Workshop – 1

Introduction

Next-Generation Sequencing (NGS) technologies have rapidly evolved and are now central to a wide array of biological and medical research. These technologies enable high-throughput sequencing of DNA and RNA, generating enormous volumes of data that hold valuable insights into genetic structure, variation, and function. However, the true power of NGS lies not just in data generation but in its correct interpretation—an area that requires specialized skills in bioinformatics.

Experimental researchers increasingly encounter the challenge of working with complex datasets but often lack the necessary informatics training to process, analyse, and interpret sequencing results. This workshop aims to bridge this skills gap by providing comprehensive training in the principles and practical approaches to NGS data analysis. Participants will learn to manage, process, and analyze sequencing data in a structured and reproducible manner using command-line environments and widely accepted methodologies.

Objectives

• Introduce the foundational principles of next-generation sequencing and its application in biological research.
• Develop hands-on skills in navigating and utilizing command-line interfaces for data handling and analysis.
• Build competency in quality assessment, read alignment, variant detection, RNA sequencing analysis, and genome assembly.
• Guide participants through workflows in genomic data analysis.

Target Audience

The workshop is designed for graduate students, early-career researchers, laboratory professionals, and educators from life sciences, particularly those working in genomics, biotechnology, microbiology, molecular biology, and related fields. Prior knowledge of programming is not required, although basic familiarity with biology and data handling is recommended.

Workshop Structure & Thematic Modules

1.     Introduction to NGS and Bioinformatics Concepts

Participants will begin with an overview of current sequencing technologies and understand how data is generated and stored. Discussions will include the types of data files encountered in NGS and the significance of each in downstream analysis. Key algorithmic concepts such as sequence alignment, data mapping, and variation detection will be explained to establish a strong theoretical foundation.

2.     Working in a UNIX/Linux Environment

This module introduces participants to a command-line interface, the primary environment for most bioinformatics analysis. They will learn to navigate directories, manage files, and execute essential commands for data manipulation. Emphasis will be placed on creating, organizing, and editing files; searching and retrieving data; and modifying file permissions for secure and efficient data handling.

3.     Quality Control and Data Pre-processing

High-throughput data often contains sequencing errors or low-quality regions. This session focuses on inspecting raw sequencing data for quality metrics and applying filtering or trimming techniques to improve dataset reliability. Participants will interpret quality scores, identify sequencing artifacts, and prepare clean data for further analysis.

4.     Sequence Alignment and Variant Analysis

A crucial part of NGS analysis involves aligning sequencing reads to a reference genome or transcriptome. In this module, participants will learn the logic behind sequence alignment, including how mismatches, gaps, and repeats are handled computationally. They will perform alignment of reads and then identify genetic variations such as single nucleotide polymorphisms (SNPs), insertions/deletions, and larger structural changes.

5.     RNA Sequencing (RNA-Seq) Analysis

This module introduces gene expression analysis through RNA-Seq workflows. Participants will learn to quantify expression levels, perform normalization, and identify differentially expressed genes across experimental conditions. They will explore data visualization techniques to interpret biological significance from gene expression profiles.

6.     Genome Assembly and Annotation

Participants will explore how sequencing reads can be used to assemble a genome, especially in cases where a reference genome is not available. They will learn the differences between reference-guided and de novo assembly approaches. Challenges such as repeat resolution, contig merging, and assembly validation will be addressed.

Resource Persons: