Overview This two-day course will discuss the following topics: The Structure of an RNAseq analysis pipeline: Raw data quality check RNAseq reads alignment Gene Expression level quantification by reads counting De novo Transcripts reconstruction
Overview R is a complete, flexible and open source system for statistical analysis and graphics, which has become a tool of choice for biologists and biomedical scientists who need to analyse and visualise large amounts of data.
Overview In this course, R programmers will learn how to create R packages, the best way to make R scripts reusable. Participants will learn how to identify and create clear, clean and usables packages in R.
One of the strengths of the Python language is the availability of mature, high-quality libraries for working with scientific data. Integration between the most popular libraries has lead to the concept of a "scientific Python stack": a collection of packages which are designed to work well together. In this workshop we will see how to leverage these libraries to efficiently work with and visualise large volumes of data.
This course provides an introduction to the tools available through the Bioconductor project for manipulating and analysing bulk RNA-seq data. We will present a workflow for the analysis RNA-seq data starting from aligned reads in bam format and producing a list of differentially-expressed genes. We will also describe the various resources available through Bioconductor to annotate, visualise and gain biological insight from the differential expression results.
Overview Several statistical methods are available that determines whether a set of genes shows statistically significant differences between two classes (two biological states, two phenotype states, two experimental conditions etc.).
This module provides an introduction to the theory and concepts of network analysis. Attendees will learn how to construct protein-protein interaction networks and subsequently use these to analyse large-scale datasets generated these to by techniques such as RNA-Seq or mass-spec proteomics. The course will focus on giving attendees hands-on experience in the use of Cytoscape and selected network analysis apps.