The programme is designed to equip NHS staff with advanced genomic knowledge and applied laboratory skills, directly linked to NHS service priorities and the evolving Genomic Medicine landscape. The Postgraduate Certificate is delivered over 1 academic year (three 20-credit modules), with learning and assessment integrated through a Digital Practice Assessment Document (PAD). The PAD provides a structured record of work-based activities mapped to Postgraduate Certificate learning outcomes and relevant areas of practice.
Learning outcomes
Introduction to Clinical Bioinformatics
- Analyse genomic data in line with current standards, best practice guidelines and demonstrate a critical awareness of how they are developed, improved, and applied to clinical bioinformatics.
- Demonstrate a critical awareness of the purpose and practice of clinical genetic/genomic variant testing.
- Apply integrative knowledge of the development, implementation strategies, file and data formats and operation of bioinformatic analysis pipelines.
- Apply bioinformatics pipelines to analyse genomic and variant data and interrogate its quality.
- 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.
- Discuss contribution of bioinformatics to patient care and the governance and ethical frameworks in place within the NHS and how they apply to bioinformatics.
Introduction to Genomics
- Explain nucleic acid and chromosome structure and function and apply the nomenclature used to describe the human genome.
- Apply integrative knowledge of patterns of inheritance.
- Evaluate the design, operation and performance of methods used to investigate genetic and genomic alterations associated with disease.
- Select for application, analyse, interpret results and report on tests used to investigate genetic and genomic alterations associated with disease in the clinical context.
Haemato-Oncology 1
- Summarise the pathogenesis, natural history and clinical management of chronic myeloid leukaemia (CML), acute myeloid leukaemia (AML), myeloproliferative neoplasms (MPN) and acute lymphoblastic leukaemia (ALL), including the mechanism of activation of oncogenes through genomic variation.
- Evaluate the design, operation and performance of laboratory approaches used to diagnose chronic myeloid leukaemia (CML), acute myeloid leukaemia (AML), myeloproliferative neoplasms (MPN) and acute lymphoblastic leukaemia (ALL).
- Critically evaluate the use of targeted therapies in the treatment of patients with haemato-oncologies.
- Critically appraise the role of molecular monitoring to assess response to therapy, detection of minimal residual disease, causes of drug resistance and choice of therapy.
- Select for application, analyse, interpret results and report on laboratory approaches used to investigate chronic myeloid leukaemia (CML), acute myeloid leukaemia (AML), myeloproliferative neoplasms (MPN) and acute lymphoblastic leukaemia (ALL) in the clinical context.
Haemato-Oncology 2
- Describe the underlying pathogenesis, clinical management, and treatment approaches for chronic lymphocytic lymphoma (B CLL), plasma cells dyscrasias, B cell lymphomas (low and high grade) and myelodysplastic syndromes (MDS).
- Critically evaluate the role of molecular genetic analysis for sub-classification of disorders.
- Appraise the design, operation and performance of laboratory approaches for the diagnosis, classification and prognosis of chronic lymphocytic lymphoma (B CLL), plasma cells dyscrasias, B cell lymphomas (low and high grade) and myelodysplastic syndromes (MDS).
- Critically evaluate the methods available for monitoring of residual disease including molecular genetic approaches and multi-parametric flow cytometry, selecting the most appropriate technique for each disorder.
- Select for application, analyse, interpret results and report on laboratory approaches used to investigate chronic lymphocytic lymphoma (B CLL), plasma cells dyscrasias, B cell lymphomas (low and high grade) and myelodysplastic syndromes (MDS) in the clinical context.
Academic modules and content
Cellular Haematology
- Haemopoiesis, erythrocyte and leucocyte function, anaemias and haematological malignancies
- Diagnostic criteria and laboratory approaches for acute and chronic leukaemias, myelodysplastic syndromes, and myeloproliferative disorders
- Critical examination of molecular mechanisms and clinical implications, including routine and emerging diagnostic technologies
Molecular Science and diagnostics
- Key molecular techniques and their applications in disease diagnosis
- qPCR, sequencing technologies, bioinformatics pipelines
- Interpretation of molecular data, diagnostic workflow design, and case-based applications
- Ethical and practical considerations in NHS laboratory implementation
Precision medicine
- Introduction to personalised healthcare in haematology
- Tumour profiling, pharmacogenomics, biomarker-led diagnostics
- Use of AI and big data in clinical decision-making
- Key NHS frameworks, including the National Genomic Test Directory
Work-based learning integration
- Learning and assessment embedded within a Digital Practice Assessment Document (PAD), mapping academic outcomes to work-based activities.
- Supervised workplace activities reviewed jointly by NHS workplace supervisors and University academic tutors.
- Emphasis on reflective practice, critical evaluation, and applied genomics in a healthcare setting.
Practical clinical skills
Introduction to Clinical Bioinformatics
- Select the analytical strategy for Haemato-Oncology clinical cases in the context of the patient history, following best practice guidelines.
- Analyse bioinformatics sequence data, to include sequencing from panel testing, exome testing and whole-genome sequencing.
- Analyse and interpret bioinformatic sequence data for clinical cases and manage incidental findings as appropriate, to include cases involving SNV, Indels, CNV, and SVs for somatic and germline variants.
- Assess quality parameters and metrics for bioinformatic sequence data to ensure validation according to national standards prior to analysis and compile error reports.
Introduction to Genomics
- Identify the testing strategy for genomic referrals in the context of patient history and other laboratory and clinical results.
- Assess the quality control process to identify assay performance trends and draw conclusions based on these trends for Internal Quality Control and External Quality Assurance schemes.
- Analyse variant interpretation data to determine pathogenicity.
- Interpret laboratory results in the context of other laboratory and clinical results and draft reports applying the correct nomenclature to genetic alternations for sequencing, copy number variants, and chromosomal analysis.
Haemato-Oncology 1
For the clinical conditions covered in this module:
- Identify the correct testing strategy required for referrals patients with haemato-oncologies based on existing test results.
- Interpret genomics results in context of other laboratory and clinical results:
- Identifying ambiguity with results
- Identifying where further testing is required
- With reference to published guidelines
- Considering quality metrics including IQC
- Report diagnostic results, identifying unexpected results.
- Prepare reports for contribution at a multidisciplinary team meeting or Genomic Tumour Advisory Board.
Haemato-Oncology 2
For clinical conditions covered in this module:
- Identify the correct testing strategy required for referrals for haemato-oncologies based on existing test results.
- Interpret genomics results in context of other laboratory and clinical results:
- Identifying ambiguity with results
- Identifying where further testing is required
- With reference to published guidelines
- Considering quality metrics including IQC
- Report diagnostic results, identifying unexpected results.
- Prepare reports for contribution at a multidisciplinary team meeting or Genomic Tumour Advisory Board.
Download the learning outcomes
PgCert in Haematology Genomics – 22KB OpenDoc