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 : 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
- 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
- Extended Analysis : Pathways analysis
- 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?)
- Extended Analysis - Compare datasets : Compare two lists of RefSeq genes (e.g., Which genes are common in the two lists?)
- 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.
Other supplementary tools can be found here