Bioinformatic; Learn Bulk RNA-Seq Data Analysis From Scratch
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Welcome to our third course “Learn Bulk RNA-Seq Data Analysis From Scratch,” a comprehensive online course designed to equip you with the skills and knowledge needed to harness the power of RNA-Seq data analysis (NGS). In this course, we delve into the captivating world of genomics and bioinformatics, empowering you to explore the intricacies of gene expression and unravel the hidden mysteries within the transcriptome.
With the advent of high-throughput sequencing technologies, RNA-Seq (NGS) has revolutionized the field of molecular biology, allowing us to decipher the intricate dance of gene expression in ways never before possible. This course serves as your gateway to understanding and interpreting the wealth of information contained within RNA-Seq data, transforming it into valuable insights and meaningful discoveries.
Bioinformatics, the multidisciplinary field at the intersection of biology and computer science, plays a pivotal role in deciphering complex biological systems. In this course, we emphasize the importance of bioinformatics methodologies and tools, which form the foundation of modern genomics research. By mastering these techniques, you will gain a competitive edge in the rapidly evolving field of life sciences.
Course Highlights:
Comprehensive Training: From raw FASTQ files to in-depth analysis, this course provides a step-by-step guide to RNA-Seq data analysis, covering the entire workflow with clarity and precision. This is not limited to RNA-Seq but to all type of NGS data.
Linux and R-Studio: Get hands-on experience with two essential tools in bioinformatics. Learn to navigate the Linux command line environment and utilize R-Studio for data processing, visualization, and statistical analysis.
Theory and Practice: We strike a perfect balance between theoretical concepts and practical application. Understand the underlying principles of RNA-Seq analysis while honing your skills through hands-on exercises and real-world examples.
Cutting-edge Techniques: Stay at the forefront of genomics research by exploring the latest advancements in RNA-Seq analysis techniques, such as differential gene expression analysis, functional enrichment analysis, and pathway analysis.
Expert Guidance: Benefit from the expertise of experienced instructors who have a deep understanding of both bioinformatics and molecular biology. Their guidance and insights will ensure a rewarding learning experience.
Interactive Learning: Engage in interactive assignments, and discussions to reinforce your understanding and interact with a vibrant community of fellow learners, fostering knowledge exchange.
Embark on this transformative journey into the world of RNA-Seq analysis and bioinformatics. Unleash the power of genomics to uncover hidden biological insights and make significant contributions to scientific research. Enroll in “Bioinformatics: Learn Bulk RNA-Seq Data Analysis From Scratch” today and equip yourself with the essential skills needed to excel in the dynamic field of bioinformatics. We assure you that all of the tools that will be used in this course will be Freely available and closely related to the course material. For most of them you do not need to sign up.
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12
Basic Workflow of RNA-Seq Data Analysis -
13Installation of Linux in Your Windows (WSL)
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14Installation of Necessary Programs In Linux Environment (Part-1)
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15Installation of Necessary Programs In Linux Environment (Part-2)
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16Installation of SAM Tools in Linux (Part-3)
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17Downloading of Timmomatic Tool
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18Quality Check of the Reads with FASTQC (Part-1)
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19Quality Check of the Reads with FASTQC (Part-2)
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20Assignment 1: FASTQC Analysis of test_udemy.fastq File
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21Use of Timmomatic Tool to Remove Poor Quality Reads
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22Assignment-2: Trimming of Poor Quality Reads
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23Use of HISAT2 for Alignment of Reads with Reference Genome
To use HISAT2, you need a reference genome. In our case, we are using RNA-Seq data from humans; therefore, we have downloaded the Human Reference Genome. Please use the the link that is shown in the video.
Note: This is open-source material, and you can download it for free. Furthermore, there will be no need for registration to download the reference genomes.
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24Assignment-3: Performing Alignment of Reads with Reference Genome
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25Downloading of GTF File to Build the Feature Count Matrix
GTF is an important file to further transform the alignment to build Feature Count Matrix. Use the link that is shown in the video.
Note: This is an open-source material and is available freely. Furthermore, you do not need to register yourself to download these files.
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26Building of Feature Count Matrix With Subread Tool
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27Assignment-4: Building Feature Count Matrix
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28How to Process Multipipe FastQ Files Using Bash Scripts
These four scripts will help you to analyze multiple FASTQ files at once. One very important thing that you should remember is that sometimes you have FASTQ files in zip folders like test.fastq.gz. In this case, we will request you to change the script1 a bit by replace fastq with fastq.gz.
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29Experimental Design of Airway Cell Line Study That will Use In DEG Analysis
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30Test Your Skill With Large FASTQ File (Optional)
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38Installation of DESeq2 in R-Studio For DEGs Analysis
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39What is CSV format & Saving MetaData File in CSV format
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40Uploading of Feature Count Matrix and Meta Data in R-Studio
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41Assignment-5: Uploading Feature Count Matrix and Meta Data in R-Studio
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42Basic Quality Check of Feature Count Matrix and Meta Data
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43Assignment-6: Basic Quality Check of Data
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44Use of DESeq2 for DEG Analysis (Part-1)
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45Assignment-7: Creating Design for Differentially Expressed Genes
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46DESeq2: Concept of Leaky Expression Part-2)
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47DESEq2: Removing Low Counts Reads Genes (Part-3)
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48Assignment-8: Dropping Rows with Low Count
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49DESeq2: Use of DESeq2 Function for DEG Analysis (Part-3)
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50Assignment-9: Use of DESeq Function
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51What is Size Factor Estimation in DESEq2 ?
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52What is dispersion Estimation in DESeq2?
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53Hypothesis testing in DESeq2 for DEG Analysis
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54Concept of P-value and P-Adjusted values
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55Getting Differentially Expressed Gene at Different Alpha Value
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56Assignment-10: Getting DEGs at 0.05 Alpha Value
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57Converting Gene IDs to Gene Name
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58Assignment-11: Converting Genes IDs to Gene Name
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59Basic Quality Check Parameters
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60Basic Concepts of PCA Plot
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61Building PCA Plot of RNA-Seq Data
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62Assignment-12: Generation of PCA Plot
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63Size Factor Estimation and Its Calculation
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64Assignment-13: Estimating Size Factor
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65Dispersion Estimates and Building of Dispresion Plot
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66Assignment-14: Building Dispersion Plot
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