About this internship
This Advanced Genomics Internship is an intensive, project-based program designed to bridge the gap between biological theory and Computational Data Science. In an era where Precision Medicine is powered by data, this course provides hands-on exposure to high-throughput Next-Generation Sequencing (NGS) pipelines and Artificial Intelligence (AI) applications. Participants will work on real-world datasets, leveraging Machine Learning algorithms to decode complex genetic architectures and identify clinical biomarkers. Our curriculum emphasizes Bioinformatics workflow automation using Python and R, ensuring you master the tools used by global biotech leaders. By integrating Deep Learning for protein structure prediction and variant calling, this internship prepares you for the high-demand Genomics Data Science market. You will move beyond basic sequence analysis into Multi-Omics data integration, focusing on scalability and reproducible research. The program concludes with a capstone project that showcases your ability to transform raw genomic data into actionable biological insights.
What you will achieve
Genomic Pipeline Development: Build automated workflows for Whole Genome Sequencing (WGS) and RNA-Seq.
AI in Biomedicine: Apply Supervised Learning for gene-disease association mapping and drug response prediction.
Structural Bioinformatics: Use tools like AlphaFold for AI-powered protein folding and 3D structure analysis.
Cloud Computing: Execute large-scale genomic analyses using AWS and Google Cloud Platform (GCP).
Clinical Variant Interpretation: Master the art of identifying and annotating pathogenic mutations using GATK and Ensemble.
The project work
Hands-on implementation of everything covered in the sessions
Industry-standard tools and workflows
Project documentation and a final presentation
Code review and optimisation sessions with your mentor
Skills you will gain
NGS
Python
Bioinformatics
AI
MachineLearning
R-Programming
Bioconductor
Linux
CRISPR-Design
Data Mining
Certification
Available
Issued by Dr. Omics
Internship curriculum
10 modules
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Topic 1 M1T1 = Introduction to Bioinformatics
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Topic 2 M1T2 = NCBI Database Overview
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Topic 3 M1T3 = Genbank Database Practical Exercises
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Topic 4 M1T4 = UCSC Genome Browser Overview
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Topic 5 M1T5 = UCSC Genome Browser Hands-on Exercises
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Topic 6 M1T6 = Pubmed Database Introduction
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Topic 7 M1T7 = Clinvar Database Overview
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Topic 8 M1T8 = KEGG Database Overview and Exercises
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Topic 9 M1T9 = Protein Databases (UniProt)
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Topic 10 M1T10 = Protein Databases (PDB)
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Topic 11 M1T11 = Online BLAST Introduction and Exercises
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Topic 12 M1T12 = Standalone BLAST Setup and Exercises
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Topic 13 M1T13 = Standalone BLAST Advanced Exercises
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Topic 14 M1T14 = Multiple Sequence Alignment with ClustalW
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Topic 15 M1T15 = Multiple Sequence Alignment with MEGA
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1) = Overview and Installation of Linux
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2) = Basic Linux Commands
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3) = Advanced Linux Commands
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4) = Package Management using Repository
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5) = Package Management using Source Code
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1) = Introduction to Python
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2)= Data Types
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3= String Handling
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4)= Data Structure
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5)=Control Structure
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6) = Function
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7)= File Handling
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8)= Data Manipulation
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9)= Data Visualization
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10)= Biopython
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1) = Introduction and Installation of R
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2)= Data Types in R
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3)= Data Structure
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4)= File Handling
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5)=Control Structure
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6) = Function
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7)= Package Management
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8)= Data Manipulation
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9)= Data Visualization
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10)= Statistical Analysis
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1)= Introduction to NGS and DNAseq
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2)= Basic Terminologies in NGS
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3)= Understanding of SRA database
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4)= Tools installation in Linux for Variation Calling
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5)= Quality control
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6)= Trimming of Reads
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7)= Indexing of Genome and Alignment of Reads
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8)= Variation calling using GATK
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9)= Variant Effect Prediction(VEP)
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10)= Variation Visualization (IGV)
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1)=Introduction to RNAseq and it’s basic terminologies
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2)=Tools installation in Linux for Gene Expression analysis
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3)=Quality control and Trimming of reads
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4)=Indexing of Genome and Alignment of Reads
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5)=Normalization of Data (Cufflinks)
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6)=Merging of Data and Differential expression of genes
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7)=Understanding of DEG results
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8)=Annotation of DEG
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9)=Functional and Pathway Enrichment Analysis
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10)=Network Analysis
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1)= Tools installation for De-novo RNAseq
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2)= Tools installation for De-novo RNAseq
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3)= Data downlading and Quality control
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4)= Assembly Creation
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5)= Abundance count estimation
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6) = Generation of count matrix and DEG
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7)= BLAST
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8)= Understanding the DEG results
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9)= Annotation of DEGs
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10)= Encrichment Analysis
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1)= Introduction to metagenomics
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2)= Tools installation for metagenomics
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3)= Data Downloading
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4)= Quality control & Trimming
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5)= Data importing in Qimme2
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6)= Data quality check using DADA2
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7)= Phylogentic Analysis
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8)= Taxonomy Analysis
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9)= Krona Plot
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10)= Phylogenetic tree construction
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1)= Introduction to MIcroarray
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2)= Introduction to Microarray
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3)= Data Downloading
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4)= Microarray Pipeline upto Normalization
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5)= Microarray Pipeline till DEG
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6)= Annotation of DEG
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7)= Encrichment Analysis
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8)= Network Analysis
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9)= Volcano Plot
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10)= Heatmap
What you need to start
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Basic understanding of Molecular Biology and Genetics.
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Familiarity with any programming language (Python or R is a plus but not mandatory).
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A laptop with a minimum of 8GB RAM for running local bioinformatics simulations.
Who this internship is for
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B.Tech/M.Tech/M.Sc Students in Biotechnology, Bioinformatics, or Life Sciences.
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Data Scientists looking to transition into the Healthcare AI and Biotech sector.
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Research Scholars aiming to enhance their thesis with advanced Computational Genomics.
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Medical Professionals interested in the technical side of Precision Oncology.
