Differential Gene Expression Analysis - Your Complete A to Z

Do you want to be a bioinformatician but don't know what it entails? Or perhaps you're struggling with biological data analysis problems? Are you confused amongst the biological, medicals, statistical and analytical terms? Do you want to be an expert in this field and be able to design biological experiments, appropriately apply the concepts and do a complete end-to-end analysis?

This is a comprehensive and all-in-one-place course that will teach you differential gene expression analysis with focus on next-generation sequencing, RNAseq and quantitative PCR (qPCR)

In this course we'll learn together one of the most popular sub-specialities in bioinformatics: differential gene expression analysis. By the end of this course you'll be able to undertake both RNAseq and qPCR based differential gene expression analysis, independently and by yourself, in R programming language. The RNAseq section of the course is the most comprehensive and includes everything you need to have the skills required to take FASTQ library of next-generation sequencing reads and end up with complete differential expression analysis. Although the course focuses on R as a biological analysis environment of choice, you'll also have the opportunity not only to learn about UNIX terminal based TUXEDO pipeline, but also online tools. Moreover you'll become well grounded in the statistical and modelling methods so you can explain and use them effectively to address bioinformatic differential gene expression analysis problems. The course has been made such that you can get a blend of hands-on analysis and experimental design experience - the practical side will allow you to do your analysis, while theoretical side will help you face unexpected problems.

Here is the summary of what will be taught and what you'll be able to do by taking this course:

• You'll learn and be able to do a complete end-to-end RNAseq analysis in R and TUXEDO pipelines: starting with FASTQ library through doing alignment, transcriptome assembly, genome annotation, read counting and differential assessment

• You'll learn and be able to do a qPCR analysis in R: delta-Ct method, delta-delta-Ct method, experimental design and data interpretation

• You'll learn how to apply the knowledge of molecular biology to solve problems in differential gene expression analysis specifically, and bioinformatics generally

• You'll learn the technical foundations of qPCR, microarray, sequencing and RNAseq so that you can confidently deal with differential gene expression data by understanding what the numbers mean

• You'll learn and be able to use two main modelling methods in R used for differential gene expression: the general linear model as well as non-parametric rank product frameworks

• You'll learn about pathway analysis methods and how they can be used for hypothesis generation

• You'll learn and be able to visualise gene expression data from your experiments