Bioinformatics Methods for Transcriptomics
Master RNA sequencing analysis with hands-on training in tools like STAR, DESeq2, and IsoQuant. Ideal for intermediate bioinformaticians.
Master RNA sequencing analysis with hands-on training in tools like STAR, DESeq2, and IsoQuant. Ideal for intermediate bioinformaticians.
This course offers comprehensive training in bioinformatics methods for analyzing transcriptomic RNA sequencing data. Covering both short read (RNA-seq) and long read (PacBio, ONT) sequencing, it addresses core transcriptomics questions: gene and transcript expression, expression levels, and differences in gene expression and splicing patterns between conditions. Students gain hands-on experience with popular tools such as STAR, PsiCLASS, DESeq2, rMATS, MntJULiP, Minimap2, and IsoQuant. The course is structured into four modules, progressing from gene expression analysis to alternative splicing analysis, transcriptome reconstruction with long reads, and differential expression and splicing analysis. Designed for intermediate-level learners, it assumes basic knowledge of command-line bioinformatics tools in a Unix-type environment.
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What you'll learn
Perform gene expression analysis of RNA-seq data using tools like STAR and DESeq2
Conduct alternative splicing analysis with rMATS and MntJULiP
Reconstruct transcriptomes using long RNA sequencing reads with FLAIR and IsoQuant
Carry out differential expression and splicing analysis with long RNA sequencing data
Visualize transcriptomic data using tools like IGV and Jutils
Apply bioinformatics methods to real-world transcriptomics datasets
Skills you'll gain
This course includes:
7.85 Hours PreRecorded video
8 assignments
Access on Mobile, Tablet, Desktop
FullTime access
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There are 4 modules in this course
This course provides a comprehensive exploration of bioinformatics methods for analyzing transcriptomic RNA sequencing data. It covers both short read (RNA-seq) and long read (PacBio, ONT) sequencing technologies. The curriculum is designed to address core transcriptomics questions, including gene and transcript expression identification, expression level quantification, and analysis of differential gene expression and splicing patterns between experimental conditions. Students will gain practical experience using popular bioinformatics tools such as STAR, PsiCLASS, DESeq2, rMATS, MntJULiP, Minimap2, and IsoQuant. The course structure progresses from fundamental gene expression analysis to advanced topics like alternative splicing analysis and transcriptome reconstruction using long reads. This intermediate-level course assumes basic familiarity with command-line bioinformatics tools in a Unix environment, making it ideal for researchers and professionals looking to enhance their skills in transcriptomics data analysis.
Course Introduction and Gene expression analysis of RNA-seq data
Module 1 · 4 Hours to complete
Alternative splicing analysis of RNA-seq data
Module 2 · 4 Hours to complete
Transcriptome reconstruction with long RNA sequencing reads
Module 3 · 4 Hours to complete
Differential expression and differential splicing analysis with long RNA sequencing reads
Module 4 · 4 Hours to complete
Fee Structure
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Instructor
Associate Professor of Medicine at Johns Hopkins University
Dr. Liliana Florea is an Associate Professor of Medicine in the McKusick-Nathans Department of Genetic Medicine at Johns Hopkins University, where she specializes in developing computational methods for analyzing large-scale sequencing data to better understand the molecular mechanisms of diseases. Her research focuses on genome comparison, gene characterization, alternative splicing variations, and identifying genetic and molecular determinants of disease. Before joining Johns Hopkins in 2011, Dr. Florea was on the faculty at George Washington University and was a key member of the team at Celera Genomics that produced the first human genome sequence. Her work has earned her a Sloan Research Fellowship in Computational and Evolutionary Molecular Biology, with funding from the National Science Foundation and National Institutes of Health. She holds a PhD from Pennsylvania State University.
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