Programme Parameters
Graduate / Intermediate
3 Days (4.5 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
High-throughput technologies like next-generation sequencing and mass spectrometry generate massive volumes of omics data. To turn this raw information into meaningful biological insights, researchers must be comfortable with scripting, data wrangling, and analysis workflows in Python and R. This course bridges that gap by focusing on practical, example-driven bioinformatics using real or realistic datasets. Every participant receives a verified e-Certificate and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
High-throughput technologies like next-generation sequencing and mass spectrometry generate massive volumes of omics data. To turn this raw information into meaningful biological insights, researchers must be comfortable with scripting, data wrangling, and analysis workflows in Python and R. This course bridges that gap by focusing on practical, example-driven bioinformatics using real or realistic datasets.
📋 Course Objectives
1. Apply bioinformatics methods to authentic research and industry problems.
2. Produce a reproducible, portfolio-ready project you can cite in a thesis, paper, or job application.
👥 Who Should Enroll?
• Master's and senior undergraduate students specializing in bioinformatics
• R&D engineers and working professionals applying bioinformatics in industry
• Academics and educators building research or teaching capacity in bioinformatics
🚀 Key Learning Outcomes
• Tangible, reproducible bioinformatics work to show supervisors or employers.
• 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
Choosing and Setting Up the Environment
• Where Python and R each genuinely have the advantage in bioinformatics
• Conda, renv and reproducible environments rather than a shared global install
• Project structure, version control and scripts over interactive one-off commands
Sequence and Variant Data
• BioPython and Biostrings for sequence handling and file parsing
• Reading VCF and BED and using pyranges or GenomicRanges for interval work
• Coordinate conventions and the strand errors that silently corrupt results
Expression Analysis
• Count matrices, DESeq2 and the reason raw counts must not be pre-normalised
• Exploratory analysis with PCA and clustering before testing anything
• Pandas and dplyr for the reshaping most of the work actually consists of
Mass Spectrometry Data
• Search output, FDR at peptide and protein level, and the protein inference problem
• Missing values in proteomics and imputation that must be reported
• Normalisation and statistics on data with far fewer features than transcriptomics
Visualisation and Reporting
• ggplot2 and matplotlib or seaborn for figures that survive review
• Volcano plots, heatmaps and MA plots read correctly
• R Markdown and Jupyter for an analysis someone else can rerun
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | BWA |
| Covered Tool / Platform | SAMtools |
| Covered Tool / Platform | GATK |
| Covered Tool / Platform | FastQC |
| Covered Tool / Platform | Trimmomatic |
| Covered Tool / Platform | R/Bioconductor |
| Covered Tool / Platform | IGV |
| Covered Tool / Platform | PLINK |
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