MG-RAST version 4—lessons learned from a decade of low-budget ultra-high-throughput metagenome analysis

Abstract As technologies change, MG-RAST is adapting. Newly available software is being included to improve accuracy and performance. As a computational service constantly running large volume scientific workflows, MG-RAST is the right location to perform benchmarking and implement algorithmic or pl...

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Bibliographic Details
Published in:Briefings in bioinformatics 2019-07, Vol.20 (4), p.1151-1159
Main Authors: Meyer, Folker, Bagchi, Saurabh, Chaterji, Somali, Gerlach, Wolfgang, Grama, Ananth, Harrison, Travis, Paczian, Tobias, Trimble, William L, Wilke, Andreas
Format: Article
Language:English
Subjects:
Algorithms
Benchmarks
Bioinformatics
Budgets
Computational Biology - methods
Data analysis
Datasets
Diamonds
High-Throughput Nucleotide Sequencing - economics
High-Throughput Nucleotide Sequencing - methods
High-Throughput Nucleotide Sequencing - statistics & numerical data
Internet
Metagenome
Metagenomics - economics
Metagenomics - methods
Metagenomics - statistics & numerical data
Sensitivity
Sequence Analysis, DNA - economics
Sequence Analysis, DNA - methods
Sequence Analysis, DNA - statistics & numerical data
Software
User-Computer Interface
Workflow
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Summary:Abstract As technologies change, MG-RAST is adapting. Newly available software is being included to improve accuracy and performance. As a computational service constantly running large volume scientific workflows, MG-RAST is the right location to perform benchmarking and implement algorithmic or platform improvements, in many cases involving trade-offs between specificity, sensitivity and run-time cost. The work in [Glass EM, Dribinsky Y, Yilmaz P, et al. ISME J 2014;8:1–3] is an example; we use existing well-studied data sets as gold standards representing different environments and different technologies to evaluate any changes to the pipeline. Currently, we use well-understood data sets in MG-RAST as platform for benchmarking. The use of artificial data sets for pipeline performance optimization has not added value, as these data sets are not presenting the same challenges as real-world data sets. In addition, the MG-RAST team welcomes suggestions for improvements of the workflow. We are currently working on versions 4.02 and 4.1, both of which contain significant input from the community and our partners that will enable double barcoding, stronger inferences supported by longer-read technologies, and will increase throughput while maintaining sensitivity by using Diamond and SortMeRNA. On the technical platform side, the MG-RAST team intends to support the Common Workflow Language as a standard to specify bioinformatics workflows, both to facilitate development and efficient high-performance implementation of the community’s data analysis tasks.
ISSN:1467-5463
1477-4054
DOI:10.1093/bib/bbx105