Day 10: Wrap-up session¶

Date: September 12, 2025
Duration: 09:00-11:40 CAT
Focus: Course conclusion, presentations, and future directions

Overview¶

The final day of the microbial genomics training course brings together all learning experiences through participant presentations, showcases ongoing research initiatives, and provides guidance for continued professional development in computational biology.

Learning Objectives¶

By the end of Day 10, you will be able to:

Present bioinformatics analysis results effectively to scientific audiences
Demonstrate mastery of key concepts covered throughout the course
Identify resources for continued learning and professional development
Connect with ongoing research initiatives and training opportunities
Plan next steps for implementing learned skills in your research
Build professional networks within the computational biology community

Schedule¶

Time (CAT)	Topic	Links
09:00	Participant presentations
11:15	Short talks NGS-Academy/AfriGen-D/eLwazi ODSP
11:40	End of the course	[Resources]

Presentation Session (09:00-11:15)¶

Presentation Guidelines¶

Each participant will deliver a 5-minute presentation covering their Day 9 analysis work:

Presentation Structure¶

Introduction (1 minute)
Research question or objective
Brief background context
Dataset description
Methods (1.5 minutes)
Analysis workflow overview
Key tools and techniques used
Parameter choices and rationale
Results (2 minutes)
Major findings from analysis
Key figures or summary statistics
Interpretation of results
Challenges & Solutions (30 seconds)
Main obstacles encountered
How they were addressed
Lessons learned

Technical Requirements¶

Format: PDF slides or live demonstration
Time limit: Strictly enforced 5 minutes
Q&A: 2-3 minutes for questions after each presentation
Backup: Have presentation files ready on USB drive

Evaluation Criteria¶

Presentations will be assessed on:

Scientific rigor: Appropriate methods and interpretation
Technical competence: Correct use of bioinformatics tools
Communication clarity: Clear explanation of complex concepts
Problem-solving: Evidence of troubleshooting and adaptation
Course integration: Application of multiple course concepts

Sample Presentation Topics¶

Based on participant data types, presentations may cover:

Genomic Analysis Examples¶

"Antimicrobial resistance profiling in Mycobacterium tuberculosis isolates"
"Phylogenetic analysis of Mycobacterium tuberculosis outbreak strains"
"Comparative genomics of Vibrio cholerae from environmental samples"
"Transmission analysis of Vibrio cholerae epidemic strains"

Methodological Examples¶

"Optimizing assembly parameters for low-coverage genomes"
"Developing Nextflow pipeline for routine surveillance"
"Quality control strategies for degraded DNA samples"

Presentation Order¶

Presentations will be grouped by analysis type to facilitate discussion:

Genomic Surveillance (9:00-9:45)
Methodological & Pipeline Development (9:45-11:15)

Special Presentations (11:15-11:40)¶

NGS-Academy Initiative¶

Overview of next-generation sequencing training programs - Advanced training opportunities - Certification pathways - Research collaboration opportunities - International exchange programs

AfriGen-D Project¶

African Genome Diversity Project updates - Current research initiatives - Collaboration opportunities for participants - Data sharing and analysis platforms - Future funding opportunities

eLwazi ODSP (Open Data Science Platform)¶

Data science infrastructure and resources - Platform capabilities and access - Training modules and resources - Research project support - Community building initiatives

Course Completion¶

Certificate Requirements¶

To receive course completion certificate, participants must:

Attend at least 8 out of 10 training days
Complete all hands-on exercises
Submit Day 9 analysis documentation
Deliver Day 10 presentation
Participate in course evaluation

Skills Assessment Summary¶

By course completion, participants will have demonstrated:

Technical Skills¶

Command line proficiency: Navigation, file management, and tool execution
Quality control: Assessment and improvement of sequencing data
Genome assembly: De novo assembly and quality assessment
Annotation: Functional and structural genome annotation
Phylogenetics: Tree construction and interpretation
Workflow development: Nextflow pipeline creation and optimization

Analytical Skills¶

Data interpretation: Drawing biological conclusions from computational results
Method selection: Choosing appropriate tools for specific analyses
Parameter optimization: Adjusting analysis parameters for data characteristics
Quality assessment: Evaluating reliability of computational results
Troubleshooting: Diagnosing and solving technical problems

