Year 3 Specialist Modules Next Generation Sequencing [10] IT for Advanced Bioinformatics Applications [10] Whole Systems Molecular Medicine [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, Professional Practice and Clinical Leadership
[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
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
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
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
Year 2: Programming [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 an object oriented programming language, e.g. Java.
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.
1. Design and code a small programme in Java or an alternative object-oriented programming language for bioinformatic application and proceed to test and debug the program in accordance with good programming practice.
2. Develop documentation and testing protocols for the program according to local practice.
3. Evaluate the program against non-functional requirements such as
maintainability, efficiency and readability, finalise the software and update and complete the documentation.
Indicative Content
• Sequential execution and programming
• Types, variable and expressions
• Execution flow control
• Separate classes
• Object-oriented design
• Introduction to graphical user interfaces
• Arrays
• Files and exceptions
• Programming testing
• An introduction to modern development and documentation tools Division: Cross-Divisional
Theme: Clinical Bioinformatics Specialism: Genomics
Year 2: Advanced Clinical Bioinformatics [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.
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. Annotate variation data in the context of a specific acquired or inherited disease or genetic investigation.
2. Develop variation data to inform a gene dossier, and enhance testing strategy for a specific patient population.
3. Develop an analysis strategy for a new service.
4. Advise 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.
Indicative content Genetics
• Genome wide association studies
• Haplotypes
• Large-scale sequencing projects, e.g. 1000 genome project, Exome Sequencing Project
• Linkage analysis, LOD scores (logarithm (base 10) of odds)
• Role of environment and genetic background in determining risks
• Personalised medicine and genetics
• Bacterial genetics and the spread of antibiotic resistance
• Detailed description of genome function – what has been learnt from Elixir
• Classification of genome variation – SNPs, CNVs
• Impact of variation on genome function – coding 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
• DECIPHER • Orphanet • DMuDB • OMIM • ECARUCA • DGV
• LOVD/UMD database software and scientific literature Pharmacogenomics
• Variation and response to drugs
• Impact of sequencing of pathogens – tracking spread of drug resistance Specific clinical analysis software
• CNV analysis
• Gene prioritisation (e.g. ToppGene, Endeavour, GeCCO)
• Missense analysis (e.g. Align GVGD, SIFT, PolyPhen, Panther, PhDSNP, MAPP)
• Splicing analysis applications (e.g. GeneSplicer, MAxEntScan, NNSplice, SSFL, HSF, NetGene2)
Disease and phenotype ontologies
• Human Phenotype Ontology (HPO)
• Orphanet
• PhenoDB
Reporting of results
• Providing reports that are clinically useful – understanding the strengths and limitations of the methodologies
• The case conference – what are the roles?
o The role of the bioinformatician within a patient case conference
Ethics, confidentiality and governance
• The challenges presented by genome data
o Specific risks of genome data
o Issues with Genome Wide Association Studies (GWAS) data and
identifiability
o Legal and governance framework for genome data in the NHS
Division: Cross-Divisional
Theme: Clinical Bioinformatics Specialism: Genomics
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, service improvement, 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.
Learning Outcomes: Knowledge and Understanding On successful completion of this module the trainee will:
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, service improvement 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.
4. Present a summary of the research project and outcome that conforms to the format of a typical scientific presentation at a national or international scientific meeting, responding to questions appropriately.
5. Prepare a summary of the research project suitable for non-specialist and lay audiences.
Indicative Content
• Critical evaluation of the literature/evidence base
• 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 with respect to bibliography and referencing of information
Division: Cross Divisional
Theme: Clinical Bioinformatics Specialism: Genomics
Year 3: Applied Next Generation Sequencing [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, while 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 trainee’s 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 the trainee 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 Next Generation Sequencing (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, Binary Alignment/Map (BAM) and Sequence Alignment/Map (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.
On successful completion of this module the trainee will: 1. Use the tools required for each stage of NGS data analysis.
2. Analyse NGS data through base calling, filtering, quality control, data validation and read mapping (eukaryotic and prokaryotic) in a clinical setting.*
3. In partnership with the relevant clinical specialist interpret NGS data through SNP, InDEL and CNV analysis and relate to phenotypic data.
*The clinical setting could include Clinical Genetics, Microbiology, Virology, Clinical Diagnostics for example targeted oncology treatments/classical genetics) Indicative Content
• Brief history of sequencing strategies
• Application of next generation data in genetics and medicine and the impact on patient care
Next generation sequencing platforms
• The genome science behind next generation sequencing – random fragments, sequencing, assembly
• Different sequencing platforms and the physical chemistry they deploy: non- optical (e.g. Ion Torrent, Nanopore and optical e.g. Illumina, 454)
• Applications of NGS Sequence assembly
• The problems of aligning short reads
• Next generation 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
• Current literature and practice around the impact of NGS tools in clinical medicine and genomics
Division: Cross-Divisional
Theme: Clinical Bioinformatics Specialism: Genomics
Year 3: IT for Advanced Bioinformatics Applications [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 trainee 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.
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
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. Identify a clinical bioinformatics requirement and develop, validate and deploy a bespoke workflow for clinical diagnostic analysis.
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