Without an after or a when.can you hear the rain?more quotes

genomes: thinking

The Outbreak Poems — artistic emissions in a pandemic

things on the side

data visualization + art
The Personal OncoGenomics Program (POG) is a research initiative to study the impact of embedding genomic sequencing into real-time treatment planning for BC patients with metastatic cancers. Based out of the BC Cancer Research Centre and the GSC, POG is a large world-class clinical research collaboration of BC Cancer oncologists, pathologists and other clinical staff, researchers and technical personnel throughout BC healthcare facilities.
Interested in more art based on the POG570 cohort from the Personal OncoGenomics Program? Check out our 5-year POG anniversary posters and desktops.

Pan-cancer genomic landscapes of advanced tumors after therapy

Pleasance, E., Titmuss, E., Williamson, L. et al. (2020) Pan-cancer analysis of advanced patient tumors reveals interactions between therapy and genomic landscapes. Nat Cancer 1:452–468.

Art is science in love.
— E.F. Weisslitz

Desktop image based on our design of the Nature Cancer April 2020 cover accompanying Pan-cancer genomic landscapes of advanced tumors after therapy. (download desktops)
Desktop image based on our design of the Nature Cancer April 2020 cover accompanying Pan-cancer genomic landscapes of advanced tumors after therapy. (download desktops)

Nature Cancer selected our design for the cover of the April 2020 issue. The design is based on the mutations of 570 cancer genomes, which we report on in "Pan-cancer genomic landscapes of advanced tumors after therapy" [1] that appears in the issue.

Mutation spectra of patients from the POG570 cohort of 570 individuals with advanced metastatic cancer. Each ellipse system represents the mutation spectrum of an individual patient. Individual ellipses in the system correspond to the number of base changes in a given class and are layered by mutation count. Ellipse angle is controlled by the proportion of mutations in a class within the sample and its size is determined by a sigmoid mapping of mutation count scaled within the layer. The opacity of each system represents the duration since the diagnosis of advanced disease. (Read about the design.)
A poster of the cover with an interpretive legend. (Get the poster)

Virus Mutations Reveal How COVID-19 Really Spread

Mon 01-06-2020

Genetic sequences of the coronavirus tell story of when the virus arrived in each country and where it came from.

Our graphic in Scientific American's Graphic Science section in the June 2020 issue shows a phylogenetic tree based on a snapshot of the data model from Nextstrain as of 31 March 2020.

Virus Mutations Reveal How COVID-19 Really Spread. Text by Mark Fischetti (Senior Editor), art direction by Jen Christiansen (Senior Graphics Editor), source: Nextstrain (enabled by data from GISAID).

Cover of Nature Cancer April 2020

Mon 27-04-2020

Our design on the cover of Nature Cancer's April 2020 issue shows mutation spectra of patients from the POG570 cohort of 570 individuals with advanced metastatic cancer.

Each ellipse system represents the mutation spectrum of an individual patient. Individual ellipses in the system correspond to the number of base changes in a given class and are layered by mutation count. Ellipse angle is controlled by the proportion of mutations in a class within the sample and its size is determined by a sigmoid mapping of mutation count scaled within the layer. The opacity of each system represents the duration since the diagnosis of advanced disease. (read more)

The cover design accompanies our report in the issue Pleasance, E., Titmuss, E., Williamson, L. et al. (2020) Pan-cancer analysis of advanced patient tumors reveals interactions between therapy and genomic landscapes. Nat Cancer 1:452–468.

Modeling infectious epidemics

Wed 06-05-2020

Every day sadder and sadder news of its increase. In the City died this week 7496; and of them, 6102 of the plague. But it is feared that the true number of the dead this week is near 10,000 ....
—Samuel Pepys, 1665

This month, we begin a series of columns on epidemiological models. We start with the basic SIR model, which models the spread of an infection between three groups in a population: susceptible, infected and recovered.

Nature Methods Points of Significance column: Modeling infectious epidemics. (read)

We discuss conditions under which an outbreak occurs, estimates of spread characteristics and the effects that mitigation can play on disease trajectories. We show the trends that arise when "flattenting the curve" by decreasing $R_0$.

Nature Methods Points of Significance column: Modeling infectious epidemics. (read)

This column has an interactive supplemental component that allows you to explore how the model curves change with parameters such as infectious period, basic reproduction number and vaccination level.

Nature Methods Points of Significance column: Modeling infectious epidemics. (Interactive supplemental materials)

Bjørnstad, O.N., Shea, K., Krzywinski, M. & Altman, N. (2020) Points of significance: Modeling infectious epidemics. Nature Methods 17:455–456.

The Outbreak Poems

Sat 04-04-2020

I'm writing poetry daily to put my feelings into words more often during the COVID-19 outbreak.

$That moment when you know a moment.$
$Branch to branch, flit, look everywhere, chirp.$
$Memory, scent of thought fleeting.$
$Distant pasts all ways in plural form.$

Deadly Genomes: Genome Structure and Size of Harmful Bacteria and Viruses

Tue 17-03-2020

A poster full of epidemiological worry and statistics. Now updated with the genome of SARS-CoV-2 and COVID-19 case statistics as of 3 March 2020.

Deadly Genomes: Genome Structure and Size of Harmful Bacteria and Viruses (zoom)

Bacterial and viral genomes of various diseases are drawn as paths with color encoding local GC content and curvature encoding local repeat content. Position of the genome encodes prevalence and mortality rate.

The deadly genomes collection has been updated with a posters of the genomes of SARS-CoV-2, the novel coronavirus that causes COVID-19.

Genomes of 56 SARS-CoV-2 coronaviruses that causes COVID-19.
Ball of 56 SARS-CoV-2 coronaviruses that causes COVID-19.
The first SARS-CoV-2 genome (MT019529) to be sequenced appears first on the poster.