Novel Bioinformatics Strategies Driving Dynamic Metaproteomic Studies.
Bioinformatics
Computational biology
Mass spectrometry
Metaproteomics
Microbiome
Proteomics
Quantification
Software
Statistics
Journal
Methods in molecular biology (Clifton, N.J.)
ISSN: 1940-6029
Titre abrégé: Methods Mol Biol
Pays: United States
ID NLM: 9214969
Informations de publication
Date de publication:
2022
2022
Historique:
entrez:
25
5
2022
pubmed:
26
5
2022
medline:
28
5
2022
Statut:
ppublish
Résumé
Constant improvements in mass spectrometry technologies and laboratory workflows have enabled the proteomics investigation of biological samples of growing complexity. Microbiomes represent such complex samples for which metaproteomics analyses are becoming increasingly popular. Metaproteomics experimental procedures create large amounts of data from which biologically relevant signal must be efficiently extracted to draw meaningful conclusions. Such a data processing requires appropriate bioinformatics tools specifically developed for, or capable of handling metaproteomics data. In this chapter, we outline current and novel tools that can perform the most commonly used steps in the analysis of cutting-edge metaproteomics data, such as peptide and protein identification and quantification, as well as data normalization, imputation, mining, and visualization. We also provide details about the experimental setups in which these tools should be used.
Identifiants
pubmed: 35612752
doi: 10.1007/978-1-0716-2124-0_22
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
319-338Informations de copyright
© 2022. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.
Références
Heyer R, Schallert K, Büdel A et al (2019) A robust and universal metaproteomics workflow for research studies and routine diagnostics within 24 h using phenol extraction, fasp digest, and the metaproteomeanalyzer. Front Microbiol 10:1883
pubmed: 31474963
pmcid: 6707425
doi: 10.3389/fmicb.2019.01883
Heyer R, Schallert K, Zoun R et al (2017) Challenges and perspectives of metaproteomic data analysis. J Biotechnol 261:24–36
pubmed: 28663049
doi: 10.1016/j.jbiotec.2017.06.1201
Stahl DC, Swiderek KM, Davis MT, Lee TD (1996) Data-controlled automation of liquid chromatography/tandem mass spectrometry analysis of peptide mixtures. J Am Soc Mass Spectrom 7:532–540
pubmed: 24203425
doi: 10.1016/1044-0305(96)00057-8
Venable JD, Dong M-Q, Wohlschlegel J et al (2004) Automated approach for quantitative analysis of complex peptide mixtures from tandem mass spectra. Nat Methods 1:39–45
pubmed: 15782151
doi: 10.1038/nmeth705
Gillet LC, Navarro P, Tate S et al (2012) Targeted data extraction of the MS/MS spectra generated by data-independent acquisition: a new concept for consistent and accurate proteome analysis. Mol Cell Proteomics 11(O111):016717
Doerr A (2014) DIA mass spectrometry. Nat Methods 12:35–35
doi: 10.1038/nmeth.3234
Eng JK, McCormack AL, Yates JR (1994) An approach to correlate tandem mass spectral data of peptides with amino acid sequences in a protein database. J Am Soc Mass Spectrom 5:976–989
pubmed: 24226387
doi: 10.1016/1044-0305(94)80016-2
Tanca A, Palomba A, Fraumene C et al (2016) The impact of sequence database choice on metaproteomic results in gut microbiota studies. Microbiome 4:51
pubmed: 27671352
pmcid: 5037606
doi: 10.1186/s40168-016-0196-8
Tanca A, Palomba A, Deligios M et al (2013) Evaluating the impact of different sequence databases on metaproteome analysis: insights from a lab-assembled microbial mixture. PLoS One 8:e82981
pubmed: 24349410
pmcid: 3857319
doi: 10.1371/journal.pone.0082981
Timmins-Schiffman E, May DH, Mikan M et al (2017) Critical decisions in metaproteomics: achieving high confidence protein annotations in a sea of unknowns. ISME J 11:309–314
pubmed: 27824341
