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PALMA: Perfect Alignments using Large Margin Algorithms

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons84153

Rätsch,  G
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons84118

Ong,  CS
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Zitation

Rätsch, G., Hepp B, Schulze, U., & Ong, C. (2006). PALMA: Perfect Alignments using Large Margin Algorithms. Proceedings of the German Conference on Bioinformatics 2006 (GCB 2006), 104-113.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0013-D04D-D
Zusammenfassung
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. We present a novel approach based on large margin learning that combines kernel based 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 the true alignment scores higher than all other alignments. In an experimental study on the alignments of mRNAs containing artificially generated micro-exons, we show that our algorithm drastically outperforms all other methods: It perfectly aligns all 4358 sequences on an hold-out set, while the best other method misaligns at least 90 of them. Moreover, our algorithm is very robust against noise in the query sequence: when deleting, inserting, or mutating up to 50 of the query sequence, it still aligns 95 of all sequences correctly, while other methods achieve less than 36 accuracy. For datasets, additional results and a stand-alone alignment tool see http://www.fml.mpg.de/raetsch/projects/palma.