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NGS and Microarray Data Analysis In Bioinformatics
Bestseller
Rating: 4.5 out of 5(45 ratings)
354 students

NGS and Microarray Data Analysis In Bioinformatics

A Practical, End-to-End Guide to Gene Expression Analysis Using NGS and Microarray Data
Bestseller
Created byShahroz Rahman
Last updated 8/2026
English
English [Auto],

What you'll learn

  • Understand the biological foundations of genomics, transcriptomics, and gene expression
  • Explain how NGS and microarray technologies generate gene expression data
  • Distinguish between different genomic data types and their appropriate analytical uses
  • Interpret common bioinformatics file formats such as FASTA, FASTQ, SAM/BAM, GFF, and VCF
  • Design and understand complete NGS and microarray analysis workflows
  • Perform quality control and preprocessing of NGS and microarray datasets
  • Understand and apply normalization methods for gene expression data
  • Analyze RNA-seq data, including read alignment, quantification, and differential expression
  • Understand alternative splicing and isoform-level complexity in NGS data
  • Visualize gene expression results using PCA, heatmaps, and volcano plots
  • Perform Variant Calling on the DNA Sequencing Data using GATK
  • Retrieve and work with real datasets from GEO and ArrayExpress databases
  • Perform microarray differential expression analysis using R, limma, and Geo2R
  • Interpret differential expression results in a biologically meaningful way
  • Identify and troubleshoot common errors in genomic data analysis workflows
  • Understand best practices for reproducibility, documentation, and data management
  • Develop confidence to read, evaluate, and reproduce published gene expression studies

Course content

3 sections • 47 lectures • 12h 28m total length

  • Introduction3:27

    Become a data-driven biologist by learning to handle omics data with microarray and NGS workflows, clean and map data, and validate results using public databases like Geo and ArrayExpress.

  • Overview of Genomics, Transcriptomics, and Functional Genomics8:02

    Explore genomics and transcriptomics, from the genome as DNA to the dynamic transcriptome, and see how NGS and microarrays enable functional genomics through bioinformatics workflows.

  • Central Dogma and Gene Expression Regulation11:12

    Explore the central dogma from DNA to RNA to protein, and how transcription factors, chromatin, alternative splicing, and post-translational regulation shape transcriptomes for bioinformatics analyses.

  • DNA Sequencing Technologies: From Sanger to Next-Generation Sequencing11:28

    Chart the shift from Sanger sequencing to next-generation sequencing, compare platforms and read lengths, and explain how data outputs drive bioinformatics pipelines.

  • The $1,000 Genome: How We Got Here and Why It Matters4:58
  • Introduction to Microarray Technology and Its Evolution11:10

    Explore how microarray technology measures thousands of genes simultaneously through hybridization and fluorescence. Trace its evolution, applications in diagnostics and research, and its shift with RNA-seq.

  • NGS vs. Microarray: Principles, Strengths, and Limitations11:47

    Compare NGS and microarray principles, strengths, and limitations to guide research questions, data types, discovery potential, and analyses, from fluorescence intensity to fastq reads and isoform resolution.

  • Biological Data Types: Genomic, Transcriptomic, and Epigenomic11:54

    Explore the three core data types—genomic, transcriptomic, and epigenomic—revealing how static DNA content, dynamic gene expression, and regulatory marks enable integrated, actionable insights in bioinformatics.

  • Key Bioinformatics File Formats (FASTA, FASTQ, GFF, SAM/BAM, VCF, etc.)14:31

    Explore essential bioinformatics file formats, including fasta, fastq, gff, gtf, sam, bam, and vcf, and learn how they map to reference genomes, annotations, alignments, and variants in ngs workflows.

  • Structure of NGS and Microarray Workflows12:04

    Outline the three main phases of the workflow for both NGS and microarray—from wet-lab data generation through computational processing to downstream interpretation—emphasizing experimental design, quality control, and data normalization.

  • Experimental Design in Genomic Studies (Replicates, Controls, Biases)10:56

    Explore the core elements of experimental design in genomic and transcriptomic studies, including replicates, controls, and biases, and learn how they shape downstream bioinformatics analysis.

  • Garbage In, Garbage Out: The Art of Experimental Design3:42
  • Introduction to Data Quality and Preprocessing Concepts12:10

    Assess and improve genomic and transcriptomic data quality through adapter trimming, quality filtering, and contamination removal, using QC tools to ensure reliable, reproducible downstream analysis.

  • Introduction to Reference Genomes, Gene Annotations, and Databases12:03

    Discover how reference genomes and gene annotations guide genomic analysis, and how databases like NCBI, Ensembl, and UCSC provide the maps and contexts for interpretation.

  • Overview of Common Bioinformatics Tools and Pipelines10:47

    Explore the heart of modern bioinformatics as you learn common NGS and microarray pipelines, from quality control and alignment to variant calling, expression analysis, and visualization.

  • Basics of Command-Line and Scripting in Bioinformatics14:56

    Learn to navigate the Linux command line, manage files with common commands, process large sequencing data with pipes, and write simple shell scripts for automated, reproducible bioinformatics workflows.

  • Data Interpretation and Biological Validation Concepts11:01

    Interpret differential genes and variants from RNA-seq or microarray by linking results to pathways, enrichment analysis, and literature, then validate computational predictions experimentally or in silico.

Requirements

  • Basic understanding of molecular biology (DNA, RNA, genes)
  • No prior experience with NGS or microarray analysis is required
  • Basic familiarity with R or command-line tools is helpful but not mandatory
  • All concepts are explained from first principles

Description

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

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