ABSTRACT
It has long been known that biological species can be identified from mass spectrometry data alone. Ten years ago, we described a method and software tool, compareMS2, for calculating a distance between sets of tandem mass spectra, as routinely collected in proteomics. This method has seen use in species identification and mixture characterization in food and feed products, as well as other applications. Here, we present the first major update of this software, including a new metric, a graphical user interface and additional functionality. The data have been deposited to ProteomeXchange with dataset identifier PXD034932.
Subject(s)
Software , Tandem Mass Spectrometry , Tandem Mass Spectrometry/methods , Proteomics/methods , AlgorithmsABSTRACT
SUMMARY: In mass spectrometry-based proteomics, accurate peptide masses improve identifications, alignment and quantitation. Getting the most out of any instrument therefore requires proper calibration. Here, we present a new stand-alone software, mzRecal, for universal automatic recalibration of data from all common mass analyzers using standard open formats and based on physical principles. AVAILABILITY AND IMPLEMENTATION: mzRecal is implemented in Go and freely available on https://github.com/524D/mzRecal. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
ABSTRACT
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.
