Meta-analysis of Liver and Heart Transcriptomic Data for Functional Annotation Transfer in Mammalian Orthologs
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...
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doaj-7c37ba5799d247249d04d2f1b7978c4e2020-11-24T21:49:18ZengElsevierComputational and Structural Biotechnology Journal2001-03702017-01-0115425432Meta-analysis of Liver and Heart Transcriptomic Data for Functional Annotation Transfer in Mammalian OrthologsPía Francesca Loren Reyes0Tom Michoel1Anagha Joshi2Guillaume Devailly3The Roslin Institute, The University of Edinburgh, Easter Bush, Midlothian, EH25 9RG, Scotland, UKThe Roslin Institute, The University of Edinburgh, Easter Bush, Midlothian, EH25 9RG, Scotland, UKCorresponding authors.; The Roslin Institute, The University of Edinburgh, Easter Bush, Midlothian, EH25 9RG, Scotland, UKCorresponding authors.; The Roslin Institute, The University of Edinburgh, Easter Bush, Midlothian, EH25 9RG, Scotland, UKFunctional 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. Keywords: Gene function, Transcriptomics, Liver, Heart, Orthologs, Paralogs, Co-expression, Gene networkshttp://www.sciencedirect.com/science/article/pii/S2001037017300478 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Pía Francesca Loren Reyes Tom Michoel Anagha Joshi Guillaume Devailly |
spellingShingle |
Pía Francesca Loren Reyes Tom Michoel Anagha Joshi Guillaume Devailly Meta-analysis of Liver and Heart Transcriptomic Data for Functional Annotation Transfer in Mammalian Orthologs Computational and Structural Biotechnology Journal |
author_facet |
Pía Francesca Loren Reyes Tom Michoel Anagha Joshi Guillaume Devailly |
author_sort |
Pía Francesca Loren Reyes |
title |
Meta-analysis of Liver and Heart Transcriptomic Data for Functional Annotation Transfer in Mammalian Orthologs |
title_short |
Meta-analysis of Liver and Heart Transcriptomic Data for Functional Annotation Transfer in Mammalian Orthologs |
title_full |
Meta-analysis of Liver and Heart Transcriptomic Data for Functional Annotation Transfer in Mammalian Orthologs |
title_fullStr |
Meta-analysis of Liver and Heart Transcriptomic Data for Functional Annotation Transfer in Mammalian Orthologs |
title_full_unstemmed |
Meta-analysis of Liver and Heart Transcriptomic Data for Functional Annotation Transfer in Mammalian Orthologs |
title_sort |
meta-analysis of liver and heart transcriptomic data for functional annotation transfer in mammalian orthologs |
publisher |
Elsevier |
series |
Computational and Structural Biotechnology Journal |
issn |
2001-0370 |
publishDate |
2017-01-01 |
description |
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. Keywords: Gene function, Transcriptomics, Liver, Heart, Orthologs, Paralogs, Co-expression, Gene networks |
url |
http://www.sciencedirect.com/science/article/pii/S2001037017300478 |
work_keys_str_mv |
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