MaxQuant goes Linux

Sinitcyn, Pavel; Tiwary, Shivani; Rudolph, Jan Daniel; Gutenbrunner, Petra; Wichmann, Christoph; Yılmaz, Şule; Hamzeiy, Hamid; Salinas, Favio; Cox, Jürgen

doi:10.1038/s41592-018-0018-y

Cited by 47 publications

(42 citation statements)

References 3 publications

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“…MaxQuant achieves high peptide mass accuracies thanks to its advanced nonlinear recalibration algorithms (21,22). It contains comfortable visualization capabilities (23) for the inspection of the raw data and runs on Windows and Linux operating systems (24).…”

Section: Introductionmentioning

confidence: 99%

MaxQuant software for ion mobility enhanced shotgun proteomics

Prianichnikov

Koch

et al. 2019

Preprint

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Ion mobility can add a dimension to LC-MS based shotgun proteomics which has the potential to boost proteome coverage, quantification accuracy and dynamic range. Required for this is suitable software that extracts the information contained in the fourdimensional (4D) data space spanned by m/z, retention time, ion mobility and signal intensity. Here we describe the ion mobility enhanced MaxQuant software, which utilizes the added data dimension. It offers an end to end computational workflow for the identification and quantification of peptides, proteins and posttranslational modification sites in LC-IMS-MS/MS shotgun proteomics data. We apply it to trapped ion mobility spectrometry (TIMS) coupled to a quadrupole time-of-flight (QTOF) analyzer. A highly parallelizable 4D feature detection algorithm extracts peaks which are assembled to isotope patterns. Masses are recalibrated with a non-linear m/z, retention time, ion mobility and signal intensity dependent model, based on peptides from the sample. A new matching between runs (MBR) algorithm that utilizes collisional cross section (CCS) values of MS1 features in the matching process significantly gains specificity from the extra dimension. Prerequisite for using CCS values in MBR is a relative alignment of the ion mobility values between the runs. The missing value problem in protein quantification over many samples is greatly reduced by CCS aware MBR.MS1 level label-free quantification is also implemented which proves to be highly precise and accurate on a benchmark dataset with known ground truth. MaxQuant for LC-IMS-MS/MS is part of the basic MaxQuant release and can be downloaded from http://maxquant.org. 3

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Section: Introductionmentioning

confidence: 99%

MaxQuant software for ion mobility enhanced shotgun proteomics

Prianichnikov

Koch

et al. 2019

Preprint

Self Cite

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“…Both Quandenser and MaxQuant were run using all 4 cores as well. For MaxQuant, we used Mono on Linux, which was reported to be at least as fast as MaxQuant on Windows [36]. On the tested datasets, running Quandenser was typically about twice as fast as MaxQuant, with respective runtimes in the range of 1−3 hours compared to 2−5.5 hours.…”

Section: Resultsmentioning

confidence: 99%

Focus on the spectra that matter by clustering of quantification data in shotgun proteomics

Käll

2018

Preprint

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In shotgun proteomics, the information extractable from label-free quantification experiments is typically limited by the identification rate and the noise level in the quantitative data. This generally causes a low sensitivity in differential expression analysis on protein level. Here, we propose a quantification-first approach for peptides that reverses the classical identification-first workflow. This prevents valuable information from being discarded prematurely in the identification stage and allows us to spend more effort on the identification process. Specifically, we introduce a method, Quandenser, that applies unsupervised clustering on both MS1 and MS2 level to summarize all analytes of interest without assigning identities. Not only does this eliminate the need for redoing the quantification for each new set of search parameters and engines, but it also reduces search time due to the data reduction by MS2 clustering. For a dataset of partially known composition, we could now employ open modification and de novo searches to identify analytes of interest that would have gone unnoticed in traditional pipelines. Moreover, Quandenser reports error rates for feature matching, which we integrated into our probabilistic protein quantification method, Triqler. This propagates error probabilities from feature to protein level and appropriately deals with the noise in quantitative signals caused by false positives and missing values. Quandenser+Triqler outperformed the state-of-the-art method MaxQuant+Perseus, consistently reporting more differentially abundant proteins at 5% FDR: 123 vs. 117 true positives with 2 vs. 25 false positives in a dataset of partially known composition; 62 vs. 3 proteins in a bladder cancer set; 8 vs. 0 proteins in a hepatic fibrosis set; and 872 vs. 661 proteins in a nanoscale type 1 diabetes set. Compellingly, in all three clinical datasets investigated, the differentially abundant proteins showed enrichment for functional annotation terms.The source code and binary packages for all major operating systems are available from https: //github.com/statisticalbiotechnology/quandenser, under Apache 2.0 license.

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“…[28] The collaborative open-source project Trans-Proteomic Pipeline (TPP) [29] allows integration of search results including Comet, OMSSA, X!Tandem, and SEQUEST. MaxQuant [30] identifies peptides via detecting mass and intensity of the peptide peaks assembled into 3D hills over the m/z retention time plane, filtered by a graph theory algorithm to identify isotope patterns. OMSSA [31] and MS GF+ [32] are also widely used since their release.…”

Section: Tools For Searching and Building Librariesmentioning

confidence: 99%

Data‐Independent Acquisition Mass Spectrometry‐Based Proteomics and Software Tools: A Glimpse in 2020

et al. 2020

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This review provides a brief overview of the development of data‐independent acquisition (DIA) mass spectrometry‐based proteomics and selected DIA data analysis tools. Various DIA acquisition schemes for proteomics are summarized first including Shotgun‐CID, DIA, MSE, PAcIFIC, AIF, SWATH, MSX, SONAR, WiSIM, BoxCar, Scanning SWATH, diaPASEF, and PulseDIA, as well as the mass spectrometers enabling these methods. Next, the software tools for DIA data analysis are classified into three groups: library‐based tools, library‐free tools, and statistical validation tools. The approaches are reviewed for generating spectral libraries for six selected library‐based DIA data analysis software tools which are tested by the authors, including OpenSWATH, Spectronaut, Skyline, PeakView, DIA‐NN, and EncyclopeDIA. An increasing number of library‐free DIA data analysis tools are developed including DIA‐Umpire, Group‐DIA, PECAN, PEAKS, which facilitate identification of novel proteoforms. The authors share their user experience of when to use DIA‐MS, and several selected DIA data analysis software tools. Finally, the state of the art DIA mass spectrometry and software tools, and the authors’ views of future directions are summarized.

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MaxQuant goes Linux

Cited by 47 publications

References 3 publications

MaxQuant software for ion mobility enhanced shotgun proteomics

MaxQuant software for ion mobility enhanced shotgun proteomics

Focus on the spectra that matter by clustering of quantification data in shotgun proteomics

Data‐Independent Acquisition Mass Spectrometry‐Based Proteomics and Software Tools: A Glimpse in 2020

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