During this one-day tutorial participants will practice basic Docker command line functionalities, eg setting up a Docker image, deploying images as “containers” and opening ports targeting pre-installed high throughput sequencing software tools. We will also introduce Amazon web services as cloud based tools hosting the pre-built Docker containers. The knowledge acquired by the participants in this tutorial should allow them to fetch and build reproducible workflows using Docker technology.
Multiple sequencing platforms, including Pacific Biosciences and Oxford Nanopore MinION, are now available to generate reads that are several kilobases-long.
The aim of this course is to familiarise the participants with long read
(also called “third generation”) sequencing technologies, their
applications and the bioinformatics tools used to assemble this kind of
data.
This course will present all the bioinformatics tools required to analyze RNA-seq gene expression data, from the raw data to the biological interpretation. This two-day course will discuss the following topics:
- Quality control and reads cleanup
- RNAseq reads mapping to genome & transcriptome
- Gene reads counting, gene & exons differential expression
- GO enrichment and pathway analysis
Several scientific applications require computing and/or storage resources that go beyond the processing power of a single multi-core machine. High performance computing (HPC) clusters provide the necessary hardware and software infrastructure to efficiently run computing intensive applications. The course gives an introduction to high performance computing using the HPC cluster at Vital-IT. Both theoretical as well as practical usage aspects will be covered.
R is a complete and flexible system for statistical analysis which has become a tool of choice for biologists and biomedical scientists, who need to analyze and visualize large amounts of data. One reason for this success is the availability of many contributed packages, which are available freely and can be installed and run directly from R. In bioinformatics, in particular, most published papers include a link to an R package implementing the methods described in the article. This "First Steps with R" course is addressed to beginners wanting to become familiar with the R environment and master the most common commands to be able to start exploring their own datasets.
With a constant evolution of technologies, laboratory biologists are faced with an increasing need of bioinformatics skills to deal with high-throughput data storage, retrieval and analysis.
Although several resources developped for such tasks have a web interface (most of the time, the first choice of biologitsts), many operations can be more efficiently handled with command lines (CLI).
Python is an open-source and general-purpose scripting language which runs on all major operating systems. It was designed to be easily read and written with comparatively simple syntax, and is thus a good choice for beginners in programming. Python is applied in many disciplines and is one of the most common languages for bioinformatics. The Python community enthusiastically maintains a rich collection of libraries/modules for everything from web development to machine learning. Other programming languages such as R have comparable functionality to Python, however some tasks are more natural (and easier!) in Python.