GLASSgo in Galaxy: high-throughput, reproducible and easy-to-integrate prediction of sRNA homologs
Schäfer, Lott, Georg, Grüning, Hess, Voß (2020) GLASSgo in Galaxy: high-throughput, reproducible and easy-to-integrate prediction of sRNA homologs Bioinformatics (IF: 5.8) 36(15) 4357-4359
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Abstract
The correct prediction of bacterial sRNA homologs is a prerequisite for many downstream analyses based on comparative genomics, but it is frequently challenging due to the short length and distinct heterogeneity of such homologs. GLobal Automatic Small RNA Search go (GLASSgo) is an efficient tool for the prediction of sRNA homologs from a single input query. To make the algorithm available to a broader community, we offer a Docker container along with a free-access web service. For non-computer scientists, the web service provides a user-friendly interface. However, capabilities were lacking so far for batch processing, version control and direct interaction with compatible software applications as a workflow management system can provide.Here, we present GLASSgo 1.5.2, an updated version that is fully incorporated into the workflow management system Galaxy. The improved version contains a new feature for extracting the upstream regions, allowing the search for conserved promoter elements. Additionally, it supports the use of accession numbers instead of the outdated GI numbers, which widens the applicability of the tool.GLASSgo is available at https://github.com/lotts/GLASSgo/ under the MIT license and is accompanied by instruction and application data. Furthermore, it can be installed into any Galaxy instance using the Galaxy ToolShed.© The Author(s) 2020. Published by Oxford University Press.
Links
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7520042http://www.ncbi.nlm.nih.gov/pubmed/32492127
http://dx.doi.org/10.1093/bioinformatics/btaa556
