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Reseach Article

Promoter Database Search using Hidden Markov Model

Published on None 2011 by Meera.A, Lalitha Rangarajan
Artificial Intelligence Techniques - Novel Approaches & Practical Applications
Foundation of Computer Science USA
AIT - Number 2
None 2011
Authors: Meera.A, Lalitha Rangarajan
fe8bb6c3-8384-45c8-8ff3-5e312da824dd

Meera.A, Lalitha Rangarajan . Promoter Database Search using Hidden Markov Model. Artificial Intelligence Techniques - Novel Approaches & Practical Applications. AIT, 2 (None 2011), 12-16.

@article{
author = { Meera.A, Lalitha Rangarajan },
title = { Promoter Database Search using Hidden Markov Model },
journal = { Artificial Intelligence Techniques - Novel Approaches & Practical Applications },
issue_date = { None 2011 },
volume = { AIT },
number = { 2 },
month = { None },
year = { 2011 },
issn = 0975-8887,
pages = { 12-16 },
numpages = 5,
url = { /specialissues/ait/number2/2830-211/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Special Issue Article
%1 Artificial Intelligence Techniques - Novel Approaches & Practical Applications
%A Meera.A
%A Lalitha Rangarajan
%T Promoter Database Search using Hidden Markov Model
%J Artificial Intelligence Techniques - Novel Approaches & Practical Applications
%@ 0975-8887
%V AIT
%N 2
%P 12-16
%D 2011
%I International Journal of Computer Applications
Abstract

A common task in bioinformatics is the comparison of biological sequences to probabilistic models in order to evaluate their similarity. Completion of genomes of most of the organisms lead to profitable comparative analyses, providing insights into non-coding regions as well as into protein coding regions of DNA. In the present work we propose a method for finding similar sequence in a database of upstream sequences of DNA. For testing purpose, we have extracted upstream sequences of different mammals of citrate synthase and actin genes and also that of cab gene in different plants. The promoter sequences are extracted from NCBI database. Motifs/ TFBS of the upstream sequences are extracted using the software tool ‘TF search’. Then probabilistic models are obtained for motif sequences by HMM method. Query motif sequence can be compared with all the motif sequences in the data base and based on maximum likelihood procedure, degree of similarity between query and all the motif sequences is obtained.

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Index Terms

Computer Science
Information Sciences

Keywords

Database Hidden Markov Model Promoter sequence pattern matching Transcription factors (TFs) Transcription factor binding sites (TFBS) Similarity measure Promoter sequence pattern matching Transcription factors (TFs) Transcription factor binding sites (TFBS) Similarity measure