Homology cluster differential expression analysis for interspecies mRNA-Seq experiments

Jonathan A. Gelfond, Joseph G. Ibrahim, Ming Hui Chen, Wei Sun, Kaitlyn Lewis, Sean Kinahan, Matthew Hibbs, Rochelle Buffenstein

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)507-516
Number of pages10
JournalStatistical Applications in Genetics and Molecular Biology
Issue number6
StatePublished - Dec 1 2015


  • RNA
  • homology
  • transcriptome

ASJC Scopus subject areas

  • Statistics and Probability
  • Molecular Biology
  • Genetics
  • Computational Mathematics


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