Genome-Wide Analysis of Transcription Factor Binding Sites Based on ChIP-Seq Data

By Anton Valouev, David S. Johnson, Andreas Sundquist, Catherine Medina, Elizabeth Anton, Serafim Batzoglou, Richard M. Myers, Arend Sidow. Nature Methods, 2008

762 citations recorded by the source corpus.

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.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/842204e75e5b04df1b056c62f0c390cea314f13b

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Citations in the corpus

A clustering approach for identification of enriched domains from histone modification ChIP-Seq data (2009, neutral): “Availability: http://home.gwu.edu/∼wpeng/Software.htm Contact: wpeng@gwu.edu Supplementary information: Supplementary data are available at Bioinformatics online.”
High Resolution Genome Wide Binding Event Finding and Motif Discovery Reveals Transcription Factor Spatial Binding Constraints (2012, methodology): “We used a human Growth Associated Binding Protein (GABP) ChIP-Seq dataset for our evaluation because GABP ChIP-Seq data were previously reported to contain homotypic events where the reads generated by multiple closely spaced binding events overlap [5].”
Genome-wide analysis of ETS-family DNA-binding in vitro and in vivo (2010, positive): “For comparison, we also analysed overlap of our ELF1 ChIP-seq peaks with recent ChIP-seq data of the ETS-family members ETS1 and GABPA in Jurkat cells (Valouev et al, 2008; Hollenhorst et al, 2009) (see Materials and methods; Supplementary Table S4 for details).”
Measuring reproducibility of high-throughput experiments (2011, methodology): “Based on the likelihood ratio test, it seems that the one-component model ﬁts the results from SISSRS, Quest and Cisgenome better, and the two-component model ﬁts the results from other peak callers.”
Chromatin organization marks exon-intron structure (2009, methodology): “, we analyzed a Solexa high-throughput sequence of sheared DNA from Jurkat cell”
Densely interconnected transcriptional circuits control cell states in human hematopoiesis (2011, positive): “In a preprocessing step, before applying QuEST, we ﬁltered duplicate reads, allowing up to 2 reads at the same location.”
Discovery and characterization of chromatin states for systematic annotation of the human genome (2010, background): “First, we found that the descriptive power of the model was appropriately spent, allocating more states to capture biologically-meaningful complexity in small regions, for example dedicating 11 states (1-11) to capture the subtleties of promoter-associated regions that only cover 1% of the genome, w”
featureCounts: an efficient general purpose program for assigning sequence reads to genomic features (2013, methodology): “DNA sequence reads arise from a variety of technologies including ChIP-seq for transcription factor binding sites (Valouev et al. , 2008), ChIP-seq for histone marks (Park, 2009), and assays that detect DNA methylation (Harris et al. , 2010).”
ChIPpeakAnno: a Bioconductor package to annotate ChIP-seq and ChIP-chip data (2010, methodology): “This facilitates subsequent PCR amplification, cloning and/or motif discovery using algorithms such as MEME [3,30].”
Next-generation sequencing: from basic research to diagnostics. (2009, neutral): “The single-base resolution of the data allows improved estimation of binding-site positions in the programsQuEST( 79 )andMACS(80).Aligneddataat the protein-binding regions typically have 2 characteristic offset peaks, each of which is populated by only forward or only reverse reads.”
Computation for ChIP-seq and RNA-seq studies (2009, background): “For example, the strand-specific structure of the tag distribution is useful to discriminate the punctate class of binding events from a variety of artifacts 9 .”
ChIP-seq guidelines and practices of the ENCODE and modENCODE consortia (2012, methodology): “We have used several peak calling algorithms and corresponding software packages, including SPP, PeakSeq, and MACs (Ji et al. 2008; Valouev et al. 2008; Zhang et al. 2008; Rozowsky et al. 2009).”