Speaker
Description
The broadening scope of high-throughput sequencing has created a skills gap: non-computational biologists who need to analyse large data sets. Chipster (http://chipster.csc.fi) offers a versatile collection of bioinformatics tools via an easy-to-use graphical user interface, allowing non-coding biologists to access the latest R-based tools for analysing RNA-seq, single-cell RNA-seq, spatial transcriptomics and microbial
community amplicon data.
Chipster is strongly aimed at teaching bioinformatics concepts, tools and workflows. We provide ready-to-use course materials including slides, exercises and data sets that also allow self-learning. The R code running behind the scenes in Chipster is available to users, which enables a smooth transition to R once skills develop.
To demonstrate how Chipster works, we present an amplicon sequence analysis workflow for microbial community data relying on the Bioconductor packages DADA2 and Phyloseq. The user starts with raw amplicon sequence data and is able to compare and visualize the structure of microbial communities using alpha diversity estimates, ordinations and multivariate statistics, including differential abundance analysis. Chipster is an open-source software and available freely as a server installation.