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). The second major theme is the study of gene expression and the resulting change in observed 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.
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 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 in an email to email@example.com. 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, Julia Panov, Jaclyn Williams
- Lectures 7
- Quizzes 2
- Duration 6 hours
- Skill level All levels
- Language English
- Students 1575
- Certificate Yes
- Assessments Yes
From biological samples to RNA reads
From raw RNA-seq reads to expression table
Lucid and detailed explanation !
I got by basics clarified about the basics of transcriptomics, various forms of RNA and isoforms. This course introduces us to the concept of RNA-SEQ and its high-throughput algorithms like TopHat, Bowtie and Cufflinks. It also introduces us to some Machine Learning algorithms like PCA. It is the best course to understand the concept of Big Data Transcriptomics.
From the basics of the Transcriptome to the logical steps of RNA-seq data analysis, the course is a fantastic start. As always the quiz benchmarks the learning process. This course is a perfect landing for somebody who wants to go deeper into RNA-SEQ data anlaysis.
Mohit Mazumder, Phd, Machine learning
Transcriptomics 1 is an excellent course to learn and understand about RNA seq data. The practical aspect of the transcriptomics course imprints a lot of confidence to the user to go out and do independent analysis in very short or no time. This is extremely useful not only for the students but also for the researchers who want to answer a question using a multi-disciplinary approach which is a common practice nowadays. The course along with the platform helps users to adapt quickly to the data type and analytics to address the unique biological question.
A great course to add to your online Bioinformatics catalogue, and for furthering your understanding of Transcriptomics.
The course is to get prepare for real-world applications of RNA-seq. It reviews methods of quantitative and qualitative analysis of RNA in a sample and how data is generated using Next Generation Sequencing