Practical NGS Data Analysis for Variant Calling using Linux

Hands-on NGS Data Analysis Learn Variant Calling using Linux pipeline and perform variant calling easily on command line
What you'll learn:
Gain a solid understanding of Next-Generation Sequencing (NGS) technology and its applications in genomics research.
Learn to set up a bioinformatics environment using Anaconda and install essential tools for NGS data analysis such as FastQC, MultiQC, Samtools, BWA, freebayes,
Acquire skills to find, download, and prepare paired-end FASTQ reads and reference genomes for analysis.
Master the use of FastQC and MultiQC for assessing the quality of raw NGS reads and generating comprehensive quality reports.
Learn to align NGS reads to a reference genome using BWA, and process SAM/BAM files with Samtools for downstream analysis.
Develop proficiency in filtering and processing alignment records, including sorting, indexing, and removing low-quality alignments.
Understand the significance of duplicate reads and learn to mark them using Samtools to ensure accurate variant calling.
Gain hands-on experience in calling genetic variants (SNPs, indels) using Freebayes and generate VCF files for further analysis.
Learn to perform basic statistical analysis on variant calls using bcftools and apply advanced filtering techniques to identify high-confidence variants.
Acquire the skills to conduct a complete NGS data analysis workflow from raw data to variant identification and interpretation.
Requirements:
Understanding of basic biological concepts, particularly DNA, genes, and genetic variation.
Basic experience with Linux/Unix command line interface. Knowledge of basic commands will be helpful.
A computer (Windows, macOS, or Linux) with a stable internet connection for downloading data and tools.
Installation of Anaconda, a distribution of Python and R for scientific computing (instructions will be provided if needed).
Any text editor such as Notepad++, Sublime Text, or VS Code for writing and editing scripts.
This course is designed to be accessible for beginners. All necessary concepts and skills will be taught from the ground up, and detailed instructions will be provided for each step of the process. No prior experience in NGS data analysis or advanced bioinformatics is required.
Description:
Unlock the Power of NGS Data Analysis for Variant Calling Using LinuxAre you ready to dive into the world of Next-Generation Sequencing (NGS) and harness its potential for groundbreaking genomic research? Our comprehensive course, "Hands-on NGS Data Analysis: Quality Control, Read Mapping, and Variant Calling," is designed to equip you with the essential skills to analyze NGS data from start to finish using Linux.In this course, you will learn how to:Set Up Your Bioinformatics Environment: Install and configure essential tools such as FastQC, MultiQC, Samtools, BWA, Freebayes, bcftools, and tabix using Anaconda, a powerful package manager for scientific computing.Download and Prepare NGS dаta: Acquire and prepare paired-end FASTQ reads and reference genomes, essential for high-quality genomic analysis.Perform Quality Control: Assess the quality of your sequencing data with FastQC and summarize results using MultiQC to ensure reliable downstream analysis.Map Reads to a Reference Genome: Align your NGS reads to a reference genome using BWA and process alignment files with Samtools for accurate variant calling.Identify and Mark Duplicates: Learn the importance of marking duplicate reads and use Samtools to enhance the accuracy of your variant calls.Call and Filter Variants: Use Freebayes for variant calling and bcftools for advanced filtering, enabling you to identify high-confidence genetic variants.Who Is This Course For?This course is perfect for:Biology and Bioinformatics Students looking to gain practical skills in NGS data analysis.Researchers and Scientists aiming to incorporate NGS data analysis into their research.Healthcare Professionals involved in genetic research or diagnostics.Data Analysts and Bioinformaticians expanding their skill set to include variant calling.IT Professionals in Life Sciences needing to understand NGS workflows and tools.Enthusiastic Beginners with a basic understanding of biology and a keen interest in bioinformatics.Why Enroll?Hands-On Experience: Practical exercises with real-world data to solidify your learning.Comprehensive Coverage: From setting up your environment to variant calling and filtering, we cover every essential step.Career Advancement: Gain valuable skills for academic research, healthcare, biotechnology, and pharmaceutical industries.Beginner-Friendly: No prior experience in NGS data analysis required. Step-by-step instructions make learning accessible for everyone.Enroll now and take the first step towards mastering NGS data analysis for variant calling. Equip yourself with the skills to make significant contributions to genomics research and open new career opportunities in the rapidly evolving field of bioinformatics.
Who this course is for:
Undergraduate and graduate students studying biology, genetics, bioinformatics, or related fields who want to enhance their practical skills in NGS data analysis.
Researchers and scientists working in genomics, molecular biology, or related disciplines who need to analyze NGS data as part of their research projects.
Professionals in the healthcare and biomedical fields who are involved in genetic research or diagnostic activities and want to learn about variant calling from NGS data.
Data analysts and bioinformaticians looking to expand their skill set to include NGS data analysis and variant calling, using industry-standard tools and techniques.
IT professionals working in the life sciences sector who need to understand the bioinformatics workflows and tools used in NGS data analysis.
Individuals who wish to teach or train others in bioinformatics and want a structured approach to NGS data analysis for variant calling.
Enthusiastic beginners with a keen interest in bioinformatics and genomics who have basic biology knowledge and want to start learning about NGS data analysis.
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