Companion to What They Don't Teach You in Bioinformatics
The part of bioinformatics that isn't the tools.
Every course teaches Seurat, DESeq2, and the Unix commands. This site is about the other half: knowing when a result is wrong, which tool to trust for which job, and how to move data between formats without quietly breaking it.
58 pages
Sanity-check guides
How to tell when an analysis is lying to you, assay by assay: batch effects, doublets, sample swaps, misleading UMAPs, and what to do once you find them.
20 pages
Tool comparisons
DESeq2 or edgeR. Seurat or Scanpy. STAR or salmon. One recommendation per pair, defended, with the cases where the other tool wins.
24 pages
File-format conversions
The exact command, the version it was checked against, and the coordinate, strand, and naming traps that silently corrupt the output.
49 pages
Glossary
Definitions written for people who have to use the term tomorrow, organised by the book's thirteen chapters.
Recently added
- How to Read a P-Value Histogram in ChIP-seq
Flat with a spike, a hill, a U, or a pile-up near 1: each shape of your differential binding p-values points at a specific broken assumption, and you can see it in one line of R.
- How to Tell If You Sequenced Deep Enough in Variant Calling
Mean coverage looks fine, the VCF looks clean, and you still cannot say whether the missing variants are absent or just unsampled.
- How to Tell If You Sequenced Deep Enough in ATAC-seq
Before you pay for another lane, find out whether your ATAC-seq libraries are out of depth or out of complexity, and whether a third replicate would help more.
- Why Your UMAP Is Misleading You in Single-Cell RNA-seq
Distances between islands, island size and the direction of a smear on your UMAP are artifacts of the embedding, and here is how to test each claim before it reaches a figure legend.
- How to Choose a Normalization Method in CUT&RUN and CUT&Tag
Your treated sample has a fraction of the control's histone mark, and your bigWigs look identical: the normalization you picked erased the biology.
- How to Choose a Normalization Method in ATAC-seq
The method you pick can turn 33 significant regions into 24,450 on the same data, so decide it on purpose and check it with an MA plot.