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Model-based analysis of ChIP-Seq (github devel-version)

With the improvement of sequencing techniques, chromatin immunoprecipitation followed by high throughput sequencing (ChIP-Seq) is getting popular to study genome-wide protein-DNA interactions. To address the lack of powerful ChIP-Seq analysis method, we present a novel algorithm, named Model-based Analysis of ChIP-Seq (MACS), for identifying transcript factor binding sites. MACS captures the influence of genome complexity to evaluate the significance of enriched ChIP regions, and MACS improves the spatial resolution of binding sites through combining the information of both sequencing tag position and orientation. MACS can be easily used for ChIP-Seq data alone, or with control sample with the increase of specificity [1].

A galaxy server was made available by the group at

Command Syntax

macs2 [-h] [--version]

Available MACS Functions

# There are seven major functions available in MACS serving as sub-commands.

callpeak:       Main MACS2 Function to Call peaks from alignment results.
bdgpeakcall:    Call peaks from bedGraph output.
bdgbroadcall:   Call broad peaks from bedGraph output.
bdgcmp:         Deduct noise by comparing two signal tracks in bedGraph.
bdgdiff:        Differential peak detection based on paired four bedgraph files.
filterdup:      Remove duplicate reads at the same position, then convert acceptable format to BED format.
predictd:       Predict d or fragment size from alignment results.
pileup:         Pileup aligned reads with a given extension size (fragment size or d in MACS language).
                Note there will be no step for duplicate reads filtering or sequencing depth scaling, so you may need to do certain post- processing.
randsample:     Randomly sample number/percentage of total reads.
refinepeak:     (Experimental) Take raw reads alignment, refine peak summits and give scores measuring balance of forward- backward tags. Inspired by SPP.


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