doi: 10.1038/ismej.2016.132
O’Leary NA, Wright MW, Brister JR et al (2016) Reference sequence (RefSeq) database at NCBI: current status, taxonomic expansion, and functional annotation. Nucleic Acids Res 44:D733–D745
pubmed: 26553804
doi: 10.1093/nar/gkv1189
Li J, Jia H, Cai X et al (2014) An integrated catalog of reference genes in the human gut microbiome. Nat Biotechnol 32:834–841
pubmed: 24997786
doi: 10.1038/nbt.2942
Kuhring M, Renard BY (2015) Estimating the computational limits of detection of microbial non-model organisms. Proteomics 15:3580–3584
pubmed: 26136362
doi: 10.1002/pmic.201400598
Jagtap P, Goslinga J, Kooren JA et al (2013) A two-step database search method improves sensitivity in peptide sequence matches for metaproteomics and proteogenomics studies. Proteomics 13:1352–1357
pubmed: 23412978
pmcid: 3633484
doi: 10.1002/pmic.201200352
Zhang X, Ning Z, Mayne J et al (2016) MetaPro-IQ: a universal metaproteomic approach to studying human and mouse gut microbiota. Microbiome 4:31
pubmed: 27343061
pmcid: 4919841
doi: 10.1186/s40168-016-0176-z
Craig R, Beavis RC (2003) A method for reducing the time required to match protein sequences with tandem mass spectra. Rapid Commun Mass Spectrom 17:2310–2316
pubmed: 14558131
doi: 10.1002/rcm.1198
Craig R, Beavis RC (2004) TANDEM: matching proteins with tandem mass spectra. Bioinformatics 20:1466–1467
pubmed: 14976030
doi: 10.1093/bioinformatics/bth092
Tyanova S, Temu T, Cox J (2016) The MaxQuant computational platform for mass spectrometry-based shotgun proteomics. Nat Protoc 11:2301–2319
pubmed: 27809316
doi: 10.1038/nprot.2016.136
Beyter D, Lin MS, Yu Y et al (2018) ProteoStorm: an ultrafast metaproteomics database search framework. Cell Syst 7:463–467
pubmed: 30268435
pmcid: 6231400
doi: 10.1016/j.cels.2018.08.009
Xiao J, Tanca A, Jia B et al (2018) Metagenomic taxonomy-guided database-searching strategy for improving metaproteomic analysis. J Proteome Res 17:1596–1605
pubmed: 29436230
doi: 10.1021/acs.jproteome.7b00894
UniProt Consortium (2021) UniProt: the universal protein knowledgebase in 2021. Nucleic Acids Res 49:D480–D489
doi: 10.1093/nar/gkaa1100
Park SKR, Jung T, Thuy-Boun PS et al (2019) ComPIL 2.0: an updated comprehensive metaproteomics database. J Proteome Res 18:616–622
pubmed: 30525664
pmcid: 7767584
doi: 10.1021/acs.jproteome.8b00722
Xu T, Park SK, Venable JD et al (2015) ProLuCID: an improved SEQUEST-like algorithm with enhanced sensitivity and specificity. J Proteome 129:16–24
doi: 10.1016/j.jprot.2015.07.001
Lam H, Deutsch EW, Eddes JS et al (2007) Development and validation of a spectral library searching method for peptide identification from MS/MS. Proteomics 7:655–667
pubmed: 17295354
doi: 10.1002/pmic.200600625
Craig R, Cortens JC, Fenyo D, Beavis RC (2006) Using annotated peptide mass spectrum libraries for protein identification. J Proteome Res 5:1843–1849
pubmed: 16889405
doi: 10.1021/pr0602085
Frewen BE, Merrihew GE, Wu CC et al (2006) Analysis of peptide MS/MS spectra from large-scale proteomics experiments using spectrum libraries. Anal Chem 78:5678–5684
pubmed: 16906711
doi: 10.1021/ac060279n
Yang Y, Liu X, Shen C et al (2020) In silico spectral libraries by deep learning facilitate data-independent acquisition proteomics. Nat Commun 11:1–11
Gessulat S, Schmidt T, Zolg DP et al (2019) Prosit: proteome-wide prediction of peptide tandem mass spectra by deep learning. Nat Methods 16:509–518
pubmed: 31133760
doi: 10.1038/s41592-019-0426-7
Pietilä S, Suomi T, Aakko J, Elo LL (2019) A data analysis protocol for quantitative data-independent acquisition proteomics. Methods Mol Biol 1871:455–465
pubmed: 30276755
doi: 10.1007/978-1-4939-8814-3_27
Aakko J, Pietilä S, Suomi T et al (2020) Data-independent acquisition mass spectrometry in metaproteomics of gut microbiota—implementation and computational analysis. J Proteome Res 19:432–436
