modernising scientific careers programme - nhs …...stp msc clinical bioinformatics version 0.4...

DRAFT

Page | 1 STP MSc Clinical Bioinformatics Version 0.4 260113

Modernising Scientific Careers Programme

MSc in CLINICAL SCIENCE

Clinical Bioinformatics

Specialism: Genomics

Learning Outcomes and

Indicative Content

2013/14

Further specialisms within this STP Theme are currently under development for 2014/15

DRAFT


Contents Section 1: Overview of Scientist Training Programme ........................................... 3

1.0 Background .................................................................................................. 3

1.1 Scientist Training Programme in Clinical Bioinformatics............................... 3

1.2 Professional Practice.................................................................................... 4

1.3 Scientist Training Programme Outcomes ..................................................... 5

Section 2: MSc Clinical Science (Clinical Bioinformatics) ...................................... 7

2.1 Clinical Bioinformatics Route Map................................................................ 7

Section 3: Scientist Training Programme Generic Modules ................................... 9

Healthcare Science ............................................................................................ 9

Research Methods ........................................................................................... 11

Section 4: Division/Theme Specific Modules ....................................................... 14

Introduction to Clinical Bioinformatics............................................................... 14

Fundamentals of Computing for Bioinformatics and the Physical Sciences ..... 17

Applying ICT in the Clinical Environment.......................................................... 19

Introduction to Health Informatics ..................................................................... 22

Section 5: MSc Specialist Modules for Clinical Bioinformatics (Genomics).......... 26

Pedagogic Background .................................................................................... 27

Programming.................................................................................................... 28

Advanced Clinical Bioinformatics...................................................................... 30

Research Project .............................................................................................. 34

Whole Systems Molecular Medicine................................................................. 36

IT for Advanced Bioinformatics Applications .................................................... 38

Next Generation Sequencing ........................................................................... 41

Appendix 1: Contributor List ................................................................................. 44

Appendix 2: Programme Amendments ................................................................ 45

Appendix 3: Good Scientific Practice ................................................................... 46

Appendix 4: Glossary ........................................................................................... 53

DRAFT


Section 1: Overview of Scientist Training Programme 1.0 Background This document sets out the proposed structure, high level learning outcomes and indicative content for the 3-year, part-time Masters in Clinical Sciences that forms part of the Scientist Training Programme (STP). The STP combines and integrates the generic professional practice learning together with an introduction to, and specialist learning in Clinical Bioinformatics. The diagram below depicts the overall STP structure.

Modernising Scientific Careers: Scientist Training Programme (STP): Diagrammatic representation of employment-based, pre-registration 3-year

NHS commissioned education and training programme

1.1 Scientist Training Programme in Clinical Bioinformatics

The diagram overleaf provides an overview of the programme each trainee in Clinical Bioinformatics will follow.

DRAFT


Modernising Scientific Careers: Scientist Training Programme (STP): Diagrammatic representation of employment based, 3-year NHS

commissioned, pre-registration Education and Training programme

1.2 Professional Practice Good Scientific Practice (GSP) sets out the principles and values on which good practice undertaken by the Healthcare Science workforce is founded and GSP is contextualised within each STP programme. Good scientific practice underpins both the MSc in Clinical Science and the work based training. Details of the professional practice components to be covered within each MSc programme are found in this document whilst the professional practice curriculum for the work based learning guides for each of the current STP programmes can be found at: http://www.networks.nhs.uk/nhs-networks/msc-framework-curricula/stp/msc-clinical-science-learning-outcomes-indicative-content-and-work-based-trainee-learning-guides-for-2012-13-trainees. Each work based learning programme contains a generic professional practice module which is developed and contextualised within each theme and specialism.

DRAFT


1.3 Scientist Training Programme Outcomes

Introduction The Scientist Training Programme (STP) is an integrated education and training programme combining academic study leading to the award of an NHS commissioned MSc in Clinical Science and a work based training programme. Successful completion of both elements of the STP will lead to the award of a Certificate of Completion of the STP (CCSTP) by the National School of Healthcare Science (NSHCS). Holders of the CCSTP will then be eligible to apply to the Academy for Healthcare Science for a Certificate of Attainment and will which in turn confers eligibility e to apply to the Health and Care Professions Council (HCPC) HCPC for registration as a Clinical Scientist. Whilst the STP programme is an integrated programme, the MSc in Clinical Science focuses on the knowledge and understanding that underpins practice and places it within the broader clinical and scientific context. The work based training programme focuses on the acquisition and development of skills alongside the application of knowledge within the healthcare science sector/specialism. The demonstration of appropriate attitudes and behaviours spans both aspects of the programme. PROGRAMME OUTCOMES Graduates of the STP will possess the essential knowledge, skills, experience and attributes required of a newly qualified Clinical Scientist. STP graduates will have clinical and specialist expertise in a specific healthcare science specialism, underpinned by broader knowledge and experience within a healthcare science division or theme. They will be competent to undertake complex scientific and clinical roles, defining and choosing investigative and clinical options, and making key judgements about complex facts and clinical situations. Many will work directly with patients and all will have an impact on patient care and outcomes. They will be involved, often in lead roles, in innovation and improvement, research and development and/or education and training. On completion of the STP all graduates should be able to demonstrate:

Professional Practice

1. Professional practice that meets the professional standards of conduct, performance and ethics defined by Good Scientific Practice and the regulator (HCPC) and is safe, lawful and effective and within the scope of practice for the role undertaken whilst maintaining fitness to practise.

2. Personal qualities that encompass communication skills, self management, self-awareness, acting with integrity and the ability to take responsibility for self-directed learning, maintaining their own health and well being, critical reflection and action planning to maintain and improve performance.

3. The ability to be an independent self-directed learner acting autonomously in a non-discriminatory manner when planning and implementing tasks at a

DRAFT


professional level, contributing to the education and training of colleagues and providing mentoring, supervision and support as appropriate.

4. The ability to work, where appropriate, in partnership with other professionals, often as part of a multidisciplinary team, supporting staff, service users and their relatives and carers whilst maintaining confidentially.

5. The ability to work with public, service users, patients and their carers as partners in their care, embracing and valuing diversity.

Scientific and Clinical Practice

6. A systematic understanding of relevant knowledge, and a critical awareness of current problems, future developments and innovation in health and healthcare science practice, much of which is at, or informed by, the forefront of their professional practice in a healthcare environment.

7. Clinical and scientific practice that applies basic, core scientific knowledge, skills and experience in a healthcare setting, places the patient and the public at the centre of care prioritising patient safety and dignity and reflecting NHS/health service values and the NHS Constitution.

8. The ability to perform appropriate diagnostic or monitoring procedures, treatment, therapy or other actions safely and skilfully adhering to applicable legislation and in compliance with local, national and international guidelines.

9. The ability to deal with complex scientific and clinical issues both systematically and creatively, make sound judgements in the absence of complete data, and to communicate their conclusions clearly to specialist and non-specialist audiences including patients and the public.

10. The ability to define and choose investigative and scientific and/or clinical options, and make key judgements about complex facts in a range of situations.

11. Originality in the application of knowledge, together with a practical understanding of how established techniques of research and enquiry are used to create and interpret knowledge in healthcare and healthcare science and their specialism.

