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This article is part of the supplement: Ninth International Conference on Bioinformatics (InCoB2010): Computational Biology

Open Access Proceedings

Refining orthologue groups at the transcript level

Yizhen Jia1, Thomas KF Wong2, You-Qiang Song1, Siu-Ming Yiu2* and David K Smith1*

Author affiliations

1 Department of Biochemistry, The University of Hong Kong, Hong Kong

2 Department of Computer Science, The University of Hong Kong, Hong Kong

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Citation and License

BMC Genomics 2010, 11(Suppl 4):S11  doi:10.1186/1471-2164-11-S4-S11

Published: 2 December 2010

Abstract

Background

Orthologues are genes in different species that are related through divergent evolution from a common ancestor and are expected to have similar functions. Many databases have been created to describe orthologous genes based on existing sequence data. However, alternative splicing (in eukaryotes) is usually disregarded in the determination of orthologue groups and the functional consequences of alternative splicing have not been considered. Most multi-exon genes can encode multiple protein isoforms which often have different functions and can be disease-related. Extending the definition of orthologue groups to take account of alternate splicing and the functional differences it causes requires further examination.

Results

A subset of the orthologous gene groups between human and mouse was selected from the InParanoid database for this study. Each orthologue group was divided into sub-clusters, at the transcript level, using a method based on the sequence similarity of the isoforms. Transcript based sub-clusters were verified by functional signatures of the cluster members in the InterPro database. Functional similarity was higher within than between transcript-based sub-clusters of a defined orthologous group. In certain cases, cancer-related isoforms of a gene could be distinguished from other isoforms of the gene. Predictions of intrinsic disorder in protein regions were also correlated with the isoform sub-clusters within an orthologue group.

Conclusions

Sub-clustering of orthologue groups at the transcript level is an important step to more accurately define functionally equivalent orthologue groups. This work appears to be the first effort to refine orthologous groupings of genes based on the consequences of alternative splicing on function. Further investigation and refinement of the methodology to classify and verify isoform sub-clusters is needed, particularly to extend the technique to more distantly related species.