Survey of metaproteomics software tools for functional microbiome analysis

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Sajulga , R , Easterly , C , Riffle , M , Mesuere , B , Muth , T , Mehta , S , Kumar , P , Johnson , J , Gruening , B A , Schiebenhoefer , H , Kolmeder , C A , Fuchs , S , Nunn , B L , Rudney , J , Griffin , T J & Jagtap , P D 2020 , ' Survey of metaproteomics software tools for functional microbiome analysis ' , PLoS One , vol. 15 , no. 11 , 0241503 . https://doi.org/10.1371/journal.pone.0241503

Title: Survey of metaproteomics software tools for functional microbiome analysis
Author: Sajulga, Ray; Easterly, Caleb; Riffle, Michael; Mesuere, Bart; Muth, Thilo; Mehta, Subina; Kumar, Praveen; Johnson, James; Gruening, Bjoern Andreas; Schiebenhoefer, Henning; Kolmeder, Carolin A.; Fuchs, Stephan; Nunn, Brook L.; Rudney, Joel; Griffin, Timothy J.; Jagtap, Pratik D.
Contributor: University of Helsinki, Veterinary Biosciences
Date: 2020-11-10
Language: eng
Number of pages: 20
Belongs to series: PLoS One
ISSN: 1932-6203
URI: http://hdl.handle.net/10138/324418
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.
Subject: 1184 Genetics, developmental biology, physiology
1182 Biochemistry, cell and molecular biology
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