Pipasic: Similarity and Expression Correction for Strain-level Identification and Quantification in Metaproteomics

Overview

Journal Bioinformatics

Publisher Oxford University Press

Specialty Biology

Date 2014 Jun 17

PMID 24931978

Citations 17

Authors

Anke Penzlin

Martin S Lindner

Joerg Doellinger

Piotr Wojtek Dabrowski

Andreas Nitsche

Bernhard Y Renard

Affiliations

Soon will be listed here.

Abstract

Motivation: Metaproteomic analysis allows studying the interplay of organisms or functional groups and has become increasingly popular also for diagnostic purposes. However, difficulties arise owing to the high sequence similarity between related organisms. Further, the state of conservation of proteins between species can be correlated with their expression level, which can lead to significant bias in results and interpretation. These challenges are similar but not identical to the challenges arising in the analysis of metagenomic samples and require specific solutions.

Results: We introduce Pipasic (peptide intensity-weighted proteome abundance similarity correction) as a tool that corrects identification and spectral counting-based quantification results using peptide similarity estimation and expression level weighting within a non-negative lasso framework. Pipasic has distinct advantages over approaches only regarding unique peptides or aggregating results to the lowest common ancestor, as demonstrated on examples of viral diagnostics and an acid mine drainage dataset.

Availability And Implementation: Pipasic source code is freely available from https://sourceforge.net/projects/pipasic/.

Contact: RenardB@rki.de

Supplementary Information: Supplementary data are available at Bioinformatics online.

Citing Articles

Moving Toward Metaproteogenomics: A Computational Perspective on Analyzing Microbial Samples via Proteogenomics.

Singer F, Kuhring M, Renard B, Muth T Methods Mol Biol. 2024; 2859:297-318.

PMID: 39436609 DOI: 10.1007/978-1-0716-4152-1_17.

Bioinformatic Workflows for Metaproteomics.

Holstein T, Muth T Methods Mol Biol. 2024; 2820:187-213.

PMID: 38941024 DOI: 10.1007/978-1-0716-3910-8_16.

Pairing metagenomics and metaproteomics to characterize ecological niches and metabolic essentiality of gut microbiomes.

Wang T, Li L, Figeys D, Liu Y ISME Commun. 2024; 4(1):ycae063.

PMID: 38808120 PMC: 11131966. DOI: 10.1093/ismeco/ycae063.

Evaluation of the Limit of Detection of Bacteria by Tandem Mass Spectrometry Proteotyping and Phylopeptidomics.

Mappa C, Alpha-Bazin B, Pible O, Armengaud J Microorganisms. 2023; 11(5).

PMID: 37317144 PMC: 10223342. DOI: 10.3390/microorganisms11051170.

PepGM: a probabilistic graphical model for taxonomic inference of viral proteome samples with associated confidence scores.

Holstein T, Kistner F, Martens L, Muth T Bioinformatics. 2023; 39(5).

PMID: 37129543 PMC: 10182852. DOI: 10.1093/bioinformatics/btad289.

References

Hettich R, Pan C, Chourey K, Giannone R . Metaproteomics: harnessing the power of high performance mass spectrometry to identify the suite of proteins that control metabolic activities in microbial communities. Anal Chem. 2013; 85(9):4203-14. PMC: 3696428. DOI: 10.1021/ac303053e. View

Renard B, Timm W, Kirchner M, Steen J, Hamprecht F, Steen H . Estimating the confidence of peptide identifications without decoy databases. Anal Chem. 2010; 82(11):4314-8. DOI: 10.1021/ac902892j. View

Lo I, Denef V, VerBerkmoes N, Shah M, Goltsman D, DiBartolo G . Strain-resolved community proteomics reveals recombining genomes of acidophilic bacteria. Nature. 2007; 446(7135):537-41. DOI: 10.1038/nature05624. View

Fouts D, Pieper R, Szpakowski S, Pohl H, Knoblach S, Suh M . Integrated next-generation sequencing of 16S rDNA and metaproteomics differentiate the healthy urine microbiome from asymptomatic bacteriuria in neuropathic bladder associated with spinal cord injury. J Transl Med. 2012; 10:174. PMC: 3511201. DOI: 10.1186/1479-5876-10-174. View

