Differential Gene Expression Analysis Your Complete A to Z
What you’ll learn
You’ll be able to apply the knowledge of molecular biology to solve problems in differential gene expression analysis specifically, and bioinfomatics generally
You’ll be able to undertake an end-to-end RNAseq analysis pipeline in R
You’ll be able to do a qPCR analysis in R
You’ll be able to do a pathway analysis
You’ll be able to design bioinformatic experiments and do data interpretation
You’ll get a solid foundation on techniques used in bioinformatics
You’ll learn statistical models and methods used in differential gene expression
Requirements
Understanding of basic molecular biology terms such as DNA, RNA, gene and protein is going to be helpful in this course
Familiarity with R programming and UNIX-like terminal command line is advantageous but not necessary as it’ll be covered
Being open-minded and ready to learn!
Description
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 assessmentYou’ll learn and be able to do a qPCR analysis in R: delta-Ct method, delta-delta-Ct method, experimental design and data interpretationYou’ll learn how to apply the knowledge of molecular biology to solve problems in differential gene expression analysis specifically, and bioinformatics generallyYou’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 meanYou’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 frameworksYou’ll learn about pathway analysis methods and how they can be used for hypothesis generationYou’ll learn and be able to visualise gene expression data from your experiments
Overview
Section 1: Introduction to the course
Lecture 1 Welcome to the course!
Lecture 2 Course structure, aims and objectives
Lecture 3 Your first bioinformatic challenge!
Lecture 4 Quiz 1 file – an explanation
Lecture 5 Quiz 1 walkthrough – undertaking a BLAST search
Section 2: Biology for differential gene expression analysis
Lecture 6 Biology for differential gene expression analysis – introduction
Lecture 7 What is this thing called bioinformatics?
Lecture 8 Overview of gene expression
Lecture 9 Translation of genetic code – theory
Lecture 10 Translation of genetic code – practice
Lecture 11 Open Reading Frame translation walk-through
Lecture 12 Gene structure and splicing
Lecture 13 Biology for differential gene expression analysis – section summary
Section 3: Sequencing, PCR and microarrays – technical foundation
Lecture 14 Lab techniques for differential gene expression analysis – intro
Lecture 15 Sequencing
Lecture 16 PCR
Lecture 17 Sanger sequencing
Lecture 18 Next generation sequencing
Lecture 19 Microarrays
Lecture 20 Lab techniques for differential gene expression analysis – section summary
Section 4: Experimental, statistical and analytical foundation plus quantitative PCR
Lecture 21 First things first – RStudio installation
Lecture 22 Differential gene expression – fundamentals
Lecture 23 Sample preparation for differential gene expression analysis
Lecture 24 Measuring gene expression with qPCR
Lecture 25 qPCR quantification of gene expression
Lecture 26 qPCR quiz – part 1 – intro
Lecture 27 qPCR quiz walkthrough – part 1
Lecture 28 Experimental considerations for differential gene expression
Lecture 29 Statistical considerations for differential gene expression
Lecture 30 Spreadsheet qPCR analysis: preserving variability of expression
Lecture 31 Data analysis in RStudio
Lecture 32 Getting ready for your next quiz: dat package deprecation and functions
Lecture 33 Is the expression of this tumour suppressor affected by this drug? – Part 2 – Q1
Lecture 34 Part 2 – Q1 Walkthrough
Lecture 35 Is the expression of this tumour suppressor affected by this drug? – Part 2 – Q2
Lecture 36 Part 2 – Q2 walkthrough
Lecture 37 Is the expression of this tumour suppressor affected by this drug? – Part 2 – Q3
Lecture 38 Part 2 – Q3 walkthrough
Lecture 39 Is the expression of this tumour suppressor affected by this drug? – Part 2 – Q4
Lecture 40 Part 2 – Q4 walkthrough
Section 5: A deep dive into RNAseq and analysis methods
Lecture 41 Intro to RNAseq
Lecture 42 Analytical advantages of RNAseq and overview of RNAseq analysis pipeline
Lecture 43 RNAseq analysis pipeline
Lecture 44 FastQC: assessing the quality of sequencing library
Lecture 45 Your FastQC exercise
Lecture 46 FastQC exercise walkthrough
Lecture 47 Alignment (TUXEDO)
Lecture 48 Alignment (Rsubread)
Lecture 49 Rsubread alignment: stringent vs. less stringent alignment criteria
Lecture 50 Strict and less strict alignment walkthrough
Lecture 51 Strict and less strict alignment quiz walkthrough
Lecture 52 Transcriptome assembly (TUXEDO)
Lecture 53 Installing Cuff-apps from TUXEDO toolkit
Lecture 54 Comparing genome annotations with Cuffcompare
Lecture 55 Cuffcompare quiz walkthrough
Lecture 56 Read counts and FPKM/RPKM
Lecture 57 Read counting (TUXEDO)
Lecture 58 Read counts (Rsubread)
Lecture 59 RPKM normalization (R)
Lecture 60 Convert read counts to RPKMs
Lecture 61 Quiz 18 – Code to get the answers walkthrough
Lecture 62 Differential gene expression (TUXEDO)
Lecture 63 General Linear Models (GLMs) for differential gene expression analysis – part 1
Lecture 64 GLMs for differential gene expression analysis – part 2
Lecture 65 GLMs for differential gene expression analysis – part 3
Lecture 66 GLMs for differential gene expression analysis – part 4
Lecture 67 Design matrices for GLMs
Lecture 68 Further read count processing for differential expression analysis
Lecture 69 Differential expression (R’s GLM): limma, edgeR
Lecture 70 Differential expression: impact of filtering & normalization
Lecture 71 Differential expression quiz walkthrough
Lecture 72 Differential expression: RankProd
Lecture 73 Differential expression with RankProd in R
Lecture 74 RankProd Quiz walkthrough
Section 6: Pathway analysis for hypothesis generation
Lecture 75 Pathway analysis – ORA + GSEA approaches
Lecture 76 Hands on ORA-based pathway analysis
Lecture 77 Hands on GSEA-based pathway analysis
Lecture 78 Using pathway analysis in hypothesis-free studies
Section 7: Gene expression visualization
Lecture 79 Visualization of gene expression data
Lecture 80 Venn diagram analysis
Lecture 81 Learn to visualize gene expression data in R
Lecture 82 Visualization of gene expression data hands on exercise
Section 8: Your capstone project
Lecture 83 Capstone project
Lecture 84 Capstone project materials
Lecture 85 Capstone project walkthrough
STEM graduates who don’t have a sufficient grasp of molecular biology and want to start a career in bioinformatics,Anybody who needs a refresher in biological foundations of bioinformatics and differential gene expression analysis,Students who want to start a higher degree (Bachelor, Masters or PhD) project related to bioinformatics,People working at pharmaceutical companies or at university and who want to learn about differential gene expression analysis,Curious learners that want to gauge bioinformatics and differential gene expression analysis
Course Information:
Udemy | English | 8h 41m | 4.45 GB
Created by: Alexander Abdulkader Kheirallah, PhD
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