🧬 35-Hour Professional Grade Program
RNA-Seq Data Analysis
Master transcriptomic data processing, Linux genomic pipelines, read alignment (HISAT2/STAR), differential gene expression (DESeq2), and publication-ready R visualizations.
35 Hours
Live Learnings
Starting 22nd August
6-Week Live Program (Sat, Sun, Wed)
Small Batch
5 – 8 Participants Only
100% Positive
5.0 Rating & Certificate
Bioinformatics Stack
Tools & Pipelines You Will Learn
NCBI SRA
Ensembl
FastQC
Trim Galore
HISAT2
STAR
featureCounts
HTSeq
DESeq2
edgeR
R Language
clusterProfiler
GSEA
Structured Syllabus
Explore Curriculum & Timelines
Module 1
Introduction to RNA-Seq & Experimental Design
- Understanding RNA-Seq principles, technologies, and clinical applications
- Experimental design: biological replicates, batch effects, sequencing depth
- File formats in RNA-Seq: FASTQ, FASTA, GTF/GFF3
- Setting up Ubuntu Linux environment & Conda workflows
Key Stack & Deliverables
Ubuntu
Conda
FASTQ
GTF/GFF3
Module 2
Command Line Basics for Genomic Data Processing
- Linux fundamentals for genomics, environment variables, relative & full paths
- Essential commands: pwd, ls, cd, mkdir, cp, mv, rm, cat, head, tail, sort, grep, awk, cut
- Assignment & hands-on file manipulation
Key Stack & Deliverables
Linux CLI
Bash
awk / grep
Sed Scripting
Module 3
Data Retrieval & Read Alignment
- Retrieving public RNA-Seq datasets from NCBI & Ensembl using SRA Toolkit and iSeq
- Quality control & trimming using FastQC and Trim Galore
- Splice-aware read alignment using HISAT2 and STAR
- Alignment QC with Qualimap, RSeQC, and RNA-SeQC
Key Stack & Deliverables
SRA Toolkit
FastQC
HISAT2
STAR
Module 4
Read Quantification & Statistical Concepts
- Gene expression quantification with featureCounts & HTSeq
- Normalization methods: CPM, TPM, RPKM/FPKM, DESeq2 median of ratios, edgeR TMM
- Statistical concepts: p-values, adjusted p-values (FDR), Log2 fold change
- Introduction to R programming for genomic analysis
Key Stack & Deliverables
featureCounts
HTSeq
TPM / CPM
R Language
Module 5
Differential Expression Analysis
- Differential Gene Expression (DGE) analysis with DESeq2
- Exploratory sample QC: Principal Component Analysis (PCA) & MDS
- Hierarchical Clustering for sample grouping
- Diagnostic plots: Dispersion plots and MA plots
Key Stack & Deliverables
DESeq2
edgeR
PCA Plots
MA Plots
Module 6 & 7
Visualization & Biological Interpretation
- Generating Volcano plots, Heatmaps, MA plots, and Venn Diagrams
- Functional enrichment analysis using clusterProfiler and GSEA
- Advanced visualizations: Dot plots, Enrichment GO plots, Ridgeline plots
- Final Q&A and course wrap-up
Key Stack & Deliverables
ggplot2
clusterProfiler
GSEA
Volcano / Heatmaps
| Date | Day | Session Topic & Hands-on Agenda |
|---|---|---|
| WEEK 1 Genomics Fundamentals, Linux CLI & Conda Setup | ||
| 22nd Aug | Saturday | Introduction of Genome Data Analysis, RNA-seq basics, Experimental Design & Ubuntu/Conda setup |
| 23rd Aug | Sunday | RNA-Seq file formats (FASTQ, FASTA, BAM, GTF) & Linux Command-Line for Genomics |
| 26th Aug | Wednesday | 💬 Doubt Clearing & Live Mentoring Session (7:00 PM - 9:00 PM IST) |
| WEEK 2 Public Data Retrieval, FastQC Quality Control & Splice Alignment | ||
| 29th Aug | Saturday | Retrieving Data from Ensembl/NCBI via SRA Toolkit & FastQC Quality Control |
| 30th Aug | Sunday | Trimming low-quality bases via Trim Galore & Read Alignment using HISAT2 / STAR |
| 2nd Sept | Wednesday | 💬 Doubt Clearing & Live Mentoring Session (7:00 PM - 9:00 PM IST) |
| WEEK 3 Alignment QC, Quantification & Normalization Concepts | ||
| 5th Sept | Saturday | Alignment QC (Qualimap, RSeQC) & Gene Quantification (featureCounts, HTSeq) |
| 6th Sept | Sunday | Normalization Techniques (CPM, TPM, RPKM, TMM) & Statistical Concepts (FDR, Log2FC) |
| 9th Sept | Wednesday | 💬 Doubt Clearing & Live Mentoring Session (7:00 PM - 9:00 PM IST) |
| WEEK 4 Introduction to R Programming & Differential Expression (DESeq2) | ||
| 12th Sept | Saturday | Introduction to R Programming for Data Analysis & Genomic Data Wrangling |
| 13th Sept | Sunday | Exploratory QC (PCA, MDS, Clustering) & Differential Expression Analysis with DESeq2 |
| 16th Sept | Wednesday | 💬 Doubt Clearing & Live Mentoring Session (7:00 PM - 9:00 PM IST) |
| WEEK 5 Publication Visualizations & Functional Pathway Enrichment | ||
| 19th Sept | Saturday | Visualizing Differentially Expressed Genes: Volcano plots, MA plots, Heatmaps, Venn Diagrams |
| 20th Sept | Sunday | Functional Enrichment Analysis using clusterProfiler & GSEA |
| 23rd Sept | Wednesday | 💬 Doubt Clearing & Live Mentoring Session (7:00 PM - 9:00 PM IST) |
| WEEK 6 Advanced Pathway Plots, Capstone Project & Course Wrap-Up | ||
| 26th Sept | Saturday | Visualization of Functional Enrichment (Bar plots, Dot plots, Ridgeline plots) & Course Wrap-Up |
Hands-On Output
Publication-Ready Visualizations You Learn
Volcano & MA Plots
Highlight statistically significant up-regulated and down-regulated genes across biological conditions.
