Frontiers in Plant Sciences: Visual Analytics of large-scale biological data (30 March – 1 April 2015)
Lecturer: PD Dr. Kay Nieselt (Center for Bioinformatics Tübingen, Integrative Transcriptomics, University of Tübingen)
Location: ETH Zürich
As biological datasets increase in size and complexity, we are moving more and more from a hypothesis-driven research paradigm to a data-driven one. As a result, exploration of that data has become even more crucial than in the past. This course is aimed at PhD students who are applying or planning to apply high throughput technologies (in particular NGS) and bioinformatics methods in their research. The aim of this 3-day course is to familiarize the participants with modern visual analytics methodologies applied to biological data and to provide hands-on training. Questions such as what is data visualization, what is visual analytics and how can we visualize biological data to gain insight in them, so that hypotheses can be generated or explored and further targeted analyses can be defined.
In this course, we will focus on omics data (mainly genomics and transcriptomics data) and combined data such as GWAS and eQTL. The course is a mixture of theoretical lectures and interactive, practical sessions. The hands-on training will introduce the most commonly applied tools in the field as well as some maybe less commonly but nonetheless very useful ones. Dependent on the participants’ programming abilities we will use GUI-based tools as well as R/Bioconductor and other scripting languages.
- Understand the process of visual analytics
- Know the basics and do’s and don’ts of visualization
- Learn how to visualize large-scale genome data
- Learn how to visualize transcriptional regulation and abundance
- Understand the challenge of GWAS and eQTL data visualization and learn new approaches to address these challenges
- Basic R knowledge required
- Know how to operate and work with a Linux system
- Other programming and scripting abilities would be nice but are not necessary
- General Introduction and Outline of Course
- Basics of Visualisation and Visual Analytics
- Whole Genome Alignment Visualisation
- Pan-Genome Computation and Visualisation
- Visualisation of Expression Data
- Mayday – Introduction
- Visualisation of Variants
Slides and Handouts Practicals
- A painless introduction to R
- Handson Basic Visualisation
- Handson Fundamental Biological Visualisation
EyeTN Bioinformatics Workshop on „Analysis of NGS data“ (30 March – 1 April 2015)
This course is aimed at PhD students who are applying or planning to apply high throughput sequencing technologies (in particular RNA-seq) and bioinformatics methods in their research.
The aim of this workshop is to familiarise the participants with modern bioinformatics data analysis methodologies and to provide hands-on training. At the end of the workshop we will hopefully have convinced you that you no longer need to be `afraid’ of large scale NGS data when it comes to analysing it.
The workshop is a mixture of theoretical lectures and practical sessions.
The hands-on training will introduce the most commonly applied tools in the field as well as some maybe less commonly but nonetheless very useful ones. We will concentrate on RNA-seq data, but we will also look at some genome NGS data.
Topics will include: data handling and quality assessment, short read alignment using Bowtie/BWA, visualisation, region identification, differential expression, clustering and classification.
We will use GUI-based or command line tools as well as R/Bioconductor if possible.
High-throughput sequencing of RNA libraries (RNA-seq) has become increasingly common and largely supplanted gene microarrays for transcriptome profiling. When processed appropriately, RNA-seq data has the potential to provide a considerably more detailed view of the transcriptome.
30 March 2015
- EyeTN part 0: General Introduction and Outline of Workshop
- EyeTN part 1: High-throughput technologies for Genomics and Transcriptomics
- EyeTN part 2: Fast mapping methods for short read data
31 March 2015
- EyeTN part 3: Identifying variants and mapping of RNA-seq data
- EyeTN part 4: RNA-seq expression value estimation and normalization
1 April 2015