Are you ready to transition from basic tools to Advanced Computational Biology and Data Science? Join our specialized 6-Month Internship Program (Level-3) designed to take you from core algorithms and Linux automation to cutting-edge Single-Cell & Bulk RNA-seq analysis using R.

📚 Course Curriculum & Modules: This internship covers 24+12 (project) classes divided into 8 intensive modules, culminating in a dedicated 3-month research project focused on High-Throughput Sequencing Data Analysis.

Module 1: Core Bioinformatics Algorithms
Understand the mathematics behind the tools. This module breaks down the logic of Sequence Alignment (Needleman–Wunsch, Smith–Waterman) and Scoring Matrices (PAM/BLOSUM). You will also explore the complex algorithms behind Genome Assembly (De Bruijn Graphs) and Structural Bioinformatics, including Protein Docking fundamentals.

Module 2: Linux Command Line Mastery
Transition from a casual user to a computational pro. You will set up a professional Linux Environment (Ubuntu/WSL) and learn to manage software using Conda/Bioconda. The module covers essential terminal commands, file permissions, and writing Shell Scripts (.sh) to automate bioinformatics pipelines.

Module 3: R Programming for Bioinformatics
Unlock the power of statistical programming. Starting from R fundamentals, you will master data manipulation using dplyr and tidyr. You will create high-quality, publication-ready figures (Heatmaps, Volcano plots) using ggplot2. Additionally, you will learn to manage biological data with Bioconductor packages like edgeR and DESeq2.

Module 4: RNA-seq Data Analysis Fundamentals
Build a strong foundation in Transcriptomics. This module covers the Central Dogma, the evolution of RNA-seq technologies, and the structure of essential file formats (FASTQ, GTF, GFF). You will also learn how to retrieve datasets from public repositories like NCBI GEO and SRA Toolkit.

Module 5: Single-Cell RNA-seq Data Analysis
Step into the future of genomics. This extensive module covers the complete Single-Cell (scRNA-seq) workflow. You will learn to process 10x Genomics data, perform Quality Control (QC), and master Dimensionality Reduction (PCA, UMAP, t-SNE). The course guides you through Clustering, Marker Gene Identification, and Cell Type Annotation, along with advanced Trajectory Analysis (Pseudotime).

Module 6: Bulk RNA-seq Data Analysis
Master the industry-standard pipeline for differential expression. You will process raw sequencing data using FastQC and Trimmomatic, followed by alignment using HISAT2/STAR. The module covers transcript quantification (Salmon/Kallisto) and rigorous Differential Gene Expression (DGE) analysis using DESeq2.

Module 7: Systems Biology & Functional Interpretation
Go beyond gene lists to biological insights. You will learn to interpret your Differentially Expressed Genes (DEGs) using Gene Ontology (GO), KEGG Pathway analysis, and Reactome. The module also introduces GSEA (Gene Set Enrichment Analysis) to understand the biological significance of your data.

Module 8: Research Project & Career Guidelines
Classes 25 - 36 (3 Months) Capstone Research Project Apply your advanced skills in a real-world scenario. You will select a specialized topic in Bulk or Single-Cell RNA-seq. Under expert mentorship, you will execute the analysis, generate publication-quality figures, and receive step-by-step guidance on Scientific Paper Writing and journal submission.