PALMA: mRNA to genome alignments using large margin algorithms

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

[1]  D. Church,et al.  Spidey: a tool for mRNA-to-genomic alignments. , 2001, Genome research.

[2]  Thorsten Joachims,et al.  Learning to Align Sequences: A Maximum-Margin Approach , 2006 .

[3]  Gunnar Rätsch,et al.  An introduction to kernel-based learning algorithms , 2001, IEEE Trans. Neural Networks.

[4]  Wei Zhu,et al.  Optimal spliced alignment of homologous cDNA to a genomic DNA template , 2000, Bioinform..

[5]  Gunnar Rätsch,et al.  Improving the Caenorhabditis elegans Genome Annotation Using Machine Learning , 2006, PLoS Comput. Biol..

[6]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1995, EuroCOLT.

[7]  Steven L Salzberg,et al.  Computational discovery of internal micro-exons. , 2003, Genome research.

[8]  Gunnar Rätsch,et al.  New Methods for Splice Site Recognition , 2002, ICANN.

[9]  Dan Gusfield,et al.  Parametric optimization of sequence alignment , 1992, SODA '92.

[10]  Tamar Schlick,et al.  New Algorithms for Macromolecular Simulation , 2006 .

[11]  Thomas Hofmann,et al.  Hidden Markov Support Vector Machines , 2003, ICML.

[12]  M. Boguski,et al.  dbEST — database for “expressed sequence tags” , 1993, Nature Genetics.

[13]  Miao Zhang,et al.  Improved spliced alignment from an information theoretic approach , 2006, Bioinform..

[14]  P. Pevzner,et al.  Gene recognition via spliced sequence alignment. , 1996, Proceedings of the National Academy of Sciences of the United States of America.

[15]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[16]  G. Rubin,et al.  A computer program for aligning a cDNA sequence with a genomic DNA sequence. , 1998, Genome research.

[17]  Jennifer Daub,et al.  Expressed sequence tags: medium-throughput protocols. , 2004, Methods in molecular biology.

[18]  Kenneth O. Kortanek,et al.  Semi-Infinite Programming: Theory, Methods, and Applications , 1993, SIAM Rev..

[19]  P. V. von Hippel,et al.  Selection of DNA binding sites by regulatory proteins. , 1988, Trends in biochemical sciences.

[20]  John D. Kececioglu,et al.  Simple and Fast Inverse Alignment , 2006, RECOMB.

[21]  Gunnar Rätsch,et al.  Advanced Lectures on Machine Learning , 2004, Lecture Notes in Computer Science.

[22]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[23]  Gunnar Rätsch,et al.  Learning Interpretable SVMs for Biological Sequence Classification , 2005, BMC Bioinformatics.

[24]  Gunnar Rätsch,et al.  An Introduction to Boosting and Leveraging , 2002, Machine Learning Summer School.

[25]  Kimberly Van Auken,et al.  WormBase: a multi-species resource for nematode biology and genomics , 2004, Nucleic Acids Res..

[26]  Sean R. Eddy,et al.  Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids , 1998 .

[27]  P. V. von Hippel,et al.  Selection of DNA binding sites by regulatory proteins. Statistical-mechanical theory and application to operators and promoters. , 1987, Journal of molecular biology.

[28]  W. J. Kent,et al.  BLAT--the BLAST-like alignment tool. , 2002, Genome research.

[29]  Gunnar Rätsch,et al.  PALMA: Perfect Alignments using Large Margin Algorithms , 2006, German Conference on Bioinformatics.

[30]  Gunnar Rätsch,et al.  RASE: recognition of alternatively spliced exons in C.elegans , 2005, ISMB.

[31]  G. Stormo Computer methods for analyzing sequence recognition of nucleic acids. , 1988, Annual Review of Biophysics and Biophysical Chemistry.

[32]  E. Myers,et al.  Basic local alignment search tool. , 1990, Journal of molecular biology.