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Novel Bioinformatics Strategies Driving Dynamic Metaproteomic Studies

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Proteomics in Systems Biology

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2456))

Abstract

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.

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References

  1. 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

    Article  PubMed  PubMed Central  Google Scholar 

  2. Heyer R, Schallert K, Zoun R et al (2017) Challenges and perspectives of metaproteomic data analysis. J Biotechnol 261:24–36

    Article  CAS  PubMed  Google Scholar 

  3. 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

    Article  CAS  PubMed  Google Scholar 

  4. 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

    Article  CAS  PubMed  Google Scholar 

  5. 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

    Google Scholar 

  6. Doerr A (2014) DIA mass spectrometry. Nat Methods 12:35–35

    Article  Google Scholar 

  7. 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

    Article  CAS  PubMed  Google Scholar 

  8. 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

    Article  PubMed  PubMed Central  Google Scholar 

  9. 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

    Article  PubMed  PubMed Central  Google Scholar 

  10. 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

    Article  CAS  PubMed  Google Scholar 

  11. 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

    Article  PubMed  Google Scholar 

  12. 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

    Article  CAS  PubMed  Google Scholar 

  13. Kuhring M, Renard BY (2015) Estimating the computational limits of detection of microbial non-model organisms. Proteomics 15:3580–3584

    Article  CAS  PubMed  Google Scholar 

  14. 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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. 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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. 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

    Article  CAS  PubMed  Google Scholar 

  17. Craig R, Beavis RC (2004) TANDEM: matching proteins with tandem mass spectra. Bioinformatics 20:1466–1467

    Article  CAS  PubMed  Google Scholar 

  18. Tyanova S, Temu T, Cox J (2016) The MaxQuant computational platform for mass spectrometry-based shotgun proteomics. Nat Protoc 11:2301–2319

    Article  CAS  PubMed  Google Scholar 

  19. Beyter D, Lin MS, Yu Y et al (2018) ProteoStorm: an ultrafast metaproteomics database search framework. Cell Syst 7:463–467

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. 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

    Article  CAS  PubMed  Google Scholar 

  21. UniProt Consortium (2021) UniProt: the universal protein knowledgebase in 2021. Nucleic Acids Res 49:D480–D489

    Article  Google Scholar 

  22. Park SKR, Jung T, Thuy-Boun PS et al (2019) ComPIL 2.0: an updated comprehensive metaproteomics database. J Proteome Res 18:616–622

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. 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

    Article  CAS  Google Scholar 

  24. 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

    Article  CAS  PubMed  Google Scholar 

  25. Craig R, Cortens JC, Fenyo D, Beavis RC (2006) Using annotated peptide mass spectrum libraries for protein identification. J Proteome Res 5:1843–1849

    Article  CAS  PubMed  Google Scholar 

  26. 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

    Article  CAS  PubMed  Google Scholar 

  27. 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

    CAS  Google Scholar 

  28. 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

    Article  CAS  PubMed  Google Scholar 

  29. 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

    Article  PubMed  Google Scholar 

  30. 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

    Article  CAS  PubMed  Google Scholar 

  31. 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

    Article  CAS  PubMed  Google Scholar 

  32. Käll L, Canterbury JD, Weston J et al (2007) Semi-supervised learning for peptide identification from shotgun proteomics datasets. Nat Methods 4:923–925

    Article  PubMed  Google Scholar 

  33. 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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. 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

    Article  CAS  PubMed  Google Scholar 

  35. Guo X, Li Z, Yao Q et al (2018) Sipros ensemble improves database searching and filtering for complex metaproteomics. Bioinformatics 34:795–802

    Article  CAS  PubMed  Google Scholar 

  36. 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

    Article  CAS  PubMed  Google Scholar 

  37. Cociorva D, Tabb L, Yates JR (2007) Validation of tandem mass spectrometry database search results using DTASelect. Curr Protoc Bioinform 13:Unit 13.4

