Improved detection of DNA motifs using a self-organized clustering of familial binding profiles

Shaun Mahony, Aaron Golden, Terry J. Smith, Panayiotis V. Benos

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Motivation: One of the limiting factors in deciphering transcriptional regulatory networks is the effectiveness of motif-finding software. An emerging avenue for improving motif-finding accuracy aims to incorporate generalized binding constraints of related transcription factors (TFs), named familial binding profiles (FBPs), as priors in motif identification methods. A motif-finder can thus be 'biased' towards finding motifs from a particular TF family. However, current motif-finders allow only a single FBP to be used as a prior in a given motif-finding run. In addition, current FBP construction methods are based on manual clustering of position specific scoring matrices (PSSMs) according to the known structural properties of the TF proteins. Manual clustering assumes that the binding preferences of structurally similar TFs will also be similar. This assumption is not true, at least not for some TF families. Automatic PSSM clustering methods are thus required for augmenting the usefulness of FBPs. Results: A novel method is developed for automatic clustering of PSSM models. The resulting FBPs are incorporated into the SOMBRERO motif-finder, significantly improving its performance when finding motifs related to those that have been incorporated. SOMBRERO is thus the only existing de novo motif-finder that can incorporate knowledge of all known PSSMs in a given motif-finding run.

Original languageEnglish (US)
Title of host publicationISMB 2005 Proceedings - 13th International Conference on Intelligent Systems for Molecular Biology
StatePublished - 2005
Externally publishedYes
Event13th International Conference on Intelligent Systems for Molecular Biology, ISMB 2005 - Detroit, MI, United States
Duration: Jun 25 2005Jun 29 2005

Other

Other13th International Conference on Intelligent Systems for Molecular Biology, ISMB 2005
CountryUnited States
CityDetroit, MI
Period6/25/056/29/05

Fingerprint

Position-Specific Scoring Matrices
Nucleotide Motifs
Transcription factors
Cluster Analysis
DNA
Transcription Factors
Gene Regulatory Networks
Structural properties
Software
Proteins

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)
  • Artificial Intelligence
  • Information Systems

Cite this

Mahony, S., Golden, A., Smith, T. J., & Benos, P. V. (2005). Improved detection of DNA motifs using a self-organized clustering of familial binding profiles. In ISMB 2005 Proceedings - 13th International Conference on Intelligent Systems for Molecular Biology

Improved detection of DNA motifs using a self-organized clustering of familial binding profiles. / Mahony, Shaun; Golden, Aaron; Smith, Terry J.; Benos, Panayiotis V.

ISMB 2005 Proceedings - 13th International Conference on Intelligent Systems for Molecular Biology. 2005.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Mahony, S, Golden, A, Smith, TJ & Benos, PV 2005, Improved detection of DNA motifs using a self-organized clustering of familial binding profiles. in ISMB 2005 Proceedings - 13th International Conference on Intelligent Systems for Molecular Biology. 13th International Conference on Intelligent Systems for Molecular Biology, ISMB 2005, Detroit, MI, United States, 6/25/05.
Mahony S, Golden A, Smith TJ, Benos PV. Improved detection of DNA motifs using a self-organized clustering of familial binding profiles. In ISMB 2005 Proceedings - 13th International Conference on Intelligent Systems for Molecular Biology. 2005
Mahony, Shaun ; Golden, Aaron ; Smith, Terry J. ; Benos, Panayiotis V. / Improved detection of DNA motifs using a self-organized clustering of familial binding profiles. ISMB 2005 Proceedings - 13th International Conference on Intelligent Systems for Molecular Biology. 2005.
@inproceedings{8deadd3212f0415988020ecd41bbf3c1,
title = "Improved detection of DNA motifs using a self-organized clustering of familial binding profiles",
abstract = "Motivation: One of the limiting factors in deciphering transcriptional regulatory networks is the effectiveness of motif-finding software. An emerging avenue for improving motif-finding accuracy aims to incorporate generalized binding constraints of related transcription factors (TFs), named familial binding profiles (FBPs), as priors in motif identification methods. A motif-finder can thus be 'biased' towards finding motifs from a particular TF family. However, current motif-finders allow only a single FBP to be used as a prior in a given motif-finding run. In addition, current FBP construction methods are based on manual clustering of position specific scoring matrices (PSSMs) according to the known structural properties of the TF proteins. Manual clustering assumes that the binding preferences of structurally similar TFs will also be similar. This assumption is not true, at least not for some TF families. Automatic PSSM clustering methods are thus required for augmenting the usefulness of FBPs. Results: A novel method is developed for automatic clustering of PSSM models. The resulting FBPs are incorporated into the SOMBRERO motif-finder, significantly improving its performance when finding motifs related to those that have been incorporated. SOMBRERO is thus the only existing de novo motif-finder that can incorporate knowledge of all known PSSMs in a given motif-finding run.",
author = "Shaun Mahony and Aaron Golden and Smith, {Terry J.} and Benos, {Panayiotis V.}",
year = "2005",
language = "English (US)",
booktitle = "ISMB 2005 Proceedings - 13th International Conference on Intelligent Systems for Molecular Biology",