Jagtap P, McGowan T, Bandhakavi S, Tu Z, Seymour S, Griffin T . Deep metaproteomic analysis of human salivary supernatant. Proteomics. 2012; 12(7):992-1001. PMC: 3517020. DOI: 10.1002/pmic.201100503. View

Lai B, Ding R, Li Y, Duan L, Zhu H . A de novo metagenomic assembly program for shotgun DNA reads. Bioinformatics. 2012; 28(11):1455-62. DOI: 10.1093/bioinformatics/bts162. View

Jeong K, Kim S, Bandeira N . False discovery rates in spectral identification. BMC Bioinformatics. 2012; 13 Suppl 16:S2. PMC: 3489529. DOI: 10.1186/1471-2105-13-S16-S2. View

Karlsson R, Davidson M, Svensson-Stadler L, Karlsson A, Olesen K, Carlsohn E . Strain-level typing and identification of bacteria using mass spectrometry-based proteomics. J Proteome Res. 2012; 11(5):2710-20. DOI: 10.1021/pr2010633. View

Lindner M, Kollock M, Zickmann F, Renard B . Analyzing genome coverage profiles with applications to quality control in metagenomics. Bioinformatics. 2013; 29(10):1260-7. DOI: 10.1093/bioinformatics/btt147. View

10.

Diament B, Noble W . Faster SEQUEST searching for peptide identification from tandem mass spectra. J Proteome Res. 2011; 10(9):3871-9. PMC: 3166376. DOI: 10.1021/pr101196n. View

11.

Lindner M, Renard B . Metagenomic abundance estimation and diagnostic testing on species level. Nucleic Acids Res. 2012; 41(1):e10. PMC: 3592424. DOI: 10.1093/nar/gks803. View

12.

Denef V, Kalnejais L, Mueller R, Wilmes P, Baker B, Thomas B . Proteogenomic basis for ecological divergence of closely related bacteria in natural acidophilic microbial communities. Proc Natl Acad Sci U S A. 2010; 107(6):2383-90. PMC: 2823883. DOI: 10.1073/pnas.0907041107. View

13.

Seifert J, Herbst F, Nielsen P, Planes F, Jehmlich N, Ferrer M . Bioinformatic progress and applications in metaproteogenomics for bridging the gap between genomic sequences and metabolic functions in microbial communities. Proteomics. 2013; 13(18-19):2786-804. DOI: 10.1002/pmic.201200566. View

14.

Wooley J, Godzik A, Friedberg I . A primer on metagenomics. PLoS Comput Biol. 2010; 6(2):e1000667. PMC: 2829047. DOI: 10.1371/journal.pcbi.1000667. View

15.

Teeling H, Glockner F . Current opportunities and challenges in microbial metagenome analysis--a bioinformatic perspective. Brief Bioinform. 2012; 13(6):728-42. PMC: 3504927. DOI: 10.1093/bib/bbs039. View

16.

Dicker L, Lin X, Ivanov A . Increased power for the analysis of label-free LC-MS/MS proteomics data by combining spectral counts and peptide peak attributes. Mol Cell Proteomics. 2010; 9(12):2704-18. PMC: 3101957. DOI: 10.1074/mcp.M110.002774. View

17.

Bradshaw R, Burlingame A, Carr S, Aebersold R . Reporting protein identification data: the next generation of guidelines. Mol Cell Proteomics. 2006; 5(5):787-8. DOI: 10.1074/mcp.E600005-MCP200. View

18.

Jagtap P, Bandhakavi S, Higgins L, McGowan T, Sa R, Stone M . Workflow for analysis of high mass accuracy salivary data set using MaxQuant and ProteinPilot search algorithm. Proteomics. 2012; 12(11):1726-30. PMC: 3618284. DOI: 10.1002/pmic.201100097. View

19.

Chourey K, Nissen S, Vishnivetskaya T, Shah M, Pfiffner S, Hettich R . Environmental proteomics reveals early microbial community responses to biostimulation at a uranium- and nitrate-contaminated site. Proteomics. 2013; 13(18-19):2921-30. DOI: 10.1002/pmic.201300155. View

20.

Renard B, Kirchner M, Steen H, Steen J, Hamprecht F . NITPICK: peak identification for mass spectrometry data. BMC Bioinformatics. 2008; 9:355. PMC: 2655099. DOI: 10.1186/1471-2105-9-355. View