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Learn Bioinformatics & Data Visualization for Research
Rating: 4.5 out of 5(18 ratings)
85 students

Learn Bioinformatics & Data Visualization for Research

Learn Bioinformatics with Zero Coding: From Data Visualization to RNA-Seq Using Web-Based Tools
Created byAsif Ali
Last updated 8/2025
English
English [Auto],

What you'll learn

  • Perform RNA-seq analysis (DEGs, clustering, GO/KEGG) using point-and-click tools
  • Create stunning biological data visualizations: heatmaps, volcano plots, network graphs
  • Convert large data tables into beautiful, publication-ready visuals
  • Interpret your data with confidence — even if you’ve never coded before
  • Introduction to Web-based statistics and data visualization tools

Course content

3 sections • 22 lectures • 3h 55m total length

  • RNA-Seq data analysis Workflow5:14

    Master an rna-seq data analysis workflow—from library construction and sequencing to quality control, mapping to a reference genome, de novo assembly, and differential expression with gene ontology and kegg pathway.

  • Introduction to iDEP (Pre-procssing, Clustering, PCA, DEG, Genome, & Clustering15:10

    Explore RNA-seq data analysis with iDEP, from CSV upload and pre-processing to PCA, DEG analysis, and genome visualization, including heat maps, GO/KEGG pathways, and WGCNA networks.

  • Running Gene expression data set in iDEP26:20

    Learn to run RNA-seq expression analysis in iDEP using counts or FPKM, preprocess data, identify degs, and explore heat maps, PCA, enrichment trees, and pathways.

  • How to analyze dataset from public databases12:56

    learn to analyze public rna-seq datasets using the edap tool, prepare expression and design files, upload data, pre-process, and interpret differential expression results.

  • Performing Gene Enrichment analysis11:09

    Use the Shining Go online tool to perform gene ontology enrichment analysis, build enrichment charts and kegg pathways, and analyze protein-protein interaction networks for genome-wide or proteome data.

  • WGCNA (Geighted Gene Co-expression Network Analysis)12:00

    Learn WGCNA via an online tool with no coding, using Arabidopsis thaliana gene counts and CSV design file to form modules, identify hub genes, and compare RPM and Mgcl2 treatments.

Requirements

  • No Programming experience is needed. Basic Genetics, Bioinformatics, and Biotechnology knowledge is required.

Description

Are you a student or researcher in biology, biotechnology, agriculture, medicine, or life sciences who struggles with data analysis due to limited coding experience? You're not alone—and you don’t need to become a data scientist to publish quality research.

I’m Dr. Asif Ali, a molecular biologist, educator, and the creator of Dr. Asif’s Molecular Biology. After completing my PhD and two postdoctoral fellowships in genomics and biotechnology, I spent years learning tools like Linux, R, and Python. Eventually, I realized that many researchers simply need a faster, simpler way to analyze and visualize their data—without getting overwhelmed by programming.

That’s why I created this course. It’s the shortcut I wish I had when I started. This course is specifically designed for students and researchers who want to generate professional-quality bioinformatics results using only web-based platforms—no software installation, no command lines, and no prior coding required.

Throughout this course, you'll learn how to use more than 20 freely available online tools to perform real-world biological data analysis. You'll be able to create high-quality heatmaps, volcano plots, bar graphs, boxplots, and other visualizations essential for your research. We'll walk through key workflows like RNA-seq analysis, WGCNA, GWAS, phylogenetic trees, and gene expression studies—all using intuitive, drag-and-drop interfaces.

You’ll also gain the confidence to interpret your outputs correctly for your thesis or manuscript. By the end, you'll be equipped to craft clean, scientifically sound figures for publications, scholarship applications, or grant proposals—without spending months learning R, Python, or Linux environments.

This course is ideal for students and researchers from biology-related fields who need practical results without programming. It is also helpful for professors seeking simple tools to enhance their teaching, and for anyone who wants to carry out complex biological analyses in a user-friendly way.

If you’re ready to save time, reduce frustration, and take your research presentation to the next level—this course is for you.

Who this course is for:

  • Undergraduate & graduate students in biology, Life sciences, and biotechnology
  • Wet-lab researchers wanting to analyze their data without programming
  • Early-career scientists writing their first RNA-seq paper
  • Anyone who wants to learn biological data analysis the easy way

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