Module information

Module details

Advanced Clinical Bioinformatics
Module code

Aim of this module

Advances in genomics are leading to a better understanding of genetic variation and the role that such variation plays in human health and infectious disease. Such insights are important in predicting inherited disease risks, understanding and classifying cancer, predicting individuals’ responses to drug treatment, or better understanding the spread of drug-resistant pathogens. This module will develop the trainee’s fundamental understanding of genetic variation and its role in disease. It will also build on the trainee’s bioinformatics knowledge of the wide range of tools and resources that are used in bioinformatics to capture this knowledge, and how such tools are used by Clinical Scientists to support patient-centred care, diagnosis and treatment. A strong emphasis will be placed on ethical and confidentiality issues with such sensitive data.

This module will enable the trainee to apply their knowledge of genetic variation and its role in disease in the context of bioinformatics and the wide range of tools and resources that are used in clinical bioinformatics to capture data and support patient-centred care, diagnosis and treatment. A strong emphasis will be placed on ethical and confidentiality issues with such sensitive data. An awareness of the impact of interpretations given on clinical and public health action should be paramount when communicating the analysis of data to multi-disciplinary teams.

 For some trainees this will involve applying their knowledge of pathogen variation in bacteria and viruses and how this relates to inference of transmission and resistance to apply bioinformatics tools and algorithms to support outbreak analysis and pathogen characterisation.

Work-based content


# Learning outcome Competency Action
# 1 Learning outcome 1 Competency

Annotate variation data in the context of a specific acquired or inherited disease or genetic investigation.

Action View
# 2 Learning outcome 1,2 Competency

Use appropriate literature to summarise the role of clinical genetics in personalised healthcare in a written report or oral presentation.

Action View
# 3 Learning outcome 2 Competency

Document the analysis process to annotate variation data in the context of a specific genetic investigation and use this to develop an enhanced testing strategy.

Action View
# 4 Learning outcome 3,4 Competency

Explain how to choose and apply major bioinformatic resources for clinical diagnostics in this disease/service area and how their results are integrated with other lines of evidence to produce clinically valid reports.

Action View
# 5 Learning outcome 3,4 Competency

Develop a strategy to modify or assemble tools, pipelines and processes for this disease/service area.

Action View
# 6 Learning outcome 3,4 Competency

Develop an implementation plan for the recommended strategy in the service/disease area.

Action View


You must complete:

  • 2 case-based discussion(s)
  • 2 of the following DOPS/ OCEs:
Use the ensemble variant effect predictor tool to annotate a VCF file See if any genes in a CMV are present in the OMIM morbid database DOPS
Annotate a splice site mutation using more than one algorithm, and explain their relative strengths weaknesses. DOPS
Interpret missense analysis results e.g. SIFT and PolyPhen DOPS
For a disease select the appropriate LSDB i.e BRCA BIC etc. Select based on ease of integration and quality DOPS
Present findings of the interpretation of a missense variant to a MDT meeting OCE

Learning outcomes

In the context of both human and microbes:

  1. Annotate variation data in the context of a specific genetic investigation.
  2. Develop variation data to inform a testing strategy for a specific patient population.
  3. Develop an analysis strategy for a new service.
  4. Advise a service with respect to the bioinformatic requirements of a new service and the strategy to deliver appropriate and clinically relevant data to support patient care.

Academic content (MSc in Clinical Science)

Important information

The academic parts of this module will be detailed and communicated to you by your university. Please contact them if you have questions regarding this module and its assessments. The module titles in your MSc may not be exactly identical to the work-based modules shown in the e-portfolio. Your modules will be aligned, however, to ensure that your academic and work-based learning are complimentary.

