Programme Parameters
Graduate / Intermediate
4 Weeks (40 Hrs)
Online (e-LMS)
• A basic understanding of the subject area and fundamental programming or scientific concepts.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
Next-Generation Sequencing Data Analysis focuses squarely on the NGS workflow that underpins modern genomics. You learn how NGS data is generated and structured, then the analysis pipeline: quality control and trimming, alignment or assembly, and the downstream analysis appropriate to the experiment — variant calling or expression quantification — and interpretation. The course emphasises the NGS-specific tools, formats and pitfalls. You finish able to run and interpret an NGS data-analysis pipeline. A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This course covers next-generation sequencing (NGS) data analysis — the NGS-specific pipeline from raw reads through alignment and variant or expression analysis to interpretation.
📋 Course Objectives
1. Understand how NGS data is generated.
2. Quality-control and trim sequencing reads.
3. Align or assemble NGS data.
4. Run variant or expression analysis.
5. Interpret NGS results and formats.
👥 Who Should Enroll?
• Genomics and molecular-biology researchers
• Bioinformatics students and staff
• Core-facility and lab analysts
• Anyone working with sequencing data
🚀 Key Learning Outcomes
• The ability to analyse NGS data.
• An NGS pipeline perspective.
• A genomics-analysis foundation.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
Foundations of Next-Generation Sequencing Data Analysis and Core Biological Principles
Analyze the fundamental principles of molecular biology and genetics to understand the basis of next-generation sequencing technologies • Evaluate the different types of next-generation sequencing platforms and their applications in various fields of research • Design experimental approaches to generate high-quality sequencing data and troubleshoot common issues in sequencing workflows
Laboratory Techniques, Protocols, and Data Collection
Configure and optimize laboratory protocols for nucleic acid extraction, library preparation, and sequencing • Develop and implement quality control measures to ensure the integrity and accuracy of sequencing data • Operate and maintain sequencing instruments, including troubleshooting and resolving technical issues
Bioinformatics Tools and Computational Analysis
Implement bioinformatics pipelines for data preprocessing, alignment, and variant calling using tools such as BWA and GATK • Analyze and interpret sequencing data using statistical and computational methods, including machine learning and data visualization techniques • Develop and apply custom scripts and workflows using programming languages such as Python and R to automate data analysis tasks
Research Methodology and Experimental Design
Design and develop research studies using next-generation sequencing technologies, including hypothesis testing and sample size calculation • Evaluate and select appropriate sequencing strategies and protocols for specific research questions and applications • Develop and implement data management plans to ensure the organization, storage, and sharing of sequencing data
Advanced Next-Generation Sequencing Data Analysis Applications and Translational Research
Apply advanced bioinformatics tools and techniques, such as single-cell RNA sequencing and chromatin immunoprecipitation sequencing, to analyze and interpret complex sequencing data • Develop and implement machine learning models to predict genomic features and identify patterns in sequencing data • Integrate and analyze multi-omics data, including genomics, transcriptomics, and proteomics, to understand complex biological systems
Regulatory Compliance, Bioethics, and Safety Standards
Evaluate and apply regulatory guidelines and standards for the use of next-generation sequencing technologies in research and clinical settings • Develop and implement bioethics and safety protocols for the handling and analysis of human subjects data and biological samples • Configure and maintain laboratory safety equipment and procedures to ensure a safe working environment
Industry Applications, Career Pathways, and Case Studies
Analyze and evaluate the applications of next-generation sequencing technologies in various industries, including biotechnology, pharmaceuticals, and healthcare • Develop and implement career development plans and strategies for professionals in the field of next-generation sequencing • Evaluate and discuss case studies of successful applications of next-generation sequencing technologies in research and industry settings
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | BWA |
| Covered Tool / Platform | GATK |
| Covered Tool / Platform | TensorFlow |
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