title: SpliceGrapher: detecting patterns of alternative splicing from RNA-Seq data in the context of gene models and EST data.
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.
authors.affiliation: Department of Computer Science, 1873 Campus Delivery, Colorado State University, Fort Collins, CO 80523-1873, USA.
authors.forename: Mark F
authors.initials: MF
authors.lastname: Rogers
authors.forename: Julie
authors.initials: J
authors.lastname: Thomas
authors.forename: Anireddy Sn
authors.initials: AS
authors.lastname: Reddy
authors.forename: Asa
authors.initials: A
authors.lastname: Ben-Hur
mesh_terms: D000465:Algorithms; D017398:Alternative Splicing / Q000235:genetics*; D017360:Arabidopsis / Q000235:genetics*; D019295:Computational Biology / Q000379:methods; D030561:Databases, Nucleic Acid; D020224:Expressed Sequence Tags; D015894:Genome, Human; D006801:Humans; D022821:RNA Splice Sites / Q000235:genetics; D012984:Software
chemical_list: D022821:RNA Splice Sites