Search

A new method, based on shotgun spectral matching of peptide tandem mass spectra, was successfully applied to the identification of different food species. The method was demonstrated to work on raw as well as processed samples from 16 mammalian and 10 bird species by counting spectral matches to spectral libraries in a reference database with one spectral library per species. A phylogenetic tree could also be constructed directly from the spectra. Nearly all samples could be correctly identified at the species level, and 100% at the genus level. The method does not use any genomic information and unlike targeted methods, no prior knowledge of genetic variation within a genus or species is necessary., (Copyright © 2016 Elsevier Ltd. All rights reserved.)

Data analysis in mass spectrometry based proteomics struggles to keep pace with the advances in instrumentation and the increasing rate of data acquisition. Analyzing this data involves multiple steps requiring diverse software, using different algorithms and data formats. Speed and performance of the mass spectral search engines are continuously improving, although not necessarily as needed to face the challenges of acquired big data. Improving and parallelizing the search algorithms is one possibility; data decomposition presents another, simpler strategy for introducing parallelism. We describe a general method for parallelizing identification of tandem mass spectra using data decomposition that keeps the search engine intact and wraps the parallelization around it. We introduce two algorithms for decomposing mzXML files and recomposing resulting pepXML files. This makes the approach applicable to different search engines, including those relying on sequence databases and those searching spectral libraries. We use cloud computing to deliver the computational power and scientific workflow engines to interface and automate the different processing steps. We show how to leverage these technologies to achieve faster data analysis in proteomics and present three scientific workflows for parallel database as well as spectral library search using our data decomposition programs, X!Tandem and SpectraST.