Peak Finder Metaserver - a novel application for finding peaks in ChIP-seq data
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- 作者：Marcin Kruczyk (5) (6)
Husen M Umer (5)
Stefan Enroth (5)
Jan Komorowski (5) (6) - 关键词：Transcription factor ; Peak finder ; ChIP ; seq ; Metaserver
- 刊名：BMC Bioinformatics
- 出版年：2013
- 出版时间：December 2013
- 年：2013
- 卷：14
- 期：1
- 全文大小：214 KB
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Husen M Umer (5)
Stefan Enroth (5)
Jan Komorowski (5) (6)
5. Department of Immunology, Genetics and Pathology, SciLifeLab Uppsala, Rudbeck Laboratory, Uppsala University, SE, 751 85, Uppsala, Sweden
6. Interdisciplinary Centre for Mathematical and Computational Modelling, University of Warsaw, Pawi艅skiego 5a Street, 02-106, Warszawa, Poland - ISSN：1471-2105
文摘
Background Finding peaks in ChIP-seq is an important process in biological inference. In some cases, such as positioning nucleosomes with specific histone modifications or finding transcription factor binding specificities, the precision of the detected peak plays a significant role. There are several applications for finding peaks (called peak finders) based on different algorithms (e.g. MACS, Erange and HPeak). Benchmark studies have shown that the existing peak finders identify different peaks for the same dataset and it is not known which one is the most accurate. We present the first meta-server called Peak Finder MetaServer (PFMS) that collects results from several peak finders and produces consensus peaks. Our application accepts three standard ChIP-seq data formats: BED, BAM, and SAM. Results Sensitivity and specificity of seven widely used peak finders were examined. For the experiments we used three previously studied Transcription Factors (TF) ChIP-seq datasets and identified three of the selected peak finders that returned results with high specificity and very good sensitivity compared to the remaining four. We also ran PFMS using the three selected peak finders on the same TF datasets and achieved higher specificity and sensitivity than the peak finders individually. Conclusions We show that combining outputs from up to seven peak finders yields better results than individual peak finders. In addition, three of the seven peak finders outperform the remaining four, and running PFMS with these three returns even more accurate results. Another added value of PFMS is a separate report of the peaks returned by each of the included peak finders.