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Bioinformatics Advance Access originally published online on May 30, 2007
Bioinformatics 2007 23(15):1892-1900; 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,{dagger}, Bettina Hepp 3,{dagger}, Cheng Soon Ong 4,1 and Gunnar Rätsch 1,*

1Friedrich Miescher Laboratory, Max Planck Society, Spemannstr. 39, 72076 Tübingen, Germany, 2University of Leipzig, Johannisgasse 26, 04103 Leipzig, Germany, 3Fraunhofer FIRST, Kekulèstr. 7, 12489 Berlin, Germany, and 4Max Planck Institute for Biological Cybernetics, Spemannstr. 38, 72076 Tübingen, Germany

*To whom correspondence should be addressed.


  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 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 Caenorhabditis 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

Contact: Gunnar.Raetsch{at}tuebingen.mpg.de

Supplementary information: Supplementary data are available at Bioinformatics online.

Associate Editor: Dmitrij Frishman

{dagger}The authors wish it to be known that, in their opinion, the first two authors should be regarded as joint First Authors.

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

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