Summary: Data-dependent acquisition (DDA) is the most common method used to control the acquisition process of shotgun proteomics experiments. While novel DDA approaches have been proposed, their evaluation is made difficult by the need of programmatic control of a mass spectrometer. An alternative is in silico analysis, for which suitable software has been unavailable. To meet this need, we have developed MSAcquisitionSimulator—a collection of C ++ programs for simulating ground truth LC-MS data and the subsequent application of custom DDA algorithms. It provides an opportunity for researchers to test, refine and evaluate novel DDA algorithms prior to implementation on a mass spectrometer

Goldfarb, Dennis

Major, Michael B.

Wang, Wei

English

PubMed

Carolina Digital Repository

Data and text miningMSAcquisitionSimulator: data-dependentacquisition simulator for LC-MS shotgunproteomicsDennis Goldfarb1, Wei Wang2,* and Michael B. Major1,3,*1Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA, 2Departmentof Computer Science, University of California, Los Angeles, CA, USA and 3Department of Cell Biology andPhysiology, Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill,NC, USA*To whom correspondence should be addressed.Associate Editor: Jonathan WrenReceived on 29 September 2015; revised on 1 December 2015; accepted on 14 December 2015AbstractSummary: Data-dependent acquisition (DDA) is the most common method used to control the ac-quisition process of shotgun proteomics experiments. While novel DDA approaches have beenproposed, their evaluation is made difficult by the need of programmatic control of a mass spec-trometer. An alternative is in silico analysis, for which suitable software has been unavailable. Tomeet this need, we have developed MSAcquisitionSimulator—a collection of Cþþ programs forsimulating ground truth LC-MS data and the subsequent application of custom DDA algorithms. Itprovides an opportunity for researchers to test, refine and evaluate novel DDA algorithms prior toimplementation on a mass spectrometer.Availability and implementation: The software is freely available from its Github repository http://www.github.com/DennisGoldfarb/MSAcquisitionSimulator/ which contains further documentationand usage instructions.Contact: weiwang@cs.ucla.edu or ben_major@med.unc.eduSupplementary information: Supplementary data are available at Bioinformatics online.1 IntroductionMass spectrometry is an analytical technique used in proteomics toidentify and quantify proteins. Many of its applications share theunderlying goal of uncovering the full protein complement in a bio-logical sample. However, for complex samples this is rarely achievedpartly because the number of ion populations exceeds that whichcontemporary instruments can individually target for sequence ana-lysis with a tandem mass spectrum scan (MS/MS) (Michalski et al.,2011). Acquisition algorithms are necessary to control the data ac-quisition process and manage the limited scan speed.Data-dependent acquisition (DDA) constitutes a major class ofdata acquisition methods. DDA methods perform a precursor scan todetermine the mass-to-charge ratio (m/z) and abundance of ionscurrently entering the mass spectrometer, followed by sequencedetermining MS/MS scans on ions from a subset of detected peaks.The standard DDA algorithm, TopN, selects ions for MS/MS scansthat contributed to peaks of greatest signal intensity from the latestprecursor scan. Several other approaches, as well as adjustments toTopN, have been proposed in order to increase peptide and proteinidentifications (Graumann et al., 2012; Liu et al., 2012; Rudominet al., 2009; Scherl et al., 2004; Zerck et al., 2013). However, TopNcontinues to be the dominant choice for acquisition control despite itsbias toward abundant proteins and relatively poor reproducibility.Currently, evaluation of novel acquisition strategies requires ac-cess to both a mass spectrometer and its application programminginterface (API). Unfortunately, few instrument vendors provide anAPI, and therefore the pool of researchers with the necessary tools toexplore this field is extremely limited. An alternative is to evaluateVC The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com 