pubmed: 31755272
doi: 10.1021/acs.jproteome.9b00606
Elias JE, Gygi SP (2007) Target-decoy search strategy for increased confidence in large-scale protein identifications by mass spectrometry. Nat Methods 4:207–214
pubmed: 17327847
doi: 10.1038/nmeth1019
Käll L, Canterbury JD, Weston J et al (2007) Semi-supervised learning for peptide identification from shotgun proteomics datasets. Nat Methods 4:923–925
pubmed: 17952086
doi: 10.1038/nmeth1113
The M, MacCoss MJ, Noble WS, Käll L (2016) Fast and accurate protein false discovery rates on large-scale proteomics data sets with percolator 3.0. J Am Soc Mass Spectrom 27:1719–1727
pubmed: 27572102
pmcid: 5059416
doi: 10.1007/s13361-016-1460-7
Mikan MP, Harvey HR, Timmins-Schiffman E et al (2020) Metaproteomics reveal that rapid perturbations in organic matter prioritize functional restructuring over taxonomy in western Arctic Ocean microbiomes. ISME J 14:39–52
pubmed: 31492961
doi: 10.1038/s41396-019-0503-z
Guo X, Li Z, Yao Q et al (2018) Sipros ensemble improves database searching and filtering for complex metaproteomics. Bioinformatics 34:795–802
pubmed: 29028897
doi: 10.1093/bioinformatics/btx601
Keller A, Nesvizhskii AI, Kolker E, Aebersold R (2002) Empirical statistical model to estimate the accuracy of peptide identifications made by MS/MS and database search. Anal Chem 74:5383–5392
pubmed: 12403597
doi: 10.1021/ac025747h
Cociorva D, Tabb L, Yates JR (2007) Validation of tandem mass spectrometry database search results using DTASelect. Curr Protoc Bioinform 13:Unit 13.4
Chatterjee S, Stupp GS, Park SKR et al (2016) A comprehensive and scalable database search system for metaproteomics. BMC Genomics 17:642
pubmed: 27528457
pmcid: 4986259
doi: 10.1186/s12864-016-2855-3
Ma B, Zhang K, Hendrie C et al (2003) PEAKS: powerful software for peptide de novo sequencing by tandem mass spectrometry. Rapid Commun Mass Spectrom 17:2337–2342
pubmed: 14558135
doi: 10.1002/rcm.1196
Frank A, Pevzner P (2005) PepNovo: de novo peptide sequencing via probabilistic network modeling. Anal Chem 77:964–973
pubmed: 15858974
doi: 10.1021/ac048788h
Fischer B, Roth V, Roos F et al (2005) NovoHMM: a hidden Markov model for de novo peptide sequencing. Anal Chem 77:7265–7273
pubmed: 16285674
doi: 10.1021/ac0508853
Kleikamp HBC, Pronk M, Tugui C et al (2021) Database-independent de novo metaproteomics of complex microbial communities. Cell Syst 12:375–383.e5
pubmed: 34023022
doi: 10.1016/j.cels.2021.04.003
Behsaz B, Mohimani H, Gurevich A et al (2020) De novo peptide sequencing reveals many cyclopeptides in the human gut and other environments. Cell Syst 10:99–108
pubmed: 31864964
doi: 10.1016/j.cels.2019.11.007
Thompson A, Schäfer J, Kuhn K et al (2003) Tandem mass tags: a novel quantification strategy for comparative analysis of complex protein mixtures by MS/MS. Anal Chem 75:1895–1904
pubmed: 12713048
doi: 10.1021/ac0262560
Ong S-E, Blagoev B, Kratchmarova I et al (2002) Stable isotope labeling by amino acids in cell culture, SILAC, as a simple and accurate approach to expression proteomics. Mol Cell Proteomics 1:376–386
pubmed: 12118079
doi: 10.1074/mcp.M200025-MCP200
Ross PL, Huang YN, Marchese JN et al (2004) Multiplexed protein quantitation in Saccharomyces cerevisiae using amine-reactive isobaric tagging reagents. Mol Cell Proteomics 3:1154–1169
pubmed: 15385600
doi: 10.1074/mcp.M400129-MCP200
Zhang X, Ning Z, Mayne J et al (2016) In vitro metabolic labeling of intestinal microbiota for quantitative metaproteomics. Anal Chem 88:6120–6125
pubmed: 27248155
doi: 10.1021/acs.analchem.6b01412
Tang J, Fu J, Wang Y et al (2020) ANPELA: analysis and performance assessment of the label-free quantification workflow for metaproteomic studies. Brief Bioinform 21:621–636
