MSPLIT-DIA: sensitive peptide identification for data-independent acquisition

Jian, Wang; Tucholska, Monika; Knight, James D.R.; Lambert, Jean-Philippe; Tate, Stephen; Larsen, Brett; Gingras, Anne‐Claude; Bandeira, Nuno

doi:10.1038/nmeth.3655

Cited by 121 publications

(152 citation statements)

References 7 publications

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“…Data analysis of uDIA can be analyzed in one of two general ways, targeted or untargeted. Targeted data analysis relies on pre-assembled MS/MS spectral libraries containing normalized retention times [38], and is done using tools such as OpenSWATH [39], Skyline [28,40], Spectronaut [38], SWATH 2.0 [41], and MSPLIT-DIA [42]. Using these tools, chimeric MS/MS spectra from uDIA are compared to spectral libraries either generated in-house or by other laboratories.…”

Section: Bioinformatic Resources For Analysis Of Data From Udiamentioning

confidence: 99%

Clinical applications of quantitative proteomics using targeted and untargeted data-independent acquisition techniques

Meyer

Schilling

2017

Expert Review of Proteomics

131

105

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Introduction While selected/multiple-reaction monitoring (SRM or MRM) is considered the gold standard for quantitative protein measurement, emerging data-independent acquisition (DIA) using high-resolution scans have opened a new dimension of high-throughput, comprehensive quantitative proteomics. These newer methodologies are particularly well suited for discovery of biomarker candidates from human disease samples, and for investigating and understanding human disease pathways. Areas covered This article reviews the current state of targeted and untargeted DIA mass spectrometry-based proteomic workflows, including SRM, parallel-reaction monitoring (PRM) and untargeted DIA (e.g., SWATH). Corresponding bioinformatics strategies, as well as application in biological and clinical studies are presented. Expert commentary Nascent application of highly-multiplexed untargeted DIA, such as SWATH, for accurate protein quantification from clinically relevant and disease-related samples shows great potential to comprehensively investigate biomarker candidates and understand disease.

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Section: Bioinformatic Resources For Analysis Of Data From Udiamentioning

confidence: 99%

Clinical applications of quantitative proteomics using targeted and untargeted data-independent acquisition techniques

Meyer

Schilling

2017

Expert Review of Proteomics

131

105

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“…The quantification accuracy of the SWATH data extracted using the extended libraries is similar to that obtained when using the local comprehensive library. It is also noted that an active area of research is to develop bioinformatics approaches that eliminate the use of reference libraries for the interpretation of mixed MS/MS spectra from DIA experiments (17,37). It remains to be determined whether these methodologies will replace the need to use reference libraries for SWATH analyses.…”

