Module information

Module details

Introduction to Clinical Bioinformatics and Genetics
Module code

Aim of this module

This rotation will provide trainees with background knowledge of genetics and a knowledge and understanding of bioinformatics tools and infrastructure. They will understand the aims and operation of a genetics laboratory service. They will understand the role of bioinformatics and the bioinformatician in supporting the laboratory service, and the effect of data and its analysis on patient care. In particular it will show how bioinformatics strategies can be used and applied to genomic and genetic data to generate information and knowledge that contributes to patient care and care pathways within a clinical setting. It will also introduce the ethical and governance framework appropriate for working with patient data in an NHS setting. 

Work-based content


# Learning outcome Competency Action
# 1 Learning outcome 1,2 Competency

Take a protein sequence and use standard bioinformatic tools to locate within a genome, annotate and infer function.

Action View
# 2 Learning outcome 1,2 Competency

Take a DNA sequence and use standard bioinformatic tools to locate within a genome, annotate and infer function, including gene prediction, transcription factor (TF) analysis, splice-site boundaries potential for copy number variants (CNVs).

Action View
# 3 Learning outcome 3 Competency

Use three clinical cases to demonstrate the application of bioinformatic tools to common genetic scenarios.

Action View
# 4 Learning outcome 2,3 Competency

Identify variation within genetic sequence data captured from various sources.

Action View
# 5 Learning outcome 2 Competency

Reconstruct and interpret the relationship between individual sequences using phylogenetic analysis.

Action View
# 6 Learning outcome 3,4,5 Competency

Analyse variants using literature and bioinformatic tools or resources to predict consequence and determine significance within patient care.

Action View
# 7 Learning outcome 1,2,3,4,5,6 Competency

Follow standard protocols or agreed procedures for sequence annotation and analysis.

Action View
# 8 Learning outcome 6 Competency

Make accurate records of all work carried out.

Action View
# 9 Learning outcome 4,5,6 Competency

Communicate results in a way that is useful to the clinical team, highlighting their findings.

Action View


You must complete:

  • 1 case-based discussion(s)
  • 1 of the following DOPS/ OCEs:
Using available tools take a fragment of DNA sequence and translate into protein sequence, search within a protein database to find matches and determine function DOPS
Annotate DNA sequence data, and for the longest transcript of the chosen gene identify the exon boundaries. DOPS
For a given gene, obtain protein sequences and use sequence alignment tools to produce a multiple sequence alignment. DOPS
Find the population frequency of a variant DOPS
Given an example of a structural variant, identify the genes and their functions, including CMVs of no clinical significance DOPS
Participate in MDT meetings with other health professionals OCE
Attend a clinic as an observer and explain your role to the patient OCE

Learning outcomes

  1. Perform analysis on DNA data and protein sequence data to infer function.
  2. Perform sequence alignment tasks followed by clustering and phylogeny.
  3. Select and apply appropriate bioinformatic tools and resources from a core subset to typical diagnostic laboratory cases, contextualised to the scope and practice of a clinical genetics laboratory.
  4. Compare major bioinformatics resources or pathogen typing and identification for clinical diagnostics and how their results can be summarised and integrated with other lines of evidence to produce clinically valid reports.
  5. Interpret evidence from bioinformatic tools and resources and integrate this into the sum of genetic information for the interpretation and reporting of test results from patients.
  6. Perform the recording of building or version numbers of resources used on a given date, including those of linked data sources, and understand the clinical relevance of this data.

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. Discuss the governance and ethical frameworks in place within the NHS and across the public health function [including, where relevant, the civil service] and how they apply to bioinformatics.
  2. Discuss and justify the importance of standards, best practice guidelines and standard operating procedures: how they are developed, improved and applied to clinical bioinformatics.
  3. Describe the structure of DNA and the functions of coding and non-coding DNA.
  4. Discuss the flow of information from DNA to RNA to protein in the cell.
  5. Describe transcription of DNA to mRNA and the protein synthesis process.
  6. Discuss the role of polymorphisms in Mendelian and complex disorders and give examples of polymorphisms involved in genetic disease.
  7. Describe appropriate bioinformatics databases capturing information on DNA, RNA and protein sequences.
  8. Explain the theory of sequence analysis and the use of genome analysis tools.
  9. Describe secondary databases in bioinformatics and their use in generating metadata on gene function.
  10. Explain fundamental bioinformatic principles, including the scope and aims of bioinformatics and its development.
  11. Explain fundamental genomic principles, including the scope and aims of genomics and its development.
  12. Discover resources linking polymorphism to disease processes and antimicrobial drug resistance and discuss and evaluate the resources that are available to the bioinformatician and how these are categorised.
  13. Discuss metadata and how it is captured in bioinformatics resources.
  14. Interpret the metadata provided by the major bioinformatics resources.
  15. Describe the use of ontologies in metadata capture and give examples of the use of ontologies for capturing information on gene function and phenotype.
  16. Identify appropriate references where published data are to be reported.
  17. Describe the biological background to diagnostic genetic testing and clinical genetics, and the role of bioinformatics.
  18. Describe the partnership of Clinical Bioinformatics and Genetics with other clinical specialisms in the investigation and management of genetic disorders and the contribution to safe and effective patient care.

