About this internship
The Advanced Genomics 6 months customized Internship at Dr. Omics Edu is a 6-month live, mentor-supervised internship in next generation sequencing (NGS) and genomics under NEP 2020. It follows a customised plan so the six months are spent on a genomics question that matches your degree and goals. You work on a real dataset and a defined research question, and you finish with a genomics or transcriptomics project from raw reads to interpreted results that you can show to an admissions panel, a guide or an employer.
In this internship you move through genomics study design and dataset selection, quality control and knowladge about pre-processing of sequencing data and alignment, assembly or quantification as per project, then apply them in your own project. The programme runs live online with 1 module, 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 achieve
Genomics study design and dataset selection
Quality control and cleaning of sequencing data
Alignment, assembly or quantification as per project
Downstream statistical and functional analysis
Data visualisation and biological interpretation
Research report or manuscript preparation
For careers: Bioinformatics analyst, genomics data analyst, NGS data scientist, research associate, and a stronger profile for M.Sc, MS or PhD admissions in genomics.
Skills you will gain
Linux
FastQC
Trimmomatic
HISAT2/STAR
BWA
SAMtools
GATK
DESeq2
R
python
Certification
Available
Issued by Dr. Omics Edu
Internship curriculum
1 module
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Linux for Beginners: A Practical Guide
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Introduction to Linux: Overview and Installation
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Essential Linux Commands for Beginners
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Advanced Linux Command-Line Techniques
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Managing Packages in Linux
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Bash Scripting, AWK and SED
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Variant Calling Pipeline: A DNA-Seq Approach
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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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Installing Tools in Linux for Variant Calling
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Quality Control of Reads
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Trimming and Filtering Reads
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Genome Indexing and Read Alignment
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Variation calling using GATK
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Predicting Variant Effects
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Variation Visualization (IGV)
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Reference-Guided RNA-Seq: A Complete Analysis Workflow
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Introduction to RNA-seq and Key Terminologies
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Setting Up Tools in Linux for Gene Expression Analysis
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Quality Control and Read Trimming
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Genome Indexing and Read Alignment
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Data Normalization Using Cufflinks
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Merging Data and Identifying Differentially Expressed Genes
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Interpretation of DEG Results
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Annotation of Differentially Expressed Genes
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Functional and Pathway Enrichment Analysis
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Network Analysis of Gene Interactions
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Targeted Microbiome Profiling: A Metagenomic Approach
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Introduction to Metagenomics Analysis
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Setting Up Tools for Metagenomics
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Data Downloading and Preprocessing
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Quality Control and Read Trimming
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Importing Data into QIIME2
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Quality Assessment Using DADA2
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Phylogenetic Diversity Analysis of Microbial Communities
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Taxonomic Classification of Sequences
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Visualization with Krona Plot
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Phylogenetic tree construction using MEGA
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CHIP-Seq Analysis
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Introduction to ChIP-Seq and Experimental Design
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Introduction to NGS Terminologies
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Public Data Access and Raw Data Download (GEO/SRA)
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Quality Control and Read Trimming
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Read Mapping to the Reference Genome
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Peak Calling with MACS2
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Peak Annotation using ChIPseeker
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Motif Analysis of ChIP-Seq Peaks
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Visualization of ChIP-Seq Data
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Differential Binding and Functional Enrichment
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ATAC-Seq Analysis
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Introduction to ChIP-Seq and Experimental Design
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Introduction to NGS Terminologies
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Public Data Access and Raw Data Download (GEO/SRA)
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Quality Control and Read Trimming
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Read Mapping to the Reference Genome
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Post-alignment Processing: Sorting, Indexing, and Filtering
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Peak Calling with MACS2
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Peak Annotation using ChIPseeker
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Visualization: BigWig Tracks and Heatmaps
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Differential Accessibility and Functional Enrichment
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Introduction to Machine Learning in Bioinformatics
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Fundamentals of Machine Learning for Genomic Data
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Linear Models and Nearest Neighbors for Pattern Recognition
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Probabilistic Machine Learning Concepts and Applications
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Support Vector Machines SVM Theory and Implementation
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Naïve Bayes Classifier Fundamentals and Bioinformatics Use Cases
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Decision Trees and Random Forest Interpretable ML Models
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Logistic Regression for Predictive Analysis in Bioinformatics
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Clustering Algorithms for Unsupervised Learning
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Validation Techniques for Machine Learning Models
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Machine Learning for Biomedical Image Analysis
What you need to start
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Basic understanding of molecular biology, genetics, and life sciences.
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No prior programming experience required; beginner-friendly Linux training is included from scratch.
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A computer with stable internet access for live interactive sessions and cloud/local tool setup.
Who this internship is for
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B.Sc, M.Sc, B.Tech, M.Tech and PhD students in biotechnology, genetics, microbiology, biochemistry, life sciences or bioinformatics, plus early-career researchers.