Section: Fig 2 Assay Library Categories and Compositionmentioning

confidence: 99%

SWATH Mass Spectrometry Performance Using Extended Peptide MS/MS Assay Libraries

Song

Pascovici

et al. 2016

Molecular & Cellular Proteomics

101

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The use of data-independent acquisition methods such as SWATH for mass spectrometry based proteomics is usually performed with peptide MS/MS assay libraries which enable identification and quantitation of peptide peak areas. Reference assay libraries can be generated locally through information dependent acquisition, or obtained from community data repositories for commonly studied organisms. However, there have been no studies performed to systematically evaluate how locally generated or repository-based assay libraries affect SWATH performance for proteomic studies. To undertake this analysis, we developed a software workflow, SwathXtend, which generates extended peptide assay libraries by integration with a local seed library and delivers statistical analysis of SWATH-quantitative comparisons. We designed test samples using peptides from a yeast extract spiked into peptides from human K562 cell lysates at three different ratios to simulate protein abundance change comparisons. SWATH-MS performance was assessed using local and external assay libraries of varying complexities and proteome compositions. These experiments demonstrated that local seed libraries integrated with external assay libraries achieve better performance than local assay libraries alone, in terms of the number of identified peptides and proteins and the specificity to detect differentially abundant proteins. Our findings show that the performance of extended assay libraries is influenced by the MS/MS feature similarity of the seed and external libraries, while statistical analysis using multiple testing corrections increases the statistical rigor needed when searching against large extended assay libraries. Data Independent Acquisition (DIA) 1 mass spectrometry workflows are gaining increasing use for proteomic analysis of model systems (1-8). The first integrated DIA and quantitative analysis protocol, termed SWATH (2) was shown to offer accurate, reproducible, and robust proteomic quantification (9 -14). DIA offers advantages over conventional IDA methods (15) by overcoming the stochastic, intensity-based selection of peptide precursors-a problem which typically leads to inconsistent peptide detection and quantitation between replicate runs. By overcoming this problem, DIA is highly suited for large-scale comparative analyses as gaps in data points between samples are mostly eliminated. These digital, extensive proteome maps can be repeatedly mined for quantitative data by extracting ion chromatograms of defined peptides postacquisition, and yields fewer quantitative missing (NA) values than IDA. An important concept in DIA analysis is use of a LC-retention time referenced spectral ion assay library to enable peptide identification from DIA generated multiplexed MS/MS spectra (10,13,16). The depth and quality of this spectral reference library directly correlates with experimental outcome, therefore we consider it is essential to explore and understand this variable in detail.The reference assay library should contain all the prior knowle...

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“…Thus, using the ‘Partial’ submission mechanism, virtually any type of proteomics data workflows can be supported. In fact, PX resources store a significant number of data sets coming from other data workflows such as Data Independent Acquisition (DIA) approaches, top-down proteomics or MS imaging (14), with MassIVE already supporting ‘complete’ submissions for some DIA workflows, such as MSPLIT-DIA's (15) analysis of SWATH-MS files.…”

Section: Expansion Of the Consortium And Updated Submission Guidelinementioning

confidence: 99%

“…Among many other options, data sets can be reanalyzed by spectral library search (MSPLIT (21)), standard database search (MS-GF+ (22)), proteogenomics database search (Enosi (23)), spectral networks (24) and MODa (25) blind search for unexpected modifications, and spectral library search of DIA data (MSPLIT-DIA (15)). Finally, it should be noted that jPOST and PRIDE also plan to re-analyze raw MS data and re-annotate it in the future.…”

Section: Representation Of Reprocessed Data Setsmentioning

confidence: 99%

The ProteomeXchange consortium in 2017: supporting the cultural change in proteomics public data deposition

et al. 2016

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The ProteomeXchange (PX) Consortium of proteomics resources (http://www.proteomexchange.org) was formally started in 2011 to standardize data submission and dissemination of mass spectrometry proteomics data worldwide. We give an overview of the current consortium activities and describe the advances of the past few years. Augmenting the PX founding members (PRIDE and PeptideAtlas, including the PASSEL resource), two new members have joined the consortium: MassIVE and jPOST. ProteomeCentral remains as the common data access portal, providing the ability to search for data sets in all participating PX resources, now with enhanced data visualization components.We describe the updated submission guidelines, now expanded to include four members instead of two. As demonstrated by data submission statistics, PX is supporting a change in culture of the proteomics field: public data sharing is now an accepted standard, supported by requirements for journal submissions resulting in public data release becoming the norm. More than 4500 data sets have been submitted to the various PX resources since 2012. Human is the most represented species with approximately half of the data sets, followed by some of the main model organisms and a growing list of more than 900 diverse species. Data reprocessing activities are becoming more prominent, with both MassIVE and PeptideAtlas releasing the results of reprocessed data sets. Finally, we outline the upcoming advances for ProteomeXchange.

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MSPLIT-DIA: sensitive peptide identification for data-independent acquisition

Cited by 121 publications

References 7 publications

Clinical applications of quantitative proteomics using targeted and untargeted data-independent acquisition techniques

Clinical applications of quantitative proteomics using targeted and untargeted data-independent acquisition techniques

SWATH Mass Spectrometry Performance Using Extended Peptide MS/MS Assay Libraries

The ProteomeXchange consortium in 2017: supporting the cultural change in proteomics public data deposition

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