Assigning spectrum-specific <i>P</i>-values to protein identifications by mass spectrometry

Spirin, Victor; Shpunt, Alexander; Gentzel, Marc; Shevchenko, Andrej; Gygi, Steven P.; Sunyaev, Shamil

doi:10.1093/bioinformatics/btr089

Cited by 31 publications

(64 citation statements)

References 26 publications

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“…However, our approach is different because it does not assume a specific parametric family [15, 16] or require the introduction of a new score function [14, 9]. These previous approaches are less general, and in some cases they might partially fail [12], In contrast, our approach is generally applicable, albeit at a computational cost.…”

Section: Discussionmentioning

confidence: 99%

“…Therefore, to preserve the necessary granularity in our scoring function we instead relied on the observation that the distribution of the null optimal PSM scores of a specific spectrum can often be well approximated by a Gumbel EVD [16]. Specifically, we fitted a location-shifted and scaled Gumbel distribution to each of our spectrum-specific ECDFs generated by the yeast real data.…”

Section: Methodsmentioning

confidence: 99%

“…Until recently, no such p-values were computed in the PSM context. Moreover, while considerable effort has of late been invested in computing such p-values [14, 1, 9, 15, 16, 12], we recently showed that there are further subtle but fundamental differences between the MHT context and the PSM one, implying that we typically cannot use FDR controlling procedures that were designed for the MHT context [? ].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Progressive Calibration and Averaging for Tandem Mass Spectrometry Statistical Confidence Estimation: Why Settle for a Single Decoy?

Keich

Noble

2017

Lecture Notes in Computer Science

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Estimating the false discovery rate (FDR) among a list of tandem mass spectrum identifications is mostly done through target-decoy competition (TDC). Here we offer two new methods that can use an arbitrarily small number of additional randomly drawn decoy databases to improve TDC. Specifically, “Partial Calibration” utilizes a new meta-scoring scheme that allows us to gradually benefit from the increase in the number of identifications calibration yields and “Averaged TDC” (a-TDC) reduces the liberal bias of TDC for small FDR values and its variability throughout. Combining a-TDC with “Progressive Calibration” (PC), which attempts to find the “right” number of decoys required for calibration we see substantial impact in real datasets: when analyzing the Plasmodium falciparum data it typically yields almost the entire 17% increase in discoveries that “full calibration” yields (at FDR level 0.05) using 60 times fewer decoys. Our methods are further validated using a novel realistic simulation scheme and importantly, they apply more generally to the problem of controlling the FDR among discoveries from searching an incomplete database.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Progressive Calibration and Averaging for Tandem Mass Spectrometry Statistical Confidence Estimation: Why Settle for a Single Decoy?

Keich

Noble

2017

Lecture Notes in Computer Science

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“…Cellular lysates offer the distinct advantage to work with a cell line, yeast, or bacteria with large amounts of proteins available for analysis (Michalski et al, 2011; Ting et al, 2011), with Saccharomyces cerevisiae being the most common cell lysate (Kellie et al, 2012; Spirin et al, 2011). Other cell lines are also used including HeLa (Wilhelm et al, 2012) and E. coli (Zhou et al, 2011).…”

Section: Biological Materials Selectionmentioning

confidence: 99%

Mass spectrometry-based proteomics and peptidomics for systems biology and biomarker discovery

Cunningham

2012

Front. Biol.

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The scientific community has shown great interest in the field of mass spectrometry-based proteomics and peptidomics for its applications in biology. Proteomics technologies have evolved to produce large datasets of proteins or peptides involved in various biological and disease progression processes producing testable hypothesis for complex biological questions. This review provides an introduction and insight to relevant topics in proteomics and peptidomics including biological material selection, sample preparation, separation techniques, peptide fragmentation, post-translation modifications, quantification, bioinformatics, and biomarker discovery and validation. In addition, current literature and remaining challenges and emerging technologies for proteomics and peptidomics are presented.

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“…However, one is most interested in the region of the score distribution where only a small fraction of false positives are allowed (typically at 1% FDR). This usually corresponds to the extreme tail of the distribution where p values are on the order of 10 Ϫ9 or lower and thus there is typically lack of sufficient data points to accurately model the tail of the score distribution (32). More recently, Kim et al (24) and Alves et al (33), in parallel, proposed a generating function approach to compute the exact score distribution of random peptide matches for any spectra without explicitly matching all peptides to a spectrum.…”

mentioning

confidence: 99%

MixGF: Spectral Probabilities for Mixture Spectra from more than One Peptide

Jian

Bourne

Bandeira

2014

Molecular & Cellular Proteomics

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In large-scale proteomic experiments, multiple peptide precursors are often cofragmented simultaneously in the same mixture tandem mass (MS/MS) spectrum. These spectra tend to elude current computational tools because of the ubiquitous assumption that each spectrum is generated from only one peptide. Therefore, tools that consider multiple peptide matches to each MS/MS spectrum can potentially improve the relatively low spectrum identification rate often observed in proteomics experiments. More importantly, data independent acquisition protocols promoting the cofragmentation of multiple precursors are emerging as alternative methods that can greatly improve the throughput of peptide identifications but their success also depends on the availability of algorithms to identify multiple peptides from each MS/MS spectrum. Here we address a fundamental question in the identification of mixture MS/MS spectra: determining the statistical significance of multiple peptides matched to a given MS/MS spectrum. We propose the MixGF generating function model to rigorously compute the statistical significance of peptide identifications for mixture spectra and show that this approach improves the sensitivity of current mixture spectra database search tools by a Ϸ30 -390%. Analysis of multiple data sets with MixGF reveals that in complex biological samples the number of identified mixture spectra can be as high as 20% of all the identified spectra and the number of unique peptides identified only in mixture spectra can be up to 35.4% of those identified in single-peptide spectra. 1, 2, 3). In typical experiments, tens of thousands to millions of MS/MS spectra are generated and enable researchers to probe various aspects of the proteome on a large scale. Part of this success hinges on the availability of computational methods that can analyze the large amount of data generated from these experiments. The classical question in computational proteomics asks: given an MS/MS spectrum, what is the peptide that generated the spectrum? However, it is increasingly being recognized that this assumption that each MS/MS spectrum comes from only one peptide is often not valid. Several recent analyses show that as many as 50% of the MS/MS spectra collected in typical proteomics experiments come from more than one peptide precursor (4, 5). The presence of multiple peptides in mixture spectra can decrease their identification rate to as low as one half of that for MS/MS spectra generated from only one peptide (6,7,8). In addition, there have been numerous developments in data independent acquisition (DIA) technologies where multiple peptide precursors are intentionally selected to cofragment in each MS/MS spectrum (9,10,11,12,13,14,15). These emerging technologies can address some of the enduring disadvantages of traditional data-dependent acquisition (DDA) methods (e.g. low reproducibility (16)) and potentially increase the throughput of peptide identification 5-10 fold (4, 17). However, despite the growing importance of mixture spectra in var...

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Assigning spectrum-specific P-values to protein identifications by mass spectrometry

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 31 publications

References 26 publications

Progressive Calibration and Averaging for Tandem Mass Spectrometry Statistical Confidence Estimation: Why Settle for a Single Decoy?

Progressive Calibration and Averaging for Tandem Mass Spectrometry Statistical Confidence Estimation: Why Settle for a Single Decoy?

Mass spectrometry-based proteomics and peptidomics for systems biology and biomarker discovery

MixGF: Spectral Probabilities for Mixture Spectra from more than One Peptide

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