Transcriptomics 1

RNA-seq: What is the transcriptome and why study it?

Molecular biologists study a broad spectrum of topics relating to the molecular regulation of cell biology, from DNA and RNA, to protein expression and protein-protein interactions. A dominant theme in molecular biology is how variation in nucleic acids (genotype) affects the biology of organisms (phenotype). A second major theme is the study of gene expression and the resulting change(s) in observed transcriptomics features of a studied sample.

Next Generation Sequencing (NGS) is a key technology enabling an unprecedented level of precision, and efficacy in understanding genomic and transcriptomic regulation. A Next Generation sequencer creates a digital file representing sequenced DNA or RNA. This file contains information where nucleotides are represented by letters corresponding to their “bases”. 

Next Generation Sequencing data analysis is often referred to as “RNA-seq”. The purpose of this analysis is to map the short reads from raw sequence files to the reference genome, and then study the abundance of RNA mapped to a particular region. The abundance can represent levels of gene or isoform expression, and can be compared between samples or groups of samples.

Transcriptomics 1: Course objectives and methodology

The goal of this course is to prepare a student for real-world applications of RNA-seq. We will review methods of both quantitative and qualitative analysis of RNA in a sample, how data is generated using Next Generation Sequencing, and use a sample project “Patient Derived Xenograft: Transcriptomic Biomarkers of Breast Cancer” to practice generating a table of expression from raw FASTq files. We will also learn to preview and visualize the resulting data using Principal Component Analysis (PCA). To perform the example analysis detailed in the course, students will need access to the T-BioInfo platform. Account information can be requested by filling out the form on server.t-bio.info site. Microsoft Excel is also used for plotting results.

We would like to thank the following people who helped to prepare the projects, develop the tools, and compile the the learning resources for this course:

Dr. Leonid Brodsky and the Tauber Bioinformatics Research Center, Dr. Claudia Copeland, Dr. Ron Ferrucci, Dr. Jo Calihan, Julia Panov, and Jaclyn Williams.