Learning outcomes

  1. Describe the biological background to diagnostic genetic testing and clinical genetics.
  2. Discover and interpret recent work regarding genetic variation and disease or disease risk.
  3. Identify key issues around confidentiality and disclosure of genetic data.
  4. Describe the legal framework in which clinical genetic testing is carried out.
  5. Discuss the data governance framework within the NHS relating to genetic data.
  6. Explain the scope and application of genetic testing and sequencing technologies, in particular massively parallel sequencing.
  7. Describe research in the fields of sequencing technologies and in the analytical areas of the epigenome, transcriptome, proteome and metabolome.
  8. Describe the analysis of whole microbial ecosystems (microbiome).
  9. Explain and critically assess the use of different ontologies for standardised annotation, including genetic feature identification, determination of genomic function and the representation of clinical phenotypes and diseases.
  10. Describe the process of developing and providing bioinformatic applications and resources in the clinical setting.
  11. Describe the development, implementation strategies and operation of bioinformatic analysis pipelines.
  12. Discuss the concept and measurement of quality applied to bioinformatic resources and data used in the clinical setting, and the representation and use of metadata, including data provenance and validation, database curation, tool performance and the effect of setting appropriate tool parameters.
  13. Discuss and justify the importance of standards, best practice guidelines and standard operating procedures: how they are developed, improved and applied to clinical bioinformatics, including awareness relevant best practice guidelines.
  14. Record appropriate references where published data are to be reported.

Indicative content


  • Genome wide association studies
  • Haplotypes
  • Large-scale sequencing projects, e.g. 1000 genome project, Exome Sequencing Project
  • Linkage analysis, LOD scores (logarithm [base 10] of odds)
  • Role of environment and genetic background in determining risks
  • Personalised medicine and genetics
  • Bacterial genetics and the spread of antibiotic resistance
  • Detailed description of genome function – what has been learnt from Elixir
  • Classification of genome variation – SNPs, CNVs
  • Impact of variation on genome function – coding versus non-coding regions 


  • The challenges of variant identification
  • Variation databases – dbSNP and its replacements
  • SNP annotation challenges
  • SNP resources in the major genome sequence repositories (Ensembl, UCSC)
  • Feature identification, including SNP analysis and transcription factor binding sites
  • Introduction to bioinformatic platforms and pipelines, e.g. Galaxy and Taverna
  • Classifying phenotype: London Database of Dysmorphology (LDD), Human Phenotype Ontology (HPO), ICD, Orphanet, Snomed-CT 

Clinical application of bioinformatics

Specific databases capturing SNP/disease associations

  • Orphanet
  • DMuDB
  • OMIM
  • DGV
  • LOVD/UMD database software and scientific literature 


  • Variation and response to drugs
  • Impact of sequencing of pathogens – tracking spread of drug resistance 

Specific clinical analysis software

  • CNV analysis
  • Gene prioritisation (e.g. ToppGene, Endeavour, GeCCO)
  • Missense analysis (e.g. Align GVGD, SIFT, PolyPhen, Panther, PhDSNP, MAPP)
  • Splicing analysis applications (e.g. GeneSplicer, MAxEntScan, NNSplice, SSFL, HSF, NetGene2) 

Disease and phenotype ontologies

  • Human Phenotype Ontology (HPO)
  • Orphanet
  • PhenoDB 

Reporting of results

  • Providing reports that are clinically useful – understanding the strengths and limitations of the methodologies
  • The case conference – what are the roles?
  • The role of the bioinformatician within a patient case conference 

Ethics, confidentiality and governance

  • The challenges presented by genome data
    • Specific risks of genome data
    • Issues with Genome Wide Association Studies (GWAS) data and identifiability
    • Legal and governance framework for genome data in the NHS

Clinical experiences

Important information

Clinical experiential learning is the range of activities trainees may undertake in order to gain the experience and evidence to demonstrate their achievement of module competencies and assessments. The list is not definitive or mandatory, but training officers should ensure, as best training practice, that trainees gain as many of these clinical experiences as possible. They should be included in training plans, and once undertaken they should support the completion of module assessments and competencies within the e-portfolio.


  • Observe a range of clinical consultations where patients with genetics disorders meet with health professionals to discuss how clinical bioinformatics can be applied in the care pathway.
  • With permission, identify a patient or family with a genetic disorder and discuss the impact of that genetic disorder on the quality of life of the patient and/or family with an appropriate clinical professional and reflect on how this experience will influence your future practice.
  • Attend and contribute to teaching sessions where Clinical Scientists are being instructed in the correct use of existing bioinformatic analysis procedures and review/report the procedures, the use of standard operating procedures and the process of interaction with Clinical Scientists.
  • Attend and contribute to multidisciplinary meetings at which the results of genetic investigations are discussed and reflect on the process, the weighting placed on different types of data, and the effect on patients’ results and care pathway.
  • Visit other pathology settings where clinical bioinformatics and genomics are increasingly being used to develop strategies for identification, diagnosis and treatment, and reflect on the future contribution of clinical bioinformatics to pathology services.