Evidence map›Paper›PMID 33170893›Full record

ArticlePloS one2020

Survey of metaproteomics software tools for functional microbiome analysis.

Ray Sajulga, Caleb Easterly, Michael Riffle, Bart Mesuere, Thilo Muth, Subina Mehta, Praveen Kumar, James Johnson, Bjoern Andreas Gruening, Henning Schiebenhoefer and 6 more

Abstract read
In one paragraph

Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.

0numbers the graph read from it
0cells of the map it votes in
21citing 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

21 citing papers in PubMed.

  1. Review
  2. Article
  3. Review
  4. Article
  5. Review
  6. Article
  7. Article
  8. Review
  9. Article
  10. Article
  11. Article
  12. Article
  13. Multi-Omics Strategies for Investigating the Microbiome in Toxicology Research.Toxicological sciences : an official journal of the Society of Toxicology · 2022
    Review
  14. Considerations for constructing a protein sequence database for metaproteomics.Computational and structural biotechnology journal · 2022
    Review
  15. Review
  16. Sample Processing for Metaproteomic Analysis of Human Gut Microbiota.Methods in molecular biology (Clifton, N.J.) · 2022
    Article
  17. Article
  18. New software tools, databases, and resources in metabolomics: updates from 2020.Metabolomics : Official journal of the Metabolomic Society · 2021
    Review
  19. Review
  20. Article
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

16 authors.

Ray SajulgaUniversity of Minnesota, Minneapolis, Minnesota, United States of America.ORCID 0000-0002-9181-4303
Caleb EasterlyUniversity of Minnesota, Minneapolis, Minnesota, United States of America.ORCID 0000-0001-7853-377X
Michael RiffleUniversity of Washington, Seattle, Washington, United States of America.ORCID 0000-0003-1633-8607
Bart MesuereGhent University, Ghent, Belgium.ORCID 0000-0003-0610-3441
Thilo MuthFederal Institute for Materials Research and Testing, Berlin, Germany.ORCID 0000-0001-8304-2684
Subina MehtaUniversity of Minnesota, Minneapolis, Minnesota, United States of America.ORCID 0000-0001-9818-0537
Praveen KumarUniversity of Minnesota, Minneapolis, Minnesota, United States of America.ORCID 0000-0002-7818-671X
James JohnsonUniversity of Minnesota, Minneapolis, Minnesota, United States of America.ORCID 0000-0002-2590-4765
Bjoern Andreas GrueningUniversity of Freiburg, Freiburg, Germany.ORCID 0000-0002-3079-6586
Henning SchiebenhoeferRobert Koch Institute, Berlin, Germany.ORCID 0000-0002-4471-9709
Carolin A KolmederUniversity of Helsinki, Helsinki, Finland.
Stephan FuchsRobert Koch Institute, Wernigerode, Germany.ORCID 0000-0002-1338-2699
Brook L NunnUniversity of Washington, Seattle, Washington, United States of America.ORCID 0000-0002-7361-4359
Joel RudneyUniversity of Minnesota, Minneapolis, Minnesota, United States of America.
Timothy J GriffinUniversity of Minnesota, Minneapolis, Minnesota, United States of America.
Pratik D JagtapUniversity of Minnesota, Minneapolis, Minnesota, United States of America.ORCID 0000-0003-0984-0973

Funding

Expanding multiplexed kinase biosensor analysis to SWATH-MS (Part 2 of 2)U24CA199347 · NCI · UNIVERSITY OF MINNESOTA · PI GRIFFIN, TIMOTHY J. · 2016 to 2019
$2.4M
NCI NIH HHS U24 CA199347
6 · The paper itself

Abstract

To gain a thorough appreciation of microbiome dynamics, researchers characterize the functional relevance of expressed microbial genes or proteins. This can be accomplished through metaproteomics, which characterizes the protein expression of microbiomes. Several software tools exist for analyzing microbiomes at the functional level by measuring their combined proteome-level response to environmental perturbations. In this survey, we explore the performance of six available tools, to enable researchers to make informed decisions regarding software choice based on their research goals. Tandem mass spectrometry-based proteomic data obtained from dental caries plaque samples grown with and without sucrose in paired biofilm reactors were used as representative data for this evaluation. Microbial peptides from one sample pair were identified by the X! tandem search algorithm via SearchGUI and subjected to functional analysis using software tools including eggNOG-mapper, MEGAN5, MetaGOmics, MetaProteomeAnalyzer (MPA), ProPHAnE, and Unipept to generate functional annotation through Gene Ontology (GO) terms. Among these software tools, notable differences in functional annotation were detected after comparing differentially expressed protein functional groups. Based on the generated GO terms of these tools we performed a peptide-level comparison to evaluate the quality of their functional annotations. A BLAST analysis against the NCBI non-redundant database revealed that the sensitivity and specificity of functional annotation varied between tools. For example, eggNOG-mapper mapped to the most number of GO terms, while Unipept generated more accurate GO terms. Based on our evaluation, metaproteomics researchers can choose the software according to their analytical needs and developers can use the resulting feedback to further optimize their algorithms. To make more of these tools accessible via scalable metaproteomics workflows, eggNOG-mapper and Unipept 4.0 were incorporated into the Galaxy platform.

Indexed as

MetagenomicsMicrobiotaProteomicsSoftwareSurveys and QuestionnairesAmino Acid SequenceDysbiosisGene OntologyPeptidesWorkflowPeptides

Identifiers

PMID33170893
PMCPMC7654790

What OpenQuestion holds

Textmetadata
LicenceCC0
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.