Research, Development and Innovation 12. A comprehensive understanding of the strengths, weaknesses and

opportunities for further development of healthcare and healthcare science as applicable to their own clinical practice, research, audit, innovation and service development which either directly or indirectly leads to improvements in patient experience, clinical outcomes and scientific practice;

13. Conceptual understanding and advanced scholarship in their specialism, enabling them to critically evaluate and critique current research and innovation methodologies and, where appropriate, propose new research questions and hypotheses;

Clinical Leadership

14. Scientific and clinical leadership based on the continual advancement of their knowledge, skills and understanding through the independent learning required for continuing professional development.

DRAFT


15. The ability to critique, analyse and solve problems, define and choose investigative and scientific and/or clinical options, and make key judgements about complex facts in a range of situations.

Section 2: MSc Clinical Science (Clinical Bioinformatics) The diagram below depicts the broad framework around which all degree programmes must be structured. However, each division within the Modernising Scientific Careers Programme (MSC) has interpreted and adapted this framework.

HIGH LEVEL FRAMEWORK MSc IN CLINICAL SCIENCE

Generic Modules: Common to all divisions of Healthcare Science

Division/Theme Specific Modules: Common to a division or theme

Specialist Modules: Specific to a specialism 2.1 Clinical Bioinformatics Route Map The STP in Clinical Bioinformatics will initially be offered in one specialism namely Genomics but further specialisms are under development for 2014/15.The route map overleaf shows how the high-level framework has been interpreted for the MSc in Clinical Science (Clinical Bioinformatics).

Research Project

Students would usually begin a work based research project in Year 2 and complete the

project in Year 3.

[30]

Research Project

Students would usually begin a work based research project in Year 2 and

complete the project in Year 3

[30]

Year 2

Specialist Practice

Year 3

Specialist Practice

Healthcare Science

Specialist Learning

with integrated Professional Practice

[20]

Research Methods

[10]

Healthcare Science

Specialist Learning with integrated Professional Practice

[30]

Year 1 Core

Modules

Healthcare Science

Integrating science and

Professional Practice

[20]

Healthcare Science

Integrating underpinning knowledge required for each rotational element with Professional Practice

[40]

Generic Division/Theme

Generic Specialism

Specialism

DRAFT


MSc Clinical Sciences (Clinical Bioinformatics)

Year 1 Year 2 Year 3

Healthcare Science [20] Research Methods [10]

Clinical Bioinformatics underpinning knowledge for rotational work based training [40]

Clinical Bioinformatics

Programming [10] Whole systems molecular medicine [10]

Advanced Clinical Bioinformatics [10]

IT for advanced bioinformatics applications [10]

Research Project in Clinical Bioinformatics [30]

Next Generation Sequencing [10]

Research Project in Clinical Bioinformatics [30]

Credits

Generic 20 10 0

Division/Theme 40 0 0

Specialism 50 60

Total 60 60 60

Route Map: MSc Clinical Science (Clinical Bioinformatics)

MSc trainees commence by following the generic modules common across all STP Programmes (blue)

together with some theme specific modules (yellow).In Year 2 of the MSc trainees specialise (orange) in

genomics

DRAFT


Section 3: Scientist Training Programme Generic Modules The generic MSc modules studied by all trainees on each of the STP programmes are Healthcare Science and Research Methods. Professional Practice is also generic and is integrated across the 3-year STP in both the MSc and work based learning programme. For details of the complete Professional Practice Curriculum and Good Scientific Practice to which the professional practice module aligns please see Appendix. Please note that at the time of preparing this draft an interim review of this Healthcare Science module is underway and an updated version will be published at the end of March 2013.

Year 1: Generic Module Healthcare Science [20 credits]

The overall aim of this introductory module is to provide trainees with knowledge and understanding of the basic science and scientific knowledge that will underpin study in any of the three divisions of healthcare science namely Life Sciences, Physical Sciences and Biomedical Engineering and Physiological Sciences within the Scientist Training Programme. This module will also introduce the frameworks underpinning professional practice across the divisions providing the building blocks for future development of professional practice in the workplace. This module will build on the knowledge, skills and experience gained during undergraduate studies with learning developed and applied further in division and specialism specific modules.1

Learning Outcomes: Knowledge and Understanding On successful completion of this module the trainee will:

1. Outline the chemical, cellular and tissue level of organisation of the body. 2. Describe the function of blood as a tissue, blood cells (types and life times). 3. Know the structure and function of the skin. 4. Know the structure and function of the skeletal system. 5. Describe the organisation, basic structure and function of the central, peripheral

and autonomic nervous system.

6. Know the normal structure and function of the respiratory system including ventilation, gaseous exchange and blood gas transport.

7. Know the normal structure and function of the heart, blood vessels and lymphatic system.

8. Know the anatomy and physiology of vision, hearing and equilibrium. 9. Know the normal structure and function of the GI tract including digestion and

absorption of food, the liver and liver function tests.

10. Know the normal structure and function of the kidney including anatomy and

1This module should build on the knowledge gained during undergraduate studies with learning

developed further in division and specialism specific modules

DRAFT


function of the endocrine system, electrolyte and acid-base balance and hormonal mechanisms and control.

11. Know the anatomy and physiology of the male and female reproductive tract. 12. Know the principles of inheritance, DNA and genetics including carrier status,

genetic crosses/pedigree/punnet squares/cross diagrams.

13. Know the cellular, tissue and systems responses to disease including cell death, inflammation, neoplasia, hypertrophy, hyperplasia, tissue responses to injury and repair.

14. Explain how factors affecting health may contribute to inequalities in health between populations.

15. Explain the basic concepts underpinning health economics and their applicability to healthcare science.

16. Know the basis of health protection including principles of surveillance. 17. Examine patients' responses to illness and treatment and consider the impact of

psychological and social factors, including culture, on health and health-related behaviour.

18. Know the basic principles of physics that underpin healthcare science e.g. ultrasound, radiation...

19. Explain the structures and processes that underpin quality assurance including quality control, assurance, quality improvement and clinical governance.

20. Know and apply basic principles of communication with respect to key features of effective patient interviews and information giving; working with groups of the population who have particular communication needs such as children, those with learning disabilities and management of emotional responses within the scientist-patient interaction

21. Know the basic principles and structures underpinning history taking and clinical examination.

22. Know and understand the importance of the concept of shared leadership and the associated personal qualities and behaviours that promote shared leadership.

23. Understand the structure and management of health and social care services and the management of local healthcare systems in the United Kingdom.

Indicative Content

• Review of the organisation, structure and function of the body

• Review of basic genetic concepts

• Review of the pathological processes underpinning common diseases: o Cell death o Inflammation o Neoplasia o Hypertrophy o Hyperplasia o Tissue response to injury and repair

• Factors affecting health and their contribution to inequalities in health between populations

• Basis of health protection including principles of surveillance.

• Patients' responses to illness and treatment including the impact of psychological, social factors and culture

• Basic principles of physics underpinning common techniques used in healthcare science e.g. ultrasound, radiation

DRAFT


• Basic principles of quality assurance including quality control, assurance, quality improvement and clinical governance.

