Topics will include: RNA-Seq and ChIP-Seq data handling, quality
assessment and visualisation; region identification and differential
expression; data integration and external databases (ENCODE);
statistical analysis using R/bioconductor.
What will I learn?
Lectures will give insight into how biological knowledge can be
generated from NGS data and illustrate different ways of analysing and
integrating such data. Practicals will consist of computer exercises
that will enable the participants to apply statistical methods to the
analysis of NGS data under the guidance of the lecturers and teaching
Familiarity with the technology and biological use cases of NGS is
required, as is prior experience with standard RNA-Seq and/or ChIP-Seq
workflows. Knowledge of R/Bioconductor and the Unix/Linux operating
system are also required.
Is it right for me?
This course is aimed at advanced PhD students and post-doctoral
researchers who are already applying next generation sequencing (NGS)
technologies and bioinformatics methods in their research.
The aim of this course is to familiarise the participants with
advanced data analysis methodologies for the interpretation and
integration of data derived from different NGS applications including
RNA-seq, ChIP-seq, DNA-methylation sequencing, genome-wide association
studies (GWAS) and DNA variant detection.