PNAS cover — Earth BioGenome Project
An enlightened perspective on our planet
contents
The design represents a genomics perspective on the biodiversity of the Earth.
The basis of the design are the shape of continents. These were taken from the award-winning Authagraph Map, which is a projection that strongly preserves shapes and areas. Wired calls it “not perfect, but close”.
I rearranged the authagraph map to fit the aspect ratio of the cover image (nearly 1:1) and allow for enough room for text on the cover. To make things interesting, I put Antarctica in the center — north is everywhere. Given how the image is constructed (read below), I had to remove some islands. Sorry, Madagascar — you're a star.
Australia has the job of representing all of Oceania, such as Inaccessible Island.
For each continent and ocean, a travelling salesman problem (TSP) was solved to find the shortest (or close to it) path that covers the area. The path joins points that were sampled at a level of detail that I thought would strike a good balance between detail and legibility.
Some continents do not have full coverage by the path &mdsah; some small islands and peninsulas were excluded (or extended). This needed to be done to give room for the ocean path between continents and to avoid situations where the ocean path crossed a landmass. The shortest solution isn't always the most appropriate.
The number of points visited for each landmass were
1,575 africa
848 antarctica
2,818 asia
475 australia
713 europe
1,674 namerica
17,354 ocean
872 samerica
26,329 ALL
I then smoothed the path using a J-spline curve.
The DNA double helix is a trope — let's embrace it.
And before you start pixel peeping: the helix doesn't have handedness. It's composed of three independent stacked layers: one layer per strand strands and one layer with bonds.
Once the helix was drawn, I added bases as circles and resized to increase visibility while avoiding overlap.
The number of base pairs per area is
nbp
australia 596
europe 872
antarctica 1,024
samerica 1,064
africa 1,911
namerica 2,033
asia 3,403
ocean 21,208
------
32,111
I mapped species sequenced as part of the Earth BioGenome Project to the path that corresponded to their habitat location. This was done using a combination of automated searches through the Global Biodiversity Information Facility (GBIF) and manual corrections.
For a given path, the length of the sequence of each species was fixed and depended on the length of the area's double helix and number of species assigned to it.
For example, in Africa we have 1,911 bases to distribute across 87 species, giving us `1911/87-1=20` bases per species. The value is rounded down to the nearest integer and the `-1` term allows for a gap between each sequence.
The ocean area is very large and has relatively few species, so we can have long sequences — 396 bases per species.
nbp species bp_per_species
australia 596 71 7
europe 872 113 6
antarctica 1,024 2 511
samerica 1,064 93 10
africa 1,911 87 20
namerica 2,033 182 10
asia 3,403 206 15
ocean 21,208 52 396
------ ---
32,111 806
Any bases left over on the helix were assigned to gaps. Initially, I thought of concatenating the sequences without any gaps. While this would allow me to represent (neglibibly) longer sequences, I was persuaded against this idea (thank you Emma!).
First, the Earth Biogenome sequencing isn't complete (nor will it ever be), so gaps in the sequence are a nice metaphor for this. Second, the gaps offer some hope of actually reading the sequences off for each species. Below is a view of one of the strands with the backbone and base pair locations within gaps shown in magenta.
In the final figure, the helix backbone in gaps is faded.
In the final figure, bases are colored by nucleotide (A, T, C, G) and region (land vs ocean).
You can download each species's sequence and its location.
Sequences were sampled from the Genbank records starting at the first non-repeated base. This was a quick way to avoid spamming the design with polytails.
For example, the sequence for the Hanuman langur
>PVII010000001.1:1-1000 Semnopithecus entellus isolate BS30 SemEnt_scaffold_0, WGS
AAAAA AAAAA AAAAA AAAAA AAAAA AAATT ATTTT GCTAA GGTCT GAGAA CTCCA GAAGG TGGTG TCGTG
----- ----- ----- ----- ----- ---++ +++++ +++++ +++++ +++++ +++++ +++++ +++++ +++++
** ***** ***** ***
was sampled by skipping the first 28 bases (A's) and uses the sequence indicated by * above.
The position and sequence for each species is shown below. The first base of the sequence is capitalized and remaining are lowercase. The numeric code below the species name is the taxon, a unique taxonomy ID used by Genbank.
A version of the above without the sequence is shown below.
Convolutional neural networks
Nature uses only the longest threads to weave her patterns, so that each small piece of her fabric reveals the organization of the entire tapestry. – Richard Feynman
Following up on our Neural network primer column, this month we explore a different kind of network architecture: a convolutional network.
The convolutional network replaces the hidden layer of a fully connected network (FCN) with one or more filters (a kind of neuron that looks at the input within a narrow window).
Even through convolutional networks have far fewer neurons that an FCN, they can perform substantially better for certain kinds of problems, such as sequence motif detection.
Derry, A., Krzywinski, M & Altman, N. (2023) Points of significance: Convolutional neural networks. Nature Methods 20:.
Background reading
Derry, A., Krzywinski, M. & Altman, N. (2023) Points of significance: Neural network primer. Nature Methods 20:165–167.
Lever, J., Krzywinski, M. & Altman, N. (2016) Points of significance: Logistic regression. Nature Methods 13:541–542.
Neural network primer
Nature is often hidden, sometimes overcome, seldom extinguished. —Francis Bacon
In the first of a series of columns about neural networks, we introduce them with an intuitive approach that draws from our discussion about logistic regression.
Simple neural networks are just a chain of linear regressions. And, although neural network models can get very complicated, their essence can be understood in terms of relatively basic principles.
We show how neural network components (neurons) can be arranged in the network and discuss the ideas of hidden layers. Using a simple data set we show how even a 3-neuron neural network can already model relatively complicated data patterns.
Derry, A., Krzywinski, M & Altman, N. (2023) Points of significance: Neural network primer. Nature Methods 20:165–167.
Background reading
Lever, J., Krzywinski, M. & Altman, N. (2016) Points of significance: Logistic regression. Nature Methods 13:541–542.
Cell Genomics cover
Our cover on the 11 January 2023 Cell Genomics issue depicts the process of determining the parent-of-origin using differential methylation of alleles at imprinted regions (iDMRs) is imagined as a circuit.
Designed in collaboration with with Carlos Urzua.
Akbari, V. et al. Parent-of-origin detection and chromosome-scale haplotyping using long-read DNA methylation sequencing and Strand-seq (2023) Cell Genomics 3(1).
Browse my gallery of cover designs.
Science Advances cover
My cover design on the 6 January 2023 Science Advances issue depicts DNA sequencing read translation in high-dimensional space. The image showss 672 bases of sequencing barcodes generated by three different single-cell RNA sequencing platforms were encoded as oriented triangles on the faces of three 7-dimensional cubes.
More details about the design.
Kijima, Y. et al. A universal sequencing read interpreter (2023) Science Advances 9.
Browse my gallery of cover designs.
Regression modeling of time-to-event data with censoring
If you sit on the sofa for your entire life, you’re running a higher risk of getting heart disease and cancer. —Alex Honnold, American rock climber
In a follow-up to our Survival analysis — time-to-event data and censoring article, we look at how regression can be used to account for additional risk factors in survival analysis.
We explore accelerated failure time regression (AFTR) and the Cox Proportional Hazards model (Cox PH).
Dey, T., Lipsitz, S.R., Cooper, Z., Trinh, Q., Krzywinski, M & Altman, N. (2022) Points of significance: Regression modeling of time-to-event data with censoring. Nature Methods 19:1513–1515.