerGetReadCountInPeaksMatrix]
	* <span style="color:#347C17"> '''Extended Analysis''' </span>: '''Cluster''' and '''visualize''' the detected peak regions using [http://icb.med.cornell.edu/wiki/index.php/Elementolab/ChIPseeqer_Cluster ChIPseeqerCluster]		* <span style="color:#347C17"> '''Extended Analysis''' </span>: '''Cluster''' and '''visualize''' the detected peak regions using [http://icb.med.cornell.edu/wiki/index.php/Elementolab/ChIPseeqer_Cluster ChIPseeqerCluster]

---

Revision as of 19:36, 4 February 2011

Elementolab/

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

• 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 : 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 ChIPseeqerGetReadCountInPeaksMatrix

• Extended Analysis : Cluster and visualize the detected peak regions using ChIPseeqerCluster

• 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 : Plot average peak numbers in gene parts: promoters, first exon, first intron, all other exons, all other introns, etc. using ChIPseeqerPlotAveragePeaksNumberInGenes

• 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