Dataset Information

Identification of context-dependent motifs by contrasting ChIP binding data.

ABSTRACT:

Motivation

DNA binding proteins play crucial roles in the regulation of gene expression. Transcription factors (TFs) activate or repress genes directly while other proteins influence chromatin structure for transcription. Binding sites of a TF exhibit a similar sequence pattern called a motif. However, a one-to-one map does not exist between each TF and motif. Many TFs in a protein family may recognize the same motif with subtle nucleotide differences leading to different binding affinities. Additionally, a particular TF may bind different motifs under certain conditions, for example in the presence of different co-regulators. The availability of genome-wide binding data of multiple collaborative TFs makes it possible to detect such context-dependent motifs.

Results

We developed a contrast motif finder (CMF) for the de novo identification of motifs that are differentially enriched in two sets of sequences. Applying this method to a number of TF binding datasets from mouse embryonic stem cells, we demonstrate that CMF achieves substantially higher accuracy than several well-known motif finding methods. By contrasting sequences bound by distinct sets of TFs, CMF identified two different motifs that may be recognized by Oct4 dependent on the presence of another co-regulator and detected subtle motif signals that may be associated with potential competitive binding between Sox2 and Tcf3.

Availability

The software CMF is freely available for academic use at www.stat.ucla.edu/?zhou/CMF.

SUBMITTER: Mason MJ

PROVIDER: S-EPMC2971577 | biostudies-literature | 2010 Nov

REPOSITORIES: biostudies-literature

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Publications

Identification of context-dependent motifs by contrasting ChIP binding data.

Mason Mike J MJ Plath Kathrin K Zhou Qing Q

Bioinformatics (Oxford, England) 20100923 22

<h4>Motivation</h4>DNA binding proteins play crucial roles in the regulation of gene expression. Transcription factors (TFs) activate or repress genes directly while other proteins influence chromatin structure for transcription. Binding sites of a TF exhibit a similar sequence pattern called a motif. However, a one-to-one map does not exist between each TF and motif. Many TFs in a protein family may recognize the same motif with subtle nucleotide differences leading to different binding affinit ...[more]

PMID: 20870645

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Project description: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 (approximately 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.

Dataset Information

Identification of context-dependent motifs by contrasting ChIP binding data.

Motivation

Results

Availability

Publications

Identification of context-dependent motifs by contrasting ChIP binding data.

Similar Datasets

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