MSc Health Data Science

For entry in the academic year beginning September 2026, the tuition fees are as follows:

• MSc (full-time)
  UK students (per annum): £15,800
  International, including EU, students (per annum): £35,700
• PGDip (full-time)
  UK students (per annum): £12,600
  International, including EU, students (per annum): £28,600
• PGDip (part-time)
  UK students (per annum): £6,300
  International, including EU, students (per annum): £14,300
• PGCert (full-time)
  UK students (per annum): £6,300
  International, including EU, students (per annum): £14,300
• PGCert (part-time)
  UK students (per annum): £3,150
  International, including EU, students (per annum): £7,150

Further information for EU students can be found on our dedicated EU page.

The course fees include all the tuition, technical support and examinations required for the course. All fees for entry will be subject to yearly review. Courses lasting more than one year may be subject to incremental rises per annum. For general fees information please visit: postgraduate fees . Always contact the department if you are unsure which fee applies to your qualification award and method of attendance.

The University permits applicants with comparable previous experience to submit an application for consideration of AP(E)L Accreditation Prior (Experiential) Learning. The maximum AP(E)L is 15 credits to a PGCert, 45 credits to a PGDip and 60 credits to a MSc.

If your AP(E)L application is successful, the University charges £30 for every 15 credits of AP(E)L. The overall tuition fee is adjusted and then the administrative charge is applied.

All students should normally be able to complete their programme of study without incurring additional study costs over and above the tuition fee for that programme. Any unavoidable additional compulsory costs totalling more than 1% of the annual home undergraduate fee per annum, regardless of whether the programme in question is undergraduate or postgraduate taught, will be made clear to you at the point of application. Further information can be found in the University's Policy on additional costs incurred by students on undergraduate and postgraduate taught programmes (PDF document, 91KB).

For the latest scholarship and bursary information please visit the fees and funding page .

The Catherine Chisholm scholarship is applicable to students from selected countries for this course. Find out more details on the scholarship page .

The University of Manchester is proud to offer six fully-funded scholarships to Women from Brunei, Cambodia, Indonesia, Lao PDR, Myanmar, the Philippines, Singapore, Thailand or Timor-Leste completing specific master's courses in STEM subjects. Please visit the STEM scholarship page for more information.

Indicative Content

Genetics/Genomics 

• Introduction to the history and scope of genomics 
• The Genome Landscape
• Nucleic Acid structure and function, including the structure and function of coding and non-coding DNA 
• The central dogma 
• From DNA, to RNA and proteins 
• Noncoding regulatory sequence: promoters, transcription factor binding sites, splice site dinucleotides, enhancers, insulators, epigenetics, Mendelian inheritance
• Genomic variation and its role in health and disease 
• Genomic technology and role of the genome in the development and treatment of disease

Sequencing 

• Types of sequencing platform, applications and limitations; 
• Understanding of the different data formats and file types used, and an overview of how these are processed in sequence within a bioinformatics pipeline
• Analysis, annotation and interpretation of sequencing data
• Gene panels versus exomes versus whole genomes
• Quality, depth and coverage considerations in sequencing

Genomic variants

• Visualise single nucleotide variants (SNV)/indels/copy number variants (CNV), and structural variants (SV) in an appropriate genome viewer
• Understanding of the annotation and concepts of read depth, insert size and read/base quality
• Review the difference between somatic and germline variants

Bioinformatic Fundamentals 

• Introduction to the history and scope of bioinformatics 
• Primary biological sequence resources, including INDSC (GenBank, EMBL, DDBJ) and
  UniProt (SwissProt and TrEMBL) 
• Genome browsers and interfaces; including Ensembl, UCSC Genome Browser, Entrez,
• Similarity/homology, theory of sequence analysis, scoring matrices, dynamic programming methods including BLAST, pairwise alignments(e.g., Smith Waterman, Needleman Wunsch) and multiple sequence alignments, 
• Feature identification including SNP analysis
• Ontologies – in particular GO, Human Phenotype Ontology (HPO) and Sequence Ontology (SO)

Clinical application of bioinformatics 

Introduction to the clinical application of bioinformatic resources, including their role and use in a clinical context.

