A single-cell RNA-sequencing training and analysis suite using the Galaxy framework

Abstract

Background The vast ecosystem of single-cell RNA-sequencing tools has until recently been plagued by an excess of diverging analysis strategies, inconsistent file formats, and compatibility issues between different software suites. The uptake of 10x Genomics datasets has begun to calm this diversity, and the bioinformatics community leans once more towards the large computing requirements and the statistically driven methods needed to process and understand these ever-growing datasets. Results Here we outline several Galaxy workflows and learning resources for single-cell RNA-sequencing, with the aim of providing a comprehensive analysis environment paired with a thorough user learning experience that bridges the knowledge gap between the computational methods and the underlying cell biology. The Galaxy reproducible bioinformatics framework provides tools, workflows, and trainings that not only enable users to perform 1-click 10x preprocessing but also empower them to demultiplex raw sequencing from custom tagged and full-length sequencing protocols. The downstream analysis supports a range of high-quality interoperable suites separated into common stages of analysis: inspection, filtering, normalization, confounder removal, and clustering. The teaching resources cover concepts from computer science to cell biology. Access to all resources is provided at the singlecell.usegalaxy.eu portal. Conclusions The reproducible and training-oriented Galaxy framework provides a sustainable high-performance computing environment for users to run flexible analyses on both 10x and alternative platforms. The tutorials from the Galaxy Training Network along with the frequent training workshops hosted by the Galaxy community provide a means for users to learn, publish, and teach single-cell RNA-sequencing analysis.

Delayed booster dosing improves human antigen-specific Ig and B cell responses to the RH5.1/AS01B malaria vaccine

“…All scRNA-seq gene expression analysis was performed using the Human Cell Atlas instance of the Galaxy biocomputing framework (https://humancellatlas.usegalaxy.eu (74, 75)) based on the “Reference-based RNA-seq data analysis” (76) and “Pre-processing of Single-Cell RNA data” (77, 78) workflow templates. Paired-end FASTQ reads were aligned to the human genome (hg19) with gene annotations from Ensembl (Homo_sapiens.GRCh37.75.gtf (79)) using Trimmomatic (80) followed by RNAStar (81).…”

Galaxy Training: A Powerful Framework for Teaching!

“…Since many of the GTN tutorials are centered around an analysis pipeline described in a recently published article, they are ideally suited to provide researchers with an update on the latest state-of-the art analysis pipelines in their domain. GTN tutorials are also frequently created as part of scientific publications by authors presenting a novel analysis method, as an additional form of documentation for the readers [22][23][24][25][26][27].…”

scFlow: A Scalable and Reproducible Analysis Pipeline for Single-Cell RNA Sequencing Data

“…Common metrics and a scalable analytical framework would better enable the integration, re-use, and re-purposing of published datasets within and across diseases to drive novel discoveries. 7 Challenges toward the development of such a pipeline include the deluge of computational techniques for key analytical steps, 8 interoperability challenges between analytical tools, 9 the extensiveness of complete parameter specifications, 10 the iterative nature of hyperparameter optimisation, 11 the complexity of software dependencies for end-to-end analyses, 12 and the need for flexibility to handle complex experimental designs. 13 To this end, we have developed scFlow, an open-source analysis pipeline comprising i) the scFlow toolkit built in R with high levels of abstraction on top of popular single-cell analysis tools (e.g.…”