Acknowledgements

This book rests on software and data that other people built and gave away. The analyses in these pages run on Biopython, NumPy, pandas, SciPy, Matplotlib, scikit-learn, R, the tidyverse and Bioconductor, and on command-line tools — BLAST+, MAFFT, BWA, SAMtools, BCFtools, Prodigal, SeqKit — maintained in many cases by very small teams for very little reward. The data comes from NCBI, EMBL-EBI, UniProt, the RCSB Protein Data Bank, the AlphaFold Protein Structure Database, STRING and ClinVar, all of which make their holdings freely available. The book itself is built with Quarto. Everyone who contributes to that ecosystem has a share in whatever is useful here.

I am grateful to the students who worked through earlier versions of this material in classrooms and workshops in Nigeria and across the continent, and who were candid about the places where it did not work. Their confusion was more useful to me than their praise, and most of the rewriting in this edition was prompted by it.

My thanks to colleagues at Sa’adu Zungur University, at the Africa Centres for Disease Control and Prevention, and in the Africa Bio Genome Project and H3Africa communities, whose work shaped my sense of what genomics in resource-limited settings actually requires. The emphasis in this book on small datasets, free tools and reproducible method comes directly from that experience.

To the open science community in Nigeria, and to everyone who has argued with me about data sharing and benefit sharing: Chapter 14 is better for those arguments.

Any errors that remain are mine. Corrections are welcome through the book’s issue tracker and will be credited in later editions.