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

Abstract：

: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.

journal_name

Comput Struct Biotechnol J

journal_title

Computational and structural biotechnology journal

authors

Reyes PFL,Michoel T,Joshi A,Devailly G

doi

10.1016/j.csbj.2017.08.002

subject

Has Abstract

pub_date

2017-08-26 00:00:00

pages

425-432

issn

2001-0370

pii

S2001-0370(17)30047-8

journal_volume

15

pub_type

杂志文章

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