RNA Seq Data Analysis: from raw reads to biological discovery
Master Next-Generation Sequencing transcriptomics workflows and AI-driven biological discovery. A comprehensive pipeline training from quality control to multi-dimensional data analysis.
Course Description
The RNA-Seq Data Analysis webinar is a precision-engineered operational masterclass designed by Dr. Omics Edu to decode high-throughput transcriptomic sequencing workflows. This comprehensive educational framework guides bioinformaticians from raw FASTQ sequence files to predictive systems biology and deep mechanistic discoveries. Participants will examine key aspects of quality control, adapter removal, read trimming, and precise transcript quantification using cutting-edge pseudo-alignment models like Salmon and Kallisto. The curriculum integrates traditional statistical approaches with modern artificial intelligence parameters to enhance downstream analytical pipelines. Attendees will acquire hands-on mastery over computational tools like featureCounts and execute structural dimensional scaling via Principal Component Analysis (PCA). By resolving complex transcriptomic multi-omics data, this program overcomes computational processing bottlenecks in molecular biology research. Ultimately, the webinar delivers an actionable intelligence blueprint enabling scientists to transform raw genomic reads into peer-reviewed publication-ready biological discoveries.
What You'll Learn
Understand the absolute end-to-end transcriptomics operational data pipeline from high-throughput instrument raw reads to cellular pathway discovery.
Implement automated quality control assessment algorithms, adapter sequence extraction, and low-quality read filtering using FastQC.
Apply advanced artificial intelligence and machine learning principles to model gene expression values and predict biological outcomes.
Execute ultra-fast transcript quantification using state-of-the-art pseudo-alignment index engines like Salmon and Kallisto.
Perform high-dimensional mathematical data reduction and PCA cluster visualizations to isolate biological variation from technical batch noise.
Curriculum
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Foundations of RNA Sequencing technologies, biological sample preparation, and analytical dataset generation.
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Operational assessment of raw sequence data quality using automated tools, trimming parameters, and adapter filtering.
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Algorithmic read counting, structural feature mapping, and pseudo-alignment indexing strategies for transcript quantification.
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Statistical normalization methodologies, sample clustering, and exploratory high-dimensional data reduction via PCA.
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Downstream computational systems biology, predictive multi-omics modeling, and functional pathway translation.
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