Literature DB >> 31657527

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

Yasset Perez-Riverol1, Pablo Moreno1.   

Abstract

The recent improvements in mass spectrometry instruments and new analytical methods are increasing the intersection between proteomics and big data science. In addition, bioinformatics analysis is becoming increasingly complex and convoluted, involving multiple algorithms and tools. A wide variety of methods and software tools have been developed for computational proteomics and metabolomics during recent years, and this trend is likely to continue. However, most of the computational proteomics and metabolomics tools are designed as single-tiered software application where the analytics tasks cannot be distributed, limiting the scalability and reproducibility of the data analysis. In this paper the key steps of metabolomics and proteomics data processing, including the main tools and software used to perform the data analysis, are summarized. The combination of software containers with workflows environments for large-scale metabolomics and proteomics analysis is discussed. Finally, a new approach for reproducible and large-scale data analysis based on BioContainers and two of the most popular workflow environments, Galaxy and Nextflow, is introduced to the proteomics and metabolomics communities.
© 2019 The Authors. Proteomics published by WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Entities:  

Keywords:  bioconda; biocontainers; bioinformatics; containers; large scale data analysis; workflows

Mesh:

Year:  2019        PMID: 31657527      PMCID: PMC7613303          DOI: 10.1002/pmic.201900147

Source DB:  PubMed          Journal:  Proteomics        ISSN: 1615-9853            Impact factor:   5.393


  50 in total

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6.  Computational tools and workflows in metabolomics: An international survey highlights the opportunity for harmonisation through Galaxy.

Authors:  Ralf J M Weber; Thomas N Lawson; Reza M Salek; Timothy M D Ebbels; Robert C Glen; Royston Goodacre; Julian L Griffin; Kenneth Haug; Albert Koulman; Pablo Moreno; Markus Ralser; Christoph Steinbeck; Warwick B Dunn; Mark R Viant
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9.  Recognizing millions of consistently unidentified spectra across hundreds of shotgun proteomics datasets.

Authors:  Johannes Griss; Yasset Perez-Riverol; Steve Lewis; David L Tabb; José A Dianes; Noemi Del-Toro; Marc Rurik; Mathias W Walzer; Oliver Kohlbacher; Henning Hermjakob; Rui Wang; Juan Antonio Vizcaíno
Journal:  Nat Methods       Date:  2016-06-27       Impact factor: 28.547

Review 10.  Navigating freely-available software tools for metabolomics analysis.

Authors:  Rachel Spicer; Reza M Salek; Pablo Moreno; Daniel Cañueto; Christoph Steinbeck
Journal:  Metabolomics       Date:  2017-08-09       Impact factor: 4.290

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  3 in total

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Journal:  Nucleic Acids Res       Date:  2022-01-07       Impact factor: 16.971

  3 in total

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