An Approach towards Promoter Database Search Using Hidden Markov Model

A common task in bioinformatics is the comparison of biological sequences to probabilistic models in order to evaluate their similarity. In the proposed work we have made an attempt to classify the given query promoter sequence by comparing with the set of promoter sequences belonging to different p...

Full description

Saved in:
Bibliographic Details
Published inComputer Networks and Information Technologies pp. 547 - 552
Main Authors Meera, A., Rangarajan, Lalitha
Format Book Chapter
LanguageEnglish
Published Berlin, Heidelberg Springer Berlin Heidelberg
SeriesCommunications in Computer and Information Science
Subjects
Expectation Maximization
Hidden markov model
maximum likelihood
promoter sequence
Online AccessGet full text

Cover

More Information
Summary:A common task in bioinformatics is the comparison of biological sequences to probabilistic models in order to evaluate their similarity. In the proposed work we have made an attempt to classify the given query promoter sequence by comparing with the set of promoter sequences belonging to different pathways. The promoter sequences are extracted from NCBI database and converted to motif (TFBS) sequences by using ‘TF SEARCH’ tool. A probabilistic model is developed for each motif sequence of the pathways considered and a database is created. Given query motif sequence is compared with all the sequences in the database and classified based on the log probability score obtained.
ISBN:3642195415
9783642195419
ISSN:1865-0929
1865-0937
DOI:10.1007/978-3-642-19542-6_107