Evidence map›Paper›PMID 24939910›Full record

ArticleHuman molecular genetics2014

Multiple evidence strands suggest that there may be as few as 19,000 human protein-coding genes.

Iakes Ezkurdia, David Juan, Jose Manuel Rodriguez, Adam Frankish, Mark Diekhans, Jennifer Harrow, Jesus Vazquez, Alfonso Valencia, Michael L Tress

Abstract read
In one paragraph

Article in Human molecular genetics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 229 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
229citing papers in PubMed, 1 pooled it
–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

229 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Trial
  3. Review
  4. Article
  5. Article
  6. Review
  7. Redefining Shiga toxin-induced human cell death as NLRP1- and gasdermin E-mediated pyroptosis.Proceedings of the National Academy of Sciences of the United States of America · 2026
    Article
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  10. Article
  11. Article
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  18. The state of the human coding gene catalogues.Database : the journal of biological databases and curation · 2025
    Article
  19. A Review of Protein Inference.Methods in molecular biology (Clifton, N.J.) · 2025
    Review
  20. Review

169 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

9 authors.

Iakes EzkurdiaUnidad de Proteómica and.
David JuanStructural Biology and Bioinformatics Programme and.
Jose Manuel RodriguezNational Bioinformatics Institute (INB), Spanish National Cancer Research Centre (CNIO), Melchor Fernández Almagro, 3, 28029, Madrid, Spain.
Adam FrankishWellcome Trust Sanger Institute, Wellcome Trust Campus, Hinxton, Cambridge CB10 1SA, UK and.
Mark DiekhansCenter for Biomolecular Science and Engineering, School of Engineering, University of California Santa Cruz (UCSC), 1156 High Street, Santa Cruz, CA 95064, USA.
Jennifer HarrowWellcome Trust Sanger Institute, Wellcome Trust Campus, Hinxton, Cambridge CB10 1SA, UK and.
Jesus VazquezLaboratorio de Proteómica Cardiovascular, Centro Nacional de Investigaciones Cardiovasculares, CNIC, Melchor Fernández Almagro, 3, 28029, Madrid, Spain.
Alfonso ValenciaStructural Biology and Bioinformatics Programme and, National Bioinformatics Institute (INB), Spanish National Cancer Research Centre (CNIO), Melchor Fernández Almagro, 3, 28029, Madrid, Spain, mtress@cnio.es valencia@cnio.es.
Michael L TressStructural Biology and Bioinformatics Programme and, mtress@cnio.es valencia@cnio.es.

Funding

GENCODE Resource ProjectU41HG007234 · NHGRI · SANGER INSTITUTE · PI FLICEK, PAUL · 2013 to 2020
$20.3M
NHGRI NIH HHS U41 HG007234
6 · The paper itself

Abstract

Determining the full complement of protein-coding genes is a key goal of genome annotation. The most powerful approach for confirming protein-coding potential is the detection of cellular protein expression through peptide mass spectrometry (MS) experiments. Here, we mapped peptides detected in seven large-scale proteomics studies to almost 60% of the protein-coding genes in the GENCODE annotation of the human genome. We found a strong relationship between detection in proteomics experiments and both gene family age and cross-species conservation. Most of the genes for which we detected peptides were highly conserved. We found peptides for >96% of genes that evolved before bilateria. At the opposite end of the scale, we identified almost no peptides for genes that have appeared since primates, for genes that did not have any protein-like features or for genes with poor cross-species conservation. These results motivated us to describe a set of 2001 potential non-coding genes based on features such as weak conservation, a lack of protein features, or ambiguous annotations from major databases, all of which correlated with low peptide detection across the seven experiments. We identified peptides for just 3% of these genes. We show that many of these genes behave more like non-coding genes than protein-coding genes and suggest that most are unlikely to code for proteins under normal circumstances. We believe that their inclusion in the human protein-coding gene catalogue should be revised as part of the ongoing human genome annotation effort.

Indexed as

Computational BiologyGenome, HumanHumansOpen Reading FramesPeptidesProteinsProteomicsPeptidesProteins

Identifiers

PMID24939910
PMCPMC4204768

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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.