| 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. https://github.com/taoliu/MACS/ |
hg clone https://toolshed.g2.bx.psu.edu/repos/iuc/macs2
| Name | Version | Type | |
|---|---|---|---|
| R | 3.1.2 | package | |
| gnu_awk | 4.1.0 | package | |
| macs2 | 2.1.0.20151222 | package | |
| numpy | 1.7.1 | package | |
| scipy | 0.12.0 | package | |
| MACS2_SCRIPT_PATH | set_environment | ||
| Name | Description | Version | Minimum Galaxy Version |
|---|---|---|---|
| Call broad peaks from bedGraph output | 2.1.0.20151222.0 | any | |
| Predict 'd' or fragment size from alignment results | 2.1.0.20151222.0 | any | |
| Randomly sample number or percentage of total reads | 2.1.0.20151222.0 | any | |
| Differential peak detection based on paired four bedgraph files | 2.1.0.20151222.0 | any | |
| Remove duplicate reads at the same position | 2.1.0.20151222.0 | any | |
| Refine peak summits and give scores measuring balance of forward- backward tags (Experimental) | 2.1.0.20151222.0 | any | |
| Call peaks from alignment results | 2.1.0.20151222.0 | any | |
| Deduct noise by comparing two signal tracks in bedGraph | 2.1.0.20151222.0 | any | |
| Call peaks from bedGraph output | 2.1.0.20151222.0 | any | |