Evidence map›Paper›PMID 32859984›Full record

ArticleNature protocols2020

A complete and flexible workflow for metaproteomics data analysis based on MetaProteomeAnalyzer and Prophane.

Henning Schiebenhoefer, Kay Schallert, Bernhard Y Renard, Kathrin Trappe, Emanuel Schmid, Dirk Benndorf, Katharina Riedel, Thilo Muth, Stephan Fuchs

Abstract read
PubMed Publisher
In one paragraph

Article in Nature protocols, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers.

0numbers the graph read from it
0cells of the map it votes in
48citing papers in PubMed
5.3field-weighted citation impact, top 3% 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

48 citing papers in PubMed, 92 citations in OpenAlex.

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  7. Microbial Omics.Progress in molecular and subcellular biology · 2026
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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

9 authors at 7 institutions in 2 countries.

Henning SchiebenhoeferBioinformatics Unit (MF1), Department for Methods Development and Research Infrastructure, Robert Koch Institute, Berlin, Germany.ORCID http://orcid.org/0000-0002-4471-9709
Kay SchallertBioprocess Engineering, Otto von Guericke University, Magdeburg, Germany.
Bernhard Y RenardBioinformatics Unit (MF1), Department for Methods Development and Research Infrastructure, Robert Koch Institute, Berlin, Germany.
Kathrin TrappeBioinformatics Unit (MF1), Department for Methods Development and Research Infrastructure, Robert Koch Institute, Berlin, Germany.
Emanuel SchmidID Computational & Data Science Support, Eidgenössische Technische Hochschule, Zurich, Switzerland.
Dirk BenndorfBioprocess Engineering, Otto von Guericke University, Magdeburg, Germany.
Katharina RiedelCenter for Functional Genomics of Microbes (CFGM), Institute of Microbiology, University of Greifswald, Greifswald, Germany.
Thilo MuthBioinformatics Unit (MF1), Department for Methods Development and Research Infrastructure, Robert Koch Institute, Berlin, Germany.
Stephan FuchsDepartment of Infectious Diseases, Robert Koch Institute, Wernigerode, Germany. fuchss@rki.de.ORCID http://orcid.org/0000-0002-1338-2699
Hasso Plattner Institute · DERobert Koch Institute · DEBoard of the Swiss Federal Institutes of Technology · CHFederal Institute For Materials Research and Testing · DEMax Planck Institute for Dynamics of Complex Technical Systems · DEOtto-von-Guericke-Universität Magdeburg · DEUniversität Greifswald · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Metaproteomics, the study of the collective protein composition of multi-organism systems, provides deep insights into the biodiversity of microbial communities and the complex functional interplay between microbes and their hosts or environment. Thus, metaproteomics has become an indispensable tool in various fields such as microbiology and related medical applications. The computational challenges in the analysis of corresponding datasets differ from those of pure-culture proteomics, e.g., due to the higher complexity of the samples and the larger reference databases demanding specific computing pipelines. Corresponding data analyses usually consist of numerous manual steps that must be closely synchronized. With MetaProteomeAnalyzer and Prophane, we have established two open-source software solutions specifically developed and optimized for metaproteomics. Among other features, peptide-spectrum matching is improved by combining different search engines and, compared to similar tools, metaproteome annotation benefits from the most comprehensive set of available databases (such as NCBI, UniProt, EggNOG, PFAM, and CAZy). The workflow described in this protocol combines both tools and leads the user through the entire data analysis process, including protein database creation, database search, protein grouping and annotation, and results visualization. To the best of our knowledge, this protocol presents the most comprehensive, detailed and flexible guide to metaproteomics data analysis to date. While beginners are provided with robust, easy-to-use, state-of-the-art data analysis in a reasonable time (a few hours, depending on, among other factors, the protein database size and the number of identified peptides and inferred proteins), advanced users benefit from the flexibility and adaptability of the workflow.

Indexed as

Data AnalysisDatabases, ProteinMicrobiotaPeptidesProteomeProteomicsSoftwareWorkflowPeptidesProteome

Identifiers

PMID32859984
OpenAlexW3082161503

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.