pubmed: 30649171
doi: 10.1093/bib/bby127
Riffle M, May DH, Timmins-Schiffman E et al (2018) MetaGOmics: a web-based tool for peptide-centric functional and taxonomic analysis of metaproteomics data. Proteomes 6:2
doi: 10.3390/proteomes6010002
Mayers MD, Moon C, Stupp GS et al (2017) Quantitative metaproteomics and activity-based probe enrichment reveals significant alterations in protein expression from a mouse model of inflammatory bowel disease. J Proteome Res 16:1014–1026
pubmed: 28052195
pmcid: 5441882
doi: 10.1021/acs.jproteome.6b00938
Cheng K, Ning Z, Zhang X et al (2017) MetaLab: an automated pipeline for metaproteomic data analysis. Microbiome 5:157
pubmed: 29197424
pmcid: 5712144
doi: 10.1186/s40168-017-0375-2
Cheng K, Ning Z, Zhang X et al (2020) MetaLab 2.0 enables accurate post-translational modifications profiling in metaproteomics. J Am Soc Mass Spectrom 31:1473–1482
pubmed: 32396346
doi: 10.1021/jasms.0c00083
Zhang X, Ning Z, Mayne J et al (2020) Widespread protein lysine acetylation in gut microbiome and its alterations in patients with Crohn’s disease. Nat Commun 11:1–12
Schiebenhoefer H, Schallert K, Renard BY et al (2020) A complete and flexible workflow for metaproteomics data analysis based on MetaProteomeAnalyzer and prophane. Nat Protoc 15:3212–3239
pubmed: 32859984
doi: 10.1038/s41596-020-0368-7
Muth T, Behne A, Heyer R et al (2015) The MetaProteomeAnalyzer: a powerful open-source software suite for metaproteomics data analysis and interpretation. J Proteome Res 14:1557–1565
pubmed: 25660940
doi: 10.1021/pr501246w
Muth T, Kohrs F, Heyer R et al (2018) MPA portable: a stand-alone software package for analyzing metaproteome samples on the go. Anal Chem 90:685–689
pubmed: 29215871
doi: 10.1021/acs.analchem.7b03544
Schneider T, Schmid E, de Castro JV et al (2011) Structure and function of the symbiosis partners of the lung lichen (Lobaria pulmonaria L. Hoffm.) analyzed by metaproteomics. Proteomics 11:2752–2756
pubmed: 21604374
doi: 10.1002/pmic.201000679
Geer LY, Markey SP, Kowalak JA et al Open mass spectrometry search algorithm. J Proteome Res 3:958–964
Van Den Bossche T, Verschaffelt P, Schallert K et al (2020) Connecting MetaProteomeAnalyzer and PeptideShaker to unipept for seamless end-to-end metaproteomics data analysis. J Proteome Res 19:3562–3566
pubmed: 32431147
doi: 10.1021/acs.jproteome.0c00136
Vaudel M, Burkhart JM, Zahedi RP et al (2015) PeptideShaker enables reanalysis of MS-derived proteomics data sets. Nat Biotechnol 33:22–24
pubmed: 25574629
doi: 10.1038/nbt.3109
Gurdeep Singh R, Tanca A, Palomba A et al (2019) Unipept 4.0: functional analysis of metaproteome data. J Proteome Res 18:606–615
pubmed: 30465426
doi: 10.1021/acs.jproteome.8b00716
Verschaffelt P, Van Den Bossche T, Martens L et al (2021) Unipept desktop: a faster, more powerful metaproteomics results analysis tool. J Proteome Res 20:2005–2009
pubmed: 33401902
doi: 10.1021/acs.jproteome.0c00855
Perez-Riverol Y, Csordas A, Bai J et al (2018) The PRIDE database and related tools and resources in 2019: improving support for quantification data. Nucleic Acids Res 47:D442–D450
pmcid: 6323896
doi: 10.1093/nar/gky1106
Deutsch EW, Csordas A, Sun Z et al (2017) The ProteomeXchange consortium in 2017: supporting the cultural change in proteomics public data deposition. Nucleic Acids Res 45:D1100–D1106
pubmed: 27924013
doi: 10.1093/nar/gkw936
Jagtap PD, Blakely A, Murray K et al (2015) Metaproteomic analysis using the galaxy framework. Proteomics 15:3553–3565
pubmed: 26058579
doi: 10.1002/pmic.201500074
Huson DH, Weber N (2013) Microbial community analysis using MEGAN. Methods Enzymol 531:465–485
pubmed: 24060133
doi: 10.1016/B978-0-12-407863-5.00021-6
