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1.
Nucleic Acids Res. 2007 Jul;35(Web Server issue):W253-8. Epub 2007 May 3.

STAMP: a web tool for exploring DNA-binding motif similarities.

Author information

  • 1Department of Computational Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA.

Abstract

STAMP is a newly developed web server that is designed to support the study of DNA-binding motifs. STAMP may be used to query motifs against databases of known motifs; the software aligns input motifs against the chosen database (or alternatively against a user-provided dataset), and lists of the highest-scoring matches are returned. Such similarity-search functionality is expected to facilitate the identification of transcription factors that potentially interact with newly discovered motifs. STAMP also automatically builds multiple alignments, familial binding profiles and similarity trees when more than one motif is inputted. These functions are expected to enable evolutionary studies on sets of related motifs and fixed-order regulatory modules, as well as illustrating similarities and redundancies within the input motif collection. STAMP is a highly flexible alignment platform, allowing users to 'mix-and-match' between various implemented comparison metrics, alignment methods (local or global, gapped or ungapped), multiple alignment strategies and tree-building methods. Motifs may be inputted as frequency matrices (in many of the commonly used formats), consensus sequences, or alignments of known binding sites. STAMP also directly accepts the output files from 12 supported motif-finders, enabling quick interpretation of motif-discovery analyses. STAMP is available at http://www.benoslab.pitt.edu/stamp.

PMID:
17478497
[PubMed - indexed for MEDLINE]
PMCID:
PMC1933206
Free PMC Article
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2.
PLoS Comput Biol. 2007 Mar 30;3(3):e61. Epub 2007 Feb 15.

DNA familial binding profiles made easy: comparison of various motif alignment and clustering strategies.

Author information

  • 1Department of Computational Biology, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America.

Abstract

Transcription factor (TF) proteins recognize a small number of DNA sequences with high specificity and control the expression of neighbouring genes. The evolution of TF binding preference has been the subject of a number of recent studies, in which generalized binding profiles have been introduced and used to improve the prediction of new target sites. Generalized profiles are generated by aligning and merging the individual profiles of related TFs. However, the distance metrics and alignment algorithms used to compare the binding profiles have not yet been fully explored or optimized. As a result, binding profiles depend on TF structural information and sometimes may ignore important distinctions between subfamilies. Prediction of the identity or the structural class of a protein that binds to a given DNA pattern will enhance the analysis of microarray and ChIP-chip data where frequently multiple putative targets of usually unknown TFs are predicted. Various comparison metrics and alignment algorithms are evaluated (a total of 105 combinations). We find that local alignments are generally better than global alignments at detecting eukaryotic DNA motif similarities, especially when combined with the sum of squared distances or Pearson's correlation coefficient comparison metrics. In addition, multiple-alignment strategies for binding profiles and tree-building methods are tested for their efficiency in constructing generalized binding models. A new method for automatic determination of the optimal number of clusters is developed and applied in the construction of a new set of familial binding profiles which improves upon TF classification accuracy. A software tool, STAMP, is developed to host all tested methods and make them publicly available. This work provides a high quality reference set of familial binding profiles and the first comprehensive platform for analysis of DNA profiles. Detecting similarities between DNA motifs is a key step in the comparative study of transcriptional regulation, and the work presented here will form the basis for tool and method development for future transcriptional modeling studies.

PMID:
17397256
[PubMed - indexed for MEDLINE]
PMCID:
PMC1848003
Free PMC Article
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