PNAS cover — Earth BioGenome Project
An enlightened perspective on our planet
contents
download | 1,162 loci records for 806 unique species submitted by 9 consortia: Bat1K Consortium, Genome 10K Community of Scientists, Human Genome Sequencing Center, Molecular Ecology Group, Tribolium Genome Sequencing Consortium, Vertebrate Genomes Project Consortium, Wellcome Sanger Institute, Wellcome Sanger Institute Data Sharing, Wellcome Sanger Tree of Life Programme.
download | 806 unique species
download | 1,121 sequence records representing short sequences from all species.
download | Geolocation mappings for each genbank record. Localization was done using GBIF. Species list of all occurrences (human observation or preserved specimen) for each continent were downloaded. If a species was observed on multiple continents it is denoted by location_type = MULTI in the file otherwise it is UNIQUE. Where a species did not have an occurrence, its habitat was determined manually through Wikipedia or other sources and denoted by SUPP. Finally, some records were corrected manually (override_location). For species with multiple locations, one was arbitrarily chosen, but excluded Europe since this continent is small on the map and has small sequence capacity (the TSP path is short).
download | The sequence and its location for each species in the design.
Propensity score weighting
It is not certain that everything is uncertain. —Blaise Pascal
We have already explored how we can mitigate bias caused by confounding variables in observational studies using propensity score (PS) matching (PSM) and propensity score weighting (PSW). However, any statistical model is only as good as its assumptions and, if it is specified incorrectly, it can itself produce biased estimates of the treatment effect.
This month, we explore double robustness, a powerful statistical concept that provides a valuable “safety net” against the risk of an incorrect model. It offers two opportunities, instead of just one, to obtain a valid estimate of the treatment effect — making it possible to draw credible causal inferences from observational data without having to depend on a single set of modeling assumptions.
Kurz, C.F., Krzywinski, M. & Altman, N. (2026) Points of significance: Double Robustness. Nat. Methods 23:868–869.
Nature Biotechnology cover
My cover design on the 7 April 2026 Nature Biotechnology issue shows the dendrogram that represents a cluster of uniquely expressed (or downregulated) genes in human naive stem cells induced from such cells. Within each dendrogram block, the genomic barcode sequence (sampled from Supplementary Table 1) is depicted with a Code 39 barcode. The highlighted barcode is one of those used for cell isolation.
Ishiguro S. et al. A multi-kingdom genetic barcoding system for precise clone isolation (2026) Nature Biotechnology 44:616–629.
Browse my gallery of cover designs.
Happy 2026 π Day—
Art for the 5%
Celebrate π Day (March 14th) and enjoy the art — but only if you're part of the 5%.
Go ahead, see what you can't see.
Ishihara's Tests for Colour Deficiency
Authentic and accurate images of Ishihara's test plates photographed (and lovingly color-corrected) from the 38-plate Ishihara's Tests for Colour Deficiency.
I also provide the position, size, and color of each circle on each test plate.
Symmetric alternatives to the ordinary least squares regression
What immortal hand or eye, could frame thy fearful symmetry? — William Blake, "The Tyger"
This month, we look at symmetric regression, which, unlike simple linear regression, it is reversible — remaining unaltered when the variables are swapped.
Simple linear regression can summarize the linear relationship between two variables `X` and `Y` — for example, when `Y` is considered the response (dependent) and `X` the predictor (independent) variable.
However, there are times when we are not interested (or able) to distinguish between dependent and independent variables — either because they have the same importance or the same role. This is where symmetric regression can help.
Luca Greco, George Luta, Martin Krzywinski & Naomi Altman (2025) Points of significance: Symmetric alternatives to the ordinary least squares regression. Nat. Methods 22:1610–1612.