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NGS and Microarray Data Analysis In Bioinformatics
Bestseller
212 students
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
Explore related topics
Course content
3 sections • 47 lectures • 12h 28m total length
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