skip to primary navigationskip to content
 

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 genomics applications, focusing on the use of next generation sequencing (NGS) for the analysis of gene expression and genomics variation in healthy and diseased individuals. Analytical workflows for processing NGS data are presented and students have the opportunity to familiarize themselves with basic statistics, computational skills, bioinformatics resources and analytical approaches needed to process, analyze and interpret NGS data. Clinical and pharmacological implications are also discussed.

For more information, visit the BBS website or contact the course organizer, Dr. .

 

Learning outcomes

By the end of this course students should be able to:

  1. Discuss the principles applied to quality control of sequencing data, alignment of sequence to the reference genome, calling and annotating sequence variants, and filtering strategies to identify pathogenic mutations in sequencing data;
  2. Identify the challenges associated with the analysis of variation data, how to treat candidate variants given known false positive/negative rates and population frequencies as well as the implications for disease diagnosis;
  3. Interrogate major database sources and be able to integrate this information with clinical data, to assess the potential pathogenic and clinical significance of the identified sequence variants;
  4. Perform basic RNA-seq data analysis and be familiar with open-source software packages for such analysis;
  5. Explain of basic concepts and methods applied in network analysis, phylogenetic analysis, deep learning andstructural bioinformatics, and
  6. Gain practical experience of the bioinformatics pipelines for variant calling, RNA-seq and network analysis.

 

2017/18 timetable:

Lecture/practical title

Type*

Lecturer

Date

Introduction to Unix

P

Morgunov

15-Jan

Introduction to bioinformatics and computational biology

L

Durbin

16-Jan

Introduction to sequencing and its applications

L

Enright

18-Jan

Introduction to R

P

Ghanbarian

22-Jan

Next generation sequencing data analysis: quality control and alignment

L

Lopez

23-Jan

Next generation sequencing data analysis: variant calling and annotation

L

Lopez

25-Jan

Introduction to NGS analysis (data formats, data manipulation, QC)

P

Grassi

29-Jan

GWAS and disease: cataloguing variation in human disease

L

Martin

30-Jan

Analyzing variation in cancer

L

Nik-Zainal

01-Feb

Analysis of variants I

P

Lopez

05-Feb

Disease diagnosis/prognosis/susceptibility - Bayesian treatment of test results

L

Turro

06-Feb

Disease gene discovery through differential expression

L

Enright

08-Feb

Analysis of variants II

P

Lopez

12-Feb

Signature matching strategies for in silico drug discovery and re-positioning

L

Iorio

13-Feb

Exploring the landscape of pharmacogenomics interactions in cancer through computational tools and data integration

L

Iorio

15-Feb

RNA-seq analysis I

P

Grassi

19-Feb

Phylogenetic analysis

L

Slodkowicz

20-Feb

Structural bioinformatics

L

Morgunov

22-Feb

RNA-seq analysis II

P

Grassi

26-Feb

What biological problems can deep learning solve?

L

Parts

01-Mar

Structural bioinformatics

P

Morgunov

05-Mar

Network analysis

L

Garcia Alonso

06-Mar

Network analysis

P

Garcia Alonso

12-Mar

* L=lecture, P=computer practical