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SpliceGrapher: detecting patterns of alternative splicing from RNA-Seq data in the context of gene models and EST data

Mark F Rogers1, Julie Thomas2, Anireddy SN Reddy2 and Asa Ben-Hur13*

Author affiliations

1 Department of Computer Science, 1873 Campus Delivery, Colorado State University, Fort Collins, CO 80523-1873, USA

2 Department of Biology and Program in Molecular Plant Biology, 1878 Campus Delivery, Colorado State University, Fort Collins, CO 80523-1878, USA

3 Department of Statistics, 1877 Campus Delivery, Colorado State University, Fort Collins, CO 80523-1877, USA

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Citation and License

Genome Biology 2012, 13:R4  doi:10.1186/gb-2012-13-1-r4

Published: 31 January 2012

Abstract

We propose a method for predicting splice graphs that enhances curated gene models using evidence from RNA-Seq and EST alignments. Results obtained using RNA-Seq experiments in Arabidopsis thaliana show that predictions made by our SpliceGrapher method are more consistent with current gene models than predictions made by TAU and Cufflinks. Furthermore, analysis of plant and human data indicates that the machine learning approach used by SpliceGrapher is useful for discriminating between real and spurious splice sites, and can improve the reliability of detection of alternative splicing. SpliceGrapher is available for download at http://SpliceGrapher.sf.net webcite.