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The University of Southampton

MEDI6215 Bioinformatics, Interpretation and Data Quality Assurance in Genome Analysis

Module Overview

The module will cover the fundamental principles of informatics and bioinformatics applied to clinical genomics, find and use major genomic and genetic data resources; use software packages, in silico tools, databases and literature searches to align sequence data to the reference genome, critically assess, annotate and interpret findings from genetic and genomic analyses. Theoretical sessions will be coupled with practical assignments of analysing and annotating predefined data sets. This is a core module that is central to the MSc programme in Genomic Medicine as it will provide the student with the skills to analyse genomic data. Upon completion of this module students will be eligible to base their MSc research project on data from the 100,000 Genomes Project data set

Aims and Objectives

Learning Outcomes

Learning Outcomes

Having successfully completed this module you will be able to:

  • Analyse 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
  • Interrogate major data sources, e.g. of genomic sequence, protein sequences, variation, pathways, (e.g. EVS, dbSNP, ClinVar, etc.) and be able to integrate with clinical data, to assess the pathogenic and clinical significance of the genome result
  • Acquire relevant basic computational skills and understanding of statistical methods for handling and analysing sequencing data for application in both diagnostic and research settings
  • Gain practical experience of the bioinformatics pipeline through the Genomics England programme.
  • Justify and defend the place of Professional Best Practice Guidelines in the diagnostic setting for the reporting of genomic variation


Aligning genome data to reference sequence using up to date alignment programmes (e.g. BWA) Assessment of data quality through application of quality control measures How to determine the analytical sensitivity and specificity of genomic tests Use of tools to call sequence variants e.g. GATK, annotation of variant-call files using established databases Filtering strategies of variants, in context of clinical data, and using publically-available control data sets Use of multiple database sources, in silico tools and literature for pathogenicity evaluation, and familiarity with the statistical programmes to support this Principles of integration of laboratory and clinical information, and place of best-practice guidelines for indicating the clinical significance of results Principles of downstream functional analysis e.g. knock-outs, and other cellular model How to analyse genomic data to identify epigenetic and other variation that modifies phenotype Practice in examples of analysis of genomic data in the Training Embassy within the Genomics England Data Centre.

Learning and Teaching

Teaching and learning methods

The module will comprise two blocks of two days' intensive on-site teaching, each followed by a period of independent study. A variety of learning and teaching methods will be adopted to promote a wide range of skills and meet the differing learning styles of the group. The on-site teaching will include seminars, practical demonstrations, discussions and exercises surrounding interpretation of data and clinical scenarios, and specialist lectures given by a range of academic and health care professionals. This will ensure a breadth and depth of perspective, giving a good balance between background theories and principles and practical experience. Off-site independent learning will take place on the virtual learning environment hosted by the UoS.

Independent Study122
Total study time150

Resources & Reading list

More General - OGT. Web Presentation - Arrays and NGS: High Resolution Analysis of the Medical Exome.

Genome browsers and tutorials - Ensembl.

Online Discussion forums - SEQanswers.

Introduction to Genomics. 

Genome browsers and tutorials - 100,000 genomes.

Genome browsers and tutorials - dbSNP.

Variant effect prediction programs- polyphen.

Introduction to Bioinformatics. 

Web-based suite of bioinformatic tools - Galaxy.

Variant annotation programs - Annovar.

Tools for viewing NGS data - IGV.


Assessment Strategy

The assessment for the module provides you with the opportunity to demonstrate achievement of the learning outcomes. There will be two assessments to include i) full analysis report and ii) genetic test report with summary of best practice guidelines. The pass mark for the module and all assessed components is 50%. If you do not achieve the pass mark on this module by achieving 50% or more in all components, you may still pass by compensation. To do this, you must achieve a qualifying mark of 40% on each assessed component. Each of the component marks is then combined, using the appropriate weighting, to give an overall mark for the module. If this overall mark is greater than or equal to 50% you will have passed the module. If your overall mark is less than 50% when the weighting has been applied to the components, you will have failed the module. If you have not achieved 40% or more on all components, you cannot use compensation and have failed the module. If you have failed the module, you will have the opportunity to submit work at the next referral (re-sit) opportunity using the method outlined below. You must achieve the pass mark in all referred components. On passing your referrals, your final module mark will be capped at 50%.


MethodPercentage contribution
Written assignment  (2000 words) 75%
Written assignment  (1000 words) 25%


MethodPercentage contribution
Report  (2000 words) 100%

Repeat Information

Repeat type: Internal & External

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