This course aims to develop fundamental understanding of bioinformatics, including core concepts such as genomic sequencing technologies, data quality control, and utilisation of bioinformatics tools for sequence analysis and visualization within a clinical context. It emphasises development of critical skills for interpreting genomic data to support diagnostics, personalised treatment, and clinical decision-making.
Building on this foundation, the course incorporates essential molecular biology concepts, such as DNA structure, gene expression, and genetic variation, providing the biological underpinning necessary to comprehend the molecular basis of diseases and their clinical relevance.
Furthermore, the course critically explores Digital Health Transformation and AI/machine learning algorithms, focusing on their theoretical principles, classifications, and the ethical considerations involved in their deployment within healthcare settings. Participants will learn to recognise key stakeholders, understand strategic frameworks like the WHO Strategy, and learn societal and ethical implications of AI-driven innovations in medicine.
Through practical modules in R scripting and Linux command-line operations, learners will acquire the skills to perform bioinformatics workflows tailored to clinical applications, enabling effective analysis, automation, and visualisation of genomic data. Ultimately, the course contributes to cultivating learners capable of developing basic understanding integrating clinical bioinformatics, molecular biology, and AI/machine learning technologies to improve patient outcomes and advance personalised healthcare.
This course aims to capacitate students, healthcare professionals, bioinformaticians, and researchers with a robust understanding of the interconnected fields of digital health transformation, AI/machine learning, and clinical bioinformatics. Participants will critically explore the ethical considerations surrounding AI deployment in genomics, ensuring responsible and ethically grounded practice.
The course provides foundational knowledge of genomic data formats—such as FASTA, FASTQ, SAM/BAM, and VCF—enabling learners to identify, differentiate, and manipulate these files proficiently. Practical skills in Linux command-line operations, including file indexing and management, will empower students to handle large genomic datasets efficiently.
A significant component of the course focuses on visualisation and analysis of genomic data using the IGV tool, with an emphasis on interpreting complex visual data to identify variants accurately. Additionally, learners will develop the capability to analyse VCF files for variant annotation, apply quality filters, and generate comprehensive reports for clinical and research applications.
Throughout the programme, learners will cultivate critical awareness of ethical issues in AI algorithms within bioinformatics, fostering responsible use of emerging technologies in genomic medicine. By integrating theoretical principles with practical skills, this course aims to produce well-rounded learners capable of advancing personalised healthcare through informed, technically competent, and ethically sound practices.
This course aims to capacitate students, healthcare professionals, bioinformaticians, and researchers with a thorough understanding of AI, and genomics. Through an integrated blend of theoretical knowledge and hands-on training, participants will critically engage with the evolving landscape of bioinformatics and genomic data analysis.
Participants will develop foundational knowledge of performance metrics of AI machine learning algorithms, including performance metrics relevant to classification tasks within bioinformatics contexts, enabling responsible evaluation and implementation of AI tools. The course underscores the importance of ethical principles, addressing data privacy, confidentiality, and the potential risks associated with genetic discrimination and regulation compliance to promote ethical research and practice.
A core componet of the course focuses on understanding genomic analysis pipelines, their structure, purpose, and ability to enhance reproducibility and efficiency in large-scale genomic data processing. Practical skills will be fostered through applying command-line tools such as FastQC for quality control, performing chromosome-specific data extraction, sequence alignment with BWA, variant calling with FreeBayes, and interpreting VCF files.
Learners will critically analyse genetic variants by annotating data with VEP and ClinVar, evaluating disease associations and variant effects, while navigating bioinformatics platforms like Galaxy for data management, workflow automation, and reproducibility assurance.
By integrating ethical considerations with technical competence, the course aims to contribute to capacitating learners/attendees with genomic medicine, ensuring responsible data stewardship, and contributing effectively to healthcare innovation through early robust bioinformatics practices.
This workshop focuses on capacitating participants with essential skills in R programming and Linux command-line operations for bioinformatics applications. Designed for students, researchers, and professionals new to these tools, the session emphasizes hands-on exercises that demonstrate how to manipulate datasets and automate workflows efficiently. Participants will learn the basics of R, including data visualization and statistical modeling, alongside fundamental Linux commands for managing bioinformatics workflows. By the end, attendees will be able to handle bioinformatics datasets, perform data preprocessing, and execute workflows, providing a strong foundation for integrating computational skills into biological research and advancing in the field of bioinformatics.
This workshop introduces participants to the critical process of secondary analysis in Next-Generation Sequencing (NGS), vital for transforming raw data into actionable genetic insights. Attendees will learn essential skills, including quality control, alignment of sequencing reads to reference genomes, and variant calling. Through hands-on sessions, participants will gain practical experience with bioinformatics workflows necessary for processing sequencing data effectively. Learning outcomes include understanding the NGS workflow, performing data quality assessments with FastQC, aligning reads with tools like BWA, and calling variants using GATK. It is suitable for bioinformatics beginners familiar with basic Linux and R, eager to advance their skills in NGS techniques.