• Health Economics

• Communications skills

• Introduction to history taking and clinical examination.

• Introduction to leadership within the NHS.

• Introduction to the structure of the NHS Year 2: Generic Module Research Methods [10 credits] The overall aim of this module is to ensure that the trainee has the knowledge, skills and experience of the role of research, development and innovation in the NHS in improving patient care including prevention, diagnostics, treatment and service delivery. On completion of this module and the research project trainees should be able to generate ideas, assess, plan, conduct, evaluate, interpret and report research and innovation projects, which includes original research and disseminate the findings and where appropriate the adoption of the findings. Trainees should also be able to use research to improve practice.


1. Discuss and critically evaluate the context within which research, development, innovation and audit are undertaken to improve patient care, promote innovation and improve service delivery.

2. Describe, compare and contrast a range of research methods/approaches including cohort studies, qualitative, quantitative, systematic review, sampling techniques and clinical trials.

3. Explain and justify current UK ethical and governance frameworks and processes spanning the conduct of human and animal research, innovation and audit.

4. Critically evaluate the literature/evidence base to identify a research question, create a new approach or technique to improve patient care or service delivery.

5. Discuss and justify the research, audit and innovation process from idea generation to dissemination/implementation including patient/user involvement and intellectual property.

6. Describe and evaluate a range of data analysis techniques to ensure the validity, reliability and appropriateness to the research aim, design and conclusion.

7. Describe how clinical guidelines are produced and the concept of evidence based practice including the role of current statutory and advisory regulatory bodies.

8. Identify potential sources of research and innovation funding for healthcare science/clinical scientists.

Learning Outcomes: Practical Skills

DRAFT


On successful completion of this module the trainee will: 1. Undertake an evidence based literature review, critically appraise the

output, draw appropriate conclusions and report the findings and where appropriate, use the findings to inform a research project.

2. Identify, discuss and critically evaluate a research, innovation or audit project that has resulted in an improvement in patient care, diagnostics or service delivery.

Indicative Content Research Methods/Approaches

• Differentiation between audit and research • Cohort studies • Qualitative • Quantitative • Systematic review • Meta-Analysis • Sampling techniques • Clinical Trials (pre-clinical to translational) • Epidemiological Studies • Study Design • Hypothesis generation and testing

Ethical and Governance Research Frameworks

• Good Clinical Practice • Human • Animal Research • Innovation • Audit.

Research, audit and innovation process

• Literature searching and referencing • Innovation pathway (Invention, Evaluation, Adoption and Diffusion) • Idea generation • Patient/user involvement • Peer/expert review • Practical and financial criteria and constraints affecting research • Dissemination/implementation • Intellectual property • Quality Assurance • Monitoring and Reporting • Archiving • Roles and responsibilities of the research/innovation team

Data analysis techniques

• Data validity, reliability and appropriateness • Application and interpretation of statistical techniques • Power calculation

DRAFT


Clinical Guidelines

• Evidence based practice • Statutory and advisory regulatory bodies.

Research and Innovation Funding

• Sources of funding including Research Councils and Charities • Grant Applications

DRAFT


Section 4: Division/Theme Specific Modules

Introduction to Clinical Bioinformatics

This section covers the division/theme specific module that will be studied by all trainees undertaking the Clinical Bioinformatics STP.

Division: Cross Divisional Theme: Bioinformatics Year 1: Introduction to Clinical Bioinformatics [40 credits] The overall aim of this module is to provide trainees with the knowledge that underpins the STP work based rotational programme in Clinical Bioinformatics. A high level description of the work based placed learning is included to provide education providers with information on how the academic and work based elements integrate. Rotational Programme Division: Cross Divisional Theme: Clinical Bioinformatics Rotation A: Introduction to Clinical Bioinformatics and Genetics [10

credits] Rotation B: Fundamentals of Computing for Bioinformatics and the

Physical Sciences [10 credits] Rotation C: Applying ICT in the Clinical Environment [10 credits] Rotation D: Introduction to Health Informatics [10 credits] Rotation A: Introduction to Clinical Bioinformatics and Genetics [10 credits] This rotation will provide trainees with a background knowledge of genetics and a knowledge and understanding of bioinformatics tools and infrastructure. 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.

Learning Outcomes: Knowledge and Understanding On successful completion of this module the trainee will: 1. Discuss the governance and ethical frameworks in place within the NHS

and how they apply to bioinformatics.

DRAFT


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 changing protein function and

expression 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. Discover resources linking polymorphism to disease processes and discuss and evaluate the resources that are available to the bioinformatician and how these are categorised.

12. Discuss metadata and how it is captured in bioinformatics resources. 13. Interpret the metadata provided by the major bioinformatics resources. 14. Describe the use of ontologies in metadata capture and give examples of

the use of ontologies for capturing information on gene function and phenotype.

15. Identify appropriate references where published data are to be reported. 16. Describe the biological background to diagnostic genetic testing and clinical

genetics, and the role of bioinformatics. 17. Describe the partnership of Clinical Bioinformatics and Genetics to other

clinical specialisms in the investigation and management of genetic disorders and the contribution to safe and effective patient care.

Learning Outcomes: Associated Work Based Learning

High level description of the work based learning that accompanies this academic module. Further details of the work based programme can be found in the work based learning guide including the Clinical Experiential Learning, Competences and Applied Knowledge and Understanding. On successful completion of this module the trainee will: 1. Explain the process of developing and providing bioinformatic applications

and resources in the clinical setting. 2. Discuss the relevance and limitations of data from specific sources to the

case(s) of interest, the influence of user interfaces on results and the limitation of methods used to validate data submissions.

3. Perform sequence analysis on DNA and protein sequence data to infer function.

4. Perform sequence alignment tasks.

DRAFT


5. Select and apply appropriate bioinformatic tools and resources from a core subset to typical diagnostic laboratory cases according to established best practice.

6. Compare major bioinformatics resources for clinical diagnostics, and how their results can be summarised and integrated with other lines of evidence to produce clinically valid reports.

7. 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.

8. 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.

9. Actively seek accurate and validated information from all available sources.

Indicative Content Genetics

• The structure and function of coding and non-coding DNA

• The central dogma

• The process of going from DNA to RNA to protein.

• Genetics variation and its role in health and disease. Bioinformatic Fundamentals

• Introduction to the history and scope of bioinformatics

• Primary biological sequence resources, including INDSC (GenBank, EMBL, DDBJ) and UniProt (SwissProt and TrEMBL)

• Data 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), multiple sequence alignments (e.g., ClustalW, T-Coffee, Muscle)

• Feature identification including SNP analysis and transcription factor binding sites

• Ontologies – in particular GO. 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

• Background and application of specialist databases and browsers

• dbSNP

• DECIPHER

• Orphanet

• DMuDB / NGRL Universal Browser

•

• OMIM

• ECARUCA

DRAFT


• DGV

• LOVD/UMD database software and scientific literature

• HGMD

• 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)

• Alamut, Cartegenia

• Capture and representation of phenotype data

• Development of a simple application for clinical bioinformatic use Standards and governance

• Data standards and formats

• IUPAC codes

• FASTA

• GenBank

• FASTQ

• SAM/BAM

• HGVS variant nomenclature

• HGNC gene nomenclature

• RefSeq/RefSeqGene

• LRG • Role and development of Standard Operating Procedures • Relevant standards (clinical, genetic, bioinformatic) for data representation

and exchange Rotation B: Fundamentals of Computing for Bioinformatics and the Physical Sciences [10 credits] Modern healthcare generates large amounts of data – data which must be managed and shared effectively. In many cases this will involve staff working with database systems, and interacting with internal or external computer scientists/software engineers to commission appropriate data management tools. In this module trainees will be introduced to the fundamental aspects of computer science needed to support data management. They will also be introduced to the principles of modern software engineering processes such that they can better engage with and support software development within the NHS.

