Diagnostic Sequencing —
Training activity: 7

Details

Identify and visualise target genomic regions and variation

Type

Entrustable training activity (ETA)

Evidence requirements

Evidence the activity has been undertaken by the trainee repeatedly, consistently, and effectively over time, in a range of situations. This may include occasions where the trainee has not successfully achieved the outcome of the activity themselves. For example, because it was not appropriate to undertake the task in the circumstances or the trainees recognised their own limitations and sought help or advice to ensure the activity reached an appropriate conclusion. ​

Reflection at multiple timepoints on the trainee learning journey for this activity.

Considerations

• Best practice guidelines
• Local processes
• Quality of the sequence alignment, variant calls and nomenclature
• Visualising structural re-arrangements, e.g., breakpoints or read pair orientation
• Additional annotations e.g., gnomad, OMIM, cosmic or ClinVar
• Troubleshoot realignments
• Limitations of algorithms

Reflective practice guidance

The guidance below is provided to support reflection at different time points, providing you with questions to aid you to reflect for this training activity. They are provided for guidance and should not be considered as a mandatory checklist. Trainees should not be expected to provide answers to each of the guidance questions listed.

Before action

• What is the goal of successfully identifying specific genomic regions and variations within them and visualising them? What specific regions or variants should you focus on, what level of detail is required for visualisation, and what visualisation tools (e.g., genome browsers like IGV) should be used?

• Have you used genome browsers or other visualisation tools for genomic data before? Are you familiar with navigating and interpreting visual representations of sequence data or variants? What possible challenges might you face e.g. handling large BAM/VCF files for visualisation? Setting up the visualisation tool correctly? Interpreting complex read alignments? Structural variants visually? Identifying specific features within dense genomic data? How might you handle these challenges? When would you need to ask for help if you struggle to load data, navigate the tool, or interpret unusual visual patterns? How do you feel about visualising genomic data?

• What specific skills do you want to develop e.g. becoming proficient in using a particular genome browser? Improving your skill in visually identifying different types of variants or complex genomic rearrangements? What specific insights do you hope to gain into how different variant types appear visually or how alignment patterns reflect underlying genomic features?
• If previous visualisation attempts were difficult, what steps did you plan to take to improve? What important information do you need to consider before embarking on the activity e.g. ensuring access to the required data files (BAM, VCF)? Appropriate visualisation software? Understanding the specific coordinates or regions of interest for the task?

In action

• As you are identifying and visualising genomic regions and variations, make a note of anything that feels surprising or different from what you anticipate. For example, does a specific data file fail to load into the visualisation tool, do you encounter unexpected visual artifacts that are not predicted, or does navigating a dense genomic region prove unexpectedly challenging? Consider how this experience compares with previous experiences of similar activities, such as other data visualisation tasks or using genome browsers. Does it feel more or less familiar?

• Identify how any unexpected developments, such as a software glitch or difficulty interpreting visual patterns, impact your immediate actions. Do you immediately consult the tool’s user manual, try an alternative visualisation setting, or seek advice from a colleague proficient in genome browsers? Do you adapt or change your visualisation approach or strategy as a result? For instance, do you try a different genome browser, adjust the zoom level or track settings, or modify your data preparation to better suit the tool? Do you find it difficult to adapt your visualisation technique on the fly? Does it affect your confidence in accurately identifying and presenting genomic features? Do you feel positive you can reach a successful conclusion?

• Do you recognise when you might need to seek immediate advice or help, such as when a complex structural variant is ambiguous even after detailed visualisation or when a technical issue with the software is beyond your current understanding? Identify what you learn as a result of the unexpected development. For instance, do you learn a new feature of a genome browser, a specific technique for visualising a challenging variant type, or a more efficient workflow for loading and interpreting genomic data visually?

On action

• Summarise the key steps involved in identifying and visualising the target genomic regions and/or variation. Describe the specific regions or variations you focused on and the tools or software you used for identification and visualisation. What types of visualisations did you create? Were there any specific events, actions, or interactions that felt important during the process?

• What specific learning can you take from this task? For example, what strengths did you demonstrate in using visualisation tools or selecting appropriate views? What skills or knowledge gaps were evident regarding different visualisation methods or representing complex variations like CNVs or SVs? How did this experience compare against previous times you have visualised genomic data? Were any previous actions for development in this area achieved? Do you feel your practice has improved? Identify any challenges you experienced, such as handling large datasets, representing complex genomic features clearly, or using specific software features, and how you reacted to them. Did these challenges affect your ability to deal with the situation? Were you able to overcome them? Was there anything significant about this activity, such as needing to seek advice or clarification on using a visualisation tool or interpreting a complex view?

• Identify the specific actions or ‘next steps’ you will take based on this experience to support your learning. What will you do differently next time you identify or visualise genomic data? Has anything changed in terms of what you would do if faced with a similar situation again? Do you need to practice using specific visualisation tools, representing certain types of variation, or creating clearer visualisations further? How will you assimilate any feedback you received on your visualisations?

Beyond action

• Reflect on the times you have needed to identify and visualise specific genomic regions or different types of variation. Have you reviewed reflections on instances where visualisation was challenging (e.g., complex structural variants)? What steps did you previously identify to improve your skills in using visualisation tools or techniques for specific variant types? Have you practised applying these improved methods? Are you consistently applying more effective strategies for visualising regions and variants based on your past learning? Has discussing different visualisation approaches or challenging cases with others changed how you approach this task?

• How has repeatedly identifying and visualising genomic regions and variations across different cases enhanced your ability to understand the data? Can you more quickly spot important features in a visualisation? How does your cumulative experience in this area prepare you for assessments that involve viewing data representations, such as a BAM file or discussions about structural variants? Do your experiences help you recognise when visualising a particular type of variation or region is particularly complex or potentially beyond your current scope, requiring assistance?

Relevant learning outcomes

#	Outcome
# 3	Outcome Select appropriate tools for next generation sequencing (NGS) analysis of inherited and acquired disease.
# 4	Outcome Analyse NGS data in a clinical setting applying appropriate quality control and data validation.