An Automated Pipeline for High-Throughput Label-Free Quantitative Proteomics

Weisser, Hendrik; Nahnsen, Sven; Grossmann, Jonas; Nilse, Lars; Quandt, Andreas; Brauer, Hendrik; Sturm, Marc; Kenar, Erhan; Kohlbacher, Oliver; Aebersold, Ruedi; Malmström, Lars

doi:10.1021/pr300992u

Cited by 151 publications

(149 citation statements)

References 45 publications

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“…The OpenMS preprocessing workflow includes centroiding, feature finding 27 , precursor correction using the identified features and MS2 denoising as described above (Figure S1). Msconvert was used for converting the raw files to mgf files without any correction.…”

Section: Methodsmentioning

confidence: 99%

In-Search Assignment of Monoisotopic Peaks Improves the Identification of Cross-Linked Peptides

Lenz

Giese

Fischer

et al. 2018

Preprint

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Cross-linking/mass spectrometry (CLMS) has gone through a maturation process similar to standard proteomics by adapting key methods such as false discovery rate control and quantification. However, methods for preprocessing mass spectrometric data of cross-linked peptides are currently underexplored. Preprocessing only requires computational efforts and can significantly improve data quality and search engine results. Our analysis shows that monoisotopic peak selection is a major weakness of current data handling approaches. High precursor masses paired with low intensities, typical of cross-linked peptides, are the main causes of the frequent misassignment of their monoisotopic peaks. We address this by 'in-search selection of the monoisotopic peak' in Xi (Xi-MPS). We compare and evaluate the performance of MaxQuant-Xi, OpenMS-Xi and Xi-MPS on three publicly available datasets. Xi-MPS always delivered the highest number of identifications with ~2 to 4-fold increase of PSMs without compromising identification accuracy as determined by FDR estimation and comparison to crystallographic models.Several approaches have been utilized to increase the numbers of identified cross-links, e.g. enriching for crosslinked peptides 1-3 , using different proteases 1,4 or optimizing fragmentation methods 5,6 . In parallel with experimental developments, data analysis has also progressed. Search software has been designed for the identification of crosslinked peptides, for example Kojak 7 , xQuest 8 , pLink 9 , XlinkX 10 or Xi 11 . In addition, cross-linking workflows can make use of preprocessing methods to improve data quality and reduce file sizes, post-processing to filter out false identifications 7,12 and custom-tailored false discovery rate (FDR) estimation [13][14][15] .Preprocessing steps that improve the data quality for peptide identification can be split into those dealing with each of the two levels of acquisition: correction of the MS1 precursor ion m/z and simplifying MS2 fragment spectra. MS2 preprocessing, e.g. by deisotoping or removing less intense peaks, is search algorithm dependent due to distinct scoring methods on the fragment spectra 16 . Correction of the MS1 precursor ion can include correction of the charge state or m/z value. However, cross-linked peptides have characteristics that may make preprocessing methods used for linear peptide identification workflows nonoptimal: high-charge states, large masses, and low abundances.Several cross-link search engines include preprocessing steps in their pipeline: In pLink, spectral quality filtering and preprocessing of the MS2 spectra is implemented by removing noise peaks. The group also published a tool for correction of monoisotopic peaks 17 , and while it was not specifically designed for cross-link detection, it is implemented in their workflow 18 . The search engine Kojak averages precursor ion signals of neighboring scans to create a composite spectrum and infer the true monoisotopic mass of the precursor. Secondly, Kojak includes processing o...

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Section: Methodsmentioning

confidence: 99%

In-Search Assignment of Monoisotopic Peaks Improves the Identification of Cross-Linked Peptides

Lenz

Giese

Fischer

et al. 2018

Preprint

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“…Case studies Some recent examples where researchers have used OpenMS to build complex analysis tools include improvements to a label-free quantification pipeline [1] , the development of a metabolomics workflow [33] , the integration of RNA cross-linking workflows [34] and the addition of a workflow for targeted data analysis using SWATH-MS [35] . In addition, OpenMS has been extended to include a probabilistic scoring engine [36] , SILAC analysis [37] , isobaric quantification, random-access XML file parsing [38] and an MS simulation framework [39] .…”

Section: Reproducible Researchmentioning

confidence: 99%

“…This flexibility usually calls for complex, multi-step analysis workflows with inter-dependent steps that need to be tailored to the experiment at hand. For example, in a discovery proteomics pipeline, the workflow steps include file conversion, centroiding, database searching, feature detection, retention time alignment, cross-run feature linking, protein inference, and statistical error control at various steps of the process [1] .…”

mentioning

confidence: 99%

OpenMS: a flexible open-source software platform for mass spectrometry data analysis

et al. 2016

Self Cite

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“…The lowered rate of converting identified peptides into those capable of being quantified with shotgun acquisition has been previously reported. [25] In this dataset, 62.7% of the shotgun sequenced peptides were matched to MS1 EICs. In contrast, 94.2% of peptides detected by the MS/MS database searches were able to be matched to a minimum of five fragment-ions in the most sensitive DIA method using 6 m/z isolation windows to analyze the 550-830 precursor m/z range.…”

Section: Evaluation Of 3 6 and 26 M/z Mass-isolation Windowsmentioning

confidence: 99%

Systematic evaluation of data‐independent acquisition for sensitive and reproducible proteomics‐a prototype design for a single injection assay

Heaven¹,

Funk²,

Cobbs³

et al. 2016

J. Mass Spectrom.

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Data-independent acquisition (DIA) based proteomics has become increasingly complicated in recent years due to the vast number of workflows described, coupled with a lack of studies indicating a rational framework for selecting effective settings to use. To address this issue and provide a resource for the proteomics community, we compared twelve DIA methods that assay tryptic peptides using various mass-isolation windows. Our findings indicate the most sensitive single injection LC-DIA method uses 6 m/z isolation windows to analyze the densely populated tryptic peptide range from 450-730 m/z, which allowed quantification of 4,465 E. coli peptides. In contrast, using the sequential windowed acquisition of all theoretical fragment-ions (SWATH) approach with 26 m/z isolation windows across the entire 400-1200 m/z range, allowed quantification of only 3,309 peptides. This reduced sensitivity with 26 m/z windows is caused by an increase in co-eluting compounds with similar precursor values detected in the same tandem MS spectra, which lowers the signal-to-noise of peptide fragment-ion chromatograms and reduces the amount of low abundance peptides that can be quantified from 410-920 m/z. Above 920 m/z, more peptides were quantified with 26 m/z windows due to substantial peptide 13 C isotope distributions that parse peptide ions into separate isolation windows. Since reproducible quantification has been a long-standing aim of quantitative proteomics, and is a so-called trait of DIA, we sought to determine whether precursor-level chromatograms used in some methods rather than their fragment-level counterparts have similar precision. Our data show extracted fragmention chromatograms are the reason DIA provides superior reproducibility.

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An Automated Pipeline for High-Throughput Label-Free Quantitative Proteomics

Cited by 151 publications

References 45 publications

In-Search Assignment of Monoisotopic Peaks Improves the Identification of Cross-Linked Peptides

In-Search Assignment of Monoisotopic Peaks Improves the Identification of Cross-Linked Peptides

OpenMS: a flexible open-source software platform for mass spectrometry data analysis

Systematic evaluation of data‐independent acquisition for sensitive and reproducible proteomics‐a prototype design for a single injection assay

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