Scalable data analysis in proteomics and metabolomics using BioContainers and workflows engines

Abstract

The recent improvements in mass spectrometry instruments and new analytical methods are increasing the intersection between proteomics and big data science. In addition, the bioinformatics analysis is becoming an increasingly complex and convoluted process 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 targeted and design for single desktop application limiting the scalability and reproducibility of the data analysis. In this paper we overview the key steps of metabolomic and proteomics data processing including main tools and software use to perform the data analysis. We discuss the combination of software containers with workflows environments for large scale metabolomics and proteomics analysis. Finally, we introduced to the proteomics and metabolomics communities a new approach for reproducible and large-scale data analysis based on BioContainers and two of the most popular workflows environments: Galaxy and Nextflow.

ProMetIS, deep phenotyping of mouse models by combined proteomics and metabolomics analysis

“…At the computational level, common formats exist for raw and pre-processed data, such as mzML 13 and mzTab 14 , respectively. Computational tools which can process either types of mass spectrometry raw data are already available 15 – 18 . Moreover, sample preparation protocols for the simultaneous extraction of proteins and metabolites have been proposed 19 – 21 enabling to combine both omics within an unique analytical strategy.…”

Genetic determinants of seed protein plasticity in response to the environment in Medicago truncatula

“…The mass spectrometry proteomics data have been deposited in the ProteomeXchange Consortium via the PRIDE (Perez‐Riverol and Moreno, 2020) partner repository (http://www.ebi.ac.uk/pride) with the data set identifier and 10.6019/. All of the other data supporting the findings of this study are available within the article and its supplementary files.…”

The META tool optimizes metagenomic analyses across sequencing platforms and classifiers

“…The copyright holder for this preprint (which this version posted July 29, 2021. ; https://doi.org/10.1101/2021.07.29.454031 doi: bioRxiv preprint composable modules (simulate, classify, evaluate) and four command types (download, build, classify, report), META prevents the "decision paralysis" inherent to more complex workflow environments and description languages 27,28 .…”

“…Example workflows can be seen in Figure 1B. By distilling the workflow to these three composable modules (simulate, classify, evaluate) and four command types (download, build, classify, report), META prevents the "decision paralysis" inherent to more complex workflow environments and description languages 27,28 .…”