Finding and Using Biological Databases
Welcome
Modern biology runs on public databases. Before you can analyse a genome, quantify a transcriptome, or reason about a protein’s structure, you have to find the data — and finding it means knowing your way around the handful of big public archives that the whole field depends on: the NCBI/GEO/SRA family, the European databases at EMBL-EBI (ENA, Ensembl, PDBe), and the structural prediction and comparison tools that sit alongside them.
This short book is a set of hands-on, self-checking tutorials that walk you through real database tasks and let you confirm you got the right answer as you go. Each chapter takes a genuine biological question, points you at the right database, and has you navigate it for yourself — retrieving accessions, downloading data, and interpreting what you find. Little multiple-choice checkpoints along the way let you make sure you’re on track before moving on.
There are three tutorials:
- Gene expression databases — using GEO and the SRA to find and download RNA-seq reads, then quantifying transcript abundance.
- Genomic databases — cross-referencing NCBI and the ENA to identify a species, find its assemblies, and download raw reads for assembly and annotation.
- Structural databases — searching PDBe for known protein structures and using structure-prediction tools to model a protein of your own.
How to use these tutorials
Work through each chapter with the relevant database open in another browser tab, and actually do the steps — the whole point is to build the muscle memory of navigating these resources yourself. When you reach a question, have a go before you reveal the answer. Where a question has a “Why?” note attached, open it once you’ve answered: it explains why the answer is what it is, which is usually more useful than the answer itself.
The book itself needs nothing installed — the tutorials and their self-check questions run entirely in your browser, and every question is answerable from what the databases show you. The tutorials do, however, walk through command-line tools (for downloading and processing data) that you’d run on your own machine in a real analysis. If you want to follow those hands-on steps for yourself rather than just read them, see the prerequisites below.
Prerequisites
What you should already know
The hands-on command-line parts assume you’re already comfortable using command-line tools.
Software
You have two options for running the command-line tools.
The easiest is the pre-built Google Colab document, which has everything ready to go: Link to Google Colab. You’ll need a Google account to use it — if you’d rather not use a personal one, you can sign up with your @tsl.ac.uk address.
Alternatively, you can install the tools on your own machine. You’ll need:
Most of these can be installed with bioconda.
Let’s get started.