Björn Grüning | bioRxiv Science

bioRxiv2016

Enhancing pre-defined workflows with ad hoc analytics using Galaxy, Docker and Jupyter

What does it take to convert a heap of sequencing data into a publishable result? First, common tools are employed to reduce primary data (sequencing reads) to a form suitable for further analyses (i.e., list of variable sites). The subsequent exploratory stage is much more ad hoc and requires development of custom scripts making it problematic for biomedical researchers. Here we describe a hybrid platform combining common analysis pathways with exploratory environments. It aims at fully encompassing and simplifying the \"raw data-to-publication\" pathway and making it reproducible.

Bioinformatics↗

bioRxiv2015

NCBI BLAST+ integrated into Galaxy

BackgroundThe NCBI BLAST suite has become ubiquitous in modern molecular biology, used for small tasks like checking capillary sequencing results of single PCR products through to genome annotation or even larger scale pan-genome analyses. For early adopters of the Galaxy web-based biomedical data analysis platform, integrating BLAST was a natural step for sequence comparison workflows.\n\nFindingsThe command line NCBI BLAST+ tool suite was wrapped for use within Galaxy, defining appropriate datatypes as needed, with the goal of making common BLAST tasks easy, and advanced tasks possible.\n\nConclusionsThis effort has been come an informal international collaborative effort, and is deployed and used on Galaxy servers worldwide. Several example use-cases are described herein.

Bioinformatics↗