Single Nucleotide Polymorphism (SNP) Detection and Analysis using NGS

Unlocking Genetic Insights: Precision SNP Detection with Next-Generation Sequencing

MODE
Mode(Online) TYPE
Self Paced LEVEL
Moderate Program Length
6 hours

Skills you will gain:

Proficiency in NGS Technologies Bioinformatics Laboratory techniques and safety skills SNP Analysis Expertise Geospatial Analysis

About Program:

The Single Nucleotide Polymorphism (SNP) Detection and Analysis Program using Next-Generation Sequencing (NGS) is an intensive program designed to equip participants with comprehensive knowledge and practical skills in identifying, analyzing, and interpreting SNP data through NGS technology. The program typically covers fundamental concepts of SNP variation, NGS data generation, preprocessing, and quality control steps, as well as diverse SNP calling methodologies. Participants engage in hands-on sessions, leveraging bioinformatics tools and software for SNP analysis, validation methods, and data interpretation. Additionally, the program explores the practical applications of SNP analysis in diverse fields like medical genetics, evolutionary biology, and personalized medicine while discussing emerging trends and challenges in SNP detection and analysis. Ultimately, it aims to empower researchers, students, and professionals with the necessary expertise to effectively utilize NGS data for SNP identification and interpretation in various genetic studies and applications.

Aim: The aim of this program is to equip participants with a deep understanding of single nucleotide polymorphism (SNP) analysis through the application of Next-Generation Sequencing (NGS) techniques. By the end of the program, participants will possess the knowledge and practical skills necessary to perform accurate and meaningful SNP analysis.

Program Objectives:

1. Understanding SNP Basics: To provide participants with a strong foundational understanding of single nucleotide polymorphisms (SNPs), their significance in genetics, and their role in genetic variation.
2. NGS Principles: To familiarize participants with the principles and methodologies of Next-Generation Sequencing (NGS), including library preparation, sequencing, and data generation.
3. SNP Detection: To instruct participants on the various techniques and algorithms used for SNP detection within NGS data, emphasizing accuracy and sensitivity.
4. Quality Control and Preprocessing: To teach participants how to perform quality control and preprocessing of NGS data to ensure reliable and high-quality SNP analysis.
5. Bioinformatics Tools: To introduce participants to bioinformatics tools and software commonly used for SNP analysis and interpretation.
6. Genomic Annotation: To help participants understand the annotation and functional analysis of SNPs, particularly how to identify potentially impactful genetic variations.
7. Population Genetics: To explore the application of SNP analysis in the context of population genetics and the study of genetic diversity.
8. Case Studies: To provide real-world examples and case studies of SNP analysis and interpretation in genetic research and disease studies.
9. Hands-On Analysis: To offer hands-on exercises or demonstrations, allowing participants to practice SNP analysis using NGS data.
10. Best Practices: To impart best practices in SNP analysis and quality assurance, ensuring the reliability and validity of results.

What you will learn?

Day 1

Introduction to NGS Technology
Overview of NGS data formats
Accessing NGS data using SRA tool kit

Day 2

Checking sequence quality using FastQC
Cleaning the sequences using cutadapt
Aligning sequences using BWA
Sorting the BAM alignment files

Day 3

Mark PCR duplicates in BAM alignment files using Picard
Use FreeBayes and Delly on the BAM files to identify variants (SNPs and indels)
Visually inspect read mapping and variant calls with the Integrated Genomics Viewer (IGV)

Note: Linux OS is required for running the tools

Fee Plan

INR 1999 /- OR USD 50

ACCESS THIS PROGRAM

Intended For :

Educational Background: Candidates typically need a bachelor’s degree in a relevant field such as genetics, genomics, molecular biology, bioinformatics, or a related discipline. Some programs may require a specific GPA or academic performance threshold.
Prerequisite Courses: Applicants may be required to have completed coursework in genetics, molecular biology, bioinformatics, and/or related subjects. Proficiency in relevant laboratory techniques and computational methods may also be necessary.
Experience: While not always mandatory, applicants with prior research experience or coursework in SNP detection, next-generation sequencing (NGS), or related areas may receive preference. This experience could be gained through internships, research projects, or work in a relevant industry.
Computer Skills: Proficiency in computer programming languages commonly used in bioinformatics, such as Python, R, or Perl, may be required or beneficial for data analysis and interpretation.
Letters of Recommendation: Applicants may need to submit letters of recommendation from professors, supervisors, or professionals who can attest to their academic abilities, research potential, and suitability for the program.
Statement of Purpose/Personal Statement: Many programs require applicants to submit a statement of purpose or personal statement outlining their academic background, research interests, career goals, and reasons for applying to the program. This allows applicants to demonstrate their motivation and alignment with the program’s objectives.
Standardized Test Scores: Depending on the institution and program requirements, applicants may need to submit standardized test scores such as the GRE (Graduate Record Examination) or relevant subject tests.
English Proficiency: For international applicants, proof of English proficiency may be required through standardized tests such as the TOEFL (Test of English as a Foreign Language) or IELTS (International English Language Testing System).
Interview: In some cases, applicants may be required to participate in an interview as part of the admissions process. This interview may assess the applicant’s academic background, research experience, technical skills, and fit for the program.

Career Supporting Skills

Bioinformatics Tools Laboratory Techniques and Safety Skills SNP Analysis Expertise Geospatial Analysis Proficiency in NGS Technologies

Program Outcomes

1. Demonstrate Proficiency: Understand the fundamental concepts of SNP analysis using NGS and demonstrate proficiency in applying these concepts to real data.
2. Perform SNP Detection: Confidently perform SNP detection and annotation using NGS data, including the ability to differentiate between true variants and sequencing errors.
3. Interpret Genetic Variants: Interpret the biological and clinical significance of detected SNPs, including their potential impact on genes and gene products.
4. Utilize Bioinformatics Tools: Use bioinformatics tools and software for SNP analysis and related bioinformatics tasks.
5. Apply Population Genetics: Apply SNP analysis techniques in the context of population genetics, including assessing genetic diversity and conducting association studies.
6. Quality Assurance: Implement best practices for quality control and quality assurance in SNP analysis, ensuring the reliability and reproducibility of results.
7. Research Readiness: Be prepared to apply the knowledge and skills gained in the program to their own research projects or work in genetics, genomics, or related fields.
8. Critical Analysis: Critically analyze and interpret SNP data, understanding its implications for genetic research, personalized medicine, and disease studies.