@article {Kim033670, author = {Bong-Hyun Kim and Jiali Zhuang and Jie Wang and Zhiping Weng}, title = {Factorbook Motif Pipeline: A de novo motif discovery and filtering web server for ChIP-seq peaks}, elocation-id = {033670}, year = {2015}, doi = {10.1101/033670}, publisher = {Cold Spring Harbor Laboratory}, abstract = {Summary High-throughput sequencing technologies such as ChIP-seq have deepened our understanding in many biological processes. De novo motif search is one of the key downstream computational analysis following the ChIP-seq experiments and several algorithms have been proposed for this purpose. However, most web-based systems do not perform independent filtering or enrichment analyses to ensure the quality of the discovered motifs. Here, we developed a web server Factorbook Motif Pipeline based on an algorithm used in analyzing ENCODE consortium ChIP-seq datasets. It performs comprehensive analysis on the set of peaks detected from a ChIP-seq experiments: (i) de novo motif discovery; (ii) independent composition and bias analyses and (iii) matching to the annotated motifs. The statistical tests employed in our pipeline provide a reliable measure of confidence as to how significant are the motifs reported in the discovery step.Availability Factorbook Motif Pipeline source code is accessible through the following URL. https://github.com/joshuabhk/factorbook-motif-pipeline}, URL = {https://www.biorxiv.org/content/early/2015/12/04/033670}, eprint = {https://www.biorxiv.org/content/early/2015/12/04/033670.full.pdf}, journal = {bioRxiv} }