Evidence map›Paper›PMID 27246681›Full record

ArticleProteomics2016

Untargeted, spectral library-free analysis of data-independent acquisition proteomics data generated using Orbitrap mass spectrometers.

Chih-Chiang Tsou, Chia-Feng Tsai, Guo Ci Teo, Yu-Ju Chen, Alexey I Nesvizhskii

Abstract read
In one paragraph

Article in Proteomics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.

0numbers the graph read from it
0cells of the map it votes in
38citing 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

38 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Article
  6. Article
  7. diaTracer enables spectrum-centric analysis of diaPASEF proteomics data.bioRxiv : the preprint server for biology · 2024
    Article
  8. Article
  9. Article
  10. Article
  11. Proteomics techniques in protein biomarker discovery.Quantitative biology (Beijing, China) · 2024
    Review
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Review
  20. Review
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

5 authors.

Chih-Chiang TsouDepartment of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.
Chia-Feng TsaiInstitute of Chemistry, Academia Sinica, Taipei, Taiwan.
Guo Ci TeoDepartment of Pathology, University of Michigan, Ann Arbor, MI, USA.
Yu-Ju ChenInstitute of Chemistry, Academia Sinica, Taipei, Taiwan.
Alexey I NesvizhskiiDepartment of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.

Funding

COMPUTATIONAL TOOLS FOR MASS SPECTROMETRY-BASED INTERACTOME DATAR01GM094231 · NIGMS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Alexey I Nesvizhskii · 2010 to 2026
$5.4M
Targeted Proteomic Approaches for Natural Product Biosynthetic Pathway DiscoveryR01GM107148 · NIGMS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI HAKANSSON, KRISTINA · 2014 to 2018
$1.4M
NIGMS NIH HHS R01 GM094231NIGMS NIH HHS R01 GM107148
6 · The paper itself

Abstract

We describe an improved version of the data-independent acquisition (DIA) computational analysis tool DIA-Umpire, and show that it enables highly sensitive, untargeted, and direct (spectral library-free) analysis of DIA data obtained using the Orbitrap family of mass spectrometers. DIA-Umpire v2 implements an improved feature detection algorithm with two additional filters based on the isotope pattern and fractional peptide mass analysis. The targeted re-extraction step of DIA-Umpire is updated with an improved scoring function and a more robust, semiparametric mixture modeling of the resulting scores for computing posterior probabilities of correct peptide identification in a targeted setting. Using two publicly available Q Exactive DIA datasets generated using HEK-293 cells and human liver microtissues, we demonstrate that DIA-Umpire can identify similar number of peptide ions, but with better identification reproducibility between replicates and samples, as with conventional data-dependent acquisition. We further demonstrate the utility of DIA-Umpire using a series of Orbitrap Fusion DIA experiments with HeLa cell lysates profiled using conventional data-dependent acquisition and using DIA with different isolation window widths.

Indexed as

Computational BiologyHEK293 CellsHeLa CellsHumansMass SpectrometryProteomicsBioinformaticsData-independent acquisition

Identifiers

PMID27246681
PMCPMC5476226

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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.