META-pipe is a complete service for the analysis of marine metagenomic data. It provides assembly of high-throughput sequence data, functional annotation of predicted genes, and taxonomic profiling. The functional annotation is computationally demanding and is therefore currently run on a high-performance computing cluster in Norway. However, additional compute resources are necessary to open the service to all ELIXIR users. We describe our approach for setting up and executing the functional analysis of META-pipe on additional academic and commercial clouds. Our goal is to provide a powerful analysis service that is easy to use and to maintain. Our design therefore uses a distributed architecture where we combine central servers with multiple distributed backends that execute the computationally intensive jobs. We believe our experiences developing and operating META-pipe provides a useful model for others that plan to provide a portal based data analysis service in ELIXIR and other organizations with geographically distributed compute and storage resources.

CESNET Prague 6 160 00 Czech Republic

CSC IT Center for Science Espoo 02150 Finland

Department of Chemistry UiT The Arctic University of Norway Tromsø Norway

Department of Computer Science UiT The Arctic University of Norway Tromsø Norway

Department of Information Technology UiT The Arctic University of Norway Tromsø Norway

Zobrazit více v PubMed

Robertsen EM, Kahlke T, Raknes IA, et al. : META-pipe - Pipeline Annotation, Analysis and Visualization of Marine Metagenomic Sequence Data. ArXiv160404103 Cs2016. Reference Source

Ondov BD, Bergman NH, Phillippy AM: Interactive metagenomic visualization in a Web browser. BMC Bioinformatics. 2011;12:385. 10.1186/1471-2105-12-385 PubMed DOI PMC

Carver T, Harris SR, Berriman M, et al. : Artemis: an integrated platform for visualization and analysis of high-throughput sequence-based experimental data. Bioinformatics. 2012;28(4):464–469. 10.1093/bioinformatics/btr703 PubMed DOI PMC

Goll J, Rusch DB, Tanenbaum DM, et al. : METAREP: JCVI metagenomics reports--an open source tool for high-performance comparative metagenomics. Bioinformatics. 2010;26(20):2631–2632. 10.1093/bioinformatics/btq455 PubMed DOI PMC

Afgan E, Baker D, van den Beek M, et al. : The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2016 update. Nucleic Acids Res. 2016;44(W1):W3–W10. 10.1093/nar/gkw343 PubMed DOI PMC

Kallio MA, Tuimala JT, Hupponen T, et al. : Chipster: user-friendly analysis software for microarray and other high-throughput data. BMC Genomics. 2011;12:507. 10.1186/1471-2164-12-507 PubMed DOI PMC

Amstutz P, Crusoe MR, Tijanić N, et al. : Common Workflow Language, v1.0. 10.6084/m9.figshare.3115156.v2 DOI

Vivian J, Rao AA, Nothaft FA, et al. : Toil enables reproducible, open source, big biomedical data analyses. Nat Biotechnol. 2017;35(4):314–316. 10.1038/nbt.3772 PubMed DOI PMC

Di Tommaso P, Chatzou M, Floden EW, et al. : Nextflow enables reproducible computational workflows. Nat Biotechnol. 2017;35(4):316–319. 10.1038/nbt.3820 PubMed DOI

Schulz WL, Durant TJ, Siddon AJ, et al. : Use of application containers and workflows for genomic data analysis. J Pathol Inform. 2016;7(1):53. 10.4103/2153-3539.197197 PubMed DOI PMC

Agafonov A, Mattila K, Tuan CD, et al. : META-pipe Cloud Setup and Execution (Version Tag: Zenodo-F1000). Zenodo. 2017. 10.5281/zenodo.1053807 PubMed DOI PMC

META-pipe cloud setup and execution