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visualization + design

Genome Informatics 2010 cover

Genome Informatics, September 15-19, 2010 / Hinxton, UK

1 · The conference program cover

The program cover shows sequences of some of the genes and viruses that appear in the 2010 Genome Informatics conference's abstracts.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
GENOME INFORMATICS 2010 FRONT COVER | The conference program cover shows sequences of some of the proteins and genes reported in the abstracts drawn as paths
Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
GENOME INFORMATICS 2010 BACK COVER | The conference program cover shows sequences of some of the proteins and genes reported in the abstracts drawn as paths

The booklet was published with a black cover background. Below is an inverted and pinkish take on the cover.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
GENOME INFORMATICS 2010 FRONT AND BACK COVER | The conference program cover shows sequences of some of the proteins and genes reported in the abstracts drawn as paths

2 · Design of the cover

2.1 · Sequence as a path

Each sequence is represented by a continuous path. The length of the path is proportional to the length of the sequence.

2.2 · Path color — GC Content

At each point on the path, color is used to show the GC content computed over a window of 20 bases at that position.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
GC CONTENT ENCODING | GC content is encoded by color

Because the GC content doesn't vary greatly, values in the range 0.2–0.6 are mapped onto hues 0–300, with GC values outside that range assigned to the start and end hues. To smooth the color mpaping, a running average is calculated across 10 adjacent samples.

2.3 · Path direction — relative GC content

Direction of the curvature of the path is determined by the GC content relative to the average GC content of the human genome.

2.4 · Path curvature — Repeat content

The magnitude of path curvature is informed by the repeat content near that location, which is calculated by determining the average frequency of 10-mers sampled within a window of 200 bases relative to their frequency in the human exon sequence.

This quantity is expressed relative to the chance of observing these 10-mers randomly and used to inform the angle of the path. Regions that are composed of 10-mers that are relatively rare are straighter than those which contain repetitive regions.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
CURVATURE SHOWS REPEATS | The degree to which the path turns is informed by how much of the sequence at that position is repeated.

The path is confined within a circular area to keep it compact, at the cost of losing translational and rotational invariance of the representation. This limitation is due to the fact that the segments of the path depend on the angle and position at which the path approaches the circular boundary.

2.5 · Interpreting structure

For genes, the transcribed sequence is shown, which includes both introns and exons.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
GENES ARE HIGH-INFORMATION AREAS | Areas of high information are more straight (fewer repeats). Where sequence for areas outside genes and in repeats tend to curl up on themselves.

The overall effect of the path encoding is a qualitative, artistic interpretation of local sequence structure. Two paths can be directly compared to interrogate differences in their corresponding sequence.

3 · Deadly genome series

The Deadly Genomes poster demonstrates how entire genomes appear when encoded as paths. The poster compares the incidence rates and mortality of harmful viruses and bacteria, such as malaria, syphilis, AIDS and SARS.

Discover all the things that are not trying to make you stronger.
The cover design uses the same approach to depicting genomes as the Deadly Genomes poster.

As on the conference covers, on the poster each genome is drawn as a path. The length of the path is proportional to the size of the genome. Every fifth base is drawn as a circle whose color is based on the GC content (fraction of guanines and cytosines). The path curvature is proportional to the repeat content and the direction of curvature is determined by whether the GC content is lower or higher than average. Genomes are labeled by disease, organism, size (in bases) and GC content. Updated with the genome of SARS-CoV-2 (Wuhan-Hu-1 isolate) and COVID-19 case statistics as of 3 March 2020."

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
DEADLY GENOMES | Genomes of harmful bacteria and viruses.

The poster was a finalist in the 2009 National Science Foundation Visualization Challenge.

news + thoughts

How Analyzing Cosmic Nothing Might Explain Everything

Thu 18-01-2024

Huge empty areas of the universe called voids could help solve the greatest mysteries in the cosmos.

