NGS Data Processing and Interpretation

Next-Generation Sequencing (NGS) technologies have revolutionized genomics by enabling rapid, high-throughput DNA and RNA sequencing. This internship provides participants with a comprehensive understanding of NGS data formats, quality control, alignment, variant calling, and biological interpretation. Hands-on training with real datasets will prepare learners to analyze and interpret data from whole-genome, RNA-seq, and targeted sequencing experiments.

🎯 Learning Objectives

By the end of the internship, participants will:

• Understand different types of NGS technologies and sequencing platforms

• Perform quality control and preprocessing of raw FASTQ data

• Align sequence reads to a reference genome

• Identify variants such as SNPs and indels

• Analyze and interpret RNA-Seq data for differential expression

• Use bioinformatics pipelines and tools for automated analysis

🧩 Program Structure

Introduction to NGS Technologies

• Overview of Illumina, Ion Torrent, Nanopore platforms

• Applications: Whole-genome sequencing, RNA-seq, ChIP-seq

• NGS workflow overview (wet lab to dry lab)

NGS File Formats and Data Types

• FASTQ, SAM/BAM, VCF

• Paired-end vs single-end reads

• Introduction to public repositories: ENA, SRA, GEO

Quality Control of Raw Reads

• Quality metrics: Phred scores, GC content, adapter contamination

• Tools: FastQC, MultiQC

• Practice: Run QC reports and interpret results

Read Trimming and Preprocessing

• Adapter removal and quality trimming

• Tools: Trimmomatic, Cutadapt

• Practice: Clean raw FASTQ files

Read Alignment to Reference Genome

• Indexing the genome

• Aligners: BWA, Bowtie2, HISAT2

• Converting and sorting SAM/BAM files using SAMtools

Variant Calling (DNA-seq Focus)

• Identify SNPs and Indels

• Tools: FreeBayes, GATK, bcftools

• Annotate variants using SnpEff, ANNOVAR

RNA-Seq Data Analysis (Expression Profiling)

• Gene expression quantification: featureCounts, HTSeq

• Differential expression analysis with DESeq2, EdgeR

• Volcano plots and heatmaps

Functional Enrichment Analysis

• Gene Ontology (GO) and Pathway Enrichment

• Tools: DAVID, Enrichr, g:Profiler

• Case Study: Differentially expressed genes in a cancer dataset

Automation and Pipelines

• Shell scripting basics for NGS

• Introduction to Galaxy, Nextflow, or Snakemake

• Creating a mini workflow

Project Presentation and Evaluation

• Project: DNA or RNA-seq dataset processing

• Present pipeline, visualizations, and biological interpretation

Tools & Software

• FastQC, Trimmomatic, Cutadapt – Preprocessing

• HISAT2, BWA, Bowtie2 – Alignment

• SAMtools, bcftools, GATK – Variant calling

• DESeq2, EdgeR – RNA-seq expression analysis

• IGV, Enrichr, Galaxy – Visualization & functional interpretation

📂 Assignments & Capstone Project

• Analyze a sample NGS dataset (DNA or RNA-seq)

• Document quality reports, alignment metrics, and gene expression results

• Submit plots, tables, and a final PDF report or presentation deck

📜 Certification

• Certificate of Completion and a Project Experience Letter upon submission and evaluation

👥 Target Audience

• Postgraduate students and researchers in genomics, bioinformatics, or molecular biology

• Professionals working with NGS data or planning NGS-based projects

• Scientists aiming to understand genomics in clinical or agricultural settings