This article is part of the supplement: Selected articles from the Twelfth Asia Pacific Bioinformatics Conference (APBC 2014): Genomics
An improved ChIP-seq peak detection system for simultaneously identifying post-translational modified transcription factors by combinatorial fusion, using SUMOylation as an example
1 Department of Computer Science, National Tsing Hua University, Hsinchu, Republic of China: Taiwan
2 Biomedical Science & Engineering Center, National Tsing Hua University, Hsinchu, Republic of China: Taiwan
3 Department of Information Engineering and Computer Science, Feng Chia University, Taichung City, Republic of China: Taiwan
4 Department of Computer Science and Information Engineering, Providence University, Sha-Lu, Republic of China: Taiwan
5 Division of Molecular and Genomic Medicine, National Health Research Institutes, Miaoli County, Republic of China: Taiwan
6 UC Davis Cancer Center, Research III Room 2400, 4645 2nd Ave, Sacramento, CA 95817, USA
7 The institute for Translational Medicine, College of Medical Science and Technology, Taipei Medical University, 250 Wu-Xin Street, Taipei City, Republic of China: Taiwan
8 Institute of Microbiology and Immunology, National Yang-Ming University, Taipei, Republic of China: Taiwan
BMC Genomics 2014, 15(Suppl 1):S1 doi:10.1186/1471-2164-15-S1-S1Published: 24 January 2014
Post-translational modification (PTM) of transcriptional factors and chromatin remodelling proteins is recognized as a major mechanism by which transcriptional regulation occurs. Chromatin immunoprecipitation (ChIP) in combination with high-throughput sequencing (ChIP-seq) is being applied as a gold standard when studying the genome-wide binding sites of transcription factor (TFs). This has greatly improved our understanding of protein-DNA interactions on a genomic-wide scale. However, current ChIP-seq peak calling tools are not sufficiently sensitive and are unable to simultaneously identify post-translational modified TFs based on ChIP-seq analysis; this is largely due to the wide-spread presence of multiple modified TFs. Using SUMO-1 modification as an example; we describe here an improved approach that allows the simultaneous identification of the particular genomic binding regions of all TFs with SUMO-1 modification.
Traditional peak calling methods are inadequate when identifying multiple TF binding sites that involve long genomic regions and therefore we designed a ChIP-seq processing pipeline for the detection of peaks via a combinatorial fusion method. Then, we annotate the peaks with known transcription factor binding sites (TFBS) using the Transfac Matrix Database (v7.0), which predicts potential SUMOylated TFs. Next, the peak calling result was further analyzed based on the promoter proximity, TFBS annotation, a literature review, and was validated by ChIP-real-time quantitative PCR (qPCR) and ChIP-reChIP real-time qPCR. The results show clearly that SUMOylated TFs are able to be pinpointed using our pipeline.
A methodology is presented that analyzes SUMO-1 ChIP-seq patterns and predicts related TFs. Our analysis uses three peak calling tools. The fusion of these different tools increases the precision of the peak calling results. TFBS annotation method is able to predict potential SUMOylated TFs. Here, we offer a new approach that enhances ChIP-seq data analysis and allows the identification of multiple SUMOylated TF binding sites simultaneously, which can then be utilized for other functional PTM binding site prediction in future.