[RG+NF][MULTI] Ngs And Microarray Data Analysis In Bioinformatics

[CZFXP]

Ngs And Microarray Data Analysis In Bioinformatics

Published 12/2025

Created by Shahroz Rahman

MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch

Level: All | Genre: eLearning | Language: English | Duration: 47 Lectures ( 12h 28m ) | Size: 8.5 GB

A Practical, End-to-End Guide to Gene Expression Analysis Using NGS and Microarray Data

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

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 You are not allowed to view links. Register or Login 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 You are not allowed to view links. Register or Login 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 You are not allowed to view links. Register or Login 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 You are not allowed to view links. Register or Login and Technologies CoveredLinux command line (for NGS workflows)GATK for Variant CallingR and BioconductorFastQCRead alignment and quantification toolsLimmaGEO and ArrayExpress databasesGEO2RPublic genomic datasetsTeaching ApproachConcept-first, workflow-oriented explanationsReal datasets from public repositoriesEmphasis on why each step is performed, not just howNo unnecessary complexity or black-box analysisFocus on reproducibility, interpretation, and best practicesAfter Completing This CourseAfter completing this course, learners will be able to:Confidently analyze NGS and microarray gene expression datasetsUnderstand and evaluate published genomic studiesDesign their own basic genomic data analysis workflowsTransition 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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