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

• 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
• ISSN: 1467-5463; 1477-4054
• DOI: 10.1093/bib/bbx105

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.