Genome Informatics 2010 cover
contents
The program cover shows sequences of some of the genes and viruses that appear in the 2010 Genome Informatics conference's abstracts.
The booklet was published with a black cover background. Below is an inverted and pinkish take on the cover.
Each sequence is represented by a continuous path. The length of the path is proportional to the length of the sequence.
At each point on the path, color is used to show the GC content computed over a window of 20 bases at that position.
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.
Direction of the curvature of the path is determined by the GC content relative to the average GC content of the human genome.
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.
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.
For genes, the transcribed sequence is shown, which includes both introns and exons.
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.
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.
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."
The poster was a finalist in the 2009 National Science Foundation Visualization Challenge.
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.
Music video for Max Cooper's Ascent
My 5-dimensional animation sets the visual stage for Max Cooper's Ascent from the album Unspoken Words. I have previously collaborated with Max on telling a story about infinity for his Yearning for the Infinite album.
I provide a walkthrough the video, describe the animation system I created to generate the frames, and show you all the keyframes
The video recently premiered on YouTube.
Renders of the full scene are available as NFTs.