Literature DB >> 33513299

Cloudy with a Chance of Peptides: Accessibility, Scalability, and Reproducibility with Cloud-Hosted Environments.

Benjamin A Neely1.   

Abstract

Cloud-hosted environments offer known benefits when computational needs outstrip affordable local workstations, enabling high-performance computation without a physical cluster. What has been less apparent, especially to novice users, is the transformative potential for cloud-hosted environments to bridge the digital divide that exists between poorly funded and well-resourced laboratories, and to empower modern research groups with remote personnel and trainees. Using cloud-based proteomic bioinformatic pipelines is not predicated on analyzing thousands of files, but instead can be used to improve accessibility during remote work, extreme weather, or working with under-resourced remote trainees. The general benefits of cloud-hosted environments also allow for scalability and encourage reproducibility. Since one possible hurdle to adoption is awareness, this paper is written with the nonexpert in mind. The benefits and possibilities of using a cloud-hosted environment are emphasized by describing how to setup an example workflow to analyze a previously published label-free data-dependent acquisition mass spectrometry data set of mammalian urine. Cost and time of analysis are compared using different computational tiers, and important practical considerations are described. Overall, cloud-hosted environments offer the potential to solve large computational problems, but more importantly can enable and accelerate research in smaller research groups with inadequate infrastructure and suboptimal local computational resources.

Entities:  

Keywords:  cloud computing; high-performance computing; proteomic workflow; remote access

Mesh:

Substances:

Year:  2021        PMID: 33513299      PMCID: PMC8637422          DOI: 10.1021/acs.jproteome.0c00920

Source DB:  PubMed          Journal:  J Proteome Res        ISSN: 1535-3893            Impact factor:   4.466


  35 in total

1.  Code developments to improve the efficiency of automated MS/MS spectra interpretation.

Authors:  Rovshan G Sadygov; Jimmy Eng; Eberhard Durr; Anita Saraf; Hayes McDonald; Michael J MacCoss; John R Yates
Journal:  J Proteome Res       Date:  2002 May-Jun       Impact factor: 4.466

2.  MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification.

Authors:  Jürgen Cox; Matthias Mann
Journal:  Nat Biotechnol       Date:  2008-11-30       Impact factor: 54.908

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.  Nextflow enables reproducible computational workflows.

Authors:  Paolo Di Tommaso; Maria Chatzou; Evan W Floden; Pablo Prieto Barja; Emilio Palumbo; Cedric Notredame
Journal:  Nat Biotechnol       Date:  2017-04-11       Impact factor: 54.908

5.  MaxQuant goes Linux.

Authors:  Pavel Sinitcyn; Shivani Tiwary; Jan Rudolph; Petra Gutenbrunner; Christoph Wichmann; Şule Yılmaz; Hamid Hamzeiy; Favio Salinas; Jürgen Cox
Journal:  Nat Methods       Date:  2018-06       Impact factor: 28.547

6.  Cloud parallel processing of tandem mass spectrometry based proteomics data.

Authors:  Yassene Mohammed; Ekaterina Mostovenko; Alex A Henneman; Rob J Marissen; André M Deelder; Magnus Palmblad
Journal:  J Proteome Res       Date:  2012-09-05       Impact factor: 4.466

7.  OpenMS: a flexible open-source software platform for mass spectrometry data analysis.

Authors:  Hannes L Röst; Timo Sachsenberg; Stephan Aiche; Chris Bielow; Hendrik Weisser; Fabian Aicheler; Sandro Andreotti; Hans-Christian Ehrlich; Petra Gutenbrunner; Erhan Kenar; Xiao Liang; Sven Nahnsen; Lars Nilse; Julianus Pfeuffer; George Rosenberger; Marc Rurik; Uwe Schmitt; Johannes Veit; Mathias Walzer; David Wojnar; Witold E Wolski; Oliver Schilling; Jyoti S Choudhary; Lars Malmström; Ruedi Aebersold; Knut Reinert; Oliver Kohlbacher
Journal:  Nat Methods       Date:  2016-08-30       Impact factor: 28.547

8.  Toil enables reproducible, open source, big biomedical data analyses.

Authors:  John Vivian; Arjun Arkal Rao; Frank Austin Nothaft; Christopher Ketchum; Joel Armstrong; Adam Novak; Jacob Pfeil; Jake Narkizian; Alden D Deran; Audrey Musselman-Brown; Hannes Schmidt; Peter Amstutz; Brian Craft; Mary Goldman; Kate Rosenbloom; Melissa Cline; Brian O'Connor; Megan Hanna; Chet Birger; W James Kent; David A Patterson; Anthony D Joseph; Jingchun Zhu; Sasha Zaranek; Gad Getz; David Haussler; Benedict Paten
Journal:  Nat Biotechnol       Date:  2017-04-11       Impact factor: 54.908

Review 9.  Scalable Data Analysis in Proteomics and Metabolomics Using BioContainers and Workflows Engines.

Authors:  Yasset Perez-Riverol; Pablo Moreno
Journal:  Proteomics       Date:  2019-12-18       Impact factor: 5.393

10.  A uniform proteomics MS/MS analysis platform utilizing open XML file formats.

Authors:  Andrew Keller; Jimmy Eng; Ning Zhang; Xiao-jun Li; Ruedi Aebersold
Journal:  Mol Syst Biol       Date:  2005-08-02       Impact factor: 11.429

View more
  2 in total

1.  ppx: Programmatic Access to Proteomics Data Repositories.

Authors:  William E Fondrie; Wout Bittremieux; William S Noble
Journal:  J Proteome Res       Date:  2021-08-03       Impact factor: 5.370

Review 2.  Deep learning neural network tools for proteomics.

Authors:  Jesse G Meyer
Journal:  Cell Rep Methods       Date:  2021-05-17
  2 in total

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