My graphic accompanying How Analyzing Cosmic Nothing Might Explain Everything in the January 2024 issue of Scientific American depicts the entire Universe in a two-page spread — full of nothing.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
How Analyzing Cosmic Nothing Might Explain Everything. Text by Michael Lemonick (editor), art direction by Jen Christiansen (Senior Graphics Editor), source: SDSS

The graphic uses the latest data from SDSS 12 and is an update to my Superclusters and Voids poster.

Michael Lemonick (editor) explains on the graphic:

“Regions of relatively empty space called cosmic voids are everywhere in the universe, and scientists believe studying their size, shape and spread across the cosmos could help them understand dark matter, dark energy and other big mysteries.

To use voids in this way, astronomers must map these regions in detail—a project that is just beginning.

Shown here are voids discovered by the Sloan Digital Sky Survey (SDSS), along with a selection of 16 previously named voids. Scientists expect voids to be evenly distributed throughout space—the lack of voids in some regions on the globe simply reflects SDSS’s sky coverage.”

voids

Sofia Contarini, Alice Pisani, Nico Hamaus, Federico Marulli Lauro Moscardini & Marco Baldi (2023) Cosmological Constraints from the BOSS DR12 Void Size Function Astrophysical Journal 953:46.

Nico Hamaus, Alice Pisani, Jin-Ah Choi, Guilhem Lavaux, Benjamin D. Wandelt & Jochen Weller (2020) Journal of Cosmology and Astroparticle Physics 2020:023.

Sloan Digital Sky Survey Data Release 12

constellation figures

Alan MacRobert (Sky & Telescope), Paulina Rowicka/Martin Krzywinski (revisions & Microscopium)

stars

Hoffleit & Warren Jr. (1991) The Bright Star Catalog, 5th Revised Edition (Preliminary Version).

cosmology

H0 = 67.4 km/(Mpc·s), Ωm = 0.315, Ωv = 0.685. Planck collaboration Planck 2018 results. VI. Cosmological parameters (2018).

Error in predictor variables

Tue 02-01-2024

It is the mark of an educated mind to rest satisfied with the degree of precision that the nature of the subject admits and not to seek exactness where only an approximation is possible. —Aristotle

In regression, the predictors are (typically) assumed to have known values that are measured without error.

Practically, however, predictors are often measured with error. This has a profound (but predictable) effect on the estimates of relationships among variables – the so-called “error in variables” problem.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Nature Methods Points of Significance column: Error in predictor variables. (read)

Error in measuring the predictors is often ignored. In this column, we discuss when ignoring this error is harmless and when it can lead to large bias that can leads us to miss important effects.

Altman, N. & Krzywinski, M. (2024) Points of significance: Error in predictor variables. Nat. Methods 20.

Background reading

Altman, N. & Krzywinski, M. (2015) Points of significance: Simple linear regression. Nat. Methods 12:999–1000.

Lever, J., Krzywinski, M. & Altman, N. (2016) Points of significance: Logistic regression. Nat. Methods 13:541–542 (2016).

Das, K., Krzywinski, M. & Altman, N. (2019) Points of significance: Quantile regression. Nat. Methods 16:451–452.

Convolutional neural networks

Tue 02-01-2024

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).

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Nature Methods Points of Significance column: Convolutional neural networks. (read)

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 20:1269–1270.

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

Tue 10-01-2023

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.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Nature Methods Points of Significance column: Neural network primer. (read)

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

Mon 16-01-2023

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.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Our Cell Genomics cover depicts parent-of-origin assignment as a circuit (volume 3, issue 1, 11 January 2023). (more)

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.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
A catalogue of my journal and magazine cover designs. (more)

Science Advances cover

Thu 05-01-2023

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.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
My Science Advances cover that encodes sequence onto hypercubes (volume 9, issue 1, 6 January 2023). (more)

Kijima, Y. et al. A universal sequencing read interpreter (2023) Science Advances 9.

Browse my gallery of cover designs.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
A catalogue of my journal and magazine cover designs. (more)
Martin Krzywinski | contact | Canada's Michael Smith Genome Sciences CentreBC Cancer Research CenterBC CancerPHSA
Google whack “vicissitudinal corporealization”
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