NGS and Microarray Data Analysis In Bioinformatics

In this course, you will learn how to analyze genomic and gene expression data using both Next-Generation Sequencing (NGS) and microarray technologies. The course is designed to take you step by step from the biological foundations of gene expression to complete, real-world data analysis workflows used in research and industry.

You will begin by building a strong conceptual understanding of genomics, transcriptomics, and functional genomics. This foundation will help you understand how biological data is generated, what different data types represent, and how experimental design influences downstream analysis. Rather than jumping directly into tools, the early part of the course focuses on helping you think like a bioinformatician.

As you progress, you will work through NGS data analysis workflows, learning how to inspect raw sequencing data, perform quality control, understand alignment and quantification steps, apply normalization methods, and interpret differential expression results. Important theoretical topics such as alternative splicing, reproducibility, documentation, and integration with other omics data are explained clearly so that you understand not only how analyses are done, but why they are done in a particular way.

In the later part of the course, you will learn microarray data analysis with a practical focus. You will work with real datasets from public repositories such as GEO and ArrayExpress, understand different data formats, perform quality control, and conduct differential expression analysis using R, limma, and Geo2R. You will also learn how to handle common data access and analysis issues that occur in real research settings.

Throughout the course, the emphasis is on workflow-based thinking, biological interpretation, and troubleshooting, rather than memorizing commands. By the end of the course, you should feel confident reading published genomic studies, working with public datasets, and performing your own basic NGS and microarray analyses in a structured and reproducible way.

Tools and Technologies Covered

• Linux command line (for NGS workflows)

• GATK for Variant Calling

• R and Bioconductor

• FastQC

• Read alignment and quantification tools

• Limma

• GEO and ArrayExpress databases

• GEO2R

• Public genomic datasets

Teaching Approach

• Concept-first, workflow-oriented explanations

• Real datasets from public repositories

• Emphasis on why each step is performed, not just how

• No unnecessary complexity or black-box analysis

• Focus on reproducibility, interpretation, and best practices

After Completing This Course

After completing this course, learners will be able to:

• Confidently analyze NGS and microarray gene expression datasets

• Understand and evaluate published genomic studies

• Design their own basic genomic data analysis workflows

• Transition smoothly into advanced topics such as single-cell analysis, long-read sequencing, or multi-omics integration

Who this course is for:

• Undergraduate and graduate students in bioinformatics, biotechnology, genetics, molecular biology, or computational biology

• Students planning to pursue research-based Master’s or PhD programs involving genomic or transcriptomic data

• Laboratory scientists who want to analyze and interpret their own sequencing or microarray data

• Beginners transitioning from wet-lab biology to computational data analysis

• Learners who want to understand real-world genomic datasets rather than only theoretical examples

• Researchers who work with public datasets and want to reproduce or reanalyze published studies

• Anyone seeking a strong conceptual foundation before moving into advanced topics such as single-cell or multi-omics analysis

• Self-learners aiming to build practical bioinformatics skills for academic or industry roles