Direct ChIP-Seq significance analysis improves target prediction

BMC Genomics

Research

Geetu Mendiratta¹  Santosh Anand²  Archana Iyer³  Manju Kustagi³  Mukesh Bansal³  Andrea Califano⁴  Raju SK Chaganti⁵  Ryan Hyunjae Kim⁵  Ritu Kushwaha⁶  Pavel Sumazin⁷ 

[1] Cancer Genetics Inc, New Jersey, USA;Department of Science and Technology, University of Sannio, Benevento, and Institute for Biomedical Technologies, National Research Council, Milan, Italy;Department of Systems Biology, and Center for Computational Biology and Bioinformatics, Columbia University, New York, NY, USA;Department of Systems Biology, and Center for Computational Biology and Bioinformatics, Columbia University, New York, NY, USA;Institute for Cancer Genetics, Columbia Genome Center, High Throughput Screening facility, Department of Biomedical Informatics, Department of Biochemistry and Molecular Biophysics, and Herbert Irving Comprehensive Cancer Center, Columbia University, New York, NY, USA;Laboratory of Immune Cell Epigenetics and Signaling, Rockefeller University, 1230 York Avenue, 10065, New York, NY, USA;Memorial Sloan-Kettering Cancer Center, 1275 York Avenue, New York, NY, USA;Texas Children's Cancer Center, Baylor College of Medicine, 77030, Houston, Texas, USA;

关键词: ChIP-Seq;    peak calling;    protein-DNA binding sites;   

DOI  :  10.1186/1471-2164-16-S5-S4

来源: Springer

【 摘 要 】

BackgroundChromatin immunoprecipitation followed by sequencing of protein-bound DNA fragments (ChIP-Seq) is an effective high-throughput methodology for the identification of context specific DNA fragments that are bound by specific proteins in vivo. Despite significant progress in the bioinformatics analysis of this genome-scale data, a number of challenges remain as technology-dependent biases, including variable target accessibility and mappability, sequence-dependent variability, and non-specific binding affinity must be accounted for.Results and discussionWe introduce a nonparametric method for scoring consensus regions of aligned immunoprecipitated DNA fragments when appropriate control experiments are available. Our method uses local models for null binding; these are necessary because binding prediction scores based on global models alone fail to properly account for specialized features of genomic regions and chance pull downs of specific DNA fragments, thus disproportionally rewarding some genomic regions and decreasing prediction accuracy. We make no assumptions about the structure or amplitude of bound peaks, yet we show that our method outperforms leading methods developed using either global or local null hypothesis models for random binding. We test prediction performance by comparing analyses of ChIP-seq, ChIP-chip, motif-based binding-site prediction, and shRNA assays, showing high reproducibility, binding-site enrichment in predicted target regions, and functional regulation of predicted targets.ConclusionsGiven appropriate controls, a direct nonparametric method for identifying transcription-factor targets from ChIP-Seq assays may lead to both higher sensitivity and higher specificity, and should be preferred or used in conjunction with methods that use parametric models for null binding.

【 授权许可】

Unknown   
© Bansal et al.; licensee BioMed Central Ltd. 2015. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.