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
Transcriptome Library Preparation and Data Analysis connects the bench and the computer for RNA sequencing. You learn the wet-lab side — how RNA is extracted, quality-checked and turned into sequencing libraries, and the choices that shape the experiment — and the dry-lab side — processing the reads through to differential expression and interpretation. The course’s distinctive value is spanning prep and analysis so you understand how library choices affect results. You finish able to reason about a transcriptomics experiment end to end. A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This course covers transcriptome library preparation and data analysis — the wet-lab preparation of RNA-seq libraries through to the computational analysis of the resulting data.
📋 Course Objectives
1. Prepare and quality-check RNA-seq libraries.
2. Understand library-prep design choices.
3. Process reads to expression data.
4. Run differential expression and interpretation.
5. Link prep decisions to analysis outcomes.
👥 Who Should Enroll?
• Molecular biologists and geneticists
• Genomics core-facility staff
• Bioinformatics students
• Anyone running RNA-seq
🚀 Key Learning Outcomes
• A prep-to-analysis transcriptomics view.
• A wet-and-dry-lab perspective.
• A transcriptomics 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 Transcriptome Library Preparation and Data Analysis
Analyze the core biological principles underlying transcriptome library preparation, including RNA extraction, purification, and quality control • Design experimental workflows for transcriptome library preparation, taking into account factors such as sample type, RNA integrity, and sequencing platform • Evaluate the impact of different library preparation protocols on downstream data analysis and interpretation
Laboratory Techniques, Protocols, and Data Collection
Implement standardized laboratory protocols for RNA extraction, library preparation, and sequencing, ensuring consistency and reproducibility • Configure and operate laboratory equipment, such as automated RNA extractors and library preparation platforms, to optimize workflow efficiency • Develop and implement quality control measures to monitor RNA integrity, library quality, and sequencing performance
Bioinformatics Tools and Computational Analysis
Apply bioinformatics tools, such as FASTQC and Trim Galore, to assess and improve RNA-seq data quality • Configure and run computational pipelines, including alignment, quantification, and differential expression analysis, using tools like HISAT2 and DESeq2 • Interpret and visualize bioinformatics results, including gene expression profiles and differential expression analysis, using tools like R and Bioconductor
Research Methodology and Experimental Design
Design and develop well-controlled experiments, including power analysis and sample size determination, to address specific research questions • Evaluate and select appropriate statistical methods and tools for data analysis, taking into account factors such as data distribution and experimental design • Develop and implement data management plans, including data storage, backup, and sharing, to ensure data integrity and accessibility
Advanced Transcriptome Library Preparation and Data Analysis Applications
Apply advanced library preparation techniques, such as single-cell RNA-seq and chromatin immunoprecipitation sequencing, to study specific biological systems • Develop and implement customized bioinformatics pipelines, using tools like Python and R, to analyze and interpret complex transcriptomic data • Integrate transcriptomic data with other omics data types, such as genomics and proteomics, to gain a more comprehensive understanding of biological systems
Regulatory Compliance, Bioethics, and Safety Standards
Evaluate and implement regulatory requirements, including IRB approval and informed consent, for human subjects research • Develop and implement laboratory safety protocols, including biosafety level 2 practices and chemical hygiene plans, to ensure a safe working environment • Apply bioethical principles, including respect for persons and beneficence, to ensure responsible and ethical conduct of research
Industry Applications, Career Pathways, and Case Studies
Analyze and discuss current industry applications of transcriptome library preparation and data analysis, including pharmaceutical and biotechnology research • Develop and implement career development plans, including networking and professional development opportunities, to pursue careers in transcriptomics • Evaluate and present case studies of successful transcriptomics research, including experimental design, data analysis, and interpretation
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | TensorFlow |
| Covered Tool / Platform | Bioconductor |
| Covered Tool / Platform | HISAT2 |
| Covered Tool / Platform | DESeq2 |
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