RNA Seq Data Analysis: from raw reads to biological discovery
Master the end-to-end transcriptomics pipeline using cutting-edge bioinformatics tools and AI-driven insights. Transform raw sequencing data into publication-ready biological discoveries and differential gene expression profiles.
Course Description
Dive deep into the world of transcriptomics with this comprehensive, hands-on masterclass designed to bridge the gap between raw sequencing data and meaningful biological insight. In this intensive webinar, you will navigate the entire high-throughput RNA-Seq workflow, starting from initial quality control of FASTQ files to advanced differential gene expression analysis. We integrate industry-standard Linux/Unix command-line tools with modern R programming and AI-assisted workflow optimization to streamline your data processing. Participants will explore critical steps including read alignment, transcript quantification, and functional enrichment analysis (GO/KEGG). By applying these methodologies to real-world datasets, you will gain the confidence to interpret complex transcriptomic landscapes and identify key biomarkers. Whether you are aiming to publish or accelerate your research, this course equips you with the exact computational competencies demanded by modern precision medicine and biotechnology sectors.
What You'll Learn
Quality Control Mastery: Identify and filter low-quality reads and adapter sequences from raw high-throughput data.
Reference Mapping: Align RNA-Seq reads to a reference genome using splice-aware alignment algorithms.
Expression Quantification: Quantify transcript and gene-level abundances accurately from mapped reads.
Statistical Modeling: Implement robust statistical frameworks in R to discover significantly altered biological pathways.
Functional Annotation: Map differentially expressed genes to biological pathways using functional enrichment analysis.
AI Tool Integration: Leverage AI prompts and LLMs to debug bioinformatics scripts and automate workflow code.
Curriculum
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RNA Seq Data Analysis: from raw reads to biological discovery
Lesson
