Fei Deng1, Lusheng Wang1, Xiaowen Liu2. 1. Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong, Department of BioHealth Informatics, Indiana University-Purdue University Indianapolis and Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN 46202, USA. 2. Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong, Department of BioHealth Informatics, Indiana University-Purdue University Indianapolis and Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN 46202, USA Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong, Department of BioHealth Informatics, Indiana University-Purdue University Indianapolis and Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN 46202, USA.
Abstract
MOTIVATION: Tandem mass spectrometry (MS) has become the method of choice for protein identification and quantification. In the era of big data biology, tandem mass spectra are often searched against huge protein databases generated from genomes or RNA-Seq data for peptide identification. However, most existing tools for MS-based peptide identification compare a tandem mass spectrum against all peptides in a database whose molecular masses are similar to the precursor mass of the spectrum, making mass spectral data analysis slow for huge databases. Tag-based methods extract peptide sequence tags from a tandem mass spectrum and use them as a filter to reduce the number of candidate peptides, thus speeding up the database search. Recently, gapped tags have been introduced into mass spectral data analysis because they improve the sensitivity of peptide identification compared with sequence tags. However, the blocked pattern matching (BPM) problem, which is an essential step in gapped tag-based peptide identification, has not been fully solved. RESULTS: In this article, we propose a fast and memory-efficient algorithm for the BPM problem. Experiments on both simulated and real datasets showed that the proposed algorithm achieved high speed and high sensitivity for peptide filtration in peptide identification by database search. CONTACT: cswangl@cityu.edu.hk or xwliu@iupui.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
MOTIVATION: Tandem mass spectrometry (MS) has become the method of choice for protein identification and quantification. In the era of big data biology, tandem mass spectra are often searched against huge protein databases generated from genomes or RNA-Seq data for peptide identification. However, most existing tools for MS-based peptide identification compare a tandem mass spectrum against all peptides in a database whose molecular masses are similar to the precursor mass of the spectrum, making mass spectral data analysis slow for huge databases. Tag-based methods extract peptide sequence tags from a tandem mass spectrum and use them as a filter to reduce the number of candidate peptides, thus speeding up the database search. Recently, gapped tags have been introduced into mass spectral data analysis because they improve the sensitivity of peptide identification compared with sequence tags. However, the blocked pattern matching (BPM) problem, which is an essential step in gapped tag-based peptide identification, has not been fully solved. RESULTS: In this article, we propose a fast and memory-efficient algorithm for the BPM problem. Experiments on both simulated and real datasets showed that the proposed algorithm achieved high speed and high sensitivity for peptide filtration in peptide identification by database search. CONTACT: cswangl@cityu.edu.hk or xwliu@iupui.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.