Pipasic: Similarity and expression correction for strain-level identification and quantification in metaproteomics

Bioinformatics

Volumn 30, Issue 12, 2014, Pages

  Penzlin, Anke ^a   Lindner, Martin S ^a   Doellinger, Joerg ^a   Dabrowski, Piotr Wojtek ^a   Nitsche, Andreas ^a   Renard, Bernhard Y ^a  

^a ROBERT KOCH INSTITUTE   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

BACTERIAL PROTEIN; PEPTIDE; PROTEOME;

ALGORITHM; ARTICLE; CHEMISTRY; CLASSIFICATION; COMPUTER PROGRAM; COWPOX VIRUS; MASS SPECTROMETRY; METABOLISM; METHODOLOGY; MICROBIOLOGY; PROTEOMICS;

ALGORITHMS; BACTERIAL PROTEINS; COWPOX VIRUS; ENVIRONMENTAL MICROBIOLOGY; MASS SPECTROMETRY; PEPTIDES; PROTEOME; PROTEOMICS; SOFTWARE;

EID: 84902437322     PISSN: 13674803     EISSN: 14602059     Source Type: Journal    
DOI: 10.1093/bioinformatics/btu267     Document Type: Article

References (34)

1. Altschul, S.F. et al. (1990) Basic local alignment search tool. J. Mol. Biol, 215, 403-410.
2. Bielow, C. et al. (2011) MSSimulator: simulation of mass spectrometry data. J. Proteome Res., 10, 2922-2929.
3. Bradshaw, R.A. et al. (2006) Reporting protein identification data: the next generation of guidelines. Mol. Cell. Proteomics, 5, 787-788.
4. Chourey, K. et al. (2013) Environmental proteomics reveals early microbial community responses to biostimulation at a uranium-and nitrate-contaminated site. Proteomics, 13, 2921-2930.
5. Denef, V.J. et al. (2010) Proteogenomic basis for ecological divergence of closely related bacteria in natural acidophilic microbial communities. Proc. Natl Acad. Sci. USA, 107, 2383-2390.
6. Diament, B.J. and Noble, W.S. (2011) Faster SEQUEST searching for peptide identification from tandem mass spectra. J. Proteome Res., 10, 3871-3879.
7. Dicker, L. et al. (2010) Increased power for the analysis of label-free LC-MS/MS proteomics data by combining spectral counts and peptide peak attributes. Mol. Cell. Proteomics, 9, 2704-2718.
8. Efron, B. et al. (2004) Least angle regression. Ann. Stat., 32, 407-499.
9. Fouts, D.E. et al. (2012) 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., 10, 174.
10. Geer, L.Y. et al. (2004) Open mass spectrometry search algorithm. J. Proteome Res., 3, 958-964.
11. Hettich, R.L. et al. (2013) Metaproteomics: harnessing the power of high performance mass spectrometry to identify the suite of proteins that control metabolic activities in microbial communities. Anal. Chem., 85, 4203-4214.
12. Huson, D.H. et al. (2007) MEGAN analysis of metagenomic data. Genome Res., 17, 377-386.
13. Jagtap, P. et al. (2012a) Deep metaproteomic analysis of human salivary supernatant. Proteomics, 12, 992-1001.
14. Jagtap, P. et al. (2012b) Workflow for analysis of high mass accuracy salivary data set using MaxQuant and ProteinPilot search algorithm. Proteomics, 12, 1726-1730.
15. Jagtap, P. et al. (2013) A two-step database search method improves sensitivity in peptide sequence matches for metaproteomics and proteogenomics studies. Proteomics, 13, 1352-1357.
16. Jeong, K. et al. (2012) False discovery rates in spectral identification. BMC Bioinformatics, 13 (Suppl. 16), S2.
17. Karlsson, R. et al. (2012) Strain-level typing and identification of bacteria using mass spectrometry-based proteomics. J. Proteome Res., 11, 2710-2720.
18. Lai, B. et al. (2012) A de novo metagenomic assembly program for shotgun DNA reads. Bioinformatics, 28, 1455-1462.
19. Lindner, M.S. and Renard, B.Y. (2013) Metagenomic abundance estimation and diagnostic testing on species level. Nucleic Acids Res., 41, e10.
20. Lindner, M.S. et al. (2013) Analyzing genome coverage profiles with applications to quality control in metagenomics. Bioinformatics, 29, 1260-1267.
21. Lo, I. et al. (2007) Strain-resolved community proteomics reveals recombining genomes of acidophilic bacteria. Nature, 446, 537-541.
22. Mehlan, H. et al. (2013) Data visualization in environmental proteomics. Proteomics, 13, 2805-2821.
23. Muth, T. et al. (2013) Searching for a needle in a stack of needles: challenges in metaproteomics data analysis. Mol. Biosystems, 9, 578-585.
24. Nesvizhskii, A.I. (2010) A survey of computational methods and error rate estimation procedures for peptide and protein identification in shotgun proteomics. J. Proteomics, 73, 2092-2123.
25. Oliphant, T.E. (2007) Python for scientific computing. Comput. Sci. Eng., 9, 10-20.
26. Renard, B.Y. et al. (2008) NITPICK: peak identification for mass spectrometry data. BMC Bioinformatics, 9, 355.
27. Renard, B.Y. et al. (2010) Estimating the confidence of peptide identifications without decoy databases. Anal. Chem., 82, 4314-4318.
28. Renard, B.Y. et al. (2012) Overcoming species boundaries in peptide identification with Bayesian information criterion-driven error-tolerant peptide search (BICEPS). Mol. Cell. Proteomics, 11, M111.014167.
29. Rooijers, K. et al. (2011) An iterative workflow for mining the human intestinal metaproteome. BMC Genomics, 12, 6.
30. Schneider, T. 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.
31. Seifert, J. et al. (2013) Bioinformatic progress and applications in metaproteoge-nomics for bridging the gap between genomic sequences and metabolic functions in microbial communities. Proteomics, 13, 2786-2804.
32. Tanner, S. et al. (2005) InsPecT: identification of posttranslationally modified pep-tides from tandem mass spectra. Anal. Chem., 77, 4626-4639.
33. Teeling, H. and Glockner, F.O. (2012) Current opportunities and challenges in mi-crobial metagenome analysis- A bioinformatic perspective. Brief. Bioinform., 13, 728-742.
34. Wooley, J.C. et al. (2010) A primer on metagenomics. PLoS Comput. Biol., 6, e1000667.