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hdl:10101/npre.2007.1251.1
2 votes

Discovery of transcription factor binding sites through integration of generic motif finders

Edward Wijaya1, Siu-Ming Yiu2, Thanh Son Ngo1, Rajaraman Kanagasabai3, & Wing-Kin Sung1

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  1. School of Computing, National University of Singapore
  2. Department of Computer Science, The University of Hong Kong
  3. Data Mining, Institute for Infocomm Research
Document Type:
Manuscript
Date:
Received 25 October 2007 09:49 UTC; Posted 25 October 2007
Subjects:
Biotechnology, Bioinformatics
Tags:
Abstract:

Locating transcription factor binding sites is a key step in understanding gene regulation. Due to its importance, many de novo motif-finding methods have been proposed. Individually, these motif finders perform unimpressively overall based on Tompa’s benchmark datasets. Moreover, these motif finders vary in their definitions of what constitute a motif, and in their methods for finding statistically overrepresented motifs. There is no clear way for biologists to choose the motif finder that is most suitable for their task. The purpose of this work is to describe a method called MotifVoter to identify transcription factor binding sites by integrating the results found by motif finders of different models. Validation of our method on Tompa’s benchmark, real metazoan and E. coli datasets show that it can improve the sensitivity significantly without sacrificing the precision. Our approach offers a practical alternative for biologists to study novel transcription factors.
The MotifVoter software is available for public use at: http://www.comp.nus.edu.sg/~bioinfo/MotifVoter

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This document is licensed to the public under the Creative Commons Attribution 2.5 License
How to cite this document:

Wijaya, Edward, Yiu, Siu-Ming, Ngo, Thanh Son, Kanagasabai, Rajaraman, and Sung, Wing-Kin. Discovery of transcription factor binding sites through integration of generic motif finders. Available from Nature Precedings <http://hdl.handle.net/10101/npre.2007.1251.1> (2007)

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