How to use the guide

This guide consists of several modules that you can take either in parallel or in order. Some modules are optional, depending on your background. Below are some tips to help you organize your time around these modules.

Molecular Biology

This module is designed for beginners in biology. If you're already familiar with the content, feel free to skip most of the sections. For those with a more advanced biology background, additional reading materials are provided that you may find useful.

Programming

• Python: This module is ideal if you're new to programming.

• R: If you've just mastered Python and don't want to feel overwhelmed, you may skip this module. The only place you'll need R in this guide is for a couple of practicals in the Functional Genomics module (e.g., gene expression data analysis). However, if you already know Python or prefer R, you can take this module. Some practicals in the Bioinformatics Algorithms module are explained using Python, but the same tasks can be easily implemented in R if you're more comfortable with it.

Statistics

Statistics can be overwhelming for beginners, requiring time, patience, and persistence for the material to sink in. It might feel frustrating to focus on statistics when you're eager to start coding for bioinformatics. If that’s the case, you could lighten the mood by taking the Bioinformatics Algorithms module first or in parallel with statistics.

On the other hand, if you choose to tackle statistics first, you'll be well-prepared with both coding and statistical thinking by the time you reach the Bioinformatics Algorithms and Functional Genomics modules, which will make those easier to master.

You can also choose to follow the theory and practicals in order or mix them up and take them in parallel—practicing each concept in the programming language you prefer, whether that’s Python or R.

Bioinformatics Algorithms

This module provides an engaging introduction to bioinformatics, teaching you how to code and process genetic sequences. You can choose to take this module before or after statistics, or even in parallel. However, it's recommended to take it before the Functional Genomics module, as it will introduce you to the algorithmic bases of the command-line tools that you'll be using for alignment and other algorithms.

Sequencing Data Analysis and Functional Genomics

This module is your first real introduction to computational biology. While it’s not all-encompassing (many data modalities and analysis techniques, such as epigenomics and metagenomics, are not included), by the end of this module, you will have the foundation to explore further topics and resources on your own.

With that, you're ready to dive in! Enjoy the journey of learning and self-improvement.