}

TY - GEN

T1 - Improved detection of DNA motifs using a self-organized clustering of familial binding profiles

AU - Mahony, Shaun

AU - Golden, Aaron

AU - Smith, Terry J.

AU - Benos, Panayiotis V.

PY - 2005

Y1 - 2005

N2 - Motivation: One of the limiting factors in deciphering transcriptional regulatory networks is the effectiveness of motif-finding software. An emerging avenue for improving motif-finding accuracy aims to incorporate generalized binding constraints of related transcription factors (TFs), named familial binding profiles (FBPs), as priors in motif identification methods. A motif-finder can thus be 'biased' towards finding motifs from a particular TF family. However, current motif-finders allow only a single FBP to be used as a prior in a given motif-finding run. In addition, current FBP construction methods are based on manual clustering of position specific scoring matrices (PSSMs) according to the known structural properties of the TF proteins. Manual clustering assumes that the binding preferences of structurally similar TFs will also be similar. This assumption is not true, at least not for some TF families. Automatic PSSM clustering methods are thus required for augmenting the usefulness of FBPs. Results: A novel method is developed for automatic clustering of PSSM models. The resulting FBPs are incorporated into the SOMBRERO motif-finder, significantly improving its performance when finding motifs related to those that have been incorporated. SOMBRERO is thus the only existing de novo motif-finder that can incorporate knowledge of all known PSSMs in a given motif-finding run.

AB - Motivation: One of the limiting factors in deciphering transcriptional regulatory networks is the effectiveness of motif-finding software. An emerging avenue for improving motif-finding accuracy aims to incorporate generalized binding constraints of related transcription factors (TFs), named familial binding profiles (FBPs), as priors in motif identification methods. A motif-finder can thus be 'biased' towards finding motifs from a particular TF family. However, current motif-finders allow only a single FBP to be used as a prior in a given motif-finding run. In addition, current FBP construction methods are based on manual clustering of position specific scoring matrices (PSSMs) according to the known structural properties of the TF proteins. Manual clustering assumes that the binding preferences of structurally similar TFs will also be similar. This assumption is not true, at least not for some TF families. Automatic PSSM clustering methods are thus required for augmenting the usefulness of FBPs. Results: A novel method is developed for automatic clustering of PSSM models. The resulting FBPs are incorporated into the SOMBRERO motif-finder, significantly improving its performance when finding motifs related to those that have been incorporated. SOMBRERO is thus the only existing de novo motif-finder that can incorporate knowledge of all known PSSMs in a given motif-finding run.

UR - http://www.scopus.com/inward/record.url?scp=84860510287&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84860510287&partnerID=8YFLogxK

M3 - Conference contribution

BT - ISMB 2005 Proceedings - 13th International Conference on Intelligent Systems for Molecular Biology

ER -