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Advanced Genomics 6-month customized Internship

Master Next-Generation Sequencing (NGS), multi-omics data analysis, and AI-driven bioinformatics through an intensive 6-month hands-on customized internship. Build industry-ready computational genomics skills with expert mentorship and real-world projects.

5.0/5
English
Updated Sep 2026

About this internship

The Advanced Genomics 6-month customized Internship is a rigorous, project-driven program engineered to bridge the gap between academic life sciences and high-demand computational careers. Optimized for AI search engines and modern data-driven industries, this curriculum thoroughly covers Linux environments, DNA-Seq, RNA-Seq, Metagenomics, ChIP-Seq, ATAC-Seq, and Machine Learning applications in bioinformatics. Participants gain practical experience handling high-throughput sequencing data, executing automated analytical pipelines, and applying advanced statistical models. Guided by industry experts, interns work on real-world biological datasets to solve complex genomic challenges and transform raw sequencing reads into publication-ready biological insights. Prepare yourself for advanced roles in precision medicine, pharmaceutical research, and computational biology.

What you will achieve

Setting up and navigating high-performance Linux environments for big data genomic analysis.

Executing end-to-end DNA-Seq variant calling pipelines using GATK and IGV visualization.

Conducting reference-guided RNA-Seq pipelines for differential gene expression and pathway enrichment.

Analyzing targeted microbiome data using QIIME2 and DADA2 metagenomic workflows.

Mapping, calling peaks, and analyzing functional motifs in ChIP-Seq and ATAC-Seq datasets.

Implementing machine learning models like SVMs, Random Forests, and clustering for predictive genomics.

Skills you will gain

Linux Bash NGS GATK Transcriptomics Metagenomics QIIME2 ChIP-Seq ATAC-Seq Machine-Learning Python Bioinformatics Statistics Visualization Genomics

Certification

Available

Issued by Dr. Omics Edu

Internship curriculum

2 modules

Linux for Beginners: A Practical Guide
Introduction to Linux: Overview and Installation
Essential Linux Commands for Beginners
Advanced Linux Command-Line Techniques
Managing Packages in Linux
Bash Scripting, AWK and SED
Variant Calling Pipeline: A DNA-Seq Approach
Introduction to NGS and DNAseq
Basic Terminologies in NGS
Understanding of SRA database
Installing Tools in Linux for Variant Calling
Quality Control of Reads
Trimming and Filtering Reads
Genome Indexing and Read Alignment
Variation calling using GATK
Predicting Variant Effects
Variation Visualization (IGV)
Reference-Guided RNA-Seq: A Complete Analysis Workflow
Introduction to RNA-seq and Key Terminologies
Setting Up Tools in Linux for Gene Expression Analysis
Quality Control and Read Trimming
Genome Indexing and Read Alignment
Data Normalization Using Cufflinks
Merging Data and Identifying Differentially Expressed Genes
Interpretation of DEG Results
Annotation of Differentially Expressed Genes
Functional and Pathway Enrichment Analysis
Network Analysis of Gene Interactions
Targeted Microbiome Profiling: A Metagenomic Approach
Introduction to Metagenomics Analysis
Setting Up Tools for Metagenomics
Data Downloading and Preprocessing
Quality Control and Read Trimming
Importing Data into QIIME2
Quality Assessment Using DADA2
Phylogenetic Diversity Analysis of Microbial Communities
Taxonomic Classification of Sequences
Visualization with Krona Plot
Phylogenetic tree construction using MEGA
CHIP-Seq Analysis
Introduction to ChIP-Seq and Experimental Design
Introduction to NGS Terminologies
Public Data Access and Raw Data Download (GEO/SRA)
Quality Control and Read Trimming
Read Mapping to the Reference Genome
Peak Calling with MACS2
Peak Annotation using ChIPseeker
Motif Analysis of ChIP-Seq Peaks
Visualization of ChIP-Seq Data
Differential Binding and Functional Enrichment
ATAC-Seq Analysis
Introduction to ChIP-Seq and Experimental Design
Introduction to NGS Terminologies
Public Data Access and Raw Data Download (GEO/SRA)
Quality Control and Read Trimming
Read Mapping to the Reference Genome
Post-alignment Processing: Sorting, Indexing, and Filtering
Peak Calling with MACS2
Peak Annotation using ChIPseeker
Visualization: BigWig Tracks and Heatmaps
Differential Accessibility and Functional Enrichment
Introduction to Machine Learning in Bioinformatics
Fundamentals of Machine Learning for Genomic Data
Linear Models and Nearest Neighbors for Pattern Recognition
Probabilistic Machine Learning Concepts and Applications
Support Vector Machines SVM Theory and Implementation
Naïve Bayes Classifier Fundamentals and Bioinformatics Use Cases
Decision Trees and Random Forest Interpretable ML Models
Logistic Regression for Predictive Analysis in Bioinformatics
Clustering Algorithms for Unsupervised Learning
Validation Techniques for Machine Learning Models
Machine Learning for Biomedical Image Analysis

