MSc Molecular Pathology / Course details

Year of entry: 2021

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Course unit details:
Next Generation Sequencing and Omics in Medicine and Disease

Unit code BIOL67562
Credit rating 15
Unit level FHEQ level 7 – master's degree or fourth year of an integrated master's degree
Teaching period(s) Semester 1
Offered by School of Biological Sciences
Available as a free choice unit? Yes


This module will provide an introduction to the basis of genotyping and detection of genetic variation. There will be an introduction to deep resequencing, including library preparation methods, sequencing chemistries and platforms. This module will provide a brief overview of methodologies for, and application of, detecting genetic changes including base substitutions (SNV) and copy number variants (CNV). This module will provide an overview of bioinformatics approaches and pipelines for the analysis of genomic data. In addition there will be an introduction to different "’omics technologies" and approaches, and strategies to prioritise the pathogenicity of variants.



By the end of this module the student will be able to:

  • Have a fundamental knowledge and understanding of the basis of genotyping and detection of genetic variation
  • Have a basic understanding of, and be able to apply, bioinformatics approaches to the analysis and contextualization of genomic data
  • Understand which methodology to utilise to detect different types of genetic variants, and be able to analyse and interpret sequencing data and contextualise genetic variants with regard to likely pathogenicity.
  • Be able to use literature and online resources to access information on disease and genetic variation
  • Describe techniques that can be applied to transcriptomics, metabolomics and proteomic analysis


Teaching and learning methods

The course contains ~30 hours lectures, tutorials and small group workshops.

Knowledge and understanding

  • Describe and critically evaluate a range of up-to-date genomic technologies and platforms used to sequence targeted parts of the genome or whole genomes
  • Annotate and interpret variants with regard to likely pathogenicity
  • Discuss the application of other techniques commonly used to interrogate genomic variation in the clinical setting
  • Appraise technology platforms for applications in medical genomics either for research or medical diagnostic purposes
  • Use publicly available bioinformatics approaches and tools to determine the likely pathogenicity of a sequence variant
  • Describe techniques that can be applied to transcriptomics, metabolomics and proteomic analysis

Intellectual skills

Critically evaluate the different ‘omics’ technologies and platforms and their application to genomic medicine and the impact of personalised medicine

Practical skills

  • Discuss and critically appraise approaches to the bioinformatics analysis and interpretation of ‘omics’ data
  • Evaluate the pathogenicity of variants identified in whole genome sequencing and other genomic technologies

Transferable skills and personal qualities

  • Develop their problem solving skills through collaboration in group working and debate.
  • Enhance their oral and written presentation skills.

Assessment methods

Method Weight
Written assignment (inc essay) 50%
Oral assessment/presentation 50%

Feedback methods

Formative feedback will be provided through practical computer workshops and small group discussions

Recommended reading

Includes but is not limited to:

  • Pugh et al. (2014) The landscape of genetic variation in dilated cardiomyopathy as surveyed by clinical DNA sequencing. Genetics in Medicine. 16:8;601-608
  • Gottlieb et al. (2014) Changing genetic paradigms: creating next-generation genetic databases as tools to understand the emerging complexities of genotype/phenotype relationships. 8:1-9
  • Newman and Black. (2014) Delivery of a clinical genomics service. Genes  5:1001-1017
  • Hall et al. (2014) Realising genomics in clinical practice. Phg Foundation. 
  • Perkins et al. Precision medicine screening using whole-genome sequencing and advanced imaging to identify disease risk in adults. PNAS April 3, 2018 115 (14) 3686-3691
  • The 1000 Genomes Project Consortium. A global reference for human genetic variation. Nature. 2015 (526):68-74.
  • Wishart DS (2016). Emerging applications of metabolomics in drug discovery and precision medicine. Nature Rev. Drug. Discov. doi: 10.1038/nrd.2016.32.
  • Chen et al. (2012). Personal omics profiling reveals dynamic molecular and medical phenotypes. Cell 148:1293-1307.
  • Trivedi, D. et al (2017) Metabolomics for the masses: the future of metabolomics in a personalised world. New Horizons In Translational Metabolomics 3(6):294-305


Study hours

Independent study hours
Independent study 150

Teaching staff

Staff member Role
Janine Lamb Unit coordinator

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