The University of Southampton
Courses

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

Module Aims

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

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

Syllabus

• 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.

Special Features

The module will be taught by an international faculty, at the forefront of their respective academic disciplines and professions. Adult learning methods will be used throughout and an emphasis placed upon interactive learning, practical demonstration and the interpretation of clinical scenarios to reinforce learning. Extensive e-learning facilities will be available to foster independent study.

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.

TypeHours
Teaching28
Independent Study122
Total study time150

Resources & Reading list

Genome browsers and tutorials - 100,000 genomes.

Online Discussion forums - SEQanswers.

Tools for viewing NGS data - IGV.

Genome browsers and tutorials - Ensembl.

Web-based suite of bioinformatic tools - Galaxy.

Variant effect prediction programs- polyphen.

Genome browsers and tutorials - dbSNP.

Introduction to Genomics. 

Variant annotation programs - Annovar.

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

Introduction to Bioinformatics. 

Assessment

Assessment Strategy

The assessment for the module provides you with the opportunity to demonstrate achievement of the learning outcomes. There will be two components to the assessment i) Report on NGS analysis, and ii) Written report. 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%.

Summative

MethodPercentage contribution
Report  (2000 words) 75%
Report  (500 words) 15%
Summary  (500 words) 10%

Referral

MethodPercentage contribution
Report 100%

Repeat Information

Repeat type: Internal & External

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