Learning Outcomes: Knowledge and Understanding On successful completion of this module the trainee will: 1. Describe the basic feature of the UNIX operating system. 2. Discuss client/server architectures.

DRAFT


3. Describe the basics of a relational database system and data normalisation. 4. Interpret simple SQL (structured query language) commands. 5. Describe a number of commonly used mark-up languages. 6. Interpret simple HTML scripts and simple XML scripts. 7. Describe functional and non-functional requirements for a system. 8. Discuss the advantages and disadvantages of UML for capturing

requirements. 9. Discuss the role of Application Programming Interfaces (APIs) in allowing

systems to communicate. 10. Give simple examples of software allowing databases to communicate with

web browsers. 11. Discuss security concerns around client server systems and system

security in the context of NHS data governance and ethics concerns. 12. Discuss and describe different software engineering models (agile,

waterfall). 13. Discuss how database systems/data management and modern software

processes contribute to healthcare science and the provision of high quality safe and effective patient care.

Learning Outcomes: Associated Work Based Learning High level description of the work based learning that accompanies this academic module. Further details of the work based programme can be found in the work based learning guide including the Clinical Experiential Learning, Competences and Applied Knowledge and Understanding. On successful completion of this module the trainee will: 1. Plan a process and assemble the requirements for a clinical information

system. 2. Express system requirements in UML. 3. Design a relational database system for a clinical information system –

ensuring an appropriate level of data normalisation. 4. Build an information system allowing web access to an SQL database 5. Construct a range of appropriate SQL commands. 6. Outline security, governance and ethics issues with web-accessible

database systems.

Indicative content Operating systems • Different common operating systems (windows, Linux, etc) • An introduction to Unix – file management, security, user management The web • How TCP/IP works • Client server architectures • HTML Database theory

DRAFT


• Capturing requirements – the idea of use cases • Basic intro to SQL • Basic intro to theory of normalisation • Database design in an SQL-92 environment • APIs for communication with databases Mark-up languages • Html and xml • Xml interpretation Security • Common database security issues • Governance and ethics associated with information systems in the NHS Software engineering • Formal processes for gathering requirements • Functional and non-functional requirements • UML and systems modelling • Effective communication of requirements • Developing software as a team • Different software development models: waterfall vs agile • Managing code effectively – version control systems Rotation C: Applying ICT in the Clinical Environment [10 credits] The role of the Clinical Scientist Information Communication Technology and Bioinformatics in Healthcare Science may include the development of novel image and signal processing applications. Scientists may also oversee the interconnection of critical patient safety computer systems, e.g. networks planning, imaging, controlling and verifying radiotherapy treatments. This module introduces the trainee to the application of ICT and Bioinformatics in the Clinical Environment and provides the knowledge that underpins this rotation*. *Initially this rotation is likely to be in a Medical Physics or Clinical Engineering Environment but may be in part of wholly be undertaken in a Physiological Sciences department.

Learning Outcomes: Knowledge and Understanding On successful completion of this module the trainee will: 1. Describe and justify the legislation that underpins safe working within the

clinical environment. 2. Discuss the basis of clinical measurement and the medical device lifecycle. 3. Discuss the basis of image formation and reconstruction. 4. Discuss the interconnectivity of computer systems within the clinical

environment.

DRAFT


5. Discuss how database systems/data management and modern software processes contribute to healthcare science and the provision of high quality safe and effective patient care.

6. Discuss and evaluate the role of the healthcare science workforce and the Clinical Scientist in innovation and service development.

Learning Outcomes: Associated Work Based Learning High level description of the work based learning that accompanies this academic module. Further details of the work based programme can be found in the work based learning guide including the Clinical Experiential Learning, Competences and Applied Knowledge and Understanding. On successful completion of this module the trainee will: 1. Participate in clinical measurement procedures effectively and safely with

due regard to the patient, health and safety, data security and governance in ICT within the context of Medical Physics and Clinical Engineering.

2. Develop a prototype image processing application 3. Manipulate data using a spreadsheet or database environment and an

appropriate programming language. 4. Use configuration control in relation to PC software installations and local

area networks, including the installation of systems and applications.

Indicative Content Information Communications Technology

• Range of general purpose computer software in common use including spreadsheets, flat-file and structured databases, online reference and collaborative resources

• Computing applied clinically including the additional safeguards when 'the computer acts as a medical device including an understanding of the role of the Medicine and Healthcare products Regulatory Agency (MHRA), the Food and Drugs Administration (FDA) and the International Electrotechnical Commission (IEC) and their role in CE Marking

• Introduction to the concept of the software lifecycle and the tools and frameworks used to specify, develop, validate and verify clinical software

• Basic principles relating to Information Communications Technology (ICT) security including firewalls, virus protection, encryption, server access and data security

• Information Governance, including NHS security policies

• Data exchange standards and be aware of some of the common standards, eg Digital Imaging and Communications in Medicine (DICOM) and Healthcare Level 7 (HL7)

• Networking systems in common clinical use and be aware of the relevant local Trust Information Technology policies

• Basic principles of applicable legislation and of local policies including the Data Protection Act, Computer Misuse Act and Freedom of Information Act

Clinical Measurement

DRAFT


• The physiology of pressure, flow, temperature, pH, blood gases, respiratory function and electrophysiology

• The physical principles underpinning these measurements

• Transducers for physiological measurement

• Calibration, traceability of standards

• Sources of error: random, systematic and human

• Sensitivity and specificity of measurement techniques

• Relationship of measurement results to clinical pathology, data processing and interpretation

Introduction to Imaging

• The physics and mathematics of image formation as it relates to: o The radiological image o CT scanning o Nuclear Medicine o PET o MRI o Ultrasound

• Introduction to image reconstruction techniques

• Introduction to image processing and analysis

• Image display characteristics

• Clinical application and a basic understanding of normal and pathological appearances within the image

• Introduction to image registration Safety

• Health and safety legislation specific to division

• Risk assessment techniques

• Chemical safety; COSHH, hazards, storage, use and disposal

• Electrical safety; medical equipment, leakage currents, fault conditions, isolation and circuit protection; biological/physiological response to electric shock; treatment of electric shock; equipment testing

• Mechanical safety; lifting gear; guards and operation of machine and hand tools, eye and ear protection; fumes, dusts, moving and handling

• Biological safety; pathological and normal specimens; blood and other tissues; equipment contamination, cleaning, cross-contamination; handling procedures and protocols

• Theatre safety; anaesthetic agents, explosion hazard, waste gas extraction, function checks, obstacles, sterility

