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

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