This workshop focuses on the essential process of tertiary analysis in Next Generation Sequencing (NGS), emphasizing the interpretation of biological significance of variants identified during secondary analysis. Participants will acquire the skills needed to annotate, filter, and prioritize variants, enabling informed decisions in both research and clinical contexts. This workshop covers tools like ANNOVAR and VEP for variant annotation, exploring how to prioritize findings based on their clinical relevance. By integrating tertiary analysis results with existing data and adhering to ACMG guidelines for reporting, attendees will enhance their capability to derive actionable insights from sequencing data. This workshop is ideal for beginners in bioinformatics with a foundation in NGS secondary analysis.
This workshop focuses on the detection and analysis of Copy Number Variations (CNVs) using Next Generation Sequencing (NGS) technologies. Participants will learn about the significance of CNVs in genetic disorders and cancer genomics, alongside methods for their detection from both long-read and short-read sequencing data. Participants will gain hands-on experience with bioinformatics techniques for CNV calling using tools like GATK-CNV and pbsv, and will visualise results with software such as IGV. The workshop also covers the clinical implications of CNVs following ACMG guidelines. Designed for bioinformatics beginners, this workshop will enhance participants’ understanding and practical skills in CNV analysis.
This workshop delves into pharmacogenomics (PGx), focusing on how genetic variations affect individual drug responses. Participants will gain hands-on experience with bioinformatics tools and workflows essential for analyzing PGx data, enabling them to improve drug efficacy and minimize adverse effects in personalized medicine. The workshop covers the basics of pharmacogenomics and its clinical relevance, guiding attendees in analyzing genetic variants associated with drug response and utilizing key databases like PharmGKB. By integrating PGx findings into clinical decision-making, participants will be equipped to advance personalized medicine. This workshop is ideal for researchers, bioinformaticians, and clinicians familiar with next-generation sequencing analysis.
This workshop provides a comprehensive introduction to gene expression data analysis in cancer research, emphasising its significance in tumor classification and biomarker discovery. Participants will gain essential knowledge and hands-on experience with bioinformatics tools and machine learning techniques to process, visualise, and interpret gene expression datasets. Attendees will learn to conduct differential expression analysis using tools like DESeq2, visualise data through heatmaps and PCA, and apply machine learning for tumor classification. Designed for bioinformatics researchers and students familiar with R programming and next-generation sequencing, this workshop aims to empower participants to uncover insights into tumor biology and enhance personalized therapeutic strategies.
This workshop provides an in-depth exploration of epigenetics, focusing on DNA methylation and its analysis using methylation arrays. Participants will gain essential skills for interpreting epigenetic data, crucial for understanding gene regulation and disease mechanisms. Aimed at researchers and students interested in epigenetics and bioinformatics, the workshop features hands-on sessions that cover data processing, normalisation, and differential methylation analysis. Attendees will learn to visualise data through heatmaps and clustering techniques while interpreting findings in biological and clinical contexts. This comprehensive approach emphasises the significance of methylation studies in advancing knowledge of complex biological processes and disease research.
This workshop focuses on Single Nucleotide Polymorphism (SNP) arrays, essential tools for exploring genetic variation, identifying disease-associated loci, and analysing tumour-specific alterations. The workshop provides a practical introduction to SNP array data analysis, combining hands-on sessions with case studies to highlight their significance in genomics research and personalized medicine. Participants will gain insights into the applications of SNP arrays in genotyping, genome-wide association studies (GWAS), and copy number variation (CNV) analysis. By the end, attendees will understand principles, perform quality control, and conduct GWAS, enabling them to analyse CNVs and tumour-specific data in clinical and research settings effectively.
The workshop aims to capacitate clinical bioinformatics regulators, healthcare policymakers, and data governance professionals with the tools to address challenges and align practices with evolving regulatory requirements, ultimately empowering them to navigate the complex regulatory landscape effectively. This workshop focuses on the regulatory frameworks governing clinical bioinformatics, a field bridging healthcare, genomics, and data science. Participants will gain insights into data governance, compliance with international standards, and the ethical considerations essential for genomic and clinical data analysis. Through interactive discussions and case studies, attendees will explore regulatory methods that ensure integrity and security in bioinformatics practices.
This workshop focuses on the transformative role of generative AI in clinical bioinformatics, particularly in genomic data analysis and personalized medicine. Participants will explore how AI techniques enhance data interpretation, variant analysis, and predictive modeling, leading to improved patient outcomes. The workshop addresses essential ethical considerations and challenges in integrating AI into clinical workflows, emphasizing the importance of ethical practices and interdisciplinary collaboration. Attendees will achieve key learning outcomes, including understanding generative AI principles, analyzing genomic data with AI tools, and evaluating the implications for personalized medicine. This workshop is tailored for clinical bioinformaticians, AI researchers, data scientists, and healthcare professionals eager to adopt AI solutions.