• Workshop safety Innovation and Service Improvement

• Role of Medical Physics and Clinical Engineering in innovation and service improvement

• Project management

• Process mapping

• Equipment lifecycle

• Specification, procurement, installation and commissioning

• Critical review of protocols, techniques and equipment

DRAFT


• Health Technology Assessment

• Horizon scanning Medical Device Lifecycle

• Health Technology Assessment

• Quality systems and standards o ISO9000 o EN13485

• Equipment Evaluation

• Medical Device Lifecycle

• Medical Devices Directive

• Risk management principles applied to medical devices Rotation D: Introduction to Health Informatics [10 credits] This rotation will provide trainees with the basic informatics knowledge and understanding of the skills and tools needed by all professionals in modern healthcare systems to provide safe, secure, high quality, effective patient-centred services. Learning will be developed and applied in the work based training and contextualised to patient care and patient safety. The content of the module is taken from the national curriculum framework “Learning to Manage Health Information: A theme for clinical education 2012 – Making a difference” (Department of Health 2012). This fourth edition of the Framework was developed in partnership with representatives of a wide range of clinical professional bodies, health informatics professionals and health educators from across the UK.

Learning Outcomes: Knowledge and Understanding On successful completion of this module the trainee will: 1. Discuss and justify the legislation, regulatory guidance and national and

local protocols relating to the security, confidentiality and appropriate sharing of patient information.

2. Explain the information governance implications for individuals and organisations of information sharing and communication between professions, with patients/clients and across organisations.

3. Discuss the principles and purpose of effective quality control and validation of data and the impact of poor quality data on management and healthcare outcomes. Identify the range, purposes, benefits and potential risks of sharing, integrating and aggregating clinical data and information.

4. Discuss the role of informatics in clinical governance. 5. Describe and evaluate the purpose, structures, use and storage of health

and care records. 6. Discuss the implications of patient access to records and clinical

information for inter-professional practice and multi-disciplinary care.

DRAFT


7. Discuss the basis, application and evaluate the limitations of the different clinical coding systems in use and the importance of high quality coded clinical data in communication and to patient safety.

8. Explain the use of clinical terms in record keeping and the role of terming on reporting and analysis.

9. Discuss the importance of ICT in supporting clinical practice and new ways of working in healthcare drawing on examples from national and local policy strategy.

10. Discuss the risks in clinical IT systems and mitigations. 11. Discuss emerging information and communication technologies and their

application in health and care.

Associated Work Based Learning Outcomes High level description of the work based learning that accompanies this academic module. Further details of the work based programme can be found in the work based learning guide including the Clinical Experiential Learning, Competences and Applied Knowledge and Understanding. On successful completion of this module the trainee will: 1. Successfully complete the national Information Governance training module

(with a score of more than 80%). 2. Perform a clinical audit and produce an audit report. 3. Advise peers and colleagues on best practice in: a. Data and information security b. Patient confidentiality c. Record sharing d. Information sharing with patients/clients e. Records access by patient 4. Assist the Chief Clinical Information Officer or other informatics lead in

managing and developing local approaches to informatics strategic planning and implementation.

5. Review opportunities for the application of tele-health or tele-medicine in the host organisation.

6. Access local and national clinical knowledge bases and care pathway guidance.

7. Send, receive and store communications containing patient/clinical information safely and securely in accordance with policy, protocols, legislation and codes.

8. Observe other professionals sharing information with patients and supporting patients to access clinical information and/or their own records.

9. Perform a review of a local clinical IT system for patient safety and security compliance.

To support the training and development of clinical professionals pre and post-registration and in response to requests from clinical educators, a suite of seven, short, free, on line learning materials have been developed, based on the content of “Learning to Manage”. They can be accessed at: www.cln.nhs.uk/eice. Organisations/academic institutions may register staff/students on the portal and then

DRAFT


track progress using the integral learning management system. Individuals can also register and access the materials.

Indicative Content • Information Governance: data and information quality, security and

confidentiality o Acts of Parliament, other legislation, Codes of Practice o Dealing with requests for information about patients/clients o Information Commissioner, o Paper based vs electronic records; o Patient identifiable data and information, o Secondary uses of data, o Audit and research, o Caldicott Guardians o Consent o Smart cards/records access o The well informed patient, the expert patient, o Encryption (principles) o Safe havens o Relationship and differences between data and information o Qualities of good data o Information system risks to patient safety o Cost of data entry errors o Secure information exchange between professionals o Sharing and communication with patients and carers

• Uses of clinical and health data and information o Patient identifiable and non-patient identifiable data and information o Health research applications o Public health, o Service planning o Cross sector care, o Patient/client centred service, o Information flows between health and social care and public health, third

sector and private sectors o Systematic approaches to improving patient care: secondary uses – SUS

– QIPP and related initiatives o Patient focused systems vs speciality, disease or procedure focused

systems o Big Data o Transparency o Information intermediaries o Clinical audit o Information for patient choice

• Health Records

o Paper vs electronic records o Patient held records o Structured and coded records - free text in records o Consent models

DRAFT


o Confidentiality and security o Impact of patient access on professionals and relationships, behavioural

issues o Record sharing – with patients and between professionals o Summary Care Record o Electronic Health Records o GP/primary care records

• The Language of Health: Clinical Coding and Terminology o Terminologies vs classifications. o Coding systems - nature, clinical applications, limitations o Accident and Emergency Coding Tables o International Classification of Diseases (ICD) o NHS dictionary of medicines and devices, OPCS Classification of

Interventions and Procedures, Read Coded Clinical Terms, SNOMED CT

o Coding quality issues and risks o Coding and patient accessible information o Coding for management - importance of coded data for supporting

business workflows and administration, eg Quality and Outcomes Framework (QOF) and Commissioning Outcomes Framework (COF).

• ICT Systems for Clinicians, Patients and the Public o eHealth, Tele-health, tele-medicine o Assistive technologies – applications, risks and issues o Clinical and decision support systems o Map of Medicine o National Laboratory Medicine Catalogue o On-line guidelines and knowledge bases o National infrastructure eg SPINE, NWW, NHS Choices, patient

portal(s) o Lorenzo o e-Prescribing o Mobile working o System design, system reliability, interoperability o “Good informatics” vs “bad informatics”

DRAFT


Section 5: MSc Specialist Modules for Clinical Bioinformatics (Genomics)

Module Titles

Year 3 Specialist Modules

IT for advanced bioinformatics applications

[10]

Whole systems molecular medicine

[10]

Next Generation Sequencing

[10]

Research Project in Clinical Bioinformatics

[30]

Year 2 Specialist Modules

Research Methods

[10]

Advanced Clinical Bioinformatics

[10]

Programming

[10]

Research Project in Clinical Bioinformatics

[30]

Year 1 Core Modules

Healthcare Science integrating science and professional practice

[20]

Introduction to Clinical Bioinformatics Underpinning knowledge for rotational work based training programme and

integrated professional practice

[40]

Generic Modules: Common to all divisions of Healthcare Science

Division/Theme Specific Modules: Common to a division or theme

Specialist Modules: Specific to a specialism

DRAFT


Pedagogic Background Bloom definitions have been used to classify learning outcomes. However, the Bloom classification has been simplified to define three broad areas – understanding, application and the creation of new understanding. The document is therefore written to describe the learning outcomes from each module at three levels: Level 1: Understanding the area:

