Gallantries Grant - Intellectual Output 2 - Large-scale data analysis, and introduction to visualisation and data modelling

Comment: What is a Learning Pathway?
A graphic depicting a winding path from a start symbol to a trophy, with tutorials along the way
We recommend you follow the tutorials in the order presented on this page. They have been selected to fit together and build up your knowledge step by step. If a lesson has both slides and a tutorial, we recommend you start with the slides, then proceed with the tutorial.

This Learning Pathway collects the results of Intellectual Output 2 in the Gallantries Project

Success Criteria:

Year 1: Introduction to large-scale analyses in Galaxy

Galaxy offers support for the analysis of large collections of data. This submodule will cover the upload, organisation, and analysis of such large sets of data and files. [SC2.1; SC1.3,5]

Lesson Slides Hands-on Recordings
Rule Based Uploader
Rule Based Uploader: Advanced
SRA Aligned Read Format to Speed Up SARS-CoV-2 data Analysis
Extracting Workflows from Histories
Using dataset collections
Automating Galaxy workflows using the command line
Creating, Editing and Importing Galaxy Workflows
Using Workflow Parameters

Year 1: Introduction to the human microbiome analyses

The human microbiome consists of a community of thousands of species of microorganisms. Sequencing of this community is often performed to identify which species of microorganism are present. This aids in diagnostics and treatment of patients. [SC2.1-3,6; SC1.4,5]

Lesson Slides Hands-on Recordings
Identification of the micro-organisms in a beer using Nanopore sequencing
16S Microbial analysis with Nanopore data

Year 1: Advanced microbiome analysis

By using more complex sequencing techniques, it is possible to not only obtain information about which organisms are present in the microbiome, but also their activity. This can e.g. aid in identification of antibiotic resistance. This more complex sequencing requires more complex data analysis [SC2.1-4,6; SC1.4,5]

Lesson Slides Hands-on Recordings
Pathogen detection from (direct Nanopore) sequencing data using Galaxy - Foodborne Edition

Year 2: Cancer Analysis

The previous submodules focused on scaling up in terms of number of samples. This submodule will focus on scaling up in terms of complexity. Cancer is a disease of the genome, it is a multifaceted and heterogeneous disease. This leads to complex datasets and analysis pipelines [SC2.3,4; SC1.5]

Lesson Slides Hands-on Recordings
Mapping and molecular identification of phenotype-causing mutations

Year 2: Intro to machine learning

Going beyond conventional statistics, many scientific data analyses benefit from machine learning techniques for modelling of datasets. This is widely used in biomedical domain. [SC2.4,5; SC1.4]

Lesson Slides Hands-on Recordings
Introduction to Machine Learning using R

Year 2: Introduction to the Galaxy visualisation framework

(This module was cancelled due to insufficiencies in the Galaxy Visualisation Framework.) Galaxy has many options for visualisation of scientific data. This module will cover how to use this framework to create and share visualisation. [SC2.2-3; SC1.1,3,6]

Lesson Slides Hands-on Recordings

Year 3: Visualisation of complex multidimensional data

For advanced visualisation, tools such as Circos may be utilized where Galaxy’s basic visualisation framework does not suffice. [SC2.2-3; SC1.5]

Lesson Slides Hands-on Recordings
Visualisation with Circos
Ploting a Microbial Genome with Circos

Year 3: Introduction to Visualisation with R and Python

When the available visualisation options do not suffice, custom plots and visualisations can be created using one of several extensive visualisation libraries available in R and Python. This module will cover the basics of using R and Python to create custom plots and visualisations. [SC2.3; SC1.1]

Lesson Slides Hands-on Recordings
Data visualisation Olympics - Visualization in R
Plotting in Python

Editorial Board

This material is reviewed by our Editorial Board:

orcid logoSaskia Hiltemann avatar Saskia Hiltemannorcid logoHelena Rasche avatar Helena Rascheorcid logoBérénice Batut avatar Bérénice Batut


This material was funded by:

Gallantries: Bridging Training Communities in Life Science, Environment and Health avatar Gallantries