Evidence map›Paper›PMID 30654742›Full record

ArticleBMC genomics2019

Peptimapper: proteogenomics workflow for the expert annotation of eukaryotic genomes.

Laetitia Guillot, Ludovic Delage, Alain Viari, Yves Vandenbrouck, Emmanuelle Com, Andrés Ritter, Régis Lavigne, Dominique Marie, Pierre Peterlongo, Philippe Potin and 1 more

Open access · goldAbstract read
In one paragraph

Article in BMC genomics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
0.7field-weighted citation impact, top 32% 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

5 citing papers in PubMed, 14 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

11 authors at 3 institutions in 1 country.

Laetitia GuillotUniv Rennes, Inserm, EHESP, Irset (Institut de recherche en santé, environnement et travail) - UMR_S 1085, F-35042, Rennes cedex, France.
Ludovic DelageSorbonne Université, UPMC, CNRS, UMR 8227, Integrative Biology of Marine Models, Biological Station, CS 90074, F-29688, Roscoff, France.
Alain ViariINRIA Grenoble-Rhône-Alpes, F-38330, Montbonnot-Saint-Martin, France.
Yves VandenbrouckUniversity Grenoble Alpes, CEA, Inserm, BIG-BGE, 38000, Grenoble, France.
Emmanuelle ComUniv Rennes, Inserm, EHESP, Irset (Institut de recherche en santé, environnement et travail) - UMR_S 1085, F-35042, Rennes cedex, France.
Andrés RitterSorbonne Université, UPMC, CNRS, UMR 8227, Integrative Biology of Marine Models, Biological Station, CS 90074, F-29688, Roscoff, France.
Régis LavigneUniv Rennes, Inserm, EHESP, Irset (Institut de recherche en santé, environnement et travail) - UMR_S 1085, F-35042, Rennes cedex, France.
Dominique MarieSorbonne Université, UPMC, CNRS, UMR 8227, Integrative Biology of Marine Models, Biological Station, CS 90074, F-29688, Roscoff, France.
Pierre PeterlongoUniversity Rennes, Inria, CNRS, IRISA, F-35042, Rennes, France.
Philippe PotinSorbonne Université, UPMC, CNRS, UMR 8227, Integrative Biology of Marine Models, Biological Station, CS 90074, F-29688, Roscoff, France.
Charles PineauUniv Rennes, Inserm, EHESP, Irset (Institut de recherche en santé, environnement et travail) - UMR_S 1085, F-35042, Rennes cedex, France. charles.pineau@inserm.fr.ORCID http://orcid.org/0000-0002-7461-5433
Centre National de la Recherche Scientifique · FRInserm · FRUniversité Claude Bernard Lyon 1 · FR

Funding

Agence Nationale de la Recherche ANR-10-BTBR-04-02
6 · The paper itself

Abstract

backgroundAccurate structural annotation of genomes is still a challenge, despite the progress made over the past decade. The prediction of gene structure remains difficult, especially for eukaryotic species, and is often erroneous and incomplete. We used a proteogenomics strategy, taking advantage of the combination of proteomics datasets and bioinformatics tools, to identify novel protein coding-genes and splice isoforms, assign correct start sites, and validate predicted exons and genes.

resultsOur proteogenomics workflow, Peptimapper, was applied to the genome annotation of Ectocarpus sp., a key reference genome for both the brown algal lineage and stramenopiles. We generated proteomics data from various life cycle stages of Ectocarpus sp. strains and sub-cellular fractions using a shotgun approach. First, we directly generated peptide sequence tags (PSTs) from the proteomics data. Second, we mapped PSTs onto the translated genomic sequence. Closely located hits (i.e., PSTs locations on the genome) were then clustered to detect potential coding regions based on parameters optimized for the organism. Third, we evaluated each cluster and compared it to gene predictions from existing conventional genome annotation approaches. Finally, we integrated cluster locations into GFF files to use a genome viewer. We identified two potential novel genes, a ribosomal protein L22 and an aryl sulfotransferase and corrected the gene structure of a dihydrolipoamide acetyltransferase. We experimentally validated the results by RT-PCR and using transcriptomics data.

conclusionsPeptimapper is a complementary tool for the expert annotation of genomes. It is suitable for any organism and is distributed through a Docker image available on two public bioinformatics docker repositories: Docker Hub and BioShaDock. This workflow is also accessible through the Galaxy framework and for use by non-computer scientists at https://galaxy.protim.eu . Data are available via ProteomeXchange under identifier PXD010618.

Indexed as

GenomeMolecular Sequence AnnotationSoftwareWorkflowAmino Acid SequenceCodonEukaryotaMass SpectrometryPeptidesProteogenomicsReproducibility of ResultsCodonPeptidesBioinformaticsGenome annotationPeptide sequence tagProteogenomicsProteomicsTandem mass spectrometry

Identifiers

PMID30654742
PMCPMC6337836
OpenAlexW2916192076

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.