• Genome browsers
• Variant databases
• Phenotype databases
• Missense and splice site prediction tools
• Locus specific databases
• CNV analysis tools
• Annotation tools

Ethics, standards and governance 

• Genomic nomenclature
• Sequence files and formats
• Variant files and formats
• Variant classification
• ISO standards
• Data protection and governance for genomic data
• Incidental findings in genomic data
   

This module will provide students with a background knowledge of human genomics, with a particular emphasis on the application to the clinical setting. We will focus on the application of next generation sequencing technologies in the clinic and how they are transforming patient care. We will introduce the basic concepts of next generation sequencing and how the resulting genomic data is analysed. We will introduce bioinformatics tools, databases and the methodology that will help to make sense of all of this clinical genomic data.

This unit is delivered via blended learning, including assessment. The course runs over 6 weeks, with a nominal 25h/week of student work.

Each week consists of:

•  An overview of the material, presenting the learning objectives for the week.
• Explanatory material (~10h of student activity/week) in the form of video lectures, papers/articles, the course text, and links to further resources, this will be delivered online via Articulate Rise
• Workshops(~3h/week). These will contain structured exercises, based on real clinical case studies and will integrate bioinformatics tools and databases. Practical data analyses will take place within online computing environments including the University eLab. Formative, feedback will be given in the sessions and via discussion boards.
• Discussion Fora (~2h/week). Students are encouraged to discuss the exercises and material in the forums where tutors will facilitate peer learning and pose questions for consideration, providing feedback/input where necessary. Discussion fora will also support the face to face workshops

Summative assessment. (5h/week) Students will analyse their own clinical case study as part of the summative assessment, this will form further practice after the face to face sessions

There is also private study of ~5h/week consisting of:
- Revision
- Coursework
- Further practice (after the tutorials)
- Independent/further study
 

Upon completion, students should/will be able to: 

Introduction to Clinical Bioinformatics and Genomics

1. Discuss the governance and ethical frameworks in place within healthcare and how they apply to clinical bioinformatics and genomics.
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. Understand the process of meiosis and mitosis, inheritance and de novo mutations.
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 the reference genome.
10. Explain fundamental bioinformatic principles, including the scope and aims of bioinformatics and its development.
11. Describe the biological background to diagnostic genomic testing and clinical genomics, and the role of bioinformatics.
12. Describe the partnership of Clinical Bioinformatics and Genomics to other clinical specialisms in the investigation and management of genetic disorders and the contribution to safe and effective patient care. 
    
     

Upon completion, students will/should be able to:

1. Critically analyse scientific and clinical data
2. Present scientific and clinical data appropriately
3. Formulate a critical argument
4. Evaluate scientific and clinical literature 
5. Apply the knowledge of clinical bioinformatics to address specific clinical problems
    

Upon completion, students should/will be able to: 

1. Present information clearly in the form of written reports.
2. Communicate complex ideas and arguments in a clear and concise and effective manner.
3. Work effectively as an individual and part of a team. 
4. Use relevant literature and electronic resources to collect, select and organise complex scientific information
5. Perform analysis on DNA data and protein sequence data to infer function. 
6. Perform sequence alignment tasks. 
7. 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. 
8. Compare major bioinformatics resources for clinical diagnostics, and show how their results can be summarised and integrated with other lines of evidence to produce clinically valid reports. 
9. 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. 
10. 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.

Upon completion, students will/should be able to: 

1. Present complex ideas in simple terms in written formats.
2. Actively seek accurate and validated information from all available sources.
3. Interpret data and convert into knowledge for use in the clinical context of individual and groups of patients.
    

Written feedback provided in grademark within 15 days  of assignment submission deadline

During face to face workshops and in discussion boards from tutors/GTAs

Molecular Biology/Genetics textbooks – look for the latest edition

1. Human Molecular Genetics, Tom Strachan and Andrew Read, Garland Science Chapters 1, 2 and 13
2. New Clinical Genetics, Andrew Read and Dian Donnai, Scion Publishing
   Journal papers

Genetics

1. What is a gene, post ENCODE? History and updated definition
   Gerstein, MB et al (2007) Genome Research 17:p669 https://doi.org/10.1101/gr.6339607
2. Non-coding RNAs: key regulators of mammalian transcription
   Kugel, JF and Goodrich, JA (2012) Trends Biochem Sci 37(4):p144 https://doi.org/10.1016/j.tibs.2011.12.003
3. http://www.nature.com/scitable/topicpage/regulation-of-mrna-splicing-by-signal-transduction-14128469
4. RNA splicing, disease and therapy https://doi.org/10.1093/bfgp/elr020

Variant interpretation

1. Standards and guidelines for variant interpretation doi:10.1038/gim.2015.30
2. Human genotype:phenotype databases https://doi.org/10.1038/nrg3932