Evidence map›Paper›PMID 28483925›Full record

ArticleMolecular & cellular proteomics : MCP2017

MSstatsQC: Longitudinal System Suitability Monitoring and Quality Control for Targeted Proteomic Experiments.

Eralp Dogu, Sara Mohammad-Taheri, Susan E Abbatiello, Michael S Bereman, Brendan MacLean, Birgit Schilling, Olga Vitek

Abstract read
In one paragraph

Article in Molecular & cellular proteomics : MCP, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. 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.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Review
  6. A framework for quality control in quantitative proteomics.bioRxiv : the preprint server for biology · 2024
    Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Methods for Proteomic Analyses of Mycobacteria.Methods in molecular biology (Clifton, N.J.) · 2021
    Article
  12. Review
  13. Article
  14. Review
  15. Article
  16. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Eralp DoguFrom the ‡College of Computer and Information Science, Northeastern University, Massachusetts 02115.
Sara Mohammad-TaheriFrom the ‡College of Computer and Information Science, Northeastern University, Massachusetts 02115.
Susan E Abbatiello¶Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142.
Michael S Bereman‖Department of Biological Sciences, Center for Human Health and the Environment, North Carolina State University, Raleigh, North Carolina 27695.
Brendan MacLean**Department of Genome Sciences, University of Washington School of Medicine, Seattle, Washington 98195.
Birgit Schilling‡‡Buck Institute for Research on Aging, Novato, California 94945; o.vitek@neu.edu bschilling@buckinstitute.org.
Olga VitekFrom the ‡College of Computer and Information Science, Northeastern University, Massachusetts 02115; o.vitek@neu.edu bschilling@buckinstitute.org.

Funding

AB SCIEX QTRAP 5500 LC MS/MS systemS10RR027953 · NCRR · BUCK INSTITUTE FOR RESEARCH ON AGING · PI GIBSON, BRADFORD WAYNE · 2010 to 2010
$500k
NCRR NIH HHS S10 RR027953
6 · The paper itself

Abstract

Selected Reaction Monitoring (SRM) is a powerful tool for targeted detection and quantification of peptides in complex matrices. An important objective of SRM is to obtain peptide quantifications that are (1) suitable for the investigation, and (2) reproducible across laboratories and runs. The first objective is achieved by system suitability tests (SST), which verify that mass spectrometric instrumentation performs as specified. The second objective is achieved by quality control (QC), which provides in-process quality assurance of the sample profile. A common aspect of SST and QC is the longitudinal nature of the data. Although SST and QC have received a lot of attention in the proteomic community, the currently used statistical methods are limited. This manuscript improves upon the statistical methodology for SST and QC that is currently used in proteomics. It adapts the modern methods of longitudinal statistical process control, such as simultaneous and time weighted control charts and change point analysis, to SST and QC of SRM experiments, discusses their advantages, and provides practical guidelines. Evaluations on simulated data sets, and on data sets from the Clinical Proteomics Technology Assessment for Cancer (CPTAC) consortium, demonstrated that these methods substantially improve our ability of real time monitoring, early detection and prevention of chromatographic and instrumental problems. We implemented the methods in an open-source R-based software package MSstatsQC and its web-based graphical user interface. They are available for use stand-alone, or for integration with automated pipelines. Although the examples focus on targeted proteomics, the statistical methods in this manuscript apply more generally to quantitative proteomics.

Indexed as

HumansInternetMass SpectrometryPeptidesProteomicsQuality ControlSoftwarePeptides

Identifiers

PMID28483925
PMCPMC5500765

What OpenQuestion holds

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Registered trials

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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.