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Short description:
Please summarise the application in a few sentences. Avoid links here. We present a novel approach based on large margin learning that combines accurate splice site predictions with common sequence alignment techniques. By solving a convex optimization problem, our algorithm -- called PALMA -- tunes the parameters of the model such that true alignments score higher than other alignments. We study the accuracy of alignments of mRNAs containing artificially generated micro-exons to genomic DNA. In a carefully designed experiment, we show that our algorithm accurately identifies the intron boundaries as well as boundaries of the optimal local alignment. It outperforms all other methods: for 5702 artificially shortened EST sequences from C. elegans and human it correctly identifies the intron boundaries in all except two cases. The best other method is a recently proposed method called exalin which misaligns 37 of the sequences. Our method also demonstrates robustness to mutations, insertions and deletions, retaining accuracy even at high noise levels.
Software version:
Biological application domain(s) (Phylogenetics, Genomics, ...):
RNA-Seq alignment
Principal bioinformatics method(s) (Assembly, Mapping, ...):
Sequence alignment,
Technology (Sanger, Illumina, 454, SOLiD, Ion Torrent, ...):
Interface (Command line, Web UI, Desktop GUI, SOAP WS, HTTP WS, API, QL):
Resource type (Command-line tool, Web application, Desktop application, Script, Suite, Workbench, Database portal, Workflow, Plug-in, Library, Web API, Web service, SPARQL endpoint):
Max Planck Society
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