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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 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, how to use RNA-seq data and be familiar with open-source software packages for such analysis;
  5. Discuss and critically evaluate statistical methods for handling and analysing sequencing data;
  6. Explain of basic concepts and methods of network analysis and modeling of biological systems; and
  7. Gain practical experience of the bioinformatics pipelines for variant calling, RNA-seq and network analysis.

 

2016/17 timetable:

Lecture/practical title

Type*

Lecturer

Date

Introduction to Unix

P

Morgunov

16-Jan

Introduction to genomics

L

Micklem

17-Jan

Introduction to sequencing and its applications

L

Kostadima

19-Jan

Introduction to R

P

Ghanbarian

23-Jan

Next generation sequencing data analysis: quality control and alignment

L

Medina

24-Jan

Next generation sequencing data analysis: variant calling and annotation

L

Medina

26-Jan

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

P

Rudolph

30-Jan

Statistics primer I: experimental design, hypothesis testing, etc

L

Morgunov

31-Jan

Statistics primer II: experimental design, hypothesis testing, etc

L

Morgunov

02-Feb

Analysis of variants I

P

Medina

06-Feb

GWAS and disease: cataloguing variation in human disease

L

Martin

07-Feb

Cataloguing and analyzing variation in cancer

L

Ross

09-Feb

Analysis of variants II

P

Medina

13-Feb

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

L

Graf

14-Feb

Disease gene discovery through differential expression

L

Kostadima

16-Feb

RNA-seq analysis I

P

Petersen

Grassi

20-Feb

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

L

Iorio

21-Feb

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

L

Iorio

23-Feb

RNA-seq analysis II

P

Petersen

Grassi

27-Feb

Network biology

L

Ross

28-Feb

Image analysis and computational neuroscience

L

Aston

02-Mar

Network analysis

P

Ross

13-Mar

Modelling biological systems

L

Le Novere

14-Mar

* L=lecture, P=computer practical