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Course
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Genome Wide Association Studies (GWAS)
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Duration
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Online - 21 Days Training [ 2 Hours Daily [ Monday To Friday ] ]
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Slots
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Our working Time is 9:00 AM to 6:00 PM Indian Time
Available slots - 9:00 AM to 11:00 AM / 11:00 AM to 1:00 PM / 2:00 PM to 4:00 PM
/ 4:00 PM to 6:00 PM
For training slots after 6 PM or before 9 am as well as
weekends training kindly mention during registration accordingly it will be
scheduled.
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Mode
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👉 For online training candidate have to install ZOOM (with remote control on
candidate system which makes 100% interactive)
👉 Run time video recording candidate can make as well as pdf manual will be
provided for future reference.
👉 All our training is 100% practical and 100% industrial and 100% interactive
which provides same
as offline learning.
👉 For doubt clear there will be extra support will be provided based on the
requirement.
👉 Certificate will be provided
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| Training Fees |
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Support
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Candidate can also discuss and possible implementation with their own genomic
data.
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Module - 1
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Advanced Bioinformatics (In-depth Industry exposure & Hands-on 100%
Practical Exposure with realtime examples)
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Topics
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📘 Introduction to Bioinformatics
- Overview of bioinformatics and its applications
- Key concepts in computational biology
- Role of bioinformatics in genomics, transcriptomics, and proteomics
📘 Understanding NGS and Genomics Bioinformatics
- Basics of Next-Generation Sequencing (NGS)
- Types of NGS data (RNA-seq, WGS, WES)
- Overview of NGS data formats: FASTQ, BAM, VCF
- Introduction to pipelines and tools for NGS data analysis
📘 Databases & Data Retrieval (NCBI and UCSC)
- Learning how to retrieve biologically correct data
- Performing complete batch retrieval (e.g., whole exome)
- NCBI: understanding gene-level data retrieval
- UCSC: handling large-scale data retrieval
- UCSC Genome Browser and Table Browser usage
- Batch Coordinate Retrieval and Genomic Data downloads
- GFF/GTF gene annotation formats and how to retrieve them
- Using BLAT for sequence-based search and alignment
📘 Gene Prediction and Functional Annotation
- Gene prediction approaches and tools
- Functional annotation using Gene Ontology (GO)
- Pathway analysis using KEGG, Reactome
- Interpreting gene sets and biological relevance
📘 Standalone/Offline BLAST for Large-Scale Genomic Data
- Installing and setting up standalone BLAST
- Running local BLAST for batch sequence alignment
- Applications in genome-wide homology searches
- Custom BLAST databases and performance optimization
📘 PCR Primer Designing and Specificity Check
- Designing accurate primers for PCR amplification
- Tools: Primer3, NCBI Primer-BLAST
- Checking primer specificity using genome-wide BLAST
- Avoiding non-target amplification through design best practices
📘 Understanding Python Programming
- Introduction to Python for bioinformatics
- Scripting basics: variables, loops, functions
- Libraries like Biopython, pandas for biological data handling
- Automating genomic workflows with Python scripts
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Module - 2
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GWAS
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Topics
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📘 Introduction to GWAS & NGS-DNASeq Variation Detection
- What is a GWAS and its purpose in identifying genetic risk factors?
- Overview of genetic variation, including SNPs and haplotypes
- Effects of genetic variation on traits and disease
- Statistical concepts: regression coefficients, p-values, statistical power
- Applications of GWAS in disease studies and trait discovery
- Introduction to NGS-based variation detection algorithms
📘 Linux Basics and Tool Installation
- Basic Linux commands for bioinformatics
- Installing tools using conda, apt, or source
- Setup and Install GWAS tools
📘 Data Handling and Quality Control
- Understanding GWAS data types (e.g., VCF files)
- Data retrieval from public repositories (e.g., NCBI SRA)
- Read quality assessment using FastQC
- Adapter and quality trimming using Cutadapt
- Alignment of reads using BWA or Bowtie
- SAM/BAM file manipulation using Samtools
- Variant calling using GATK
- Merging VCFs from multiple samples
- Visualization of variants using IGV (Integrative Genomics Viewer)
- Quality control for samples, variants, and genotypes
📘 Association Analysis and Statistical Methods (PLINK / Hail)
- Detecting associations between genetic variants and phenotypes
- Adjusting for population structure and confounding variables
- Understanding and calculating Linkage Disequilibrium (LD)
- Imputation of missing genotypes in GWAS data
- Performing GWAS meta-analysis
- Analysis of rare variants
- Introduction to Mendelian Randomization for causal inference
📘 Interpretation and Visualization of Results
- Creating Manhattan and QQ plots to visualize GWAS results
- Interpreting association signals and identifying causal variants
- Functional annotation of variants (e.g., using VEP or Annovar)
- Building and analyzing polygenic risk scores
- Translating findings to biological function and clinical relevance
- Understanding and interpreting output from GWAS and meta-analysis
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Preparation
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- GWAS : https://en.wikipedia.org/wiki/Genome-wide_association_study
- NCBI : https://ncbiinsights.ncbi.nlm.nih.gov/tag/gwas/
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Instructor
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Industry Experienced
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Target Audiance
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This course is designed for graduate students, postdoctoral researchers, and
professionals working in the fields of conservation biology, evolutionary
genomics, and population genetics or any life sciences who are interested in
applying genomic tools to real-world conservation challenges.
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Contact
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Please write us at
info@arraygen.com or call
or whatsapp us on mobile +91-9673625446 if you need any clarification or for any custom training based on candidate reference paper
or candidate own content/tools.
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