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Bioinformatics, Interpretation and Data Quality Assurance in Genome Analysis

Course Type
Postgraduate, Continuing professional development, Module

Module overview

The aim of this module is to enable students to gain the knowledge and understanding required to critically analyse bioinformatics data in genomic research, develop the skills to formulate their own research questions, as well as to collect, apply and interpret NHS genomic data sets using a basic range of statistical and bioinformatics techniques.

The module will cover the fundamental principles of informatics, the impact of bioinformatics on clinical genomics, and the ethical, legal and social issues that need to be considered. Students will be expected to be able to find and use major genomic and genetic data resources; use software packages and analysis tools for big data and undertake literature searches to critically assess, annotate and interpret findings from sequence data and genetic variants. Theoretical sessions will be coupled with practical exercises involving the analysis and annotation of predefined data sets.

This module will equip the student with the essential skills to analyse genomic data, applying professional best practice guidelines. Upon completion of this module students will be able to understand how bioinformatics is used to analyse, interpret and report genomic data in a clinical context. Students will also be equipped to utilise the 100,000 Genomes Project data set if relevant for their research project.

Module content

  • Overview of genomic data flow from the patient, through to the laboratory, to the clinician and then back to patient.
  • Assessment of data quality through application of quality control and statistical measures.
  • Aligning genome data to reference sequence using up-to-date alignment algorithms.
  • Measures to determine the analytical sensitivity and specificity of genomic tests.
  • Tools to call sequence variants, and annotate variant-call files using established resources.
  • Use of multiple database sources (for example, EVS, dbSNP, ClinVar etc.), data integration tools, clinical literature, and statistical evidence to prioritise variants for pathogenicity.
  • Principles of integration of laboratory and clinical information, and location of best practice guidelines for indicating the clinical significance of results (for example, American College of Medical Genetic and Genomics (ACMG), and UK guidelines for bioinformatics and variant classification).
  • Approaches for assessing the functional effect of variants.
  • Basic statistical concepts, including probability and hypothesis testing.
  • Examples of large data sets available through cloud computing, for example, training embassy within the Genomic England data centre.
  • Ethical and legal issues, including patient identifiable data and information, and the relationship between data and information (de-identified vs anonymous).
  • Secure information exchange between professionals.


15 Masters level credits

Module attendance required

Teaching delivered over 5 days.

Module dates

Semester 1

Module Leads

Dr Deena Gendoo (Senior Lecturer in Computational Biology & Bioinformatics)
Dr Mathew Coleman (Reader in Tumour Cell Biology, Institute of Cancer and Genomic Sciences)

Module Prerequisites

This module can be taken as a stand-alone assessed or non-assessed course.   

You should have a good honours degree in a life sciences subject, although we will consider applicants with alternative qualifications and professional experience within the health service or other relevant background. You should either take  (An Introduction to Human Genetics and Genomics) and (Omics Techniques and their Application to Genomic Medicine) first, or be able to show you have equivalent knowledge and understanding to enable you to benefit from this module. 

Please contact the Programme Administrator for further information at 

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