For scientists
Field guides for bench scientists
Powerful computational tools are now one download away, and so are their traps. These guides are for people who run experiments for a living and want answers they can trust.
Each guide is built around the mistakes I have watched good scientists make, myself included, and around one rule that I now apply to everything: a negative result only means something when a positive control has gone through exactly the same analysis.
They are not programming courses. They assume you can run a tool, or can ask someone or something to run it for you. They are about what to ask, what to check, and when not to believe the answer.
AlphaFold for experimentalists
How to get real answers about proteins and complexes from predicted structures, and how to tell when a prediction is fooling you.
Public RNA-seq and microarray data: reanalysis and GSEA done properly
Thousands of datasets are free to reuse. How to check that the samples are what the labels say, and how to read a pathway result without over-reading it.
Pooled CRISPR screens: from read counts to hits you can trust
Screens are expensive. What a hit is, what a bottleneck does to your statistics, and how to choose what to validate.
Status
The chapters are written and are being checked before publication.