Dataset Information

Tracing the evolution of lineage-specific transcription factor binding sites in a birth-death framework.

ABSTRACT: Changes in cis-regulatory element composition that result in novel patterns of gene expression are thought to be a major contributor to the evolution of lineage-specific traits. Although transcription factor binding events show substantial variation across species, most computational approaches to study regulatory elements focus primarily upon highly conserved sites, and rely heavily upon multiple sequence alignments. However, sequence conservation based approaches have limited ability to detect lineage-specific elements that could contribute to species-specific traits. In this paper, we describe a novel framework that utilizes a birth-death model to trace the evolution of lineage-specific binding sites without relying on detailed base-by-base cross-species alignments. Our model was applied to analyze the evolution of binding sites based on the ChIP-seq data for six transcription factors (GATA1, SOX2, CTCF, MYC, MAX, ETS1) along the lineage toward human after human-mouse common ancestor. We estimate that a substantial fraction of binding sites (?58-79% for each factor) in humans have origins since the divergence with mouse. Over 15% of all binding sites are unique to hominids. Such elements are often enriched near genes associated with specific pathways, and harbor more common SNPs than older binding sites in the human genome. These results support the ability of our method to identify lineage-specific regulatory elements and help understand their roles in shaping variation in gene regulation across species.

SUBMITTER: Yokoyama KD

PROVIDER: S-EPMC4140645 | biostudies-literature | 2014 Aug

REPOSITORIES: biostudies-literature

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Tracing the evolution of lineage-specific transcription factor binding sites in a birth-death framework.

Yokoyama Ken Daigoro KD Zhang Yang Y Ma Jian J

PLoS computational biology 20140821 8

Changes in cis-regulatory element composition that result in novel patterns of gene expression are thought to be a major contributor to the evolution of lineage-specific traits. Although transcription factor binding events show substantial variation across species, most computational approaches to study regulatory elements focus primarily upon highly conserved sites, and rely heavily upon multiple sequence alignments. However, sequence conservation based approaches have limited ability to detect ...[more]

PMID: 25144359

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Project description:Computational prediction of nucleotide binding specificity for transcription factors remains a fundamental and largely unsolved problem. Determination of binding positions is a prerequisite for research in gene regulation, a major mechanism controlling phenotypic diversity. Furthermore, an accurate determination of binding specificities from high-throughput data sources is necessary to realize the full potential of systems biology. Unfortunately, recently performed independent evaluation showed that more than half the predictions from most widely used algorithms are false. We introduce a graph-theoretical framework to describe local sequence similarity as the pair-wise distances between nucleotides in promoter sequences, and hypothesize that densely connected subgraphs are indicative of transcription factor binding sites. Using a well-established sampling algorithm coupled with simple clustering and scoring schemes, we identify sets of closely related nucleotides and test those for known TF binding activity. Using an independent benchmark, we find our algorithm predicts yeast binding motifs considerably better than currently available techniques and without manual curation. Importantly, we reduce the number of false positive predictions in yeast to less than 30%. We also develop a framework to evaluate the statistical significance of our motif predictions. We show that our approach is robust to the choice of input promoters, and thus can be used in the context of predicting binding positions from noisy experimental data. We apply our method to identify binding sites using data from genome scale ChIP-chip experiments. Results from these experiments are publicly available at http://cagt10.bu.edu/BSG. The graphical framework developed here may be useful when combining predictions from numerous computational and experimental measures. Finally, we discuss how our algorithm can be used to improve the sensitivity of computational predictions of transcription factor binding specificities.

Dataset Information

Tracing the evolution of lineage-specific transcription factor binding sites in a birth-death framework.

Publications

Tracing the evolution of lineage-specific transcription factor binding sites in a birth-death framework.

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