Bloom terms: Definition, Knowledge and Comprehension Level 2: Application of the knowledge: Bloom terms: Application and Analysis Level: Creating new knowledge/understanding/strategies: Bloom terms: Synthesis and evaluation Effectively in each area we want to check that the trainee understands the area, can effectively apply the tools and make sense of the results returned, and has a deep enough knowledge to help guide the service towards new strategies/techniques where appropriate. The learning outcomes have been put in this order for each of the modules. However it should also be pointed out that we are not attempting to reach the final level in the Bloom taxonomy for all modules. For example in the programming module we want them to be able to understand and apply programming skills – not necessarily to develop new programming paradigms. Modules that aim to reach level 2:

• Introduction to clinical bioinformatics

• Fundamentals of Computing for Bioinformatics and the Physical Sciences

• Programming Modules that aim to reach level 3:

• Advanced clinical bioinformatics

• Next generation sequencing

• IT for Advanced Bioinformatics Applications

• Whole systems molecular medicine

DRAFT


MSc Year 2 Specialist Practice These modules provide the trainee with the knowledge and understanding that underpins and is applied to the specialist work based learning programme.

Division: Cross Divisional Theme: Clinical Bioinformatics Specialism: Genomics Module: Programming Year 2 [10 Credits] Bioinformatics and physical science in medicine are fast moving areas. It is often the case that specific tools and resources that would be useful in a clinical setting are not available commercially. Therefore the ability to be able to develop safe and effective code for use within the trainee’s organisation is an important part of the skill set of an effective information scientist. This module will provide trainees with a sound introduction to programming and safe and effective software development practice.

Learning Outcomes: Knowledge and Understanding On successful completion of this module the trainee will: 1. Express a clear understanding of the basic principles of the Java

programming language. 2. Discuss the need for a development process. 3. Discuss the role of testing programs and good documentation.

Associated Work Based Learning Outcomes High level description of the work based learning that accompanies this academic module. Further details of the work based programme can be found in the work based learning guide including the Clinical Experiential Learning, Competences and Applied Knowledge and Understanding. On successful completion of this module the trainee will: 1. Design and code small Java programs, which meet simple requirements

expressed in English. 2. Test and debug simple Java programs. 3. Write informal justifications for the programs they design. 4. Evaluate programs against non-functional requirements such as

maintainability, efficiency and readability.

Indicative Content

• Sequential execution and programming

DRAFT


• Ttypes, variable and expressions

• execution flow control

• separate methods

• separate classes

• Object oriented design

• Introduction to graphical user interfaces

• Arrays

• Files and Exceptions

• Programming testing

• An introduction to modern development and documentation tools

DRAFT


Division: Cross Divisional Theme: Clinical Bioinformatics Specialism: Genomics Module: Advanced Clinical Bioinformatics Year 2 [10 Credits] Advances in genomics are leading to a better understanding of genetic variation and the role that such variation plays in human health and 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.

Learning Outcomes: Knowledge and Understanding On successful completion of this module the trainee will: 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.

DRAFT


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.

Associated Work Based Learning Outcomes High level description of the work based learning that accompanies this academic module. Further details of the work based programme can be found in the work based learning guide including the Clinical Experiential Learning, Competences and Applied Knowledge and Understanding. On successful completion of this module the trainee will: 1. Explain how to choose and apply major bioinformatic resources for clinical

diagnostics, and how their results are integrated with other lines of evidence to produce clinically valid reports.

2. Select and apply appropriate bioinformatic tools and resources from a core subset to typical diagnostic laboratory cases according to established best practice.

3. Develop or assemble tools, pipelines and processes for specific laboratory applications.

4. Develop standard operating procedures as part of the laboratory management process.

5. Provide guidance and training for scientists in the clinical use of bioinformatic tools.

6. Identify, acquire and process appropriate genetic data sets for clinical use 7. 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.

8. Interpret evidence from bioinformatic tools and resources and integrate this into the sum of genetic information for the interpretation and reporting of results from pathogen sequencing.

9. Perform the recording of building or version numbers of resources used on a given date, including those of linked data sources, and understanding of the clinical relevance of this data.

10. Discuss the role of a bioinformatician in patient care and as part of a patient care team.

On successful completion of this module the trainee will also be able to apply their knowledge to create new knowledge or strategies by: 11. Appraising the relevance and limitations of data from specific sources to the

case(s) of interest, the influence of user interfaces on results and the limitation of methods used to validate data submissions.

12. Advising a genetics service within a hospital with respect to the bioinformatic requirements of a new clinical service and the strategy to deliver appropriate and clinically relevant data to support patient care.

DRAFT


13. Advise a genetics service on the capture and standardisation of patient phenotype data.

14. Summarising, evaluating and interpreting data governance, patient confidentiality and legal frameworks regarding bioinformatics genetics analysis pipeline.

15. Evaluating novel bioinformatics tools from outside vendors against clinical need.

16. Formulating the role of clinical genetics in personalised health care. 17. Developing a simple application to answer a specific laboratory requirement.

Indicative content

Genetics • Genome wide association studies • Haplotypes • Large-scale sequencing projects, e.g. 1000 genome project, Exome

Sequencing Project • Linkage analysis, LOD scores • 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 vs non coding regions

Bioinformatics

• 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

o DECIPHER o Orphanet o DMuDB o OMIM o ECARUCA o DGV o LOVD/UMD database software and scientific literature

Pharmacogenomics – variation and response to drugs

DRAFT


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

• 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? o A bioinformatician’s role within a patient case conference

Ethics, confidentiality and governance

• The challenges presented by genome data o Specific risks of genome data o Issues with GWAS data and identifiability o Legal and governance framework for genome data in the NHS

DRAFT


Division: Cross Divisional Theme: Clinical Bioinformatics Specialism: Genomics Module: Year 2 and 3: Research Project [60 credits]

The overall aim of this module, building on the Research Methods module is for the trainee to undertake a research project that shows originality in the application of knowledge, together with a practical understanding of how established techniques of research and enquiry are used to create and interpret knowledge in a specialism of healthcare science. The research project may span scientific or clinical research, translational research, operational and policy research, clinical education research, innovation, service development or supporting professional service users to meet the expected learning outcomes. Research projects should be designed to take into account the research training required by individual trainees and the needs of the department in which the research is to be conducted.


1. Discuss the stages of the research and innovation process from conceptualisation to

dissemination and if appropriate translation into practice

2. Describe the purpose and importance of different kinds of research including scientific or clinical research; translational research; operational and policy research; clinical education research; innovation; service development; and supporting professional service users and relate these to the roles undertaken by Clinical Scientists in the trainee’s specialism.

3. Discuss and evaluate the use of reference manager systems. 4. Justify the rationale for research governance and ethical frameworks when

undertaking research or innovation in the NHS.

5. Describe the process and requirements for publication in a peer reviewed journal and the current system of grading research publications.

Learning Outcomes: Practical Skills On successful completion of this module the trainee will: 1. Design, plan and undertake a research project to test a hypothesis from

conception to completion/archiving in accordance with ethical and research governance regulations drawing on expert advice where necessary and involving patients and service users.

