Genome-wide identification of in vivo protein–DNA binding sites from ChIP-Seq data

By R. Jothi, Suresh Cuddapah, A. Barski, Kairong Cui, Keji Zhao. Nucleic Acids Research, 2008

600 citations recorded by the source corpus.

ChIP-Seq, which combines chromatin immunoprecipitation (ChIP) with ultra high-throughput massively parallel sequencing, is increasingly being used for mapping protein–DNA interactions in-vivo on a genome scale. Typically, short sequence reads from ChIP-Seq are mapped to a reference genome for further analysis. Although genomic regions enriched with mapped reads could be inferred as approximate binding regions, short read lengths (∼25–50 nt) pose challenges for determining the exact binding sites within these regions. Here, we present SISSRs (Site Identification from Short Sequence Reads), a novel algorithm for precise identification of binding sites from short reads generated from ChIP-Seq experiments. The sensitivity and specificity of SISSRs are demonstrated by applying it on ChIP-Seq data for three widely studied and well-characterized human transcription factors: CTCF (CCCTC-binding factor), NRSF (neuron-restrictive silencer factor) and STAT1 (signal transducer and activator of transcription protein 1). We identified 26 814, 5813 and 73 956 binding sites for CTCF, NRSF and STAT1 proteins, respectively, which is 32, 299 and 78% more than that inferred previously for the respective proteins. Motif analysis revealed that an overwhelming majority of the identified binding sites contained the previously established consensus binding sequence for the respective proteins, thus attesting for SISSRs’ accuracy. SISSRs’ sensitivity and precision facilitated further analyses of ChIP-Seq data revealing interesting insights, which we believe will serve as guidance for designing ChIP-Seq experiments to map in vivo protein–DNA interactions. We also show that tag densities at the binding sites are a good indicator of protein–DNA binding affinity, which could be used to distinguish and characterize strong and weak binding sites. Using tag density as an indicator of DNA-binding affinity, we have identified core residues within the NRSF and CTCF binding sites that are critical for a stronger DNA binding.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/68a416e11fec1760d4c4cd19478fb63cd04a9d3f

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Citations in the corpus

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.”
High Resolution Genome Wide Binding Event Finding and Motif Discovery Reveals Transcription Factor Spatial Binding Constraints (2012, methodology): “We compared GEM’s spatial resolution to six well known ChIPSeq analysis methods, including GPS [8], SISSRs [6], MACS [4], cisGenome [7], QuEST [5] and PeakRanger [9].”
Inferring direct DNA binding from ChIP-seq (2012, background): “) A primary objective of many ChIP-seq experiments is determining the in vivo DNA-binding affinity of the ChIP-ed TF, and it has been shown that ChIP-seq tag densities are predictive of protein–DNA binding affinity (5).”
A clustering approach for identification of enriched domains from histone modification ChIP-Seq data (2009, unknown): “We demonstrate the application of this unbiased method in important issues in ChIP-Seq data analysis, such as data normalization for quantitative comparison of levels of epigenetic modifications across cell types and growth conditions.”
CTCF-promoted RNA polymerase II pausing links DNA methylation to splicing (2011, unknown): “Global effects of intragenic CTCF on splicing Although studies of CTCF function have been largely restricted to intergenic activities, CTCF ChIP-seq studies found that approximately 40–45% of CTCF binding sites are located intragenicall”
Chromatin organization marks exon-intron structure (2009, methodology): “We used site identification from short sequence reads (SISSRs”
CTCF: Master Weaver of the Genome (2009, unknown): “Not all data are consistent with this theme, however, as CTCF displays markedly similar distribution patterns to the transcriptional repressor NRSF and activator STAT1 (Jothi et al., 2008).”
Evaluation of Algorithm Performance in ChIP-Seq Peak Detection (2010, methodology): “One straightforward approach is to calculate the fold enrichment of ChIP tags over normalized control tags in candidate regions, to account for the fluctuating background signal [16,18,27,32].”
The bi-functional microRNA miR-9/miR-9* regulates REST and CoREST and is down-regulated in Huntington’s Disease (2008, background): “, 2007), we screened a panel of predicted REST-regulated miRNAs (Jothi et al., 2008) in samples collected from healthy control and HD patient brains (HD grades 1– 4).”
Identifying ChIP-seq enrichment using MACS (2012, methodology): “For example, to find peak candidates, many methods including SISSRs 22 , USeq 12 and MACS 13 identify clusters consisting of reads that overlap or are located within a fixed distance.”
Global analysis of the insulator binding protein CTCF in chromatin barrier regions reveals demarcation of active and repressive domains. (2008, methodology): “CTCF-binding sites overlap extensively between cell types To identify the CTCF-bound genomic sites at high resolution, we analyzed ChIP-seq data from HeLa and Jurkat cells generated in this study, along with the ChIP-seq data from resting human CD4+ T cells (Barski et al. 2007) using SISSRs (Jothi e”
Comprehensive Genome-wide Protein-DNA Interactions Detected at Single Nucleotide Resolution (2011, background): “Where CTCF binds in a genome and its DNA recognition sequence have been the subject of some dispute (Bao et al., 2008; Barski et al., 2007; Cuddapah et al., 2009; Jothi et al., 2008; Kim et al., 2007).”