NGS DATA ANALYSIS & FUNCTIONAL GENOMICS

Introduction

We will explore Next-Generation Sequencing (NGS) data analysis, starting from raw data processing to final interpretation. We will begin by learning how to handle and analyze raw NGS data, understanding the main data formats used to store genomic information. We will cover Whole Genome (WG) data handling, focusing on the key steps of alignment and variant calling. By the end of this course, you will have a solid understanding of the complete workflow involved in NGS data analysis, including the tools and techniques essential for accurate and efficient genomic data processing.

Why Do We Need NGS Data?

1. Genome Assembly Genome assembly involves reconstructing the complete sequence of an organism's DNA from small, overlapping fragments generated by sequencing. This is essential for studying organisms without a reference genome or for improving existing assemblies. NGS provides the high-throughput data needed to piece together these fragments accurately, enabling a comprehensive view of an organism's genome.

2. Variant Detection Variant detection identifies differences between an individual's genome and a reference genome, such as single nucleotide polymorphisms (SNPs), insertions, deletions, or structural variations. These variants are crucial for understanding genetic diversity, disease mechanisms, and traits of interest in both research and clinical settings. NGS's sensitivity and resolution make it ideal for uncovering even rare or complex variants. Learn more about genetic variation here

3. Gene Expression Analysis Gene expression analysis determines which genes are actively transcribed and at what levels under specific conditions or in different tissues. NGS, provides a precise and quantitative method for measuring gene expression. This allows researchers to identify gene regulation patterns, compare expression across samples, and study pathways involved in biological processes and diseases.

Beyond these, NGS has many more applications, such as epigenomics, metagenomics, and transcriptome profiling, making it an indispensable tool in modern biology.