1269Bioinformatics, 32(8), 2016, 1269–1271doi: 10.1093/bioinformatics/btv745Advance Access Publication Date: 17 December 2015Applications Notemethods with in silico simulations. Existing simulator software formass spectrometry proteomics has focused on generating ground truthdata and realistic signals for precursor and MS/MS scans (Bielowet al., 2011; Noyce et al., 2013; Schulz-Trieglaff et al., 2008; Smithet al., 2015). However, the size of the simulations is limited due tospeed, memory and/or disk space requirements. Most importantly,the simulated MS/MS spectra lead to nearly perfect peptide-spectrum-matches (PSMs) when analyzed with existing database search algo-rithms. The reasons for this include the absence of co-fragmentationfrom neighboring ions within an isolation window, the difficulty ofpredicting fragmentation patterns, and potentially other not yetunderstood phenomena. This limitation makes evaluating any acquisi-tion strategy impractical as the metrics for success are based on thenumber of confident peptide and protein identifications.Here, we present an acquisition simulator that produces PSMs withrealistic peptide sequence and probability assignments for DDA strat-egies by foregoing fragmentation simulation and instead directly gener-ating PSMs based on the precursor ion fractions of MS/MS scans. Itbuilds upon previous work on LC-MS simulations and scales to largerdatasets due to probabilistic models of ion generation and subsequentpruning of rare ions. This allows for an increased number of proteins,peptides and post-translational modifications that can be simulated.2 MethodsMSAcquisitionSimulator consists of three standalone command-lineprograms. The first, FASTASampler, assists users in creating aFASTA file containing the proteins to be included in the simulation.Users select a protein FASTA file, the distribution of protein abun-dance and the number of proteins to be sampled. The output con-tains a random subset of proteins with each header appended with a‘#’ followed by the protein’s abundance.Next, GroundTruthSimulator uses the previously createdFASTA file and a configuration file containing simulation param-eters to simulate digestion, residue modifications, retention time,chromatographic elution profiles, ionization efficiency and chargeand isotope distributions. It outputs the tab delimited ground truthdata on the generated ions, which will be used for testing acquisitionalgorithms. In contrast to previous simulators, there are no limits onmissed cleavages, enzymatic termini or dynamic modifications. Aprobabilistic approach is taken instead and rare peptides below auser-defined threshold are efficiently pruned. Details on the prob-ability calculations are in the Supplementary methods.Finally, the AcquisitionSimulator program takes as input the pre-viously generated ground truth file and another configuration file.This program simulates a user-defined DDA algorithm on the groundtruth data. It models ion accumulation, precursor scan spectra, scantime durations and database search PSMs (Supplementary Fig. S1).Currently, MS/MS fragmentation spectra are not simulated. Instead,the PSMs are generated by sampling from the list of precursorsisolated in an MS/MS scan and candidate peptides from a databasesearch. This approach leads to true positives, false positives and re-verse decoy matches similar to real experiments (Supplementary Figs.S2–S4 ). Ion accumulation is simulated by numerically integrating anion’s elution profile. Ion transmission rates are not modeled and areassumed to be 100%. The elapsed time for a scan is equal to the scanoverhead time plus the larger of either the injection time or the transi-ent time. This models the scan time for a QExactive-like instrument.The output includes an mzML file and a PSM graph file, which isused as input for the Fido protein inference algorithm (Serang et al.,2010). Speed and memory usage comparisons with existing simula-tion software are provided in Supplementary Figure S5.3 Case studyTo demonstrate the utility of MSAcquisitionSimulator, we evaluatedthree simple DDA algorithms—TopN, RandomN and WeightedN.TopN selects the most abundant precursor peaks for MS/MS scans,RandomN samples