2. Analyse the data using appropriate methods and statistical techniques and interpret, critically discuss and draw conclusions from the data.

3. Prepare a written project that describes and critically evaluates the research project clearly identifying the strengths and weaknesses.

DRAFT


4. Present a summary of the research project and outcome that conforms to the format of a typical scientific presentation at a national o r international scientific meeting, responding to questions appropriately.

5. Prepare a summary of the research project suitable for non-specialist and lay audience.

Indicative Content • Critical evaluate of the literature/evidence base • Reference management • Identification of a research question • Research ethics and regulatory requirements including issues related to

access and use of information • Data protection and confidentiality guidelines • Patient Safety • Patient Consent • Sources of funding/grants • Peer review/expert advice • Possible risks and balancing risk vs benefit • Project management techniques and tools • Roles and responsibilities of those involved in the research • Monitoring and reporting • Data Analysis • Data Interpretation • Criteria/metric for assessing and grading research data and publications in the

Scientific, NHS and HE Sectors • Range of formats and modes of presentation of data • Requirements for publications submitted to scientific, education and similar

journals • Current conventions in respect of bibliography and referencing of information

DRAFT


Division: Cross Divisional Theme: Clinical Bioinformatics Specialism: Genomics Module: Whole Systems Molecular Medicine Year 3 [10 Credits] It is becoming increasingly clear that diseases and disease processes are complex and involve many interactions within the genome, across metabolic pathways and between the individual and the environment. Such considerations are important if the consequences of variations observed within an individual’s genome are to be effectively assessed. Rapid advancements in areas such as functional genomics and systems biology are now providing new insights into such processes. However accessing these methodologies requires the use of forms of mathematics that have not been traditionally used within genetic medicine. This module will develop and strengthen the trainee’s mathematical and modelling skills and introduce them to functional genomics and systems biology strategies and the ways in which they can be applied in medicine for improved patient care.

Learning Outcomes: Knowledge and Understanding On successful completion of this module the trainee will: 1. Discuss the role of gene networks in specific genetic diseases 2. Describe the importance of genetic background in developing an

understanding of the role of a specific allele or mutation. 3. Describe the range of resources available to describe metabolic networks 4. Describe resources available that describe gene-gene and protein-protein

interactions 5. Describe the range of networks in which a particular gene might participate. 6. Describe the application of basic differential equation modelling techniques for

describing metabolic networks 7. Discuss the available repositories of models used in systems biology. 8. Describe the importance of parameter selection in modelling. 9. Describe a range of modelling strategies used to describe pathways.

Associated Work Based Learning Outcomes High level description of the work based learning that accompanies this academic module. Further details of the work based programme can be found in the work based learning guide including the Clinical Experiential Learning, Competences and Applied Knowledge and Understanding. On successful completion of this module the trainee will: 1. Develop and explore a differential equation model of a simple metabolic

pathway.

DRAFT


2. Take an existing metabolic or signalling pathway model and explore its behaviour.

3. Use the existing literature to determine the pathways in which a gene might participate.

4. Use the existing literature to determine parameters needed to explore a particular pathway model.

At the end of this a trainee will be able to apply their knowledge to create new knowledge or strategies by: 5. Developing hypotheses around the potential phenotype of a mutation based

on an analysis of the pathways and interactions in which it could participate. 6. Evaluating the literature around systems biology pathway modelling to

improve understanding of genetic disease processes 7. Determining the sensitivity of pathways to changes in gene function through

the analysis of stability analyses 8. Applying systems biology strategies to improve identification of disease

specific mutations from next generation sequencing data. 9. Providing strategic advice to clinical genetics service leaders in developing

systems strategies for interpreting next generation and other large scale sequencing data.

Indicative content

Mathematics: • Basic calculus • Building simple models using differential equations • Software tools for solving simple differential equation models (Matlab, Copasi) Bioinformatics pathway tools: • Databases of metabolic networks (Kegg, Panther etc) • Databases of gene interactions (string etc.) • Gene ontology and pathway analysis • Strategies for determining whether a pathway is over-represented in a set of

genes (Fisher exact t-test, methods based on gene lists). Systems biology • Introduction to SBML (Systems Biology Markup Language) • Repositories of pathway models • Determining model parameters from the literature • Stability analysis of ODE models (Jacobians)

DRAFT


Division: Cross Divisional Theme: Clinical Bioinformatics Specialism: Genomics Module: IT for Advanced Bioinformatics Applications Year 3 [10 Credits] The volume of data being generated by new functional genomics and next generation sequencing methodologies is unprecedented in medicine. The challenges of being able to capture and integrate this data effectively such that it can be used effectively require solutions beyond those that have typically been used in clinical medicine. The trainees will be introduced to modern computational methodologies for handling and integrating large data. This will involve them in developing a good understanding of data description standards (through ontologies) and data federation methodologies. Workflow systems will be introduced as tools for industrial scale bioinformatics analyses – as well as a discussion of cloud based computer solutions for extending the compute resource available within the NHS. A strong focus will be placed on the ethical and governance issues raised by using such technologies within an NHS setting.

Learning Outcomes: Knowledge and Understanding On successful completion of this module the trainee will: 1. Describe basic cloud computing infrastructure. 2. Describe the philosophy behind minimum information standards used to

capture functional genomics data. 3. Describe international data repositories for genetic and functional genomics

data. 4. Discuss the basic principles of ontologies for describing meta-data. 5. Describe the use of ontologies for capturing disease phenotype information. 6. Discuss strategies for genetic data analysis over large scale heterogeneous

data. 7. Describe a range of modern computational workflow systems. 8. Discuss the application of workflow systems to next generation sequence

analysis. 9. Discuss issues of data quality in medicine. 10. Discuss the importance of data quality for patient safety. 11. Describe the ethical and governance regulations relating to data capture in

the NHS. 12. Describe the ethical and governance concerns regarding data integration in

the NHS. 13. Describe basic principles of data encryption and international data encryption

standards in medicine. 14. Discuss the importance of information governance for patient safety.

Associated Work Based Learning Outcomes

DRAFT


High level description of the work based learning that accompanies this academic module. Further details of the work based programme can be found in the work based learning guide including the Clinical Experiential Learning, Competences and Applied Knowledge and Understanding. On successful completion of this module the trainee will:

1. Deploy a simple workflow system to perform a sequence analysis task 2. Perform a genetic analysis over a range of functional genomics data sets (for

example correlating SNP and gene expression data). 3. Compare and contrast the strengths and weaknesses of data warehouses vs

federated data integration strategies for genetics and genomics data in the NHS.

At the end of this a trainee will be able to apply their knowledge to create new knowledge or strategies by: 4. Developing a data and meta-data capture strategy for a genomics laboratory. 5. Perform a strategic analysis of the computational requirements of a genomics

laboratory. 6. Evaluating cloud computing solutions as to their fitness for purpose within the

security and IT governance frameworks of the NHS. 7. Evaluating data integration strategies as to their fitness for purpose within the

security and IT governance frameworks of the NHS. 8. Creating simple workflows capable of integrating and analysing clinical

functional genomics data.

