Why read this book

There are many bioinformatics books. Here is what is different about this one, so you can decide quickly whether it is the one you need.

Everything in it was run. The figures were generated by the code printed beside them, on data that ships with the book. The alignment scores, the read counts, the p-values, the RMSD values, the tool output — all of it came out of an actual execution. This sounds like a low bar. It is not: a great deal of teaching material contains code that has never been executed and output that was written by hand, and readers lose days discovering this the hard way.

It teaches judgement, not just procedure. Knowing how to run DESeq2 is a few lines. Knowing that your experimental design confounds batch with condition, that your principal component one is library size, or that a significant GO term is a hypothesis rather than a result — that is the part that takes years to acquire by osmosis, and this book tries to compress it. Several chapters deliberately demonstrate a wrong analysis before the right one, because the failure mode is the lesson.

It covers the whole working pipeline, not a slice. The command line, Python, R and Bioconductor, databases, alignment, genome annotation, sequencing data, protein structure, networks, statistics, machine learning, clinical and pathogen genomics, and the ethics of human genomic data. These are presented as one connected skill rather than as separate specialisms, because in practice a single project touches most of them.

It is reproducible by construction. The book is a Quarto project. You can clone it, rebuild it, change an analysis and see what happens. The data provenance is recorded with checksums. The software environment is pinned. The habits the book asks you to adopt are the habits it was itself written with.

It is free to read and free to run. Every tool used is open source, every dataset is public and small, and the online edition is openly licensed.

This book is for you if

you generate biological data and want to analyse it yourself rather than wait for a collaborator;
you are a student who needs working skills for a project or thesis;
you can program but do not know the biology, the file formats or the conventions;
you teach bioinformatics and want material with exercises and solutions that can be run in a classroom;
you are a clinician or public-health scientist meeting genomic data in your work.

It is probably not for you if

you want a reference manual for a specific tool — read its documentation;
you want algorithm theory at the level of a computer science course;
you want a survey of the literature rather than a set of skills;
you are looking for point-and-click instructions for web servers.

# Why read this book {.unnumbered} There are many bioinformatics books. Here is what is different about this one, so you can decide quickly whether it is the one you need. **Everything in it was run.** The figures were generated by the code printed beside them, on data that ships with the book. The alignment scores, the read counts, the p-values, the RMSD values, the tool output — all of it came out of an actual execution. This sounds like a low bar. It is not: a great deal of teaching material contains code that has never been executed and output that was written by hand, and readers lose days discovering this the hard way. **It teaches judgement, not just procedure.** Knowing how to run DESeq2 is a few lines. Knowing that your experimental design confounds batch with condition, that your principal component one is library size, or that a significant GO term is a hypothesis rather than a result — that is the part that takes years to acquire by osmosis, and this book tries to compress it. Several chapters deliberately demonstrate a wrong analysis before the right one, because the failure mode is the lesson. **It covers the whole working pipeline, not a slice.** The command line, Python, R and Bioconductor, databases, alignment, genome annotation, sequencing data, protein structure, networks, statistics, machine learning, clinical and pathogen genomics, and the ethics of human genomic data. These are presented as one connected skill rather than as separate specialisms, because in practice a single project touches most of them. **It is reproducible by construction.** The book is a Quarto project. You can clone it, rebuild it, change an analysis and see what happens. The data provenance is recorded with checksums. The software environment is pinned. The habits the book asks you to adopt are the habits it was itself written with. **It is free to read and free to run.** Every tool used is open source, every dataset is public and small, and the online edition is openly licensed. ## This book is for you if {.unnumbered} - you generate biological data and want to analyse it yourself rather than wait for a collaborator; - you are a student who needs working skills for a project or thesis; - you can program but do not know the biology, the file formats or the conventions; - you teach bioinformatics and want material with exercises and solutions that can be run in a classroom; - you are a clinician or public-health scientist meeting genomic data in your work. ## It is probably not for you if {.unnumbered} - you want a reference manual for a specific tool — read its documentation; - you want algorithm theory at the level of a computer science course; - you want a survey of the literature rather than a set of skills; - you are looking for point-and-click instructions for web servers.