from a uniform random distribution of theobserved peaks and WeightedN samples from a random distributionweighted by observed peak intensity. Dynamic exclusion was enabled,and precursor scans were de-isotoped prior to MS/MS selection deci-sions. FASTASampler was executed on the human proteome providedby UniProtKB using a lognormal abundance distribution and 50% ofthe proteins, resulting in 45 809 protein sequences. Default configur-ation files were used with both GroundTruthSimulator andAcquisitionSimulator. TopN resulted in the greatest number of confi-dent protein and peptide identifications, closely followed byWeightedN, and RandomN provided far fewer protein identifications(Fig. 1). RandomN’s poor performance stemmed from the challengesin targeting low abundant ions. The wide MS/MS isolation window of2 m/z captured neighboring abundant ions and created spectra domi-nated by peptides whose monoisotopic mass fell outside the small pre-cursor mass tolerance used to simulate the database search. Inaddition, fewer scans were performed due to increased injection time.4 ConclusionDDA simulation will assist in the development and assessment ofnovel methods. The next generation of algorithms will likely furtherintegrate data generation with data analysis, such as real-time pep-tide sequencing and protein inference. They may also become moregoal-oriented, seeking to identify subsets of proteins, specific modifi-cations or to improve quantification. Their sophistication may alsocome at a computational cost too great for their implementation oncontemporary mass spectrometers. For such a scenario,MSAcquisitionSimulator will be a great tool for their evaluation.AcknowledgementsWe would like to thank Leonard McMillan for useful discussions and mem-bers of the Major Lab for proofreading the manuscript.FundingThis work has been supported by the National Cancer Institute [Grant num-ber R21-CA178760-01].Conflict of Interest: none declared.0100020003000010002000300001000200030000.5 0.6 0.7 0.8 0.9 1.0protein probabilityNumber of proteinsTopNRandomNWeightedNFig. 1. Distribution of protein probabilities using various DDA algorithms onsimulated data. Fido was used for protein inference with parameters alpha ¼0.1, beta ¼ 0.01 and gamma ¼ 0.51270 D.Goldfarb et al.ReferencesBielow,C. et al. (2011) MSSimulator: simulation of mass spectrometry data. J.Proteome Res., 10, 2922–2929.Graumann,J. et al. (2012) A framework for intelligent data acquisition andreal-time database searching for shotgun proteomics. Mol. Cell Proteomics,11, M111.013185.Liu,H. et al. (2012) Automated iterative MS/MS acquisition: a tool for im-proving efficiency of protein identification using a LC-MALDI MS work-flow. Anal. Chem., 83, 6286–6293.Michalski,A. et al. (2011) More than 100,000 detectable peptide species elutein single shotgun proteomics runs but the majority is inaccessible to data-dependent LC-MS/MS. J. Proteome Res., 10, 1785–1793.Noyce,A.B. et al. (2013) Mspire-Simulator: LC-MS shotgun proteomic simula-tor for creating realistic gold standard data. J. Proteome Res., 12, 5742–5749.Rudomin,E.L. et al. (2009) Directed sample interrogation utilizing an accuratemass exclusion-based data-dependent acquisition strategy (AMEx).J. Proteome Res., 8, 3154–3160.Scherl,A. et al. (2004) Nonredundant mass spectrometry: a strategy to inte-grate mass spectrometry acquisition and analysis. Proteomics, 4,917–927.Schulz-Trieglaff,O. et al. (2008) LC-MSsim–a simulation software for liquidchromatography mass spectrometry data. BMC Bioinformatics, 9, 423.Serang,O. et al. (2010) Efficient marginalization to compute protein posteriorprobabilities from shotgun mass spectrometry data. J. Proteome Res., 9,5345–5357.Smith,R. et al. (2015) JAMSS: proteomics mass spectrometry simulation inJava. Bioinformatics, 31, 791–793.Zerck,A. et al. (2013) Optimal precursor ion selection for LC-MALDI MS/MS. BMC Bioinformatics, 14, 56.MSAcquisitionSimulator 1271

MSAcquisitionSimulator: data-dependent acquisition simulator for LC-MS shotgun proteomics

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MSAcquisitionSimulator: data-dependent acquisition simulator for LC-MS shotgun proteomics

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