Differential Gene Expression Using Ngs Data For Beginners

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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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