Structure of the book

The book has sixteen chapters in eight parts. The parts are ordered so that each one supplies something the next one needs, but most chapters can be read on their own if you already have the prerequisite.

Part I — Foundations

Chapter 1 defines the field as it is actually practised, works through the arithmetic that makes computation unavoidable, traces how the methods grew with the data, and sets up your computer. By the end of it you will have run your first analysis.

Chapter 2 covers the molecular biology that shows up in files: reading frames, reverse complements, splicing, coordinate systems, and the vocabulary of variation. Every concept is tied to something that will otherwise bite you later.

Part II — Biological data and databases

Chapter 3 is about the archives: what distinguishes a primary archive from a curated knowledgebase, what accession numbers and versions actually mean, how to read a record, and how to retrieve data programmatically without abusing a public service.

Part III — Sequence analysis

Chapter 4 derives pairwise alignment from first principles, implements it, then shows what scoring matrices do, how BLAST makes search tractable, what an E-value means, how multiple alignment works and how a phylogeny follows from one.

Part IV — The bioinformatics toolchain

Chapter 5 teaches the command line as a working environment: streams and pipes, the text-processing tools, scripting, and job submission on a cluster.

Chapter 6 teaches Python through biological data, ending with a reusable module and a command-line tool of your own.

Chapter 7 teaches R and Bioconductor: the tidyverse, ggplot2 as a grammar, and the Bioconductor classes — GRanges, SummarizedExperiment — that the analysis chapters rely on.

Part V — Genomes, reads and structures

Chapter 8 goes from assembled sequence to annotated genome, including a real comparison between an ab initio gene prediction and the reference annotation.

Chapter 9 is the longest chapter: sequencing data end to end, from FASTQ and quality encodings through alignment, SAM/BAM anatomy and variant calling, to a complete differential expression analysis.

Chapter 10 computes real quantities from protein coordinates, and takes predicted structures seriously enough to explain what their confidence measures do and do not mean.

Part VI — Systems biology and data science

Chapter 11 treats biological networks quantitatively and implements enrichment analysis from first principles so that you know what the tools are doing.

Chapter 12 covers the statistics and machine learning that omics data demands, with particular attention to the ways high-dimensional data produces confident nonsense.

Part VII — Applications and responsible practice

Chapter 13 applies all of it to cancer genomics, rare disease diagnostics, pharmacogenomics and pathogen surveillance.

Chapter 14 addresses privacy, consent, governance, equity and data sovereignty — the obligations that attach to human genomic data.

Part VIII — Consolidation

Chapter 15 is a capstone project carried out in full, including the parts that usually go unrecorded: the dead ends, the environment capture, the write-up.

Chapter 16 synthesises the whole book, provides a graded question bank with worked answers, and sets out learning pathways for different starting points.

Appendices

A glossary of every technical term used in the book; the software environment and how to install it; the provenance of every bundled dataset; and a command and function reference for quick lookup.

Reading paths

If you are	Read
New to both biology and computing	1, 2, 5, 6, 3, 4, then Part V onward
A biologist new to computing	1, 2, 5, 6 or 7, 3, 4, 9, then by interest
A programmer new to biology	2, 3, 4, 8, 9, 10, then Part VI
Focused on RNA-seq	2, 5, 7, 9, 11, 12
Focused on pathogen genomics	2, 3, 4, 5, 8, 9, 13, 14
Teaching a one-semester course	One chapter per week, 1–15, with Chapter 16 for assessment

# Structure of the book {.unnumbered} The book has sixteen chapters in eight parts. The parts are ordered so that each one supplies something the next one needs, but most chapters can be read on their own if you already have the prerequisite. ## Part I — Foundations {.unnumbered} **Chapter 1** defines the field as it is actually practised, works through the arithmetic that makes computation unavoidable, traces how the methods grew with the data, and sets up your computer. By the end of it you will have run your first analysis. **Chapter 2** covers the molecular biology that shows up in files: reading frames, reverse complements, splicing, coordinate systems, and the vocabulary of variation. Every concept is tied to something that will otherwise bite you later. ## Part II — Biological data and databases {.unnumbered} **Chapter 3** is about the archives: what distinguishes a primary archive from a curated knowledgebase, what accession numbers and versions actually mean, how to read a record, and how to retrieve data programmatically without abusing a public service. ## Part III — Sequence analysis {.unnumbered} **Chapter 4** derives pairwise alignment from first principles, implements it, then shows what scoring matrices do, how BLAST makes search tractable, what an E-value means, how multiple alignment works and how a phylogeny follows from one. ## Part IV — The bioinformatics toolchain {.unnumbered} **Chapter 5** teaches the command line as a working environment: streams and pipes, the text-processing tools, scripting, and job submission on a cluster. **Chapter 6** teaches Python through biological data, ending with a reusable module and a command-line tool of your own. **Chapter 7** teaches R and Bioconductor: the tidyverse, ggplot2 as a grammar, and the Bioconductor classes — `GRanges`, `SummarizedExperiment` — that the analysis chapters rely on. ## Part V — Genomes, reads and structures {.unnumbered} **Chapter 8** goes from assembled sequence to annotated genome, including a real comparison between an *ab initio* gene prediction and the reference annotation. **Chapter 9** is the longest chapter: sequencing data end to end, from FASTQ and quality encodings through alignment, SAM/BAM anatomy and variant calling, to a complete differential expression analysis. **Chapter 10** computes real quantities from protein coordinates, and takes predicted structures seriously enough to explain what their confidence measures do and do not mean. ## Part VI — Systems biology and data science {.unnumbered} **Chapter 11** treats biological networks quantitatively and implements enrichment analysis from first principles so that you know what the tools are doing. **Chapter 12** covers the statistics and machine learning that omics data demands, with particular attention to the ways high-dimensional data produces confident nonsense. ## Part VII — Applications and responsible practice {.unnumbered} **Chapter 13** applies all of it to cancer genomics, rare disease diagnostics, pharmacogenomics and pathogen surveillance. **Chapter 14** addresses privacy, consent, governance, equity and data sovereignty — the obligations that attach to human genomic data. ## Part VIII — Consolidation {.unnumbered} **Chapter 15** is a capstone project carried out in full, including the parts that usually go unrecorded: the dead ends, the environment capture, the write-up. **Chapter 16** synthesises the whole book, provides a graded question bank with worked answers, and sets out learning pathways for different starting points. ## Appendices {.unnumbered} A glossary of every technical term used in the book; the software environment and how to install it; the provenance of every bundled dataset; and a command and function reference for quick lookup. ## Reading paths {.unnumbered} | If you are | Read | |---|---| | New to both biology and computing | 1, 2, 5, 6, 3, 4, then Part V onward | | A biologist new to computing | 1, 2, 5, 6 or 7, 3, 4, 9, then by interest | | A programmer new to biology | 2, 3, 4, 8, 9, 10, then Part VI | | Focused on RNA-seq | 2, 5, 7, 9, 11, 12 | | Focused on pathogen genomics | 2, 3, 4, 5, 8, 9, 13, 14 | | Teaching a one-semester course | One chapter per week, 1–15, with Chapter 16 for assessment |