Bioinformatics RNA-seq Analysis: From Raw Reads to Biological Insights
Master complete RNA-seq analysis workflows from quality control to biological interpretation. Learn industry-standard tools, differential expression analysis, and functional enrichment using real datasets and cloud platforms.
Workshop Description
This intensive 4-day workshop provides researchers with comprehensive training in RNA-seq data analysis from raw sequencing reads to biological interpretation. Designed for biologists and researchers with basic computational skills, this course covers the complete RNA-seq pipeline using industry-standard tools and best practices.
You'll master essential bioinformatics concepts including quality control, read alignment, quantification, differential expression analysis, and functional enrichment. The workshop emphasizes hands-on experience with real datasets, using command-line tools, R/Bioconductor packages, and cloud-based platforms to ensure reproducible and publication-ready analyses.
By the end of this workshop, you'll be confident performing end-to-end RNA-seq analysis, interpreting results in biological context, creating publication-quality figures, and applying these skills to your own research datasets. All software, datasets, and analysis scripts are provided, with lifetime access to materials and continued support.
Instructor
Course Fee
$449.0
Maximum Seats
20
Duration
4 half-days
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Time
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A comprehensive 4-day journey through modern RNA-seq analysis workflows and interpretation
Format: Each day is approximately 4 hours with hands-on analysis using real RNA-seq datasets on cloud platforms
Prerequisites: Basic biology background and familiarity with R or Python recommended. Some command-line experience helpful but not required. We'll provide quick tutorials for essential skills
What you'll learn: Complete RNA-seq pipeline from raw reads to biological insights, quality control, alignment strategies, quantification methods, statistical analysis, and functional interpretation
Materials provided: All datasets, analysis scripts, reference materials, and cloud computing access. Lifetime access to updated protocols and continued community support
Day 1: RNA-seq Fundamentals & Quality Control
Understand RNA-seq technology and perform essential quality assessments
RNA-seq Overview (45 min): Sequencing technologies, experimental design considerations, single-end vs paired-end reads, library preparation methods, and common experimental pitfalls
File Formats & Data Structure (30 min): FASTQ, SAM/BAM, GTF/GFF formats, understanding sequencing metadata, and organizing bioinformatics projects
Quality Control with FastQC (1 hr): Assessing read quality, identifying common issues (adapter contamination, low quality regions), interpreting quality metrics, and batch processing multiple samples
Read Preprocessing (1 hr): Trimming adapters and low-quality bases with Trimmomatic, filtering strategies, and quality improvement verification
Introduction to Command Line (45 min): Essential Unix commands for bioinformatics, file manipulation, and basic scripting for reproducible workflows
Day 2: Read Alignment & Quantification
Map reads to reference genomes and quantify gene expression
Reference Genomes & Annotation (30 min): Choosing appropriate references, understanding genome builds, annotation files (GTF/GFF), and handling model vs non-model organisms
Alignment Strategies (45 min): Splice-aware aligners (STAR, HISAT2), alignment parameters, handling splice junctions, and alignment quality metrics
Hands-on Alignment (1.5 hrs): Indexing reference genomes, running STAR alignment, understanding output files, and assessing alignment quality with samtools and RSeQC
Gene Quantification (1 hr): Count-based methods (htseq-count, featureCounts), abundance estimation (Salmon, Kallisto), and choosing quantification strategies
Quality Assessment (1 hr): MultiQC for comprehensive quality reports, identifying problematic samples, and documenting analysis decisions
Day 3: Differential Expression Analysis
Statistical analysis and identification of differentially expressed genes
Introduction to R/Bioconductor (30 min): Setting up R environment, essential Bioconductor packages (DESeq2, edgeR, limma), and data import strategies
Experimental Design & Statistics (45 min): Understanding biological vs technical replicates, power analysis, batch effects, and statistical models for RNA-seq
DESeq2 Analysis Pipeline (2 hrs): Data preprocessing, normalization methods, dispersion estimation, statistical testing, multiple testing correction, and results interpretation
Visualization & Exploration (1 hr): PCA plots, heatmaps, volcano plots, MA plots using ggplot2 and specialized packages, and identifying patterns in expression data
Advanced Considerations (15 min): Handling complex experimental designs, time-course experiments, and multi-factor analyses
Day 4: Functional Analysis & Biological Interpretation
Transform gene lists into biological insights and publication-ready results
Gene Set Enrichment (1.5 hrs): Gene Ontology (GO) analysis, KEGG pathway enrichment, using clusterProfiler and enrichR, statistical considerations, and avoiding common pitfalls
Advanced Functional Analysis (1 hr): Gene Set Enrichment Analysis (GSEA), pathway visualization with pathview, network analysis, and integrating multiple data types
Visualization & Reporting (1 hr): Creating publication-quality figures, R Markdown reports for reproducible analysis, and integrating results across analysis steps
Case Study & Integration (30 min): Complete analysis walkthrough with real biological dataset, from hypothesis to biological conclusions, troubleshooting common issues
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