academic.oup.com/bioinformatics...
Motivation Ancestral Recombination Graphs (ARGs) represent the interwoven paths of genetic ancestry for a set of recombining sequences. The ability to capture the evolutionary history of samples makes ARGs valuable in a wide range of applications in population and statistical genetics. ARG-based approaches are increasingly becoming a part of genetic data analysis pipelines due to breakthroughs enabling ARG inference at biobank-scale. However, there is a lack of visualisation tools, which are crucial for validating inferences and generating hypotheses. Results We present tsbrowse, an open-source Python web-app for the interactive visualisation of the fundamental building-blocks of ARGs, i.e., nodes, edges and mutations. We demonstrate the application of tsbrowse to various data sources and scenarios, and highlight its key features of browsability along the genome, user interactivity, and scalability to very large sample sizes. Availability: Python package https://pypi.org/project/tsbrowse/ Development version: https://github.com/tskit.dev/tsbrowse Documentation: https://tskit.dev/tsbrowse/docs ### Competing Interest Statement The authors have declared no competing interest.
We have decided to sunset openSNP at the end of April. While triggered by the sale of #23andme, @PhilippBayer @i_dabble & I had been thinking about this for a while.
Ultimately, we think that it's the most responsible act of data stewardship given the state of the world. I've written a retrospective of the last 14 years of the project:
As this blog post is being published, we are sending out emails to all openSNP users with some news: OpenSNP will be turned off โ and with that also delete all the data stored on it โ on April 30, 2025. Given that the project has been part of my...
Interesting to see the numbers for cloud vs self hosted for people with predictable compute needs:
Since it took us years to get into the cloud in the first place, I originally imagined it would take us years to get out as well. But all that work to containerize our applications and prepare them for the cloud actually turned out to make it relatively easy to exit. And now, after six months of effort, it's done. We're out. The last a...
Great stuff here from Adam Rutherford on the science (or not) of what 23andMe was actually doing & why.
The real question is about the vast amount of genetic data they collected. It will get sold to someone - but who, and what will *they* use it for?
Is it possible to compute with arrays that are 100x larger than memory and still achieve good performance? ๐คฏ
With the new compute engine in Python-Blosc2, you can! ๐ Check out our blog post for more details: https://ironarray.io/blog/compute-bigger
Compress Better, Compute Bigger!
Big news: we are setting up a new non-profit organization to run bioRxiv and medRxiv. It's called openRxiv [no it's not a new preprint server; it's a dedicated organization to oversee the servers] http://openrxiv.org
This change reflects the importance of the servers and the need to ensure their long-term ability to serve all the scientists who have voted with their feet. Thanks to all of you who made this happen!
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