Binding site graphs: a new graph theoretical framework for prediction of transcription factor binding sites.

Binding site graphs: a new graph theoretical framework for prediction of transcription factor binding sites. Research Abstract Details 

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Binding site graphs: a new graph theoretical framework for prediction of transcription factor binding sites. Abstract Text:

Timothy E Reddy,Charles DeLisi,Boris E Shakhnovich,Timothy E Reddy,Charles DeLisi,Boris E Shakhnovich,

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.

Binding site graphs: a new graph theoretical framework for prediction of transcription factor binding sites. Publishing Authors By Initials

TE Reddy,C DeLisi,BE Shakhnovich,TE Reddy,C DeLisi,BE Shakhnovich,

For similar proteins: transcription factors research abstracts see: proteins: transcription factors research

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Binding site graphs: a new graph theoretical framework for prediction of transcription factor binding sites. Journal Published:

PUBLICATION TYPE: Research Support, N.I.H., Extr

Journal: PLoS computational biology

VOLUME: 3

Page Numbers: e90

Journal Abbreviation: PLoS Comput. Biol.

ISSN: 1553-7358

DAY: 10

MONTH: 04

YEAR: 2007

Binding site graphs: a new graph theoretical framework for prediction of transcription factor binding sites. Information

Number of References:

LANGUAGE: eng

NlmUniqueID: 101238922

Binding site graphs: a new graph theoretical framework for prediction of transcription factor binding sites. Keywords Mesh Terms:

KEYWORDS: Transcription Factors

MESH TERMS: genetics

Chemical & Substance for Abstract: Binding site graphs: a new graph theoretical framework for prediction of transcription factor binding sites. Information

Substance Name: Transcription Factors

Registry Number: 0

Grant and Affiliation Information for Binding site graphs: a new graph theoretical framework for prediction of transcription factor binding sites.

AFFILIATION: Program in Bioinformatics and Systems Biology, Boston University, Boston, Massachusetts, United States of America.

Country: United States

AGENCY: United States PHS

GRANT: J50 01–130021

ACRONYM: GM

MEDLINETA: PLoS Comput Biol

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