RNA SEQ Data Analysis

This course provides a comprehensive understanding of RNA SEQ DATA ANALYSIS and equips students with the necessary skills to  analyze RNA SEQ experiments. It combines theoretical knowledge with practical applications, preparing students to contribute to the rapidly evolving field of genomics and Data analysis

Registration

Educator

UJJAWAL SOLANKI

Difficulty

Intermediate

Mode

Live

language

Language

English

About Course

This course provides a comprehensive understanding of RNA SEQ DATA ANALYSIS and equips students with the necessary skills to  analyze RNA SEQ experiments. It combines theoretical knowledge with practical applications, preparing students to contribute to the rapidly evolving field of genomics and Data analysis

RNA SEQ Data Analysis

Day 01

Introduction to RNA-seq & Experimental Design

  • What is RNA sequencing?
  • Bulk RNA-seq vs single-cell RNA-seq
  • RNA-seq experimental workflow
  • Biological vs technical replicates
  • Experimental groups and controls
  • Sequencing technologies
  • FASTQ file structure
  • Phred quality score
  • Single-end vs paired-end sequencing
  • Stranded vs unstranded libraries

Day 02

Obtaining RNA-seq Data & Linux Fundamentals

  • NCBI SRA and GEO
  • SRA accession types: SRR, SRP, SRS, PRJNA
  • Reference genome and annotation
  • Metadata and sample information
  • Search SRA
  • Download data using SRA Toolkit
  • Convert SRA → FASTQ
  • Understand paired-end files
  • Linux commands:

Day 03

Quality Control of Raw Reads

  • Why quality control is important
  • Per-base sequence quality
  • GC content
  • Sequence duplication
  • Adapter contamination
  • Overrepresented sequences
  • Run FastQC
  • Analyze FastQC reports
  • Run MultiQC
  • Compare multiple samples
  • Identify potential sequencing problems

Day 04

Read Trimming & Post-trimming QC

  • Adapter contamination
  • Low-quality bases
  • Quality trimming vs adapter trimming
  • Why aggressive trimming can be harmful
  • Use Fastp
  • Perform adapter removal
  • Quality filtering
  • Generate Fastp reports
  • Run FastQC again
  •  Compare pre- and post-trimming quality

Day 05

Reference Genome & RNA-seq Alignment

  • Genome reference
  • GTF/GFF annotation
  • Transcript vs gene
  • Mapping reads to genome
  • Splice-aware alignment
  • HISAT2 vs STAR
  • Alignment metrics
  • Download/reference genome preparation
  • Build or use HISAT2 index
  • Align reads
  • Generate SAM/BAM
  • Understand alignment statistics

Day 06

BAM Processing & Alignment QC

  • SAM vs BAM
  • BAM sorting
  • BAM indexing
  • Mapping rate
  • Properly paired reads
  • Multi-mapped reads

  • Using Samtools:

    SAM → BAM → Sort → Index → Statistics

    • samtools view
    • samtools sort
    • samtools index
    • samtools flagstat
    samtools stats

Day 07

Gene-level Quantification

  • Gene expression quantification
  • Read counting
  • Exon-level vs gene-level counting
  • FeatureCounts
  • Count matrix
  • TPM, FPKM and raw counts
  • Why DESeq2 requires raw counts
  • Run featureCounts
  • Generate gene count matrix
  • Understand annotation/GTF
  • Remove unwanted annotation columns
  • Construct sample metadata
  •  

Day 08

Introduction to R & RNA-seq Count Matrix

  • Why R is widely used for RNA-seq
  • R objects
  • Data frames
  • Matrices
  • Factors
  • Metadata
  • Count matrix structure
  • Import count matrix
  • Import metadata
  • Check sample names
  • Match metadata with expression data
  • Remove unwanted columns
  • Filter low-count genes

Day 09

Exploratory Data Analysis & Normalization

  • Why normalization is required
  • Library size
  • Size factors
  • Variance stabilization
  • PCA
  • Sample clustering
  • Batch effects
  • Create DESeqDataSet
  • Estimate size factors
  • Normalize counts
  • VST/rlog
  • PCA plot
  • Sample correlation
  • Hierarchical clustering
  • Heatmap

Day 10

Differential Gene Expression Analysis

  • Null hypothesis
  • Fold change
  • Log2 fold change
  • p-value
  • Multiple testing
  • FDR / adjusted p-value
  • Biological vs statistical significance
  • DESeq2 methodology
    • Run DESeq2
    • Define experimental contrast
    • Extract results
    • Apply thresholds, e.g.:

    padj < 0.05

    |log2FC| ≥ 1

    • Identify:
      • Upregulated genes
      • Downregulated genes
      • Significant genes

Day 11

DEG Visualization

  • Volcano plots
  • MA plots
  • Heatmaps
  • Expression patterns
  • Why visualization matters
    • MA plot
    • Volcano plot
    • DEG heatmap
    • Top 20/50 DEG heatmap
    • Upregulated/downregulated gene plots

    Tools : ggplot2 + ComplexHeatmap/pheatmap

Day 12

Functional Enrichment Analysis

  • Gene Ontology
  • Biological Process
  • Molecular Function
  • Cellular Component
  • KEGG pathway
  • Over-representation analysis
  • Background gene set
    • GO enrichment
    • KEGG enrichment
    • Upregulated DEG enrichment
    • Downregulated DEG enrichment

    Generate:

    • Dot plot
    • Bar plot
    • Enrichment plot
    • Gene-concept network

Day 13

Advanced Biological Interpretation

  • GSEA vs ORA
  • Why GSEA can detect subtle changes
  • Ranked gene lists
  • Pathway-level interpretation
  • Gene set enrichment
  • GSEA
  • Hallmark pathway analysis
  • GO-GSEA
  • KEGG-GSEA
  • Enrichment plots

Day 14

Complete RNA-seq Pipeline & Reproducibility

  • Reproducible bioinformatics
  • Conda environments
  • Version control
  • Workflow management
  • Pipeline automation
  • Nextflow introduction
  • Directory organization

Day 15

Complete Mini Project & Report Writing

  • How to interpret RNA-seq results
  • How to write an RNA-seq analysis report
  • Figure legends
  • Results vs Discussion
  • Common RNA-seq mistakes
  • Biological validation
  • Limitations of computational analysis
  •  

Educator - Ujjawal Solanki

Ujjawal is a Bioinformatics professional with a robust foundation in multi-omics and computational biology. Holding a Master’s in Biotechnology, he excels in microbiome analysis and has contributed to diverse projects in phylogenomics, network biology, and transcriptomics. With a strong research background and a passion for blending theoretical knowledge with practical applications, Ujjawal is dedicated to inspiring students in genomics, microbiome research, and data-driven biological discoveries. He is committed to advancing bioinformatics education and promoting a deeper understanding of computational methods across various life sciences domains.

Students Reviews

RNA SEQ DATA ANALYSIS COURSE

Mode - Online

Certificate Provided

Hours 15-20 Hours

Time 7:00 PM IST

Last Date 20 August 2026

Course Start 25 August 2026

Recorded lecture Provided after session

Certificate

FAQ

Session Joining link will be send to your register email on the day of session start you can join session by clicking joining link.

Yes! All of our Live sessions are recorded and you can access recorded session after live classes, recorded session link will be send to your respective email after live classes. recorded session will be available for 30 days after live classes

All Session are evening sessions time of session is between 7:00 to 8:00 PM IST.

Please read refund policy in refund policy section page.

No, any short Prerequisites knowledge you required to join the course.

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