Meta-analysis of Liver and Heart Transcriptomic Data for Functional Annotation Transfer in Mammalian Orthologs

Pía Francesca Loren Reyes, Tom Michoel, Anagha Joshi*, Guillaume Devailly

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

Functional annotation transfer across multi-gene family orthologs can lead to functional misannotations. We hypothesised that co-expression network will help predict functional orthologs amongst complex homologous gene families. To explore the use of transcriptomic data available in public domain to identify functionally equivalent ones from all predicted orthologs, we collected genome wide expression data in mouse and rat liver from over 1500 experiments with varied treatments. We used a hyper-graph clustering method to identify clusters of orthologous genes co-expressed in both mouse and rat. We validated these clusters by analysing expression profiles in each species separately, and demonstrating a high overlap. We then focused on genes in 18 homology groups with one-to-many or many-to-many relationships between two species, to discriminate between functionally equivalent and non-equivalent orthologs. Finally, we further applied our method by collecting heart transcriptomic data (over 1400 experiments) in rat and mouse to validate the method in an independent tissue.

Original languageEnglish
Pages (from-to)425-432
Number of pages8
JournalComputational and Structural Biotechnology Journal
Early online date26 Aug 2017
Publication statusE-pub ahead of print - 26 Aug 2017

Keywords / Materials (for Non-textual outputs)

  • Co-expression
  • Gene function
  • Gene networks
  • Heart
  • Liver
  • Orthologs
  • Paralogs
  • Transcriptomics


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