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Reseach Article

A Comparison Study of Transcription Factor– DNA Binding Models

by Sudha Narang
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 1 - Number 27
Year of Publication: 2010
Authors: Sudha Narang
10.5120/500-816

Sudha Narang . A Comparison Study of Transcription Factor– DNA Binding Models. International Journal of Computer Applications. 1, 27 ( February 2010), 42-47. DOI=10.5120/500-816

@article{ 10.5120/500-816,
author = { Sudha Narang },
title = { A Comparison Study of Transcription Factor– DNA Binding Models },
journal = { International Journal of Computer Applications },
issue_date = { February 2010 },
volume = { 1 },
number = { 27 },
month = { February },
year = { 2010 },
issn = { 0975-8887 },
pages = { 42-47 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume1/number27/500-816/ },
doi = { 10.5120/500-816 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T19:49:05.869438+05:30
%A Sudha Narang
%T A Comparison Study of Transcription Factor– DNA Binding Models
%J International Journal of Computer Applications
%@ 0975-8887
%V 1
%N 27
%P 42-47
%D 2010
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The comparison study is drawn between two widely used motif representations i.e. Positional Weight Matrices (PWM) and Consensus Sequences. In the case of motif finding, where the binding sites are not known a priori but the algorithm must search a large space of possible binding sites, the PWM model may be difficult to learn as the search space is very large even for the PWM of short length (R^N for a PWM of length N, where R is the space of real numbers between 0 to 1). Optimization methods used to search for the best PWM may converge to a local minimum. On the other hand the consensus sequence has a smaller search space (15^N for a motif of length N) which is easier to search for the global optimum.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Positional Weight Matrices Consensus Sequences Stroke Width Optimization methods