Evidence map›Paper›PMID 25357241›Full record

ReviewNature methods2014

Proteogenomics: concepts, applications and computational strategies.

Alexey I Nesvizhskii

Abstract readReview
In one paragraph

Review in Nature methods, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 379 papers.

0numbers the graph read from it
0cells of the map it votes in
379citing 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

379 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Article
  5. Review
  6. Review
  7. Journal of ginseng research · 2026
    Review
  8. Review
  9. Article
  10. Article
  11. Review
  12. Review
  13. Article
  14. Article
  15. Review
  16. Article
  17. Review
  18. Review
  19. Benchmarking Software for DDA-PASEF Immunopeptidomics.Molecular & cellular proteomics : MCP · 2026
    Article
  20. Article

319 more citing papers are in PubMed but not listed here.

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

1 author.

Alexey I Nesvizhskii1] Department of Pathology, University of Michigan, Ann Arbor, Michigan, USA. [2] Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, 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
NIGMS NIH HHS R01 GM094231NIGMS NIH HHS R01-GM-094231
6 · The paper itself

Abstract

Proteogenomics is an area of research at the interface of proteomics and genomics. In this approach, customized protein sequence databases generated using genomic and transcriptomic information are used to help identify novel peptides (not present in reference protein sequence databases) from mass spectrometry-based proteomic data; in turn, the proteomic data can be used to provide protein-level evidence of gene expression and to help refine gene models. In recent years, owing to the emergence of new sequencing technologies such as RNA-seq and dramatic improvements in the depth and throughput of mass spectrometry-based proteomics, the pace of proteogenomic research has greatly accelerated. Here I review the current state of proteogenomic methods and applications, including computational strategies for building and using customized protein sequence databases. I also draw attention to the challenge of false positive identifications in proteogenomics and provide guidelines for analyzing the data and reporting the results of proteogenomic studies.

Indexed as

Databases, Nucleic AcidDatabases, ProteinGenetic VariationGenomicsHigh-Throughput Nucleotide SequencingMass SpectrometryProtein IsoformsProteomeProteomicsSequence Analysis, ProteinProtein IsoformsProteome

Identifiers

PMID25357241
PMCPMC4392723

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

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.