Differential Gene Expression Using Ngs Data For Beginners

Free Download What you'll learn
complete introduction of Next generation sequencing
First-generation sequencing A brief history of NGS
Second-generation sequencing Third-generation sequencing
Limitations of Next generation sequencing
Technologies used in Third-generation sequencing
Impact and applications of NGS: Opening the doors into the world of "omics"
Recent advances in DNA sequencing technology
Use cloud-based platform Galaxy to analyze large sequencing datasets
Use of different tools for analysis of gene expression
Use FastQC and Trimmomatic to improve data quality
You will be using Genomic tools called bowtie2 for analysis of NGS
Gene expression profiles will be got on in the end of analysis
Formats of NGS Data will be learned
Concepts of Generations of Sequencing
Requirements
Just a little about bioinformatics
Bioinformaticians starting out their research
Description
This course aims to provide an introduction to differential gene expression analysis using next-generation sequencing (NGS) data. The course will cover the basics of RNA sequencing (RNA-Seq) and will teach participants how to perform differential gene expression analysis using bioinformatics tools.Participants will learn the entire process, including quality control of raw sequencing data, mapping reads to a reference genome, quantifying gene expression, and statistical analysis to identify differentially expressed genes. The course will also introduce participants to the principles of data normalization and provide hands-on training in the use of popular bioinformatics tools such as R, DESeq2, and edgeR.Throughout the course, participants will work with real-world datasets and learn to interpret and visualize results generated from differential gene expression analysis. The course will also cover some of the common challenges encountered in differential gene expression analysis and discuss strategies for overcoming them.By the end of this course, participants will have gained the knowledge and skills necessary to perform differential gene expression analysis using NGS data, and will be able to confidently use bioinformatics tools to analyze their own RNA-Seq datasets.Prerequisites:This course is designed for beginners who have little to no experience with NGS data analysis. Participants should have a basic understanding of molecular biology and genetics, as well as some familiarity with R programming language.Course Duration:The course is designed to be completed in 4 weeks, with a time commitment of approximately 6-8 hours per week. The course will consist of pre-recorded lectures, hands-on exercises, and discussion forums.Course Goals:Understand the principles of RNA-Seq and differential gene expression analysisPerform quality control of NGS data and read mapping to reference genomeQuantify gene expression and perform statistical analysis to identify differentially expressed genesLearn normalization strategies for RNA-Seq data analysisInterpret and visualize results from differential gene expression analysisGain familiarity with popular bioinformatics tools such as R, DESeq2, and edgeRIdentify common challenges encountered in differential gene expression analysis and learn strategies for overcoming them.Course Materials:All course materials, including lecture videos, exercise materials, and datasets, will be provided through an online learning platform. Participants will also have access to a discussion forum where they can interact with instructors and other students.
Overview
Section 1: NGS Intro and what is Sequencing!
Lecture 1 Introduction of Course Section
Lecture 2 Introduction to next-generation sequencing
Lecture 3 History of NGS and First Generation Sequencing
Lecture 4 Second-Generation Sequencing
Lecture 5 3rd Generation Sequencing
Lecture 6 NGS Services Platforms
Lecture 7 Bioinformatics DNA and RNA data analysis
Lecture 8 Performance of NGS platforms and sequencing errors
Lecture 9 Impact and applications of NGS in Genomics
Lecture 10 Applications in field of Microbiology
Lecture 11 Applications in field of Oncology
Lecture 12 Applications in field of Agriculture Genomics
Lecture 13 Future of Genomics
Lecture 14 Limitations of NGS
Section 2: Hands On Next Generation Sequencing Using Galaxy Servers
Lecture 15 What is NGS and why we are using the NGS for data analysis?
Lecture 16 NGS Workflow
Lecture 17 SRA Database introduction
Lecture 18 SRA File
Lecture 19 Galaxy Server And Objects
Lecture 20 Getting Onto Galaxy
Lecture 21 Tools For NGS Data Analysis
Lecture 22 Getting SRA Runs From Databases And platform
Lecture 23 Ncbi Genome To Galaxy
Lecture 24 Getting Sra Runs To Galaxy
Lecture 25 Fastqc Tool To Dataset Generated Dataset
Lecture 26 Trimmomatic Tool On Dataset
Lecture 27 Alignment/genome Mapping
Lecture 28 Abundance Estimation Tool On Dataset
Lecture 29 From Values To Visuals (Heatmap)
Biology and Chemistry at high school graduate level,Beginner bioinformatics student who wanna work in this field




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