Executive Development Programme in Next-Generation Sequencing Data Analysis

This course is designed for senior scientists, research managers, and executives in the biotechnology and healthcare sectors aiming to enhance their understanding of next-generation sequencing (NGS) data analysis. Participants will gain practical skills in data preprocessing, alignment, variant calling, and interpretation, equipped to lead data-driven decision-making in their organizations.

Upon completion, attendees will be proficient in using advanced NGS tools and platforms, understanding the implications of NGS data in clinical and research settings, and will have the knowledge to foster innovation and improve patient outcomes through informed genomic strategies.

1. 1. Introduction to Next-Generation Sequencing (NGS): Learners will be introduced to the basics of NGS technologies and workflows, including types of NGS platforms and sequencing chemistries. They will gain foundational knowledge to understand the data generated from NGS experiments.
2. 2. Data Quality Control and Preprocessing: This module covers the essential steps in ensuring NGS data quality, including quality control metrics and preprocessing techniques to clean and align raw sequencing reads. Learners will gain hands-on experience with tools like FastQC and Trimmomatic.
3. 3. Basic Bioinformatics Analysis: Learners will explore essential bioinformatics tools and pipelines for basic NGS analysis, such as read alignment and variant calling. They will learn to use software like BWA and GATK to perform these tasks.
4. 4. Advanced Variant Calling and Annotation: This module delves into advanced methods for variant calling and annotation, focusing on single nucleotide variants (SNVs) and small insertions/deletions (Indels). Learners will apply tools like FreeBayes and Annovar to analyze genetic variations.
5. 5. Transcriptome Analysis: Learners will study techniques for analyzing RNA-seq data, including gene expression quantification and differential expression analysis. They will use tools like Salmon and DESeq2 to perform these analyses.
6. 6. Epigenetic Data Analysis: This module introduces learners to the analysis of epigenetic data from NGS techniques such as ChIP-seq and DNA methylation arrays. They will learn to use software like MACS2 and MethylKit to analyze these data types.
7. 7. Practical Application of NGS Data Analysis in Research: Through a case study approach, learners will apply NGS data analysis techniques to real-world research scenarios, focusing on hypothesis-driven analysis and interpretation of results. They will work on projects using data from various biological studies.
8. 8. Hands-on NGS Data Analysis Using Cloud Computing: Learners will gain practical experience in using cloud computing resources for NGS data analysis, focusing on cost-effective and scalable solutions. They will use platforms like AWS and Google Cloud for data storage and processing.
9. 9. Machine Learning in NGS Data Analysis: This module introduces learners to the application of machine learning techniques for NGS data analysis, including classification and clustering of genomic data. They will use tools like scikit-learn and TensorFlow to build predictive models.
10. 10. Ethical Considerations and Reporting of NGS Data: Learners will explore ethical and regulatory considerations in NGS data analysis, including data privacy, consent, and reporting standards. They will learn best practices for transparent and responsible data reporting.