On the detection and refinement of transcription factor binding sites using ChIP-Seq data

Coupling chromatin immunoprecipitation (ChIP) with recently developed massively parallel sequencing technologies has enabled genome-wide detection of protein–DNA interactions with unprecedented sensitivity and specificity. This new technology, ChIP-Seq, presents opportunities for in-depth analysis of transcription regulation. In this study, we explore the value of using ChIP-Seq data to better detect and refine transcription factor binding sites (TFBS). We introduce a novel computational algorithm named Hybrid Motif Sampler (HMS), specifically designed for TFBS motif discovery in ChIP-Seq data. We propose a Bayesian model that incorporates sequencing depth information to aid motif identification. Our model also allows intra-motif dependency to describe more accurately the underlying motif pattern. Our algorithm combines stochastic sampling and deterministic ‘greedy’ search steps into a novel hybrid iterative scheme. This combination accelerates the computation process. Simulation studies demonstrate favorable performance of HMS compared to other existing methods. When applying HMS to real ChIP-Seq datasets, we find that (i) the accuracy of existing TFBS motif patterns can be significantly improved; and (ii) there is significant intra-motif dependency inside all the TFBS motifs we tested; modeling these dependencies further improves the accuracy of these TFBS motif patterns. These findings may offer new biological insights into the mechanisms of transcription factor regulation.

[1]  Alexander Varshavsky,et al.  Mapping proteinDNA interactions in vivo with formaldehyde: Evidence that histone H4 is retained on a highly transcribed gene , 1988, Cell.

[2]  Rodger Staden,et al.  Methods to define and locate patterns of motifs in sequences , 1988, Comput. Appl. Biosci..

[3]  G. Stormo,et al.  Identifying protein-binding sites from unaligned DNA fragments. , 1989, Proceedings of the National Academy of Sciences of the United States of America.

[4]  T. D. Schneider,et al.  Sequence logos: a new way to display consensus sequences. , 1990, Nucleic acids research.

[5]  A. A. Reilly,et al.  An expectation maximization (EM) algorithm for the identification and characterization of common sites in unaligned biopolymer sequences , 1990, Proteins.

[6]  Renato Paro,et al.  Mapping polycomb-repressed domains in the bithorax complex using in vivo formaldehyde cross-linked chromatin , 1993, Cell.

[7]  Jun S. Liu,et al.  Detecting subtle sequence signals: a Gibbs sampling strategy for multiple alignment. , 1993, Science.

[8]  Charles Elkan,et al.  Fitting a Mixture Model By Expectation Maximization To Discover Motifs In Biopolymer , 1994, ISMB.

[9]  Jun S. Liu,et al.  The Collapsed Gibbs Sampler in Bayesian Computations with Applications to a Gene Regulation Problem , 1994 .

[10]  Jun S. Liu,et al.  Bayesian Models for Multiple Local Sequence Alignment and Gibbs Sampling Strategies , 1995 .

[11]  Ronald W. Davis,et al.  Quantitative Monitoring of Gene Expression Patterns with a Complementary DNA Microarray , 1995, Science.

[12]  Jun S. Liu,et al.  Gibbs motif sampling: Detection of bacterial outer membrane protein repeats , 1995, Protein science : a publication of the Protein Society.

[13]  D. Lockhart,et al.  Expression monitoring by hybridization to high-density oligonucleotide arrays , 1996, Nature Biotechnology.

[14]  Michael Gribskov,et al.  Combining evidence using p-values: application to sequence homology searches , 1998, Bioinform..

[15]  G. Church,et al.  Finding DNA regulatory motifs within unaligned noncoding sequences clustered by whole-genome mRNA quantitation , 1998, Nature Biotechnology.

[16]  H. Bussemaker,et al.  Building a dictionary for genomes: identification of presumptive regulatory sites by statistical analysis. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[17]  John J. Wyrick,et al.  Genome-wide location and function of DNA binding proteins. , 2000, Science.

[18]  R. Tibshirani,et al.  Significance analysis of microarrays applied to the ionizing radiation response , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[19]  H. Bussemaker,et al.  Regulatory element detection using correlation with expression , 2001, Nature Genetics.

[20]  D. Botstein,et al.  Genomic binding sites of the yeast cell-cycle transcription factors SBF and MBF , 2001, Nature.

[21]  J. Valverde.  Molecular Modelling: Principles and Applications , 2001 .

