Published 3/2023MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHzLanguage: English | Size: 877.95 MB | Duration: 1h 46m
Learn about Next-generation sequencing and use of Galaxy Server to analyze your datasets for Differential Gene Expresion 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 strats 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 bners 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 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 strats 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 strats 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,Bner bioinformatics student who wanna work in this field HomePage:
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