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Bioinformatics Advance Access published online on May 30, 2007
Bioinformatics, doi:10.1093/bioinformatics/btm275
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© 2007 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

PALMA: mRNA to Genome Alignments using Large Margin Algorithms

Uta Schulze 1,2,*, Bettina Hepp 3,*, Cheng Soon Ong 1,4 and Gunnars Räetsch 1,{dagger}

1 Friedrich Miescher Laboratory, Max Planck Society, Spemannstr. 39, 72076 Tübingen, Germany
2 University of Leipzig, Augustusplatz 10/11, 04109 Leipzig, Germany
3 Fraunhofer FIRST, Kekulèstr. 7, 12489 Berlin, Germany
4 Max Planck Institute for Biological Cybernetics, Spemannstr. 38, 72076 Tübingen, Germany

{dagger}To whom correspondence should be addressed. Gunnar Rätsch, E-mail: Gunnar.Raetsch{at}tuebingen.mpg.de


  Abstract

Motivation: Despite many years of research on how to properly align sequences in the presence of sequencing errors, alternative splicing and micro-exons, the correct alignment of mRNA sequences to genomic DNA is still a challenging task.

Results: We present a novel approach based on large margin learning that combines accurate plice 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.

Availability: Datasets for training, evaluation and testing, additional results and a stand-alone alignment tool implemented in C++ and python are available at http://www.fml.mpg.de/raetsch/projects/palma.

Associate Editor: Dr. GCBReview2006 Issue

* authors contributed equally

Received on November 9, 2006; revised on May 16, 2007; accepted on May 16, 2007

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