Bioinformatics & omics
Each omics layer is shaped by the physics of its own measurement; seeing where costs you what; and inference has a boundary worth stating before the next dataset arrives.
Thirteen subjects about measurement, and they agree on one thing: each omics layer is shaped by the physics of its own measurement, and the artefacts follow from it. Proteins cannot be amplified, which is why proteomics is harder than genomics. Metabolites share no common backbone, so no single method sees the layer at all. Every artefact of single-cell data — dropout, doublets, ambient RNA — is a consequence of how the cell got into the tube. In cryo-EM the microscope is rarely the limit; the ice and the molecule’s own flexibility are.
The second idea: seeing where something is costs you what it is, and the other way round. Spatial capture is unbiased but mixes cells; imaging is precise but sees only a panel.
The third: inference has a boundary worth stating. Stacking omics layers multiplies noise more often than signal; a model trained on sequence alone learns structure until it stops; DNA is an extraordinary archive and a poor computer.
Start with bioinformatics and multi-omics: it asks why combined layers so rarely multiply signal, and that question frames every method page here.
Articles 13
| Bioinformatics and multi-omics Why stacking omics layers rarely multiplies the signal, and usually multiplies the noise. |
| Biocomputing and DNA computing DNA is an extraordinary archive and a poor computer, and the reasons are physical. |
| Biological AI and bioinformation services Why a model trained on sequence alone learns structure, and where that inference stops. |
| Genomics and DNA design What can be ordered as DNA is decided by chemistry, and what it does is decided by more than codons. |
| Long-read sequencing Two very different physical measurements, one shared advantage: the read is longer than the problem. |
| Metabolomics The only omics layer whose molecules share no common backbone — which is why no single method sees it. |
| Mass-spectrometry proteomics Proteins cannot be amplified, and that single fact makes proteomics harder than genomics. |
| Single-cell omics Every artefact of the method — dropout, doublets, ambient RNA, stress response — is a consequence of how the cell got into the tube. |
| Spatial epigenomics Reading chromatin state in place, where the measurement is nearly binary and the per-cell data are sparser than anything in transcriptomics. |
| Spatial transcriptomics and spatial multi-omics Two families with opposite compromises: capture that is unbiased but mixes cells, imaging that is precise but sees only a panel. |
| Structural biology and cryo-electron microscopy The microscope is rarely the limit; the thin film of ice and the molecule's own flexibility are. |
| Toxicity screening and toxicogenomics Using a molecular response as a stand-in for harm, and the concordance problem that decides whether it works. |
| Direct RNA: reading the molecule as it is Nanopore sequencing can read RNA without copying it first — which removes amplification bias and makes base modifications part of the signal rather than a separate assay. |