Genome-wide analysis of transcription factor binding sites based on ChIP-Seq data - PubMed (original) (raw)

Genome-wide analysis of transcription factor binding sites based on ChIP-Seq data

Anton Valouev et al. Nat Methods. 2008 Sep.

Abstract

Molecular interactions between protein complexes and DNA mediate essential gene-regulatory functions. Uncovering such interactions by chromatin immunoprecipitation coupled with massively parallel sequencing (ChIP-Seq) has recently become the focus of intense interest. We here introduce quantitative enrichment of sequence tags (QuEST), a powerful statistical framework based on the kernel density estimation approach, which uses ChIP-Seq data to determine positions where protein complexes contact DNA. Using QuEST, we discovered several thousand binding sites for the human transcription factors SRF, GABP and NRSF at an average resolution of about 20 base pairs. MEME motif-discovery tool-based analyses of the QuEST-identified sequences revealed DNA binding by cofactors of SRF, providing evidence that cofactor binding specificity can be obtained from ChIP-Seq data. By combining QuEST analyses with Gene Ontology (GO) annotations and expression data, we illustrate how general functions of transcription factors can be inferred.

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Figures

Figure 1

Figure 1

QuEST’s representation of ChIP-Seq data using density profiles.. (A) GABP ChIP-Seq reads from the promoter and CpG island of the Nitric oxide synthase interacting protein gene. Hypothetical GABP binding in five cells and the corresponding DNA fragments with sequencing reads. Below, actual read data. Forward reads are displayed as small blue bands and reverse reads as small maroon bands. (B) Forward (blue) and reverse (maroon) Read Density Profiles derived from the read data contribute to the Combined Density Profile (orange). The zero x-coordinate corresponds to coordinate 54775300 of human Chromosome 19, NCBI build 36.

Figure 2

Figure 2

Reproducibility and robustness of QuEST results assessed by comparison between two independent NRSF data sets. (A) Correlation between NRSF polyclonal and NRSF monoclonal peak scores (rho = 0.97) with the inset expanding the portion near the graph origin. (B) Bar chart of the distance between NRSF polyclonal and NRSF monoclonal peak call positions.

Figure 3

Figure 3

Resolution of QuEST as quantified by the distance between QuEST peak calls and TFBS motif centers. Histograms in each panel represent the distribution of peak distances to the nearest high-scoring motif.

Figure 4

Figure 4

Motif analysis results. Each panel displays significantly overrepresented motif Weblogos for each of the three transcription factors. Pie-charts show the fraction of peaks with motifs in close proximity to the peak (< 100 bps). Histograms show the distribution of the motif number within 100 bps of the peak.

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