Evidence map›Paper›PMID 27581094›Full record

ArticleJournal of proteome research2016

Detection of Missing Proteins Using the PRIDE Database as a Source of Mass Spectrometry Evidence.

Alba Garin-Muga, Leticia Odriozola, Ana Martínez-Val, Noemí Del Toro, Rocío Martínez, Manuela Molina, Laura Cantero, Rocío Rivera, Nicolás Garrido, Francisco Dominguez and 4 more

Open access · hybridAbstract read
In one paragraph

Article in Journal of proteome research, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
2.3field-weighted citation impact, top 13% of its field
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

10 citing papers in PubMed, 16 citations in OpenAlex.

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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

14 authors at 5 institutions in 1 country.

Alba Garin-MugaProteomics and Bioinformatics Unit, Center for Applied Medical Research, University of Navarra , 31008, Pamplona, Spain.
Leticia OdriozolaProteomics and Bioinformatics Unit, Center for Applied Medical Research, University of Navarra , 31008, Pamplona, Spain.
Ana Martínez-ValProteomics Unit, Spanish National Cancer Research Centre , 28029, Madrid, Spain.
Noemí Del ToroEuropean Molecular Biology Laboratory, European Bioinformatics Institute , Wellcome Trust GenomeCampus, Hinxton, Cambridge, CB10 1SD, U.K.
Rocío MartínezProteomics and Bioinformatics Unit, Center for Applied Medical Research, University of Navarra , 31008, Pamplona, Spain.
Manuela MolinaProteomics and Bioinformatics Unit, Center for Applied Medical Research, University of Navarra , 31008, Pamplona, Spain.
Laura CanteroProteomics Unit (SCSIE), University of Valencia , 46010, Valencia, Spain.
Rocío RiveraAndrology Laboratory and Sperm Bank, Instituto Universitario IVI , 46015, Valencia, Spain.
Nicolás GarridoAndrology Laboratory and Sperm Bank, Instituto Universitario IVI , 46015, Valencia, Spain.
Francisco DominguezFundación IVI/INCLIVA , 46010, Valencia, Spain.
Manuel M Sanchez Del PinoBiochemistry Department, University of Valencia , 46010, Valencia, Spain.
Juan Antonio VizcaínoEuropean Molecular Biology Laboratory, European Bioinformatics Institute , Wellcome Trust GenomeCampus, Hinxton, Cambridge, CB10 1SD, U.K.
Fernando J CorralesProteomics and Bioinformatics Unit, Center for Applied Medical Research, University of Navarra , 31008, Pamplona, Spain.
Victor SeguraProteomics and Bioinformatics Unit, Center for Applied Medical Research, University of Navarra , 31008, Pamplona, Spain.
Universidad de Navarra · ESUniversitat de València · ESValencian Infertility Institute · ESINCLIVA Health Research Institute · ESSpanish National Cancer Research Centre · ES

Funding

Wellcome TrustWellcome Trust WT101477MA
6 · The paper itself

Abstract

The current catalogue of the human proteome is not yet complete, as experimental proteomics evidence is still elusive for a group of proteins known as the missing proteins. The Human Proteome Project (HPP) has been successfully using technology and bioinformatic resources to improve the characterization of such challenging proteins. In this manuscript, we propose a pipeline starting with the mining of the PRIDE database to select a group of data sets potentially enriched in missing proteins that are subsequently analyzed for protein identification with a method based on the statistical analysis of proteotypic peptides. Spermatozoa and the HEK293 cell line were found to be a promising source of missing proteins and clearly merit further attention in future studies. After the analysis of the selected samples, we found 342 PSMs, suggesting the presence of 97 missing proteins in human spermatozoa or the HEK293 cell line, while only 36 missing proteins were potentially detected in the retina, frontal cortex, aorta thoracica, or placenta. The functional analysis of the missing proteins detected confirmed their tissue specificity, and the validation of a selected set of peptides using targeted proteomics (SRM/MRM assays) further supports the utility of the proposed pipeline. As illustrative examples, DNAH3 and TEPP in spermatozoa, and UNCX and ATAD3C in HEK293 cells were some of the more robust and remarkable identifications in this study. We provide evidence indicating the relevance to carefully analyze the ever-increasing MS/MS data available from PRIDE and other repositories as sources for missing proteins detection in specific biological matrices as revealed for HEK293 cells.

Indexed as

Databases, ProteinAortaComputational BiologyFemaleFrontal LobeHEK293 CellsHumansMalePlacentaPregnancyProteomeProteomicsRetinaSpermatozoaTandem Mass SpectrometryProteomeC-HPPmissing proteinsMS/MS proteomicsPRIDE database

Identifiers

PMID27581094
PMCPMC5099979
OpenAlexW2508281659

What OpenQuestion holds

Textmetadata
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.