ASaiM-MT: A validated and optimized ASaiM workflow for metatranscriptomics analysis within Galaxy framework

Subina Mehta, Pratik D. Jagtap, Marie Crane, Emma Leith, Bérénice Batut, Saskia Hiltemann, Magnus O. Arntzen, Benoit J. Kunath, Phillip B. Pope, Francesco Delogu, Ray W Sajulga, Praveen Kumar, James E. Johnson, Timothy J. Griffin

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

The Earth Microbiome Project (EMP) aided in understanding the role of microbial communities and the influence of collective genetic material (the 'microbiome') and microbial diversity patterns across the habitats of our planet. With the evolution of new sequencing technologies, researchers can now investigate the microbiome and map its influence on the environment and human health. Advances in bioinformatics methods for next-generation sequencing (NGS) data analysis have helped researchers to gain an in-depth knowledge about the taxonomic and genetic composition of microbial communities. Metagenomic-based methods have been the most commonly used approaches for microbiome analysis; however, it primarily extracts information about taxonomic composition and genetic potential of the microbiome under study, lacking quantification of the gene products (RNA and proteins). On the other hand, metatranscriptomics, the study of a microbial community's RNA expression, can reveal the dynamic gene expression of individual microbial populations and the community as a whole, ultimately providing information about the active pathways in the microbiome. In order to address the analysis of NGS data, the ASaiM analysis framework was previously developed and made available via the Galaxy platform. Although developed for both metagenomics and metatranscriptomics, the original publication demonstrated the use of ASaiM only for metagenomics, while thorough testing for metatranscriptomics data was lacking. In the current study, we have focused on validating and optimizing the tools within ASaiM for metatranscriptomics data. As a result, we deliver a robust workflow that will enable researchers to understand dynamic functional response of the microbiome in a wide variety of metatranscriptomics studies. This improved and optimized ASaiM-metatranscriptomics (ASaiM-MT) workflow is publicly available via the ASaiM framework, documented and supported with training material so that users can interrogate and characterize metatranscriptomic data, as part of larger meta-omic studies of microbiomes.

Original languageEnglish (US)
Article number103
JournalF1000Research
Volume10
DOIs
StatePublished - Apr 19 2021

Bibliographical note

Funding Information:
We would like to thank European Galaxy team for the help in the support during Galaxy implementation. We would also like to thank Bj?rn A. Gr?ning (University of Freiburg, Germany) for helping us during the implementation of the workflow in the European Galaxy platform.

Publisher Copyright:
© 2021 Mehta S et al.

Keywords

  • Functional analysis
  • Galaxy
  • Metatranscriptomics
  • Microbiome

PubMed: MeSH publication types

  • Journal Article
  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, Non-P.H.S.

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