[22]  J. Liu,et al.  Phylogenetic footprinting of transcription factor binding sites in proteobacterial genomes. , 2001, Nucleic acids research.

[23]  G. Stormo,et al.  Non-independence of Mnt repressor-operator interaction determined by a new quantitative multiple fluorescence relative affinity (QuMFRA) assay. , 2001, Nucleic acids research.

[24]  Douglas L. Brutlag,et al.  BioProspector: Discovering Conserved DNA Motifs in Upstream Regulatory Regions of Co-Expressed Genes , 2000, Pacific Symposium on Biocomputing.

[25]  G. Church,et al.  Nucleotides of transcription factor binding sites exert interdependent effects on the binding affinities of transcription factors. , 2002, Nucleic acids research.

[26]  G A Whitmore,et al.  A Statistical Model for Investigating Binding Probabilities of DNA Nucleotide Sequences Using Microarrays , 2002, Biometrics.

[27]  Jun S. Liu,et al.  An algorithm for finding protein–DNA binding sites with applications to chromatin-immunoprecipitation microarray experiments , 2002, Nature Biotechnology.

[28]  G. Stormo,et al.  Additivity in protein-DNA interactions: how good an approximation is it? , 2002, Nucleic acids research.

[29]  Jun S. Liu,et al.  Discovery of Conserved Sequence Patterns Using a Stochastic Dictionary Model , 2003 .

[30]  F. P. Roth,et al.  A non-parametric model for transcription factor binding sites. , 2003, Nucleic acids research.

[31]  Jun S. Liu,et al.  Integrating regulatory motif discovery and genome-wide expression analysis , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[32]  Shane T. Jensen,et al.  BioOptimizer: a Bayesian scoring function approach to motif discovery , 2004, Bioinform..

[33]  Qing Zhou,et al.  Modeling within-motif dependence for transcription factor binding site predictions , 2004, Bioinform..

[34]  A. Wada,et al.  The effects of guanine and cytosine variation on dinucleotide frequency and amino acid composition in the human genome , 2005, Journal of Molecular Evolution.

[35]  William Stafford Noble,et al.  Assessing computational tools for the discovery of transcription factor binding sites , 2005, Nature Biotechnology.

[36]  T. Mikkelsen,et al.  Genome-wide maps of chromatin state in pluripotent and lineage-committed cells , 2007, Nature.

[37]  Michael Q. Zhang,et al.  Analysis of the Vertebrate Insulator Protein CTCF-Binding Sites in the Human Genome , 2007, Cell.

[38]  Allen D. Delaney,et al.  Genome-wide profiles of STAT1 DNA association using chromatin immunoprecipitation and massively parallel sequencing , 2007, Nature Methods.

[39]  Dustin E. Schones,et al.  High-Resolution Profiling of Histone Methylations in the Human Genome , 2007, Cell.

[40]  A. Mortazavi,et al.  Genome-Wide Mapping of in Vivo Protein-DNA Interactions , 2007, Science.

[41]  Steven J. M. Jones,et al.  FindPeaks 3.1: a tool for identifying areas of enrichment from massively parallel short-read sequencing technology , 2008, Bioinform..

[42]  Clifford A. Meyer,et al.  Model-based Analysis of ChIP-Seq (MACS) , 2008, Genome Biology.

[43]  Raja Jothi,et al.  Genome-wide identification of in vivo protein–DNA binding sites from ChIP-Seq data , 2008, Nucleic acids research.

[44]  S. Batzoglou,et al.  Genome-Wide Analysis of Transcription Factor Binding Sites Based on ChIP-Seq Data , 2008, Nature Methods.

[45]  Heejung Shim,et al.  Integrating quantitative information from ChIP-chip experiments into motif finding. , 2008, Biostatistics.

[46]  David A. Nix,et al.  Empirical methods for controlling false positives and estimating confidence in ChIP-Seq peaks , 2008, BMC Bioinformatics.

[47]  P. Park,et al.  Design and analysis of ChIP-seq experiments for DNA-binding proteins , 2008, Nature Biotechnology.

[48]  Hyungwon Choi,et al.  Hierarchical hidden Markov model with application to joint analysis of ChIP-chip and ChIP-seq data , 2009, Bioinform..

[49]  Raymond K. Auerbach,et al.  PeakSeq enables systematic scoring of ChIP-seq experiments relative to controls , 2009, Nature Biotechnology.