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Updated: May 25, 2026

Using RNA-sequencing to Detect Novel Splice Variants Related to Drug Resistance in In Vitro Cancer Models
Published on: December 9, 2016
SpliceGrapher: detecting patterns of alternative splicing from RNA-Seq data in the context of gene models and EST
Mark F Rogers1, Julie Thomas, Anireddy Sn Reddy
1Department of Computer Science, 1873 Campus Delivery, Colorado State University, Fort Collins, CO 80523-1873, USA.
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.
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