MACS: Model-based Analysis for ChIP-Seq

Latest Release:

• Github:
• PyPI:
• Bioconda:
• Debian Med:

Introduction

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 presented the 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 a control sample with the increase of specificity. Moreover, as a general peak-caller, MACS can also be applied to any "DNA enrichment assays" if the question to be asked is simply: where we can find significant reads coverage than the random background.

Please find MACS3 documentations through MACS3 website.

Contribute

Please read our CODE OF CONDUCT and How to contribute documents. If you have any questions, suggestion/ideas, or just want to have conversions with developers and other users in the community, we recommend using the MACS Discussions instead of posting to our Issues page.

Ackowledgement

MACS3 project is sponsored by . And we particularly want to thank the user community for their supports, feedbacks and contributions over the years.

Citation

2008: Model-based Analysis of ChIP-Seq (MACS)