Evidence map›Paper›PMID 25482958›Full record

ArticleJournal of proteome research2015

On the importance of well-calibrated scores for identifying shotgun proteomics spectra.

Uri Keich, William Stafford Noble

Erratum issuedAbstract read
In one paragraph

Article in Journal of proteome research, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

16 citing papers in PubMed.

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  10. Controlling the FDR in imperfect matches to an incomplete database.Journal of the American Statistical Association · 2018
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  12. Progressive calibration and averaging for tandem mass spectrometry statistical confidence estimation: Why settle for a single decoy?Research in computational molecular biology : ... Annual International Conference, RECOMB ... : proceedings. RECOMB (Conference : 2005- ) · 2017
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

2 authors.

Uri KeichSchool of Mathematics and Statistics F07, University of Sydney , NSW 2006, Australia.
William Stafford Noble

Funding

TrainingP41GM103533 · NIGMS · UNIVERSITY OF WASHINGTON · PI MACCOSS, MICHAEL · 2012 to 2021
$20.7M
Machine learning analysis of tandem mass spectraR01GM096306 · NIGMS · UNIVERSITY OF WASHINGTON · PI NOBLE, WILLIAM STAFFORD · 2011 to 2014
$2.5M
NIGMS NIH HHS P41 GM103533NIGMS NIH HHS P41GM103533NIGMS NIH HHS R01 GM096306NIGMS NIH HHS R01GM096306
6 · The paper itself

Abstract

Identifying the peptide responsible for generating an observed fragmentation spectrum requires scoring a collection of candidate peptides and then identifying the peptide that achieves the highest score. However, analysis of a large collection of such spectra requires that the score assigned to one spectrum be well-calibrated with respect to the scores assigned to other spectra. In this work, we define the notion of calibration in the context of shotgun proteomics spectrum identification, and we introduce a simple, albeit computationally intensive, technique to calibrate an arbitrary score function. We demonstrate that this calibration procedure yields an increased number of identified spectra at a fixed false discovery rate (FDR) threshold. We also show that proper calibration of scores has a surprising effect on a previously described FDR estimation procedure, making the procedure less conservative. Finally, we provide empirical results suggesting that even partial calibration, which is much less computationally demanding, can yield significant increases in spectrum identification. Overall, we argue that accurate shotgun proteomics analysis requires careful attention to score calibration.

Indexed as

ProteomicsCalibrationSaccharomyces cerevisiae ProteinsSaccharomyces cerevisiae Proteinsfalse discovery ratescore calibrationSpectrum identification

Identifiers

PMID25482958
PMCPMC4324453

What OpenQuestion holds

Textmetadata
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.