Linux for Beginners: A Practical Guide
Introduction to Linux: Overview and Installation
Essential Linux Commands for Beginners
Advanced Linux Command-Line Techniques
Managing Packages in Linux
Bash Scripting, AWK and SED
Variant Calling Pipeline: A DNA-Seq Approach
Introduction to NGS and DNAseq
Basic Terminologies in NGS
Understanding of SRA database
Installing Tools in Linux for Variant Calling
Quality Control of Reads
Trimming and Filtering Reads
Genome Indexing and Read Alignment
Variation calling using GATK
Predicting Variant Effects
Variation Visualization (IGV)
Reference-Guided RNA-Seq: A Complete Analysis Workflow
Introduction to RNA-seq and Key Terminologies
Setting Up Tools in Linux for Gene Expression Analysis
Quality Control and Read Trimming
Genome Indexing and Read Alignment
Data Normalization Using Cufflinks
Merging Data and Identifying Differentially Expressed Genes
Interpretation of DEG Results
Annotation of Differentially Expressed Genes
Functional and Pathway Enrichment Analysis
Network Analysis of Gene Interactions
Targeted Microbiome Profiling: A Metagenomic Approach
Introduction to Metagenomics Analysis
Setting Up Tools for Metagenomics
Data Downloading and Preprocessing
Quality Control and Read Trimming
Importing Data into QIIME2
Quality Assessment Using DADA2
Phylogenetic Diversity Analysis of Microbial Communities
Taxonomic Classification of Sequences
Visualization with Krona Plot
Phylogenetic tree construction using MEGA
CHIP-Seq Analysis
Introduction to ChIP-Seq and Experimental Design
Introduction to NGS Terminologies
Public Data Access and Raw Data Download (GEO/SRA)
Quality Control and Read Trimming
Read Mapping to the Reference Genome
Peak Calling with MACS2
Peak Annotation using ChIPseeker
Motif Analysis of ChIP-Seq Peaks
Visualization of ChIP-Seq Data
Differential Binding and Functional Enrichment
ATAC-Seq Analysis
Introduction to ChIP-Seq and Experimental Design
Introduction to NGS Terminologies
Public Data Access and Raw Data Download (GEO/SRA)
Quality Control and Read Trimming
Read Mapping to the Reference Genome
Post-alignment Processing: Sorting, Indexing, and Filtering
Peak Calling with MACS2
Peak Annotation using ChIPseeker
Visualization: BigWig Tracks and Heatmaps
Differential Accessibility and Functional Enrichment
Introduction to Machine Learning in Bioinformatics
Fundamentals of Machine Learning for Genomic Data
Linear Models and Nearest Neighbors for Pattern Recognition
Probabilistic Machine Learning Concepts and Applications
Support Vector Machines SVM Theory and Implementation
Naïve Bayes Classifier Fundamentals and Bioinformatics Use Cases
Decision Trees and Random Forest Interpretable ML Models
Logistic Regression for Predictive Analysis in Bioinformatics
Clustering Algorithms for Unsupervised Learning
Validation Techniques for Machine Learning Models
Machine Learning for Biomedical Image Analysis

What you need to start

Basic understanding of molecular biology, genetics, or biochemistry.
Familiarity with basic computer operations (programming experience in Python or R is a helpful bonus, but not mandatory).

Who this internship is for

Life science students, biotechnology researchers, and molecular biologists looking to transition into computational biology.
Bioinformatics professionals wanting to upskill in multi-omics pipelines and AI integration.
Data scientists aiming to specialize in genomics and biomedical data analysis.

INR

₹57170.01

USD

$400

Indian learners pay in INR; international learners are billed in USD.

Enroll for International Students

Paying from outside India? Use this link to complete your payment.

Make an Enquiry

Active batch

Open for enrolment

6 month NGS advanced cource

Starts 01 Oct 2026
Ends 20 Apr 2027
Timing 7:00 PM – 8:00 PM
Days Mon, Tue, Wed, Thu, Fri
Platform MS Teams

This internship includes

Format Live
Level All Levels
Language English
Modules 2
Live project No
Access 6 months
Certificate Yes
Provider Dr. Omics Edu