Approaches for Containerized Scientific Workflows in Cloud Environments with Applications in Life Science

Spjuth, Ola; Capuccini, Marco; Carone, Matteo; Larsson, Anders; Schaal, Wesley; Novella, Jon Ander; Stein, Oliver; Ekmefjord, Morgan; Tommaso, Paolo Di; Floden, Evan; Notredame, Cédric; Moreno, Pablo; Khoonsari, Payam Emami; Herman, Stephanie; Kultima, Kim; Lampa, Samuel

doi:10.20944/preprints202001.0378.v1

Cited by 1 publication

(1 citation statement)

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“…Increasing interest in workflow development systems that track data and software provenance, enable scalability and reproducibility, and re-entrant code (Wratten et al, 2021) have led to the development of several workflow languages, largely inspired by GNU Make (Amstutz et al, 2016;Köster & Rahmann, 2012;Stallman & McGrath, 1991). Nextflow is a Domain Specific Language (Di Tommaso et al, 2017) that currently leads workflow systems in terms of ease of scripting and submitting to cloud computing resources (Fjukstad & Bongo, 2017;Jackson et al, 2021;Leipzig, 2017;Spjuth et al, 2020). A key benefit of Nextflow compared to earlier workflow languages is being able to submit jobs to a local machine, an HPC, or cloud-based compute environments.…”

Section: Mainmentioning

confidence: 99%

“polishCLR: a Nextflow workflow for polishing PacBio CLR genome assemblies”

Chang

Stahlke

Chudalayandi

et al. 2022

Preprint

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Long-read sequencing has revolutionized genome assembly, yielding highly contiguous, chromosome-level contigs. However, assemblies from some third generation long read technologies, such as Pacific Biosciences (PacBio) Continuous Long Reads (CLR), have a high error rate. Such errors can be corrected with short reads through a process called polishing. Although best practices for polishing non-model de novo genome assemblies were recently described by the Vertebrate Genome Project (VGP) Assembly community, there is a need for a publicly available, reproducible workflow that can be easily implemented and run on a conventional high performance computing environment. Here, we describe polishCLR (https://github.com/isugifNF/polishCLR), a reproducible Nextflow workflow that implements best practices for polishing assemblies made from CLR data. PolishCLR can be initiated from several input options that extend best practices to suboptimal cases. It also provides re-entry points throughout several key processes including identifying duplicate haplotypes in purge_dups, allowing a break for scaffolding if data are available, and throughout multiple rounds of polishing and evaluation with Arrow and FreeBayes. PolishCLR is containerized and publicly available for the greater assembly community as a tool to complete assemblies from existing, error-prone long-read data.

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