Indicative content


  • Introduction to the history and scope of genomics
  • The Genome Landscape
  • The structure and function of coding and non-coding DNA
  • The central dogma
  • From DNA to RNA and proteins
  • Non-coding regulatory sequence: promoters, transcription factor binding sites, splice site dinucleotides, enhancers, insulators
  • Genetic variation and its role in health and disease 


  • Types of sequencing, applications and limitations; Sanger versus short read
  • Analysis, annotation and interpretation
  • Panel versus exome versus whole-genome resequencing 


  • Basic statistics applied to clinical genetics/genomics
  • Hardy-Weinberg, Bayes theorem, risks in pedigrees
  • Hidden Markov Models
  • Evolutionary Models
  • Mathematical basis of phylogentic tree construction 

Bioinformatic fundamentals

  • Introduction to the history and scope of bioinformatics
  • Primary biological sequence resources, including International Nucleotide Sequence Database Collaboration (INSDC) (GenBank, EMBL, DDBJ) and UniProt (SwissProt and TrEMBL)
  • Genome browsers and interfaces, including Ensembl, University of California, Santa Cruz (UCSC) Genome Browser, Entrez
  • Similarity/homology, theory of sequence analysis, scoring matrices, dynamic programming methods, including Basic Local Alignment Search Tool (BLAST), pairwise alignments (e.g. Smith Waterman, Needleman Wunsch), multiple sequence alignments (e.g. ClustalW, T-Coffee, Muscle), BLAT (BLAST-like Alignment Tool)
  • Feature identification, including single-nucleotide polymorphism (SNP) analysis and transcription factor binding sites and their associated TF binding sequence motifs
  • Ontologies – in particular Gene Ontology (GO), Human Phenotype Ontology (HPO), SnomedCT 

Clinical application of bioinformatics

  • Introduction to the clinical application of bioinformatic resources, including its role and use in a medical context in molecular genetics, cytogenetics and next generation sequencing for data manipulation and analysis, and genotyping microarrays (also used to predict copy number variants, CNVs)
  • Background and application of specialist databases and browsers
    • Single-Nucleotide Polymorphism Database (dbSNP)
    • Orphanet
    • Diagnostic Mutation Database (DMuDB)/NGRL Universal Browser
    • ClinVar (
    • OMIM
    • Database of Genomic Variants (DGV)
    • Leiden Open (Source) Variation Database (LOVD)/Universal Mutation database (UMD) database software and scientific literature
    • Human Gene Mutation Database (HGMD)
    • Stanford HIV Drug Resistance Database
  • 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)
    • Commercially available software (e.g. NextGENe, Alamut, Cartegenia)
  • Capture and representation of phenotype data
  • Development of a simple application for clinical bioinformatic use 

Standards and governance

  • Data standards and formats
  • International Union of Pure and Applied Chemistry (IUPAC) codes
  • GenBank
  • Sequence Alignment/Map (SAM)/ Binary Alignment/Map (BAM)/CRAM
  • Variant Call Format(VCF)
  • General Feature Format (GFF)
  • BED format
  • Human Genome Variation Society (HGVS) variant nomenclature
  • Human Genome Nomenclature Committee (HGNC) gene nomenclature
  • RefSeq/RefSeqGene
  • Locus Reference Genomic (LRG)
  • Role and development of standard operating procedures
  • Relevant standards (clinical, genetic, bioinformatic)

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 clinical consultation(s) where patients with genetic disorders meet with health professionals to discuss their diagnosis and care, and reflect on the positive aspects of each
  • 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
  • Attend 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
  • Gain experience of each of the following and personally reflect on the importance, application and effect on genetic services and patient care:
    • the scope and function of the genetics laboratory
    • the requirements and implementation of bioinformatic analysis strategies
    • investigation of genetic variants using in-silico techniques
    • annotation of DNA and protein sequences
    • use of standard protocols in analysis of genetic results recording of results and preparation of reports for clinical use.