Evidence map›Paper›PMID 41493144›Full record

ArticleProteomics2026

Brownotate, a Comprehensive Solution to Generate Protein Sequence Databases for Any Species.

Adrien Brown, Alexandre Burel, Sarah Cianférani, Christine Carapito, Fabrice Bertile

Abstract read
In one paragraph

Article in Proteomics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Adrien BrownLaboratoire de Spectrométrie de Masse Bioorganique (LSMBO), IPHC, UMR7178, Université de Strasbourg, CNS, Strasbourg, France.ORCID 0009-0006-2688-968X
Alexandre BurelLaboratoire de Spectrométrie de Masse Bioorganique (LSMBO), IPHC, UMR7178, Université de Strasbourg, CNS, Strasbourg, France.
Sarah CianféraniLaboratoire de Spectrométrie de Masse Bioorganique (LSMBO), IPHC, UMR7178, Université de Strasbourg, CNS, Strasbourg, France.
Christine CarapitoLaboratoire de Spectrométrie de Masse Bioorganique (LSMBO), IPHC, UMR7178, Université de Strasbourg, CNS, Strasbourg, France.
Fabrice BertileLaboratoire de Spectrométrie de Masse Bioorganique (LSMBO), IPHC, UMR7178, Université de Strasbourg, CNS, Strasbourg, France.

Funding

French Proteomic Infrastructure ANR-10-INSB-08-03
6 · The paper itself

Abstract

Proteomics is strengthening research in biology and the diversification of the model organisms studied is very promising for fully understanding the complexity of biological principles. However, the lack of protein sequence databases for many species is a major bottleneck. Existing computational solutions are usually incomplete and/or only usable by bioinformaticians. We have built an open-source, user-friendly pipeline, called Brownotate, which allows anyone to generate protein sequence databases for any species as long as sequencing information is available. The pipeline can extract already existing protein sequences, but also automatically annotate any genome assembly or assemble and annotate any DNA sequence dataset. By testing the pipeline with numerous sequencing and assembly datasets covering a large part of the phylogenetic tree, we show that Brownotate generates fragmented but good quality assemblies and good quality annotations when compared to reference data. By comparing the use of protein databases generated by Brownotate or downloaded from NCBI to interpret proteomic data, we show very comparable results. The Brownotate pipeline is, therefore, an important new addition to the proteomics toolbox. The pipeline and its web interface are freely available at https://github.com/LSMBO/Brownotate and https://github.com/LSMBO/brownotate-app, respectively. SUMMARY: This study evaluated the performance of a newly developed pipeline, Brownotate, for the assembly and annotation of sequencing data for multiple species, from prokaryotes to eukaryotes. We compared their fragmentation level (assembly) and completeness based on evolutionary expectations of gene content, and we evaluated their overlap. Brownotate generated fragmented, slightly less complete assemblies. However, the overlap of proteins predicted was very good, despite an excess of predicted sequences of small size with Brownotate. In addition, the interpretation of proteomics data downloaded from PRIDE repository for 27 species was found to lead to very similar results regardless of the origin of the protein sequencing database used, whether it was generated by Brownotate or downloaded from NCBI. Brownotate, made available to the community, will, therefore, be a tool of choice to mitigate the lack of an appropriate protein sequence database for many species, and allow proteomists to analyse without delay samples from species for which only sequencing data are available.

Indexed as

Computational BiologyDatabases, ProteinProteomicsSequence Analysis, ProteinSoftwareAnimalsMolecular Sequence AnnotationPhylogenygenome annotationgenome assemblypipelineprotein databaseproteomics

Identifiers

PMID41493144
PMCPMC13106930

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LicenceCC BY
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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.