YAMP: a containerized workflow enabling reproducibility in metagenomics research
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Abstract
YAMP ("Yet Another Metagenomics Pipeline") is a user-friendly workflow that enables the analysis of whole shotgun metagenomic data while using containerization to ensure computational reproducibility and facilitate collaborative research. YAMP can be executed on any UNIX-like system and offers seamless support for multiple job schedulers as well as for the Amazon AWS cloud. Although YAMP was developed to be ready to use by nonexperts, bioinformaticians will appreciate its flexibility, modularization, and simple customization.
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Now published in GigaScience doi: 10.1093/gigascience/giy072
Alessia Visconti 1Department of Twin Research and Genetic Epidemiology, King’s College LondonFind this author on Google ScholarFind this author on PubMedSearch for this author on this siteORCID record for Alessia ViscontiFor correspondence: alessia.visconti@kcl.ac.ukTiphaine C. Martin 1Department of Twin Research and Genetic Epidemiology, King’s College LondonFind this author on Google ScholarFind this author on PubMedSearch for this author on this siteMario Falchi 1Department of Twin Research and Genetic Epidemiology, King’s College LondonFind this author on Google ScholarFind this author on PubMedSearch for this author on this site
A version of this preprint has been published in the Open Access journal GigaScience (see paper https://doi.org/10.1093/gigascience/giy072 ), …
Now published in GigaScience doi: 10.1093/gigascience/giy072
Alessia Visconti 1Department of Twin Research and Genetic Epidemiology, King’s College LondonFind this author on Google ScholarFind this author on PubMedSearch for this author on this siteORCID record for Alessia ViscontiFor correspondence: alessia.visconti@kcl.ac.ukTiphaine C. Martin 1Department of Twin Research and Genetic Epidemiology, King’s College LondonFind this author on Google ScholarFind this author on PubMedSearch for this author on this siteMario Falchi 1Department of Twin Research and Genetic Epidemiology, King’s College LondonFind this author on Google ScholarFind this author on PubMedSearch for this author on this site
A version of this preprint has been published in the Open Access journal GigaScience (see paper https://doi.org/10.1093/gigascience/giy072 ), where the paper and peer reviews are published openly under a CC-BY 4.0 license.
These peer reviews were as follows:
Reviewer 1: http://dx.doi.org/10.5524/REVIEW.101208 Reviewer 2: http://dx.doi.org/10.5524/REVIEW.101209
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