RT Journal Article
SR Electronic
T1 Meta-analysis of liver transcriptomic data to identify mammalian functional orthologs
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 123414
DO 10.1101/123414
A1 Pía Francesca Loren Reyes
A1 Tom Michoel
A1 Anagha Joshi
A1 Guillaume Devailly
YR 2017
UL http://biorxiv.org/content/early/2017/04/03/123414.abstract
AB Identifying which orthologs share functions from sequence alone can be challenging, notably in case of paralogous genes families. 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 predict functionally equivalent 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.