Professional Skills¶

Documentation: Maintaining analysis logs and reproducible workflows
Presentation: Communicating results to scientific audiences
Collaboration: Working effectively in computational research teams
Continuous learning: Accessing resources for ongoing skill development

Post-Course Resources¶

Immediate Support (Next 3 months)¶

Email support: Continued access to trainer expertise
Online forum: Participant discussion platform
Monthly virtual meetups: Progress sharing and troubleshooting
Resource sharing: Access to course materials and datasets

Long-term Development¶

Advanced training: Information about specialized workshops
Research collaboration: Connections to ongoing projects
Professional networks: Links to regional and international communities
Career opportunities: Job postings and fellowship announcements

Recommended Next Steps¶

For Beginners¶

Practice with additional datasets
Complete online tutorials for specific tools
Join local bioinformatics user groups
Consider formal coursework in computational biology

For Intermediate Users¶

Develop specialized analysis pipelines
Contribute to open-source bioinformatics projects
Attend specialized conferences and workshops
Mentor others in computational skills

for Advanced Users¶

Lead research projects using learned techniques
Develop novel analytical methods
Teach and train others in the community
Collaborate on large-scale genomics initiatives

Course Evaluation¶

Feedback Categories¶

Content Assessment¶

Relevance to research needs
Appropriate level of technical detail
Balance of theory and practical application
Currency of methods and tools

Delivery Evaluation¶

Trainer expertise and communication
Hands-on exercise quality
Technical support adequacy
Course pacing and organization

Impact Measurement¶

Confidence in using bioinformatics tools
Likelihood of applying learned skills
Interest in advanced training
Recommendations to colleagues

Improvement Suggestions¶

Participants are encouraged to provide specific suggestions for: - Additional topics to cover - Alternative teaching methods - Better integration of concepts - Enhanced practical exercises - Improved course materials

Networking and Community Building¶

Contact Information Exchange¶

WhatsApp group for ongoing communication
LinkedIn professional network connections
GitHub collaboration on analysis projects
Research ResearchGate connections

Regional Initiatives¶

South African Bioinformatics Society: Local meetings and conferences
H3ABioNet: Pan-African bioinformatics network
ISCB Regional Student Groups: International student connections
Local university partnerships: Ongoing collaboration opportunities

Final Remarks¶

Key Takeaways¶

Bioinformatics is a journey: Continuous learning and adaptation required
Community matters: Collaboration accelerates learning and research
Documentation is crucial: Reproducible research starts with good record-keeping
Practice makes perfect: Regular use of skills prevents deterioration
Stay current: Field evolves rapidly, ongoing education essential

Words of Encouragement¶

The computational biology field welcomes contributors from diverse backgrounds. Your unique perspective, combined with the skills learned in this course, positions you to make meaningful contributions to understanding microbial genomics and its applications to human health.

Course Legacy¶

This training represents an investment in the future of computational biology in Africa. As you apply these skills in your research and career, you become part of a growing network of scientists advancing genomic medicine and public health through computational approaches.

Acknowledgments¶

Training Team Recognition¶

Special thanks to the course instructors: - Ephifania Geza: Lead instructor and course coordinator - Arash Iranzadeh: Technical instruction and phylogenomics expertise
- Sindiswa Lukhele: Sequencing technologies and quality control - Mamana Mbiyavanga: HPC systems and workflow development - Bethlehem Adnew: Guest expertise on tuberculosis genomics

Institutional Support¶

University of Cape Town Computational Biology Division
CIDRI-Africa research infrastructure
High-performance computing facility access
Guest lecture coordination and logistics

Community Contributions¶

Dataset providers and research collaborators
Open-source software developers
International training program partnerships
Participant engagement and peer learning

Contact Information¶

Immediate Questions¶

Course Coordinator: Ephifania Geza
Email: ephifania.geza@uct.ac.za

Technical Support¶

HPC and Workflows: Mamana Mbiyavanga
Email: mamana.mbiyavanga@uct.ac.za

General Inquiries¶

Training Program: microbial-genomics-training@uct.ac.za

Follow-up Resources¶

Course Materials: GitHub repository access maintained
Discussion Forum: Access links provided via email
Newsletter: Quarterly updates on opportunities and resources

Final Learning Outcome: Completion of this intensive training program provides participants with both the technical skills and professional network needed to pursue independent research in microbial genomics, contributing to advances in infectious disease understanding, antimicrobial resistance surveillance, and public health genomics.