Röst HL, Sachsenberg T, Aiche S et al (2016) OpenMS: a flexible open-source software platform for mass spectrometry data analysis. Nat Methods 13:741–748
pubmed: 27575624
doi: 10.1038/nmeth.3959
Grüning B, Chilton J, Köster J et al (2018) Practical computational reproducibility in the life sciences. Cell Syst. 6:631–635
pubmed: 29953862
pmcid: 6263957
doi: 10.1016/j.cels.2018.03.014
Berthold MR, Cebron N, Dill F et al (2007) KNIME: the Konstanz information miner. In: Studies in classification, data analysis, and knowledge organization (GfKL 2007). Springer
Sachsenberg T, Herbst FA, Taubert M et al (2015) MetaProSIP: automated inference of stable isotope incorporation rates in proteins for functional metaproteomics. J Proteome 14:619–627
doi: 10.1021/pr500245w
Deutsch EW, Mendoza L, Shteynberg D et al (2015) Trans-proteomic pipeline, a standardized data processing pipeline for large-scale reproducible proteomics informatics. Proteomics Clin Appl 9:745–754
pubmed: 25631240
pmcid: 4506239
doi: 10.1002/prca.201400164
Rabe A, Gesell Salazar M, Michalik S et al (2019) Metaproteomics analysis of microbial diversity of human saliva and tongue dorsum in young healthy individuals. J Oral Microbiol 11:1654786
pubmed: 31497257
pmcid: 6720020
doi: 10.1080/20002297.2019.1654786
Välikangas T, Suomi T, Elo LL (2018) A systematic evaluation of normalization methods in quantitative label-free proteomics. Brief Bioinform 19:1–11
pubmed: 27694351
Willforss J, Chawade A, Levander F (2019) NormalyzerDE: online tool for improved normalization of omics expression data and high-sensitivity differential expression analysis. J Proteome Res 18:732–740
pubmed: 30277078
doi: 10.1021/acs.jproteome.8b00523
Polpitiya AD, Qian W-J, Jaitly N et al (2008) DAnTE: a statistical tool for quantitative analysis of -omics data. Bioinformatics 24:1556–1558
pubmed: 18453552
doi: 10.1093/bioinformatics/btn217
Marion S, Desharnais L, Studer N et al (2020) Biogeography of microbial bile acid transformations along the murine gut. J Lipid Res 61:1450–1463
pubmed: 32661017
pmcid: 7604727
doi: 10.1194/jlr.RA120001021
Karpievitch YV, Dabney AR, Smith RD (2012) Normalization and missing value imputation for label-free LC-MS analysis. BMC Bioinform 13:1–9
doi: 10.1186/1471-2105-13-S16-S5
Lazar C, Gatto L, Ferro M et al (2016) Accounting for the multiple natures of missing values in label-free quantitative proteomics data sets to compare imputation strategies. J Proteome Res 15:1116–1125
pubmed: 26906401
doi: 10.1021/acs.jproteome.5b00981
Liu M, Dongre A (2020) Proper imputation of missing values in proteomics datasets for differential expression analysis. Brief Bioinform 22:bbaa112
doi: 10.1093/bib/bbaa112
Wang S, Li W, Hu L et al (2020) NAguideR: performing and prioritizing missing value imputations for consistent bottom-up proteomic analyses. Nucleic Acids Res 48:e83–e83
pubmed: 32526036
pmcid: 7641313
doi: 10.1093/nar/gkaa498
Graw S, Tang J, Zafar MK et al (2020) proteiNorm—a user-friendly tool for normalization and analysis of TMT and label-free protein quantification. ACS Omega 5:25625–25633
pubmed: 33073088
pmcid: 7557219
doi: 10.1021/acsomega.0c02564
Nesvizhskii AI, Aebersold R (2005) Interpretation of shotgun proteomic data: the protein inference problem. Mol Cell Proteomics 4:1419–1440
pubmed: 16009968
doi: 10.1074/mcp.R500012-MCP200
Serang O, Noble W (2012) A review of statistical methods for protein identification using tandem mass spectrometry. Stat Interface 5:3–20
pubmed: 22833779
pmcid: 3402235
doi: 10.4310/SII.2012.v5.n1.a2
Carbon S, Douglass E, Dunn N et al (2019) The gene ontology resource: 20 years and still GOing strong. Nucleic Acids Res 47:D330–D338
doi: 10.1093/nar/gky1055
Bairoch A (2000) The ENZYME database in 2000. Nucleic Acids Res 28:304–305
pubmed: 10592255
pmcid: 102465
doi: 10.1093/nar/28.1.304
