Discovery of regulatory elements is improved by a discriminatory approach

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Discovery of regulatory elements is improved by a discriminatory approach. / Valen, Eivind; Sandelin, Albin; Winther, Ole; Krogh, Anders.

In: PloS Computational Biology, Vol. 5, No. 11, 2009, p. e1000562.

Research output: Contribution to journalJournal articleResearchpeer-review

Harvard

Valen, E, Sandelin, A, Winther, O & Krogh, A 2009, 'Discovery of regulatory elements is improved by a discriminatory approach', PloS Computational Biology, vol. 5, no. 11, pp. e1000562. https://doi.org/10.1371/journal.pcbi.1000562

APA

Valen, E., Sandelin, A., Winther, O., & Krogh, A. (2009). Discovery of regulatory elements is improved by a discriminatory approach. PloS Computational Biology, 5(11), e1000562. https://doi.org/10.1371/journal.pcbi.1000562

Vancouver

Valen E, Sandelin A, Winther O, Krogh A. Discovery of regulatory elements is improved by a discriminatory approach. PloS Computational Biology. 2009;5(11):e1000562. https://doi.org/10.1371/journal.pcbi.1000562

Author

Valen, Eivind ; Sandelin, Albin ; Winther, Ole ; Krogh, Anders. / Discovery of regulatory elements is improved by a discriminatory approach. In: PloS Computational Biology. 2009 ; Vol. 5, No. 11. pp. e1000562.

Bibtex

@article{a87ec5f0e65611deba73000ea68e967b,
title = "Discovery of regulatory elements is improved by a discriminatory approach",
abstract = "A major goal in post-genome biology is the complete mapping of the gene regulatory networks for every organism. Identification of regulatory elements is a prerequisite for realizing this ambitious goal. A common problem is finding regulatory patterns in promoters of a group of co-expressed genes, but contemporary methods are challenged by the size and diversity of regulatory regions in higher metazoans. Two key issues are the small amount of information contained in a pattern compared to the large promoter regions and the repetitive characteristics of genomic DNA, which both lead to {"}pattern drowning{"}. We present a new computational method for identifying transcription factor binding sites in promoters using a discriminatory approach with a large negative set encompassing a significant sample of the promoters from the relevant genome. The sequences are described by a probabilistic model and the most discriminatory motifs are identified by maximizing the probability of the sets given the motif model and prior probabilities of motif occurrences in both sets. Due to the large number of promoters in the negative set, an enhanced suffix array is used to improve speed and performance. Using our method, we demonstrate higher accuracy than the best of contemporary methods, high robustness when extending the length of the input sequences and a strong correlation between our objective function and the correct solution. Using a large background set of real promoters instead of a simplified model leads to higher discriminatory power and markedly reduces the need for repeat masking; a common pre-processing step for other pattern finders.",
author = "Eivind Valen and Albin Sandelin and Ole Winther and Anders Krogh",
year = "2009",
doi = "10.1371/journal.pcbi.1000562",
language = "English",
volume = "5",
pages = "e1000562",
journal = "P L o S Computational Biology (Online)",
issn = "1553-7358",
publisher = "Public Library of Science",
number = "11",

}

RIS

TY - JOUR

T1 - Discovery of regulatory elements is improved by a discriminatory approach

AU - Valen, Eivind

AU - Sandelin, Albin

AU - Winther, Ole

AU - Krogh, Anders

PY - 2009

Y1 - 2009

N2 - A major goal in post-genome biology is the complete mapping of the gene regulatory networks for every organism. Identification of regulatory elements is a prerequisite for realizing this ambitious goal. A common problem is finding regulatory patterns in promoters of a group of co-expressed genes, but contemporary methods are challenged by the size and diversity of regulatory regions in higher metazoans. Two key issues are the small amount of information contained in a pattern compared to the large promoter regions and the repetitive characteristics of genomic DNA, which both lead to "pattern drowning". We present a new computational method for identifying transcription factor binding sites in promoters using a discriminatory approach with a large negative set encompassing a significant sample of the promoters from the relevant genome. The sequences are described by a probabilistic model and the most discriminatory motifs are identified by maximizing the probability of the sets given the motif model and prior probabilities of motif occurrences in both sets. Due to the large number of promoters in the negative set, an enhanced suffix array is used to improve speed and performance. Using our method, we demonstrate higher accuracy than the best of contemporary methods, high robustness when extending the length of the input sequences and a strong correlation between our objective function and the correct solution. Using a large background set of real promoters instead of a simplified model leads to higher discriminatory power and markedly reduces the need for repeat masking; a common pre-processing step for other pattern finders.

AB - A major goal in post-genome biology is the complete mapping of the gene regulatory networks for every organism. Identification of regulatory elements is a prerequisite for realizing this ambitious goal. A common problem is finding regulatory patterns in promoters of a group of co-expressed genes, but contemporary methods are challenged by the size and diversity of regulatory regions in higher metazoans. Two key issues are the small amount of information contained in a pattern compared to the large promoter regions and the repetitive characteristics of genomic DNA, which both lead to "pattern drowning". We present a new computational method for identifying transcription factor binding sites in promoters using a discriminatory approach with a large negative set encompassing a significant sample of the promoters from the relevant genome. The sequences are described by a probabilistic model and the most discriminatory motifs are identified by maximizing the probability of the sets given the motif model and prior probabilities of motif occurrences in both sets. Due to the large number of promoters in the negative set, an enhanced suffix array is used to improve speed and performance. Using our method, we demonstrate higher accuracy than the best of contemporary methods, high robustness when extending the length of the input sequences and a strong correlation between our objective function and the correct solution. Using a large background set of real promoters instead of a simplified model leads to higher discriminatory power and markedly reduces the need for repeat masking; a common pre-processing step for other pattern finders.

U2 - 10.1371/journal.pcbi.1000562

DO - 10.1371/journal.pcbi.1000562

M3 - Journal article

C2 - 19911049

VL - 5

SP - e1000562

JO - P L o S Computational Biology (Online)

JF - P L o S Computational Biology (Online)

SN - 1553-7358

IS - 11

ER -

ID: 16239672