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Undergraduate Training

Bioinformatics teaching in undergraduate degrees is crucial if we want to better prepare the next generation of graduate students to tackle the challenges of modern science.

We contribute to a number of NST subjects throughout the year and are the organizers of the NST Part II BBS Bioinformatics Minor Subject.

NST Part II BBS Bioinformatics minor (128)

This Minor Subject aims at providing an introduction to the field of bioinformatics, focusing on applications related to the study of complex disease genetics and the recent advances made in this field since the introduction of next-generation sequencing (NGS) technologies.

We will first introduce fundamental concepts in bioinformatics and then how NGS technologies can be applied to the study of human population genetics, genomics and its clinical applications.

Then we will focus on functional analysis at the genomic level. Strategies for the identification of genomic variants using NGS will be explored, providing an introduction to the basic workflows for variant identification. Emphasis will be put on variants’ annotation to infer a variant’s biological relevance and consequently its potential diagnostic and therapeutic value. The challenges associated with the analysis and interpretation of genomic variants will be discussed. We will also introduce relevant public databases and the outcomes of large sequencing projects, which have provided new insights into the landscape of functional variation and genetic association.

Students will also learn about bioinformatics methods for RNA sequencing as well as network analysis and how the latter is used to acquire a functional understanding of the deregulation of signalling networks in diseases. In addition, drug developments based on the knowledge acquired through genomics approaches will be discussed as well as fundamental principles of evolutionary genomics, machine learning and structural bioinformatics.

The course will consist of 14 lectures and 9 computer-based practical sessions. During the practical sessions, students will use the Unix command-line environment and the R project for statistical computing.

After attending this module, students will not be independent in the analysis of complex biological data but will have acquired the critical thinking needed to understand what the analysis of genomic data entails, what are the strengths and weaknesses of different analysis strategies, and will be equipped with a basic set of bioinformatics skills that will enable them to explore and interpret genomic data, as well as other types of biological data, available in the public domain.

For more information, visit the BBS website or email us at the .

 

2019/20 timetable:

Lecture/practical title

Type*

Lecturer

Date

Introduction to Unix

P

Morgunov

13-Jan

Data Science Approaches to Biological Systems

L

Babu

16-Jan

Introduction to R - part I

P

van Rongen

20-Jan

Next generation sequencing data analysis: quality control and alignment

L

Steif

21-Jan

Next generation sequencing data analysis: variant calling and annotation 

L

Steif

23-Jan

Analysis of variants

P

Steif

27-Jan

Analysing variation in cancer 

L

Rueda

28-Jan

Genetic association methods for rare diseases

L

Turro

30-Jan

Introduction to R - part II

P

van Rongen

03-Feb

Statistics for large datasets and multiple comparisons

L

Castle

04-Feb

Disease gene discovery through differential expression 

L

Enright

06-Feb

RNA-seq analysis

P

Enright

10-Feb

Single cell transcriptomics

L

Talavera-Lopez

13-Feb

RNA-seq analysis - focus on downstream analysis 

P

Enright

17-Feb

Image analysis

L

Uhlmann

18-Feb

Machine learning - part I 

L

Mohorianu

20-Feb

Machine learning 

P

Mohorianu

24-Feb

Machine learning - part II

L

Mohorianu

25-Feb

Network biology

L

Porras

27-Feb

Network analysis

P

Perfetto

02-Mar

Computational proteomics 

L

Lilley

03-Mar

Structural bioinformatics

L

Morgunov

05-Mar

Structural bioinformatics

P

Morgunov

09-Mar

* L=lecture, P=computer practical