    Google Scholar 

  38. Chatterjee S, Stupp GS, Park SKR et al (2016) A comprehensive and scalable database search system for metaproteomics. BMC Genomics 17:642

    Article  PubMed  PubMed Central  Google Scholar 

  39. 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

    Article  CAS  PubMed  Google Scholar 

  40. Frank A, Pevzner P (2005) PepNovo: de novo peptide sequencing via probabilistic network modeling. Anal Chem 77:964–973

    Article  CAS  PubMed  Google Scholar 

  41. Fischer B, Roth V, Roos F et al (2005) NovoHMM: a hidden Markov model for de novo peptide sequencing. Anal Chem 77:7265–7273

    Article  CAS  PubMed  Google Scholar 

  42. Kleikamp HBC, Pronk M, Tugui C et al (2021) Database-independent de novo metaproteomics of complex microbial communities. Cell Syst 12:375–383.e5

    Article  CAS  PubMed  Google Scholar 

  43. 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

    Article  CAS  PubMed  Google Scholar 

  44. 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

    Article  CAS  PubMed  Google Scholar 

  45. 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

    Article  CAS  PubMed  Google Scholar 

  46. 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

    Article  CAS  PubMed  Google Scholar 

  47. Zhang X, Ning Z, Mayne J et al (2016) In vitro metabolic labeling of intestinal microbiota for quantitative metaproteomics. Anal Chem 88:6120–6125

    Article  CAS  PubMed  Google Scholar 

  48. 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

    Article  CAS  PubMed  Google Scholar 

  49. 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

    Article  Google Scholar 

  50. 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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Cheng K, Ning Z, Zhang X et al (2017) MetaLab: an automated pipeline for metaproteomic data analysis. Microbiome 5:157

    Article  PubMed  PubMed Central  Google Scholar 

  52. 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

    Article  CAS  PubMed  Google Scholar 

  53. 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

    Google Scholar 

  54. 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

    Article  CAS  PubMed  Google Scholar 

  55. 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

    Article  CAS  PubMed  Google Scholar 

  56. 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

    Article  CAS  PubMed  Google Scholar 

  57. 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

    Article  CAS  PubMed  Google Scholar 

  58. Geer LY, Markey SP, Kowalak JA et al Open mass spectrometry search algorithm. J Proteome Res 3:958–964

    Google Scholar 

  59. 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

    Article  PubMed  Google Scholar 

  60. Vaudel M, Burkhart JM, Zahedi RP et al (2015) PeptideShaker enables reanalysis of MS-derived proteomics data sets. Nat Biotechnol 33:22–24

    Article  CAS  PubMed  Google Scholar 

  61. Gurdeep Singh R, Tanca A, Palomba A et al (2019) Unipept 4.0: functional analysis of metaproteome data. J Proteome Res 18:606–615

    Article  CAS  PubMed  Google Scholar 

  62. 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

    Article  CAS  PubMed  Google Scholar 

  63. 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

    Article  PubMed Central  Google Scholar 

  64. 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

    Article  CAS  PubMed  Google Scholar 

  65. Jagtap PD, Blakely A, Murray K et al (2015) Metaproteomic analysis using the galaxy framework. Proteomics 15:3553–3565

    Article  CAS  PubMed  Google Scholar 

  66. Huson DH, Weber N (2013) Microbial community analysis using MEGAN. Methods Enzymol 531:465–485

    Article  CAS  PubMed  Google Scholar 

  67. 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

    Article  PubMed  Google Scholar 

  68. Grüning B, Chilton J, Köster J et al (2018) Practical computational reproducibility in the life sciences. Cell Syst. 6:631–635

    Article  PubMed  PubMed Central  Google Scholar 

  69. 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

    Google Scholar 

  70. 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

    Article  CAS  Google Scholar 

  71. 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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. 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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Välikangas T, Suomi T, Elo LL (2018) A systematic evaluation of normalization methods in quantitative label-free proteomics. Brief Bioinform 19:1–11