Indicative content Computational infrastructure: • Data encryption and data encryption standards • Governance and security issues for large data in the NHS. • Basic cloud computing architectures (Software as service, Compute as

service etc). • Public and private cloud architectures (including commercial systems such as

Azure and EC2) • A basic introduction to workflows in computer science. • An introduction to workflow tools (Taverna, Galaxy etc) Functional genomics and genomics data sets • The concept of meta-data • The role of minimum information standards to allow effective sharing • Tools to capture minimal information data (XML) • An introduction to ontologies • Community annotation through ontology • Interoperating with ontologies • Strategies for large scale data integration • The pros and cons of data warehouses vs data integration over distributed

heterogeneous data • Examples of ontology driven data integration

DRAFT


• Examples of data warehouses for genomic integration (Ensembl) Workflows • The basic theory of computational workflows • The architecture of workflow systems • Examples of workflows in genetics (Galaxy assembly of next generation

sequencing data) • Analysis of current literature and data integration and workflows in genetics

and medicine

DRAFT


Division: Cross Divisional Theme: Clinical Bioinformatics Specialism: Genomics Module: Next Generation Sequencing Year 3 [10 Credits] There is a revolution occurring in genome sequencing. The cost of sequencing an entire human genome has dropped dramatically to the point at which it can be applied within the NHS to underpin diagnosis and treatment. Such strategies have also become very important in areas such as understanding the spread of antibiotic resistant bacteria across hospitals. However, whilst the cost of generating the data has dropped, the cost of analysing and interpreting such data has become one of the key bottlenecks in deploying this exciting new technology. This module will develop the trainees’ understanding of genome technology. It will also give them an understanding of the techniques needed to follow best practice in assembling genomic data from the current version of these technologies, and will provide trainees with tools and strategies for converting these data into clinically useful information. A strong emphasis will be placed on understanding the ethical and data governance challenges faced by this new – and very personal – data.

Learning Outcomes: Knowledge and Understanding On successful completion of this module the trainee will: 1. Describe the main NGS platforms and the methodologies that they use. 2. Discuss the applications of NGS in the clinical setting, including genome wide

association studies, whole exome sequencing, targeted resequencing and profiling of bacterial pathogens.

3. Describe the strengths of each NGS platform to solve different biological problems.

4. Describe how samples are prepared for sequencing. 5. Describe the basic principles of NGS data analysis, bioinformatic approaches,

challenges in storage and data transfer. 6. Describe the various data file types such as fastq, BAM and SAM files and

describe tools available for conversion of file types. 7. Describe the ethical and governance regulations relating to data capture in

the NHS. 8. Describe the ethical and governance concerns regarding data integration in

the NHS.

Associated Work Based Learning Outcomes High level description of the work based learning that accompanies this academic module. Further details of the work based programme can be found in the work based learning guide including the Clinical Experiential Learning, Competences and Applied Knowledge and Understanding.

DRAFT


On successful completion of this module the trainee will: 1. Analyse NGS data through base calling, quality control, data validation and

read mapping (eukaryotic and prokaryotic). 2. Describe the tools available for each stage of NGS data analysis. 3. Interpret NGS data through SNP, InDEL and CNV analysis and relate to

phenotypic data. At the end of this a trainee will be able to apply their knowledge to create new knowledge or strategies by: 4. Evaluate commercial tools versus open-source software for analysing NGS

data. 5. Describe the requirement for automated NGS data analysis pipelines. 6. Evaluate the requirement for best practice and reproducible NGS

bioinformatics workflows and the need to be able to share NGS data. 7. Appraise commercial and open source software for creating NGS analysis

pipelines. 8. Evaluate a bioinformatics pipeline for suitability in clinical diagnostics.

Indicative content Brief history of sequencing strategies Application of next generation data in genetics and medicine and the impact on patient care Next gen sequencing platforms • The genome science behind next generation sequencing – random

fragments, sequencing, assembly • Different sequencing platforms and the physical chemistry they deploy

(Illumina, Ion Torrent etc) • Applications of next gen sequencing Sequence assembly • The problems of aligning short reads • Next gen alignment strategies – Bowtie, BWA, SOAP, Burrows-Wheeler, de

Bruijn graphs • Data formats for next generation data – BAM, SAM, fastq • Sequence interpretation • SNP detection, cnv detection Data handling and data governance • Workflows for next generation analysis • Data quality in next generation data • Presenting next generation data • Models of use of next generation technology within the NHS • Issues of patient consent and what analyses are ethical

DRAFT


Current literature and practice around the impact of next generation sequencing tools in clinical medicine and genomics

DRAFT


Appendix 1: Contributor List Members of the STP MSc and Work Based Programme in Clinical Bioinformatics Development of the STP Programme (MSc Clinical Sciences and Work Based programme) for Clinical Bioinformatics has been coordinated by the Modernising Scientific Careers team and the National School of Healthcare Science working with NHS and Higher Education colleagues. The professionals who have contributed to the development of this Scientist Training Programme since 2012 include: Andy Brass The University of Manchester Angela Davies The Nowgen Centre, Manchester Val Davison National School of Healthcare Science Rob Andrew Devereau St Marys Hospital, Manchester Paul Ganney University College London Ira Laketic-Ljubojevic DH Informatics, Leeds Dominic McMullan West Midlands Regional Genetic Laboratory Di Millen DH Informatics, Leeds Robert Newton NHS National Genetics Education and

Development Centre, Birmingham Andrew Reilly Clatterbridge Cancer Centre, Wirral Lee Silcock West Midlands Regional Genetic Laboratory Paul Taylor University College London Steven Wood Royal Hallamshire Hospital, Sheffield

Modernising Scientific Careers Professional Advisors Dr Derek Pearson Dr Graham Beastall National School of Healthcare Science Professional Leads Dr Chris Gibson Theresa Fail Contributing Professional Bodies

DRAFT


Appendix 2: Programme Amendments

MSc Clinical Sciences (Clinical Bioinformatics)

Amendments Following First Publication

DRAFT


Appendix 3: Good Scientific Practice

Good Scientific Practice Section 1: The purpose of this document There are three key components to the Healthcare Science workforce in the UK:

1. Healthcare Science Associates and Assistants who perform a diverse range of task based roles with appropriate levels of supervision.

2. Healthcare Science Practitioners have a defined role in delivering and reporting quality assured investigations and interventions for patients, on samples or on equipment in a healthcare science specialty, for example Cardiac Physiology, Blood Sciences or Nuclear Medicine. They also provide direct patient care and more senior Healthcare Science Practitioners develop roles in specialist practice and management.

3. Healthcare Scientists are staff that have clinical and specialist expertise in a specific clinical discipline, underpinned by broader knowledge and experience within a healthcare science theme. Healthcare scientists undertake complex scientific and clinical roles, defining and choosing investigative and clinical options, and making key judgements about complex facts and clinical situations. Many work directly with patients. They are involved, often in lead roles, in innovation and improvement, research and development and education and training. Some pursue explicit joint academic career pathways, which combined clinical practice and academic activity in research, innovation and education.

This document sets out the principles and values on which good practice undertaken by the Healthcare Science workforce

modernising scientific careers programme - nhs …...stp msc clinical bioinformatics version 0.4...

Documents