Training in biology is incomplete today
without acquiring strong bioinformatics skills

We help you master bioinformatics using tutorials and hands-on trainings on cutting-edge topics. To try, please sign-up below for our introductory R module.

Learn R and Bioinformatics

The following remotely-taught R modules are designed specifically for the biologists. Each module is short and focussed, where you see immediate value compared to your current workflow.

  • MODULE 1. R for Biology

    This introductory module helps you get started with R, a powerful software tool for biological data analysis. We will cover the absolute basics and share with you the essence of why R is so useful. You will also receive information on the best online resources to continue learning.

    Check here for details.

  • MODULE 2. NGS - RNAseq Analysis using R

    This module shows teaches you to process tabular data from RNAseq experiments using R. On completion, you will be able to replace Excel or other spreadsheet programs with R and gain efficiency in your data analysis.

    Check here for details.

  • MODULE 3. Advanced RNAseq Analysis

    This module teaches you to use various R-based tools for RNAseq data analysis. You will learn about mapping (kallisto), differential expression analysis (DESeq2, edgeR, sleuth), annotation, clustering, GO analysis and other biological analyses of data.

    Check here for details.

  • MODULE 4. Data Visualization in R

    Visual representation of data is an important aspect of exploratory data analysis. This module shows how to use the powerful and versatile ggplot library for this purpose. You will also learn methods to prepare publication-quality figures using ggplot.

    Check here for details.

  • MODULE 5. Statistical Analysis in R

    This module helps you learn statistical concepts and perform statistical analysis of biological data using R. You will create random datasets, learn about statistical distributions, Student's t test, analysis of variance (ANOVA), linear regression, Principle Component Analysis (PCA) and clustering analysis.

    Check here for details.

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