Bioinformatics for Biologists: Analysing and Interpreting Genomics Datasets

Gain practical data science skills

Bioinformatics is crucial in helping to understand large and complex sets of biological data.

On this three-week course, you’ll develop your knowledge of bioinformatics and gain practical experience in scaling up analysis and working with large and complex datasets effectively.

With this knowledge, you’ll have the skills to tackle real-world genomics research challenges, enhancing your career prospects in fields such as genomics medicine, bioinformatics, and data science.

Develop your understanding of DNA sequencing

You’ll start by gaining an introduction to sequencing technologies and current sequencing outputs.

Dive into the rich history of DNA sequencing and how it has evolved in the Next-Generation Sequencing (NGS) era before gaining practical skills in mapping sequencing data to a reference genome.

Delve into workflow management and analysis

Next, you’ll unpack bioinformatics workflows as you learn how to use workflow management software such as Nextflow.

With this knowledge, you’ll gain skills to adapt existing workflows to your specific needs and optimise your analysis processes.

Explore data analysis with R

Finally, you’ll explore downstream analysis with the versatile R programming language. You’ll learn how to work with pipeline outputs in R to conduct in-depth analysis and visualisation.

Guided by the experts at Wellcome Connecting Science and using hands-on exercises, you’ll finish the course with both the knowledge and practical skills to effectively handle and analyse sequence datasets.

This course is designed for those with some prior knowledge of bioinformatics. We recommend starting with our Bioinformatics for Biologists: An Introduction to Linux, Bash Scripting, and R course.

You will need access to a Linux based operating system and some prior knowledge of Linux command line will be needed for you to benefit completely from the learning on this course.

This course will be particularly useful for a genomics researcher, molecular biologist, bioinformatics practitioner, or anyone looking to pursue a career in data science.