Transcriptomics 2

Transcriptomics 2

Transcriptomics 2 is a continuation of the Transcriptomics 1 and it focuses on finding differences in gene expression. We will start with differential gene expression and continue to look at typical challenges that can be resolved using other methods. We will look at t-test, then use DESeq2 to run a differential gene expression pipeline and then use Factor Regression Analysis. As a result, you will learn to detect obvious differences between pre-set groups as well as expand that idea to more subtle differences represented by factors that might interact with each other.

We will continue to use the PDX cancer project that was started in Course 1.

Major Trends and Applications

Next Generation Sequencing (NGS) and machine learning are key technologies enabling an unprecedented level of power, precision, and efficacy in understanding genomic regulation. Utilizing these tools will provide a more robust understanding of genetic diseases, expression profiles. The application of NGS data has applications outside of medicine as well, such as in agrotechnology. NGS utilized in Agrotech can help provide more information about insects and climate change.

Course objectives and methodology

The goal of this course is to further expose interested individuals to the field of big-data analysis to biologists and clinicians who currently lack the ability to work with this large scale of data, and further develop the analytical skills gained from Transcriptomics 1.

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 info@pine-biotech.com. Microsoft Excel is also used for some basic analysis of pipeline results.

We realize that the backgrounds of people interested in this course will be diverse, we want to provide sufficient background for the hands-on exercises that are included in these course. For the exercises, we will be using the T-BioInfo platform that hides the complexity of code and algorithms behind an intuitive visual interface. This part of the course will be dedicated to a detailed description of the analysis steps that are combined into pipelines on the platform. As a result, participants will gain independence and will be able to analyze their own data.

Our courses are designed to be accessible to diverse audiences, so we are limiting the number of technical concepts that some might find useful. Additional technical terminology and expanded review can be found on other websites that we will provide links to for additional reading.

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