Literature DB >> 35612752

Novel Bioinformatics Strategies Driving Dynamic Metaproteomic Studies.

Caitlin M A Simopoulos1, Daniel Figeys1,2, Mathieu Lavallée-Adam3.   

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.
© 2022. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.

Entities:  

Keywords:  Bioinformatics; Computational biology; Mass spectrometry; Metaproteomics; Microbiome; Proteomics; Quantification; Software; Statistics

Mesh:

Year:  2022        PMID: 35612752     DOI: 10.1007/978-1-0716-2124-0_22

Source DB:  PubMed          Journal:  Methods Mol Biol        ISSN: 1064-3745


  84 in total

1.  Targeted data extraction of the MS/MS spectra generated by data-independent acquisition: a new concept for consistent and accurate proteome analysis.

Authors:  Ludovic C Gillet; Pedro Navarro; Stephen Tate; Hannes Röst; Nathalie Selevsek; Lukas Reiter; Ron Bonner; Ruedi Aebersold
Journal:  Mol Cell Proteomics       Date:  2012-01-18       Impact factor: 5.911

2.  Automated approach for quantitative analysis of complex peptide mixtures from tandem mass spectra.

Authors:  John D Venable; Meng-Qiu Dong; James Wohlschlegel; Andrew Dillin; John R Yates
Journal:  Nat Methods       Date:  2004-09-29       Impact factor: 28.547

3.  An approach to correlate tandem mass spectral data of peptides with amino acid sequences in a protein database.

Authors:  J K Eng; A L McCormack; J R Yates
Journal:  J Am Soc Mass Spectrom       Date:  1994-11       Impact factor: 3.109

4.  Data-controlled automation of liquid chromatography/tandem mass spectrometry analysis of peptide mixtures.

Authors:  D C Stahl; K M Swiderek; M T Davis; T D Lee
Journal:  J Am Soc Mass Spectrom       Date:  1996-06       Impact factor: 3.109

Review 5.  Challenges and perspectives of metaproteomic data analysis.

Authors:  Robert Heyer; Kay Schallert; Roman Zoun; Beatrice Becher; Gunter Saake; Dirk Benndorf
Journal:  J Biotechnol       Date:  2017-06-27       Impact factor: 3.307

6.  A Robust and Universal Metaproteomics Workflow for Research Studies and Routine Diagnostics Within 24 h Using Phenol Extraction, FASP Digest, and the MetaProteomeAnalyzer.

Authors:  Robert Heyer; Kay Schallert; Anja Büdel; Roman Zoun; Sebastian Dorl; Alexander Behne; Fabian Kohrs; Sebastian Püttker; Corina Siewert; Thilo Muth; Gunter Saake; Udo Reichl; Dirk Benndorf
Journal:  Front Microbiol       Date:  2019-08-16       Impact factor: 5.640

7.  Evaluating the impact of different sequence databases on metaproteome analysis: insights from a lab-assembled microbial mixture.

Authors:  Alessandro Tanca; Antonio Palomba; Massimo Deligios; Tiziana Cubeddu; Cristina Fraumene; Grazia Biosa; Daniela Pagnozzi; Maria Filippa Addis; Sergio Uzzau
Journal:  PLoS One       Date:  2013-12-09       Impact factor: 3.240

8.  The impact of sequence database choice on metaproteomic results in gut microbiota studies.

Authors:  Alessandro Tanca; Antonio Palomba; Cristina Fraumene; Daniela Pagnozzi; Valeria Manghina; Massimo Deligios; Thilo Muth; Erdmann Rapp; Lennart Martens; Maria Filippa Addis; Sergio Uzzau
Journal:  Microbiome       Date:  2016-09-27       Impact factor: 14.650

9.  Reference sequence (RefSeq) database at NCBI: current status, taxonomic expansion, and functional annotation.

Authors:  Nuala A O'Leary; Mathew W Wright; J Rodney Brister; Stacy Ciufo; Diana Haddad; Rich McVeigh; Bhanu Rajput; Barbara Robbertse; Brian Smith-White; Danso Ako-Adjei; Alexander Astashyn; Azat Badretdin; Yiming Bao; Olga Blinkova; Vyacheslav Brover; Vyacheslav Chetvernin; Jinna Choi; Eric Cox; Olga Ermolaeva; Catherine M Farrell; Tamara Goldfarb; Tripti Gupta; Daniel Haft; Eneida Hatcher; Wratko Hlavina; Vinita S Joardar; Vamsi K Kodali; Wenjun Li; Donna Maglott; Patrick Masterson; Kelly M McGarvey; Michael R Murphy; Kathleen O'Neill; Shashikant Pujar; Sanjida H Rangwala; Daniel Rausch; Lillian D Riddick; Conrad Schoch; Andrei Shkeda; Susan S Storz; Hanzhen Sun; Francoise Thibaud-Nissen; Igor Tolstoy; Raymond E Tully; Anjana R Vatsan; Craig Wallin; David Webb; Wendy Wu; Melissa J Landrum; Avi Kimchi; Tatiana Tatusova; Michael DiCuccio; Paul Kitts; Terence D Murphy; Kim D Pruitt
Journal:  Nucleic Acids Res       Date:  2015-11-08       Impact factor: 16.971

10.  Critical decisions in metaproteomics: achieving high confidence protein annotations in a sea of unknowns.

Authors:  Emma Timmins-Schiffman; Damon H May; Molly Mikan; Michael Riffle; Chris Frazar; H R Harvey; William S Noble; Brook L Nunn
Journal:  ISME J       Date:  2016-11-08       Impact factor: 10.302

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