Distinguishing Species Using GC Contents in Mixed DNA or RNA Sequences. (17th July 2018)

• Record Type: Journal Article
• Authors: Karimi, Kamran; Wuitchik, Daniel M; Oldach, Matthew J; Vize, Peter D
• Is Part Of: Evolutionary bioinformatics online. Volume 14(2018)
• Publication Date: 2018-07-17
• Subjects: classifying species -- DNA -- RNA -- GC contents -- SVM -- C4.5 decision tree; Bioinformatics -- Periodicals; Evolutionary computation -- Periodicals; Genetic programming (Computer science) -- Periodicals; Computational Biology; Evolution, Molecular; Bioinformatics; Electronic journals; Periodicals; Fulltext; Internet Resources; Periodicals; Periodicals; 576.8
• DOI: 10.1177/1176934318788866
• Languages: English
• ISSNs: 1176-9343

Abstract

With the advent of whole transcriptome and genome analysis methods, classifying samples containing multiple origins has become a significant task. Nucleotide sequences can be allocated to a genome or transcriptome by aligning sequences to multiple target sequence sets, but this approach requires extensive computational resources and also depends on target sequence sets lacking contaminants, which is often not the case. Here, we demonstrate that raw sequences can be rapidly sorted into groups, in practice corresponding to genera, by exploiting differences in nucleotide GC content. To do so, we introduce GCSpeciesSorter, which uses classification, specifically Support Vector Machines (SVM) and the C4.5 decision tree generator, to differentiate sequences. It also implements a secondary BLAST feature to identify known outliers. In the test case presented, a hermatypic coral holobiont, the cnidarian host includes various endosymbionts. The best characterized and most common of these symbionts are zooxanthellae of the genus Symbiodinium. GCSpeciesSorter separates cnidarian from Symbiodinium sequences with a high degree of accuracy. We show that if the GC contents of the species differ enough, this method can be used to accurately distinguish the sequences of different species when using high-throughput sequencing technologies.