Skip to main content

SpliceGrapher

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3334585/figure/F1/

Methods

linear single view single scale single focus segregated partial abstraction no arrangement no interconnection

Tool

Paper

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


Rogers MF, Thomas J, Reddy AS, Ben-Hur A. SpliceGrapher: detecting patterns of alternative splicing from RNA-Seq data in the context of gene models and EST data. Genome Biol. genomebiology.biomedcentral.com; 2012;13: R4.

Cited by: 103

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.