Scalable workflows and reproducible data analysis for genomics
| Autor: | Francesco Strozzi, Dominique Belhachemi, George Githinji, Steffen Möller, Pjotr Prins, Paolo Di Tommaso, Ricardo Wurmus, Roel Janssen, Geert Smant, Joep de Ligt, Michael R. Crusoe |
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| Přispěvatelé: | Anisimova, M. |
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Data Analysis
Big data Cloud computing Evolutionary biology 02 engineering and technology computer.software_genre Workflow engine Workflow Software Snakemake 0303 health sciences Genomics Biological Evolution Nextflow Technology Platforms Bioinformatics 0206 medical engineering CWL 03 medical and health sciences Humans Laboratorium voor Nematologie Bioconda 030304 developmental biology Guix Workflow Language Common Workflow Language GNU Guix business.industry Parallelization Computational Biology Reproducibility of Results Informàtica biològica Pipeline (software) Virtual machine MrBayes Genòmica Cardiovascular and Metabolic Diseases Debian Linux Container (abstract data type) MPI Laboratory of Nematology Software engineering business computer Cluster computing 020602 bioinformatics |
| Zdroj: | Evolutionary Genomics Evolutionary Genomics. Humana Press Inc. Methods in Molecular Biology ISBN: 9781493990733 |
| Popis: | Biological, clinical, and pharmacological research now often involves analyses of genomes, transcriptomes, proteomes, and interactomes, within and between individuals and across species. Due to large volumes, the analysis and integration of data generated by such high-throughput technologies have become computationally intensive, and analysis can no longer happen on a typical desktop computer. In this chapter we show how to describe and execute the same analysis using a number of workflow systems and how these follow different approaches to tackle execution and reproducibility issues. We show how any researcher can create a reusable and reproducible bioinformatics pipeline that can be deployed and run anywhere. We show how to create a scalable, reusable, and shareable workflow using four different workflow engines: the Common Workflow Language (CWL), Guix Workflow Language (GWL), Snakemake, and Nextflow. Each of which can be run in parallel. We show how to bundle a number of tools used in evolutionary biology by using Debian, GNU Guix, and Bioconda software distributions, along with the use of container systems, such as Docker, GNU Guix, and Singularity. Together these distributions represent the overall majority of software packages relevant for biology, including PAML, Muscle, MAFFT, MrBayes, and BLAST. By bundling software in lightweight containers, they can be deployed on a desktop, in the cloud, and, increasingly, on compute clusters. By bundling software through these public software distributions, and by creating reproducible and shareable pipelines using these workflow engines, not only do bioinformaticians have to spend less time reinventing the wheel but also do we get closer to the ideal of making science reproducible. The examples in this chapter allow a quick comparison of different solutions. |
| Databáze: | OpenAIRE |
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