NDLI: A Data Mining Approach to Predicting Phylum for Microbial Organisms Using Genome-Wide Sequence Data

Content Provider	IEEE Xplore Digital Library
Author	Kotamarti, R.M. Raiford, D.W. Raymer, M.L. Dunham, M.H.
Copyright Year	2009
Abstract	Genomic sequencing projects are generating vast stores of data that provide opportunities and challenges in data analysis. Investigations of trends in codon usage have proven to be a rich area of study in this field. There are a number of methods for isolating codon usage bias in microbial organisms, each designed to capture a specific aspect of the bias. We posit that each species has evolved under the influence of a unique set of environmental constraints that has governed the shaping of the organism's codon usage. Analysis of codon usage data should, therefore, provide insights into the selection process at work influencing genomic composition. To this end, we describe the large-scale mining of genome-level data from several codon usage bias isolation techniques to determine whether this information can be used to predict the phylum and class to which each organism belongs. Successful prediction is an indication that the forces molding the codon usage of a given phylum/class are indeed distinctive, and that it would be of use in understanding the evolutionary forces involved. Additionally, it supports using this method to aid in, and validate existing taxonomic classification techniques.
Starting Page	161
Ending Page	167
File Size	4752483
Page Count	7
File Format	PDF
ISBN	9780769536569
DOI	10.1109/BIBE.2009.14
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-06-22
Publisher Place	Taiwan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Data mining Organisms Genomics Bioinformatics Sequences Microorganisms Databases Data analysis Computer science Genetic engineering
Content Type	Text
Resource Type	Article

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