Mooradian AD, van der Post S, Naegle KM, Held JM (2020) ProteoClade: a taxonomic toolkit for multi-species and metaproteomic analysis. PLoS Comput Biol 16:e1007741
pubmed: 32150535
pmcid: 7082058
doi: 10.1371/journal.pcbi.1007741
Saunders JK, Gaylord DA, Held NA et al (2020) METATRYP v 2.0: metaproteomic least common ancestor analysis for taxonomic inference using specialized sequence assemblies-standalone software and web servers for marine microorganisms and coronaviruses. J Proteome Res 19:4718–4729
pubmed: 32897080
pmcid: 7640959
doi: 10.1021/acs.jproteome.0c00385
Saito MA, Saunders JK, Chagnon M et al (2021) Development of an ocean protein portal for interactive discovery and education. J Proteome Res 20:326–336
pubmed: 32897077
doi: 10.1021/acs.jproteome.0c00382
Ogata H, Goto S, Sato K et al (1999) KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res 27:29–34
pubmed: 9847135
pmcid: 148090
doi: 10.1093/nar/27.1.29
Galperin MY, Wolf YI, Makarova KS et al (2021) COG database update: focus on microbial diversity, model organisms, and widespread pathogens. Nucleic Acids Res 49:D274–D281
pubmed: 33167031
doi: 10.1093/nar/gkaa1018
Huerta-Cepas J, Szklarczyk D, Heller D et al (2019) EggNOG 5.0: a hierarchical, functionally and phylogenetically annotated orthology resource based on 5090 organisms and 2502 viruses. Nucleic Acids Res 47(D1):D309–D314
pubmed: 30418610
doi: 10.1093/nar/gky1085
The UniProt Consortium (2019) UniProt: a worldwide hub of protein knowledge. Nucleic Acids Res 47:D506–D515
doi: 10.1093/nar/gky1049
Blakeley-Ruiz JA, Erickson AR, Cantarel BL et al (2019) Metaproteomics reveals persistent and phylum-redundant metabolic functional stability in adult human gut microbiomes of Crohn’s remission patients despite temporal variations in microbial taxa, genomes, and proteomes. Microbiome 7:18
pubmed: 30744677
pmcid: 6371617
doi: 10.1186/s40168-019-0631-8
Easterly CW, Sajulga R, Mehta S et al (2019) MetaQuantome: an integrated, quantitative metaproteomics approach reveals connections between taxonomy and protein function in complex microbiomes. Mol Cell Proteomics 18:S82–S91
pubmed: 31235611
pmcid: 6692774
doi: 10.1074/mcp.RA118.001240
Simopoulos CMA, Ning Z, Zhang X et al (2020) pepFunk: a tool for peptide-centric functional analysis of metaproteomic human gut microbiome studies. Bioinformatics 36:4171–4179
pubmed: 32369596
doi: 10.1093/bioinformatics/btaa289
Bolyen E, Dillon M, Bokulich N et al (2019) Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat Biotechnol 37:852–857
pubmed: 31341288
pmcid: 7015180
doi: 10.1038/s41587-019-0209-9
Rechenberger J, Samaras P, Jarzab A et al (2019) Challenges in clinical metaproteomics highlighted by the analysis of acute leukemia patients with gut colonization by multidrug-resistant enterobacteriaceae. Proteomes 7:2
pubmed: 30626002
pmcid: 6473847
doi: 10.3390/proteomes7010002
Starke R, Bastida F, Abadía J et al (2017) Ecological and functional adaptations to water management in a semiarid agroecosystem: a soil metaproteomics approach. Sci Rep 7:1–16
doi: 10.1038/s41598-017-09973-w
Li L, Ning Z, Zhang X et al (2020) RapidAIM: a culture- and metaproteomics-based rapid assay of individual microbiome responses to drugs. Microbiome 8:33
pubmed: 32160905
pmcid: 7066843
doi: 10.1186/s40168-020-00806-z
Li L, Chang L, Zhang X et al (2020) Berberine and its structural analogs have differing effects on functional profiles of individual gut microbiomes. Gut Microbes 11:1348–1361
pubmed: 32372706
pmcid: 7524264
doi: 10.1080/19490976.2020.1755413
Li L, Ryan J, Ning Z et al (2020) A functional ecological network based on metaproteomics responses of individual gut microbiomes to resistant starches. Comput Struct Biotechnol J 18:3833–3842
pubmed: 33335682
pmcid: 7720074
doi: 10.1016/j.csbj.2020.10.042