On the Monte-Carlo Expectation Maximization for Finding Motifs in DNA Sequences

Finding conserved locations or motifs in genomic sequences is of paramount importance. Expectation maximization (EM)-based algorithms are widely employed to solve motif finding problems. The present study proposes a novel initialization technique and model-shifting scheme for Monte-Carlo-based EM me...

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Bibliographic Details
Published inIEEE journal of biomedical and health informatics Vol. 19; no. 2; pp. 677 - 686
Main Authors Maiti, Aniruddha, Mukherjee, Anirban
Format Journal Article
LanguageEnglish
Published United States IEEE 01.03.2015
Subjects
Algorithms
Biological system modeling
Biomedical measurement
Clustering algorithms
Computational modeling
Databases, Factual
DNA - analysis
DNA - chemistry
Humans
Markov processes
Monte Carlo Method
Monte Carlo methods
Nucleotide Motifs - genetics
Sequence Alignment
Sequence Analysis, DNA - methods
Silicon
Monte-Carlo
motif finding
expectation maximization (EM)
DNA
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Summary:Finding conserved locations or motifs in genomic sequences is of paramount importance. Expectation maximization (EM)-based algorithms are widely employed to solve motif finding problems. The present study proposes a novel initialization technique and model-shifting scheme for Monte-Carlo-based EM methods for motif finding. Two popular EM-based motif finding algorithms are compared to the proposed method, which offers improved motif prediction accuracy on a synthetic dataset and a true biological dataset.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:2168-2194
2168-2208
DOI:10.1109/JBHI.2014.2322694