Python/R for Bioinformatics: Genomics, Transcriptomics & Proteomics Data
From Code to Biological Insight: Python & R for Real Omics Data
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 workshop bridges that gap by focusing on practical, example-driven bioinformatics using real or realistic datasets.
Participants will learn how to import, clean, explore, and analyze genomics, transcriptomics, and proteomics data using widely adopted libraries and packages. Topics include sequence data handling, basic alignment/annotation outputs, gene expression analysis, simple differential expression pipelines, and basic visualization (heatmaps, PCA, volcano plots). The emphasis is on hands-on coding, reproducible workflows, and interpreting results in a biological context.
Aim
This workshop aims to train participants in using Python and R for practical bioinformatics analysis across genomics, transcriptomics, and proteomics datasets. It focuses on data handling, preprocessing, visualization, and basic statistical and machine-learning workflows for high-throughput omics data. Through hands-on sessions, participants will learn how to work with FASTA/FASTQ, count matrices, differential expression data, and proteomics tables. The goal is to build confidence in using open-source tools and scripting to solve real biological questions.
Workshop Objectives
- Use Python and R to import, clean, and explore genomics, transcriptomics, and proteomics datasets.
- Work with common bioinformatics file formats (FASTA, FASTQ, VCF, count matrices, expression tables).
- Perform basic statistical analysis and visualization (PCA, clustering, heatmaps, boxplots, volcano plots).
- Implement simple pipelines for differential gene/protein expression and functional interpretation.
- Build reproducible scripts and notebooks that can be adapted to their own datasets and projects.
Workshop Structure
Day 1 –Workshop Orientation & Bioinformatics Overview
- Genomics vs transcriptomics vs proteomics
- FASTA, FASTQ, BAM, VCF, GTF/GFF
- Count matrices (RNA-seq), basic proteomics tables, Importing data (CSV/TSV, text files)
- Basic wrangling: filtering, subsetting, joins/merges, Histograms, boxplots, scatterplots
- Hands On:Load a small gene expression/count matrix, Inspect structure, summary statistics, simple plot
Day 2 – Genomics & Transcriptomics Data Analysis /RNA-seq / Transcriptomics
- From raw reads to count matrix: basic QC (FastQC idea), alignment, quantification; check read quality & mapping rates.
- Experimental design: controls vs treated, groups; biological vs technical replicates.
- Normalization & DE: library size methods, TPM/FPKM idea, DESeq2/edgeR normalization and basic DE script outline.
- Extracting significant genes: up/down-regulated lists.
- Pathway enrichment: GO/KEGG concepts + hands-on by sending DE gene list to an online enrichment tool.
Day 3 – Proteomics & Intro to Multi-Omics Integration
- Types of proteomics experiments: label-free vs labeled (concept only).
- Proteomics data tables: protein IDs, intensities, missing values; basic handling + log-transform & normalization.
- Differential abundance: simple stats (t-test/ANOVA), volcano/box plots; mapping significant proteins to pathways/functions (with one example).
- Integrative view: matching gene–protein IDs and outline of gene vs protein correlation plots in R/Python.
Who Should Enrol?
- Undergraduate/postgraduate degree in Microbiology, Biotechnology, Bioinformatics, Computational Biology, Genomics, or related fields.
- Professionals in healthcare, pharma, diagnostics, biotech, or omics-based research labs.
- Data scientists and AI/ML engineers interested in applying coding and analytics to biological and omics datasets.
- Individuals with a keen interest in the convergence of life sciences, coding, and data-driven biological research.
Important Dates
Registration Ends
01/05/2026
IST 7:00 PM
Workshop Dates
01/05/2026 – 01/07/2026
IST 8:00 PM
Workshop Outcomes
- Gain practical experience using Python and R for omics data analysis.
- Learn to handle real-world genomics, transcriptomics, and proteomics datasets.
- Be able to run basic differential expression and exploratory analyses.
- Produce publication-ready plots and summaries for omics data.
- Develop reusable scripts/notebooks that can be adapted for future research projects.
Meet Your Mentor(s)
DR. HARISHCHANDER ANANDARAM
Dr Harishchander Anandaram is an Assistant Professor at Centre for Excellence in Computational Engineering and Networking, Amrita Vishwa Vidyapeetham, Coimbatore, Tamil Nadu, India. He received his Ph.D. Degree in Bioengineering from Sathyabama Institute of Science and Technology, Chennai, in 2020. . . .
Fee Structure
Student Fee
₹1399 | $55
Ph.D. Scholar / Researcher Fee
₹1999 | $65
Academician / Faculty Fee
₹2999 | $75
Industry Professional Fee
₹3999 | $90
What You’ll Gain
- Live & recorded sessions
- e-Certificate upon completion
- Post-workshop query support
- Hands-on learning experience
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