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
NGS and Transcriptomic Data Analysis: From Raw Reads to Biology follows the full journey from a sequencer’s output to biological meaning. You learn the shared NGS foundation — quality control, trimming and alignment — then the transcriptomic path: quantification, differential expression, and functional interpretation that turns counts into biology. The course emphasises connecting each computational step back to the biological question. You finish able to run an NGS transcriptomic analysis from raw reads through to interpreted results. A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This course covers NGS and transcriptomic data analysis from raw reads to biological insight — the combined sequencing-and-expression pipeline end to end.
📋 Course Objectives
1. Quality-control and trim raw NGS reads.
2. Align and quantify transcriptomic data.
3. Run differential expression analysis.
4. Interpret results functionally.
5. Connect analysis to the biological question.
👥 Who Should Enroll?
• Genomics and molecular-biology researchers
• Bioinformatics students and staff
• Core-facility analysts
• Anyone analysing RNA-seq
🚀 Key Learning Outcomes
• A raw-reads-to-biology transcriptomics skill.
• A full NGS transcriptomic workflow.
• 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 NGS and Transcriptomic Data Analysis
Analyze genomic and transcriptomic data to identify patterns and correlations with biological processes • Develop a comprehensive understanding of core biological principles underlying NGS and transcriptomic data analysis • Evaluate the quality and integrity of raw sequencing data to ensure accuracy and reliability in downstream analyses
Laboratory Techniques, Protocols, and Data Collection
Configure laboratory equipment and protocols for high-throughput sequencing and transcriptomic data collection • Design and optimize experimental workflows to generate high-quality sequencing data • Implement quality control measures to ensure the integrity and consistency of collected data
Bioinformatics Tools and Computational Analysis
Apply bioinformatics tools and algorithms to analyze and interpret NGS and transcriptomic data • Develop and implement custom computational pipelines to analyze and visualize large-scale biological data • Integrate multiple data types and sources to identify complex biological relationships and patterns
Research Methodology and Experimental Design
Design and develop well-controlled experiments to test hypotheses and validate research findings • Evaluate and select appropriate statistical and computational methods for data analysis and interpretation • Develop a comprehensive understanding of research methodology and experimental design principles in the context of NGS and transcriptomic data analysis
Advanced NGS and Transcriptomic Data Analysis Applications
Apply advanced bioinformatics tools and techniques to analyze and interpret complex biological data • Develop and implement machine learning and deep learning models to identify patterns and correlations in large-scale biological data • Integrate NGS and transcriptomic data with other omics data types to gain a comprehensive understanding of biological systems
Regulatory Compliance, Bioethics, and Safety Standards
Evaluate and implement regulatory compliance and bioethics guidelines in the context of NGS and transcriptomic data analysis • Develop and implement safety protocols and standards to ensure the secure handling and storage of biological samples and data • Analyze and mitigate potential risks and liabilities associated with NGS and transcriptomic data analysis
Industry Applications, Career Pathways, and Case Studies
Apply NGS and transcriptomic data analysis skills and knowledge to real-world industry applications and case studies • Develop a comprehensive understanding of career pathways and professional opportunities in the field of NGS and transcriptomic data analysis • Evaluate and discuss the latest trends and advancements in the field of NGS and transcriptomic data analysis
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | TensorFlow |
| Covered Tool / Platform | Bioconductor |
| Covered Tool / Platform | Illumina |
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