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Journal of Integrative Bioinformatics

Editor-in-Chief: Schreiber, Falk / Hofestädt, Ralf

Managing Editor: Sommer, Björn

Ed. by Baumbach, Jan / Chen, Ming / Orlov, Yuriy / Allmer, Jens

Editorial Board: Giorgetti, Alejandro / Harrison, Andrew / Kochetov, Aleksey / Krüger, Jens / Ma, Qi / Matsuno, Hiroshi / Mitra, Chanchal K. / Pauling, Josch K. / Rawlings, Chris / Fdez-Riverola, Florentino / Romano, Paolo / Röttger, Richard / Shoshi, Alban / Soares, Siomar de Castro / Taubert, Jan / Tauch, Andreas / Yousef, Malik / Weise, Stephan

4 Issues per year


CiteScore 2017: 0.77

SCImago Journal Rank (SJR) 2017: 0.336

Open Access
Online
ISSN
1613-4516
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Volume 11, Issue 2

Issues

Probabilistic Latent Semantic Analysis Applied to Whole Bacterial Genomes Identifies Common Genomic Features

J. Rusakovica
  • School of Computing Science, and Centre for Synthetic Biology and Bioexploitation, Newcastle University, Newcastle upon Tyne, NE1 7RU, United Kingdom of Great Britain and Northern Ireland
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ J. Hallinan
  • School of Computing Science, and Centre for Synthetic Biology and Bioexploitation, Newcastle University, Newcastle upon Tyne, NE1 7RU, United Kingdom of Great Britain and Northern Ireland
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ A. Wipat
  • Corresponding author
  • School of Computing Science, and Centre for Synthetic Biology and Bioexploitation, Newcastle University, Newcastle upon Tyne, NE1 7RU, United Kingdom of Great Britain and Northern Ireland
  • Email
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ P. Zuliani
  • School of Computing Science, and Centre for Synthetic Biology and Bioexploitation, Newcastle University, Newcastle upon Tyne, NE1 7RU, United Kingdom of Great Britain and Northern Ireland
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
Published Online: 2016-10-18 | DOI: https://doi.org/10.1515/jib-2014-243

Summary

The spread of drug resistance amongst clinically-important bacteria is a serious, and growing, problem [1]. However, the analysis of entire genomes requires considerable computational effort, usually including the assembly of the genome and subsequent identification of genes known to be important in pathology. An alternative approach is to use computational algorithms to identify genomic differences between pathogenic and non-pathogenic bacteria, even without knowing the biological meaning of those differences. To overcome this problem, a range of techniques for dimensionality reduction have been developed. One such approach is known as latent-variable models [2]. In latent-variable models dimensionality reduction is achieved by representing a high-dimensional data by a few hidden or latent variables, which are not directly observed but inferred from the observed variables present in the model. Probabilistic Latent Semantic Indexing (PLSA) is an extention of LSA [3]. PLSA is based on a mixture decomposition derived from a latent class model. The main objective of the algorithm, as in LSA, is to represent high-dimensional co-occurrence information in a lower-dimensional way in order to discover the hidden semantic structure of the data using a probabilistic framework.

In this work we applied the PLSA approach to analyse the common genomic features in methicillin resistant Staphylococcus aureus, using tokens derived from amino acid sequences rather than DNA. We characterised genome-scale amino acid sequences in terms of their components, and then investigated the relationships between genomes and tokens and the phenotypes they generated. As a control we used the non-pathogenic model Gram-positive bacterium Bacillus subtilis.

About the article

Published Online: 2016-10-18

Published in Print: 2014-06-01


Citation Information: Journal of Integrative Bioinformatics, Volume 11, Issue 2, Pages 93–105, ISSN (Online) 1613-4516, DOI: https://doi.org/10.1515/jib-2014-243.

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© 2014 The Author(s). Published by Journal of Integrative Bioinformatics.. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. BY-NC-ND 4.0

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