About this course
The Pharmacogenomics: A Research-Oriented Approach to Genomic Medicine and Drug Response (6 months) at Dr. Omics Edu is a 6-month live, mentor-supervised internship in pharmacogenomics and genomic medicine under NEP 2020. Twelve modules link genomic variation to drug response, which is a growing area of precision medicine. You work on a real dataset and a defined research question, and you finish with a research project linking genetic variation to drug response that you can show to an admissions panel, a guide or an employer.
In this internship you move through foundations of pharmacogenomics and precision medicine, genetic variants and drug metabolism (pharmacokinetics) and using PharmGKB, ClinVar, and dbSNP resources, then apply them in your own project. The programme runs live online with 12 modules, so you can ask your mentor questions as you work. It is open to learners at every level, and mentors adjust the pace to your background.
What you will learn
Foundations of pharmacogenomics and precision medicine
Genetic variants and drug metabolism (pharmacokinetics)
Using PharmGKB, ClinVar and dbSNP resources
Variant annotation and clinical interpretation
Genotype to phenotype and drug-response analysis
A research project on genomic medicine and drug response
For careers: Pharmacogenomics analyst, clinical genomics associate, precision medicine researcher, and a strong profile for pharma and genomic medicine roles.
Skills you will gain
PharmGKB
ClinVar
dbSNP
Ensembl VEP
R
Certification
Available
Issued by EIMT
Course curriculum
12 modules
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Module 1: PI – Introduction to Pharmacogenomics
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Module 2: PB – Fundamentals of Bioinformatics
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Module 3: PL – Databases & Basics of Linux Operating System
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Module 4: PP – Python for Pharmacogenomic Data Analysis
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Module 5: PR – R for Data Analysis in Pharmacogenomics
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Module 6: PGWAS – Introduction to Genome-Wide Association Studies (GWAS)
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Module 7: PDD – Pharmacogenomic Applications in Drug Discovery
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Module 8: PCT – Pharmacogenomics in Cancer Treatment
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Module 9: PVC – Variant Calling Analysis using GATK
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Module 10: PCI – Clinical Implementation of Pharmacogenomics & Regulatory Guidelines
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Final Project: PRP – Research Project in Computational Pharmacogenomics
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1) Overview of genomics and pharmacogenomics
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2) How the genome conveys information to the rest of the body
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3) Basic concepts about genetic diseases and mutations
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4) Basic principles of genetics
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5) Introduction to Genomes, Variation and Population Genetics,Introduction to 1000Genome project and ENCODE
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6) Introduction to NCBI database
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7) Introduction to DbSNP
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8) Clinvar Database
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9) Association studies in Pharmacogenomics, Linking NGS/Microarray/other technologies to bedside, Analyzing gene mutations
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10) Role of Pharmacogenomics in Drug development
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Introduction to Bioinformatics
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Introduction to Genomics data resources : Gene,protein database
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Ensembl genome database
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UCSC database
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Genome Browser
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Alignment tools- A)BLAST(online)
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BLAST(standalone) Introduction
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Multiple sequence Alignment(clustalW)
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Multiple sequence Alignment(MEGA)
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Primer Designing using PRIMER3 tool and validation using blast
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PubMed Database
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KEGG database
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UniProt Database
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Pharmacogenomic databases (e.g., PharmGKB, DrugBank)
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Bioinformatics Tools for Drug Response Prediction(PolyPhen-2, SIFT)
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Introduction to Python
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Data Types
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String Handling
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Data Structure
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Control Structure
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Function
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File Handling
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Data Manipulation
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Data Visualization
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Biopython
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Introduction and Installation of R
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Data Types in R
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Data Structure
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File Handling
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Control Structure
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Function
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Package Management
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Data Manipulation
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Data Visualization
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Statistical Analysis
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Sequencing Technologies its applications,Introduction to NGS and DNAseq,
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Basic Terminologies in NGS
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Understanding of SRA database
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Tools installation in Linux for Variation Calling
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Quality control (FastQC)
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Trimming of Reads (Trimmomatic)
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Indexing of Genome (BWA) and Alignment of Reads (BWA)
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Variation calling using GATK
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Variant Effect Prediction(VEP)
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Variation Visualization (IGV)
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Introduction to RNAseq and it’s basic terminologies
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Tools installation in Linux for Gene Expression analysis
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Quality control and Trimming of reads
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Indexing of Genome and Alignment of Reads
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Normalization of Data (Cufflinks)
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Merging of Data and Differential expression of genes
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Understanding of DEG results
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Annotation of DEG
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Functional and Pathway Enrichment Analysis
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Network Analysis
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Introduction to Drug Development
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Compound Databases in Drug Discovery
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Principles of Pharmacokinetics
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Pharmacodynamics and Drug Action
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Drug Metabolism and Pharmacokinetic Modeling
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Pharmacovigilance and Drug Safety
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Pharmacogenomics and Drug Target Selection
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Pharmacogenomics in Clinical Trials
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Pharmacogenomics for Predicting Drug Efficacy and Safety
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Application of NOMAD in Pharmacogenomics
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Genetic Basis of Cancer
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Targeted Therapies and Biomarkers
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Genomic Profiling Techniques
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The Cancer Genome Atlas (TCGA)
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Functional Genomics in Cancer Research (CRISPR/Cas9 Technology)
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Introduction to GWAS
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Impact of genetic diversity on individual drug responses.
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Methods and Technologies in GWAS
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Clinical Implications
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GWAS analysis using real data
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Installation of HAIL package
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Plots before quality check
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Quality control based on Sample level
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genotype quality and Variants level
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GWAS analysis and Annotations
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Foundations of Clinical Pharmacogenomics,Stategies,Interpretation of Data
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Different Genotyping Technologies
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Overview of regulatory bodies governing pharmacogenomics, FDA (U.S. Food and Drug Administration), EMA (European Medicines Agency)
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CPIC (Clinical Pharmacogenetics Implementation Consortium) for drug-gene pairs and dosing recommendations.
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Challenges and opportunities in Clinical Implementation Introduction to Regulatory Guidelines
What you need to start
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A foundational background in Biology, Genetics, or Pharmacy.
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Basic understanding of Molecular Biology concepts.
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Familiarity with statistics (Introduction to Bio-statistics is a plus).
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No prior coding experience is required, though interest in Python for Data Science is encouraged.
Who this course is for
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B.Pharm, M.Pharm, Pharm.D, M.Sc and PhD students in pharmacy, pharmacology, genetics, biotechnology or life sciences, and clinical researchers.