Expression Heatmaps
Display hierarchical clustering of gene expression patterns across multiple experimental samples.
PCA & MDS Clustering
Evaluate sample-level quality control, variance, and batch effect distribution in low-dimensional space.
Venn Diagrams & GO Nets
Overlay gene overlaps between experimental groups and build biological pathway networks.
Student Experience & Reviews
What Our Researchers & Interns Say
Niveditha S
The Oxford College of Science
Bengaluru, India
Bengaluru, India
"This internship has been a valuable learning experience, providing exposure to genome data analysis and real world datasets. It has helped me understand key concepts like RNA-seq data handling and bioinformatics workflows, while also encouraging me to build my foundational skills further."
Bhovisha Jivan
University of the Witwatersrand
Johannesburg, South Africa
Johannesburg, South Africa
"Although I have some background in bioinformatics, this is my first time doing hands-on RNA-seq analysis. The internship has introduced me to several new tools and practical workflows, and the course is structured in a way that makes the learning process feel clear and manageable without feeling rushed."
Dr. Subbulakshmi
Tamil Nadu Agricultural University
Coimbatore, India
Coimbatore, India
"This genome data analysis internship was my first real dive into working with RNA sequencing data. I worked with large-scale sequencing datasets and gained hands-on experience in analysis, gene expression analysis, and data visualization. Grateful for the mentorship and exposure to real-world bioinformatics workflows."
Namrata Mistry
University of Calcutta
Kolkata, West Bengal
Kolkata, West Bengal
"The mentorship was invaluable for me as one can have answers to their basic questions asked during session without any judgement and most importantly it is beginner friendly. Another factor is one gets a doubt clearing session besides initial classes. Overall, I am looking forward to applying it to my research."
Sanni Mouksine
University of Abomey-Calavi
Benin Republic, Africa
Benin Republic, Africa
"I used to consider Linux and Ubuntu confusing when we just started, now it appears interesting and easy to use as a biologist. And I'm looking forward to apply the concepts and workflow learned during this program to real world problems."
Niveditha S
The Oxford College of Science
Bengaluru, India
Bengaluru, India
"This internship has been a valuable learning experience, providing exposure to genome data analysis and real world datasets. It has helped me understand key concepts like RNA-seq data handling and bioinformatics workflows, while also encouraging me to build my foundational skills further."
Bhovisha Jivan
University of the Witwatersrand
Johannesburg, South Africa
Johannesburg, South Africa
"Although I have some background in bioinformatics, this is my first time doing hands-on RNA-seq analysis. The internship has introduced me to several new tools and practical workflows, and the course is structured in a way that makes the learning process feel clear and manageable without feeling rushed."
Dr. Subbulakshmi
Tamil Nadu Agricultural University
Coimbatore, India
Coimbatore, India
"This genome data analysis internship was my first real dive into working with RNA sequencing data. I worked with large-scale sequencing datasets and gained hands-on experience in analysis, gene expression analysis, and data visualization. Grateful for the mentorship and exposure to real-world bioinformatics workflows."
Namrata Mistry
University of Calcutta
Kolkata, West Bengal
Kolkata, West Bengal
"The mentorship was invaluable for me as one can have answers to their basic questions asked during session without any judgement and most importantly it is beginner friendly. Another factor is one gets a doubt clearing session besides initial classes. Overall, I am looking forward to applying it to my research."
Sanni Mouksine
University of Abomey-Calavi
Benin Republic, Africa
Benin Republic, Africa
"I used to consider Linux and Ubuntu confusing when we just started, now it appears interesting and easy to use as a biologist. And I'm looking forward to apply the concepts and workflow learned during this program to real world problems."
Registration
Reserve Your Seat For Live Batch
Indian Participants
UPI Direct Transfer
Small Batch (5-8 participants max)
Students (UG / PG / PhD):
₹2,499
Working Professionals:
₹3,499
UPI ID for Payment:
9582327689@okbizaxisAfter completing payment via Google Pay/PhonePe/Paytm, please send a screenshot for instant confirmation.
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Foreign Participants
Razorpay Checkout
International Credit/Debit Card
Students (UG / PG / PhD):
$30 USD
Working Professionals:
$40 USD
Razorpay Gateway Link:
https://rzp.io/rzp/XPdWyeVSecure online checkout for international registration.
Pay via Razorpay
Logistics & FAQ
Frequently Asked Questions
What are the class timings & schedule?
▼
Classes run from August 22, 2026 to September 26, 2026. Live sessions are held every Saturday, Sunday, and Wednesday from 7:00 PM to 9:00 PM IST. Dedicated Wednesday sessions are focused on doubt clearing.
Will I receive a course certificate?
▼
Yes, an official Biosyntiya Verified E-Certificate of Completion will be awarded to all participants upon completing the training program.
How will I access course materials & datasets?
▼
All necessary Linux command scripts, R pipelines, sample FASTQ datasets, and LMS resource materials will be provided via email and our Learning Portal.
What is the batch size and attendance policy?
▼
To ensure personalized mentoring, batch size is capped at 5 to 8 participants. Live attendance is strongly encouraged. Schedule updates are shared directly in the dedicated WhatsApp group.
Welcome to Biosyntiya Solutions
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