Homology cluster differential expression analysis for interspecies mRNA-Seq experiments

Gelfond Jonathan A. (), Ibrahim Joseph G., Chen Ming-Hui, Sun Wei, Lewis Kaitlyn, Kinahan Sean, Hibbs Matthew and Buffenstein Rochelle
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Gelfond Jonathan A.: Department of Epidemiology and Biostatistics, UT Health Science Center San Antonio, San Antonio, TX 78229, USA
Ibrahim Joseph G.: Department of Biostatistics, School of Public Health University of North Carolina Chapel Hill, Chapel Hill, NC 27599, USA
Chen Ming-Hui: Department of Statistics, University of Connecticut, Storrs, CT 06269, USA
Sun Wei: Fred Hutchinson Cancer Center, Seattle, WA 98109, USA
Lewis Kaitlyn: Department of Epidemiology and Biostatistics, UT Health Science Center San Antonio, San Antonio, TX 78229, USA
Kinahan Sean: Department of Computer Science, Trinity University, San Antonio, TX 78212, USA
Hibbs Matthew: Department of Computer Science, Trinity University, San Antonio, TX 78212, USA
Buffenstein Rochelle: Calico Labs, San Francisco, CA 94080, USA

Statistical Applications in Genetics and Molecular Biology, 2015, vol. 14, issue 6, 507-516

Abstract: There is an increasing demand for exploration of the transcriptomes of multiple species with extraordinary traits such as the naked-mole rat (NMR). The NMR is remarkable because of its longevity and resistance to developing cancer. It is of scientific interest to understand the molecular mechanisms that impart these traits, and RNA-sequencing experiments with comparator species can correlate transcriptome dynamics with these phenotypes. Comparing transcriptome differences requires a homology mapping of each transcript in one species to transcript(s) within the other. Such mappings are necessary, especially if one species does not have well-annotated genome available. Current approaches for this type of analysis typically identify the best match for each transcript, but the best match analysis ignores the inherent risks of mismatch when there are multiple candidate transcripts with similar homology scores. We present a method that treats the set of homologs from a novel species as a cluster corresponding to a single gene in the reference species, and we compare the cluster-based approach to a conventional best-match analysis in both simulated data and a case study with NMR and mouse tissues. We demonstrate that the cluster-based approach has superior power to detect differential expression.

Keywords: homology; RNA; transcriptome (search for similar items in EconPapers)
Date: 2015
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DOI: 10.1515/sagmb-2014-0056

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