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Detection and Employment of Biological Sequence Motifs
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Author(s): Marjan Trutschl (Louisiana State University – Shreveport, USA & Louisiana State University Health – Shreveport, USA), Phillip C. S. R. Kilgore (Louisiana State University – Shreveport, USA), Rona S. Scott (Louisiana State University Health – Shreveport, USA), Christine E. Birdwell (Louisiana State University Health – Shreveport, USA)and Urška Cvek (Louisiana State University – Shreveport, USA & Louisiana State University Health – Shreveport, USA)
Copyright: 2015
Pages: 31
Source title:
Big Data Analytics in Bioinformatics and Healthcare
Source Author(s)/Editor(s): Baoying Wang (Waynesburg University, USA), Ruowang Li (Pennsylvania State University, USA)and William Perrizo (North Dakota State University, USA)
DOI: 10.4018/978-1-4666-6611-5.ch005
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Abstract
Biological sequence motifs are short nucleotide or amino acid sequences that are biologically significant and are attractive to scientists because they are usually highly conserved and result in structural and regulatory implications. In this chapter, the authors show practical applications of these data, followed by a review of the algorithms, techniques, and tools. They address the nature of motifs and elucidate on several methods for de novo motif discovery, covering the algorithms based on Gibbs sampling, expectation maximization, Bayesian inference, covariance models, and discriminative learning. The authors present the tools and their requirements to weigh their individual benefits and challenges. Since interpretation of a large set of results can pose significant challenges, they discuss several methods for handling data that span from visualization to integration into pipelines and curated databases. Additionally, the authors show practical applications of these data with examples.
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