    PubMed  Google Scholar 

  74. 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

    Article  CAS  PubMed  Google Scholar 

  75. Polpitiya AD, Qian W-J, Jaitly N et al (2008) DAnTE: a statistical tool for quantitative analysis of -omics data. Bioinformatics 24:1556–1558

    Article  CAS  PubMed  Google Scholar 

  76. 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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Karpievitch YV, Dabney AR, Smith RD (2012) Normalization and missing value imputation for label-free LC-MS analysis. BMC Bioinform 13:1–9

    Article  Google Scholar 

  78. 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

    Article  CAS  PubMed  Google Scholar 

  79. Liu M, Dongre A (2020) Proper imputation of missing values in proteomics datasets for differential expression analysis. Brief Bioinform 22:bbaa112

    Article  Google Scholar 

  80. 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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. 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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Nesvizhskii AI, Aebersold R (2005) Interpretation of shotgun proteomic data: the protein inference problem. Mol Cell Proteomics 4:1419–1440

    Article  CAS  PubMed  Google Scholar 

  83. Serang O, Noble W (2012) A review of statistical methods for protein identification using tandem mass spectrometry. Stat Interface 5:3–20

    Article  PubMed  PubMed Central  Google Scholar 

  84. 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

    Article  CAS  Google Scholar 

  85. Bairoch A (2000) The ENZYME database in 2000. Nucleic Acids Res 28:304–305

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  86. 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

    Article  PubMed  PubMed Central  Google Scholar 

  87. 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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. 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

    Article  CAS  PubMed  Google Scholar 

  89. Ogata H, Goto S, Sato K et al (1999) KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res 27:29–34

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. 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

    Article  CAS  PubMed  Google Scholar 

  91. 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

    Article  CAS  PubMed  Google Scholar 

  92. The UniProt Consortium (2019) UniProt: a worldwide hub of protein knowledge. Nucleic Acids Res 47:D506–D515

    Article  Google Scholar 

  93. 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

    Article  PubMed  PubMed Central  Google Scholar 

  94. 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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  95. 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

    Article  CAS  PubMed  Google Scholar 

  96. 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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  97. 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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  98. 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

    Article  CAS  Google Scholar 

  99. 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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  100. 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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  101. 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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

This work was supported by Natural Sciences and Engineering Research Council of Canada Discovery grants to M.L.A. and D.F. Funding from the Government of Canada through Genome Canada and the Ontario Genomics Institute (OGI-156), the Natural Sciences and Engineering Research Council of Canada (NSERC, grant no. 210034), and the Ontario Ministry of Economic Development and Innovation (ORF-DIG-14405) to D.F. C.M.A.S. was funded by a stipend from the NSERC CREATE in Technologies for Microbiome Science and Engineering (TECHNOMISE) Program.

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  1. Department of Biochemistry, Microbiology and Immunology and Ottawa Institute of Systems Biology, University of Ottawa, Ottawa, ON, Canada

    Caitlin M. A. Simopoulos, Daniel Figeys & Mathieu Lavallée-Adam

  2. School of Pharmaceutical Sciences, University of Ottawa, Ottawa, ON, Canada

    Daniel Figeys

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  1. Caitlin M. A. Simopoulos
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  2. Daniel Figeys
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Correspondence to Mathieu Lavallée-Adam .

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  1. Department of Molecular and Cellular Biology, University of Guelph, Guelph, ON, Canada

    Jennifer Geddes-McAlister

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Simopoulos, C.M.A., Figeys, D., Lavallée-Adam, M. (2022). Novel Bioinformatics Strategies Driving Dynamic Metaproteomic Studies. In: Geddes-McAlister, J. (eds) Proteomics in Systems Biology. Methods in Molecular Biology, vol 2456. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-2124-0_22

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  • DOI: https://doi.org/10.1007/978-1-0716-2124-0_22

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