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Computational methods and algorithms for phage metagenomic data analysis

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Item Summary

Title: Computational methods and algorithms for phage metagenomic data analysis
Authors: Belcaid, Mahdi
Keywords: bioinformatic tools
phage genomics
Issue Date: May 2012
Publisher: [Honolulu] : [University of Hawaii at Manoa], [May 2012]
Abstract: Bacteriophages, or viruses that infect bacteria, are by far the most abundant entities on Earth. Nevertheless, despite their abundance, the diversity of these organisms remains unknown and most existing bioinformatic tools, which are usually developed for cellular microbes, are particularly inefficient for analyzing them. As a step towards filling this gap, we provide in this research a set of methods and algorithms that strive to contribute a new momentum to phage genomics research. Our scientific contributions are three-fold. Firstly, we show, using statistical modeling as well as simulations, that separating a metagenome into non-overlapping fractions of organisms has the potential to substantially reduce the number of assembly singletons. Secondly, we present the mosaic graphs as a novel method to illustrate the prevalence of recombination events in phage communities and to highlight complex reassortment scenarios across phages. Mosaic graphs can be constructed in linear time and can account for an extensive number of recombinations.
Furthermore, these representations can be used as a platform to infer the recombination scenarios for a community of phages. Lastly, we propose a new linear-time heuristic to compute the distance across tandemly repeated words. This heuristic operates by tapping into the existing diversity across orthologous and paralogous repeats and efficiently reconstructs all the possible parsimonious scenarios that can discriminate between ancient and recent mutation events across the repeated words. The applications and algorithms introduced in this research examine phages from different perspectives and can thus be beneficial for understanding various aspects of phage biology.
Description: Ph.D. University of Hawaii at Manoa 2012.
Includes bibliographical references.
Appears in Collections:Ph.D. - Computer Science

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