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This article is part of the supplement: UT-ORNL-KBRIN Bioinformatics Summit 2012

Open Access Meeting abstract

Finding long protein products of alternatively spliced genes

Neil Moore1, Jerzy W Jaromczyk1* and Christopher L Schardl2

Author Affiliations

1 Department of Computer Science, University of Kentucky, Lexington, KY 40506, USA

2 Department of Plant Pathology, University of Kentucky, Lexington, KY 40506, USA

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BMC Bioinformatics 2012, 13(Suppl 12):A12  doi:10.1186/1471-2105-13-S12-A12


The electronic version of this article is the complete one and can be found online at: http://www.biomedcentral.com/1471-2105/13/S12/A12


Published:31 July 2012

© 2012 Moore et al; licensee BioMed Central Ltd.

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Background

In eukaryotes, pre-mRNA molecules undergo splicing, which is the removal of sequences called introns to produce mature mRNA transcripts whose open reading frames (ORFs) may then be translated to proteins. Often this splicing step may be performed in many ways - a situation known as alternative splicing [1,2] that can be described by structures such as splice graphs [3]. Alternative splicings, even those that differ only slightly, may result in proteins with substantially different biological properties [4,5].

Materials and methods

We have developed an algorithm for finding the longest ORFs of alternatively spliced transcripts described by splice graphs. Our algorithm executes in time linear in the size of the splice graph (and therefore optimal), determining the splicings that result in an open reading frame encoding a maximal-length protein for that gene. We show how our algorithm may be used to help identify biologically interesting protein products from RNA-seq data.

Acknowledgements

This work was partially supported by NIGMS Grant 1R01GM086888-01, Kentucky NSF-EPSCoR Grant 0814194, NSF Grant EF-0523661, and USDA-NRA Grant 2005-35319-16141.

An earlier version of this work [6] was presented at presented at the Workshop on Integrative Data Analysis in Systems Biology in 2011.

References

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