Bioinformatic; Learn Bulk RNA-Seq Data Analysis From Scratch

سرفصل های دوره

Best Bioinformatics course for Students, Academia and Industry Professionals to learn RNA-Seq Data Analysis From Zero

1. Course Introduction And Disclaimer

1. Course Introduction, Disclaimer And Important Message to Our Learners

2. Module-1 Basics of Molecular Biology (Optional)

1. What is DNA

2. Where is DNA Located in Our Cells

3. What is Role of DNA

4. Difference Between Eukaryotic and Prokaryotic Genes

5. What is Inside of Gene (Coding Regions of DNA)

6. Post Transcriptional Modifications

3. Introduction of RNA-Seq

1. Why There is Need of RNA-Seq Analysis

2. Basic Workflow of RNA-Seq Analysis

3. Next Generation Sequencing Workflow

4. Basic File Obtained During RNA-Seq Analysis

4. Practical Demonstration of RNA-Seq Reads To Feature Count Matrix In Linux

1. Basic Workflow of RNA-Seq Data Analysis

2. Installation of Linux in Your Windows (WSL)

3.1 Commands to Install Necessary Programs In Linux Environment.pdf

3. Installation of Necessary Programs In Linux Environment (Part-1)

4. Installation of Necessary Programs In Linux Environment (Part-2)

5. Installation of SAM Tools in Linux (Part-3)

6. Downloading of Timmomatic Tool

7.1 Command to Perform FASTQC Analysis.txt

7. Quality Check of the Reads with FASTQC (Part-1)

8.1 test udemy.zip

8. Quality Check of the Reads with FASTQC (Part-2)

9. Assignment 1 FASTQC Analysis of test udemy.fastq File.html

10.1 Command to Perform Trimming Analysis.txt

10. Use of Timmomatic Tool to Remove Poor Quality Reads

11. Assignment-2 Trimming of Poor Quality Reads.html

12.1 Command to Perform Alignment Using HISAT2.txt

12. Use of HISAT2 for Alignment of Reads with Reference Genome

13. Assignment-3 Performing Alignment of Reads with Reference Genome.html

14. Downloading of GTF File to Build the Feature Count Matrix

15.1 Command to Build The Feature Count Matrix.txt

15. Building of Feature Count Matrix With Subread Tool

16. Assignment-4 Building Feature Count Matrix.html

17.1 script1.zip

17.2 script2.zip

17.3 script3.zip

17.4 script4.zip

17. How to Process Multipipe FastQ Files Using Bash Scripts

18. Experimental Design of Airway Cell Line Study That will Use In DEG Analysis

5. Basic Concepts of R and R-Studio

1. Introduction of the Section

2. Installation of R and R-Studio

3. Setting Working Directory in R-Studio

4. Basic Data Types Used in R

5. Creating a Variable

6. What is Package And Function in R

7.1 R-Code to Bioconductor and DESeq2 in R-Studio.txt

7. Brief Introduction of Bioconductor

6. Differential Expression of Gene Analysis in R Using DESeq2 Package

1. Installation of DESeq2 in R-Studio For DEGs Analysis

2. What is CSV format And Saving MetaData File in CSV format

3. Uploading of Feature Count Matrix and Meta Data in R-Studio

4. Assignment-5 Uploading Feature Count Matrix and Meta Data in R-Studio.html

5. Basic Quality Check of Feature Count Matrix and Meta Data

6. Assignment-6 Basic Quality Check of Data.html

7. Use of DESeq2 for DEG Analysis (Part-1)

8. Assignment-7 Creating Design for Differentially Expressed Genes.html

9. DESeq2 Concept of Leaky Expression Part-2)

10. DESEq2 Removing Low Counts Reads Genes (Part-3)

11. Assignment-8 Dropping Rows with Low Count.html

12. DESeq2 Use of DESeq2 Function for DEG Analysis (Part-3)

13. Assignment-9 Use of DESeq Function.html

14. What is Size Factor Estimation in DESEq2

15. What is dispersion Estimation in DESeq2

16. Hypothesis testing in DESeq2 for DEG Analysis

17. Concept of P-value and P-Adjusted values

18. Getting Differentially Expressed Gene at Different Alpha Value

19. Assignment-10 Getting DEGs at 0.05 Alpha Value.html

20. Converting Gene IDs to Gene Name

21. Assignment-11 Converting Genes IDs to Gene Name.html

7. Quality Checking of RNA-Seq Data

1. Basic Quality Check Parameters

2. Basic Concepts of PCA Plot

3. Building PCA Plot of RNA-Seq Data

4. Assignment-12 Generation of PCA Plot.html

5. Size Factor Estimation and Its Calculation

6. Assignment-13 Estimating Size Factor.html

7. Dispersion Estimates and Building of Dispresion Plot

8. Assignment-14 Building Dispersion Plot.html

8. Analysis of Gene Expression Data

1. Basic Understanding of Tidyverse And ggplo2

2. Installation of Tidyverse And ggplot2 and Sample Dataset

3. Basic Functionality of Tidyverse Functions; Filter, Arrange, and Mutate

4. Basic Functionality of ggplot2 to Build the Plots

5. Building MA Plot

6. Assignment-15 Building MA Plot for DEGs.html

7. Getting Idea About Best Genes

8. Assignment-16 Extraction of top 30 Best Genes.html

9. Building Volcano Plot-Part1

10. Building Volcano Plot -Part2

11. Assignment-17 Volcano Plot of Data.html

12. Building HeatMap of DEGs

13. Assignment-18 HeatMap of Best 30 DEGs.html

14. Simple Gene Ontology and Pathway Analysis of Genes

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تولید کننده: Udemy