I want to spell this out in case the implications aren't clear:
This means all public tools/webapps of GISAID data (all the ones you've been used to seeing thru the pandemic, as far as we can tell) are prohibited.
The file allowed this. Cut that - cut off all tools the public & others were using.
Delighted to see our paper studying the evolution of plasmids over the last 100 years, now out! Years of work by Adrian Cazares, also Nick Thomson @sangerinstitute.bsky.social - this version much improved over the preprint. Final version should be open access, apols.
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We're looking for highly motivated candidates for PhD positions in the physics of living systems.
Our group uses theoretical physics to understand how cells collectively self-organize.
If you're interested, get in touch & check out the Biozentrum PhD Fellowship program, deadline October 12th!
Willing to join us @pasteur.fr for a PhD for a project on how interactions between mobile genetic elements shape bacterial adaptation? Subject to be tailored to candidates with keen interest in evolution, genomics, computational biology, microbiology. Check www.pasteur.fr/en/education...
It was a great pleasure to contribute to this work by Jemma Fendley, @mmolari.bsky.social, and Boris Shraiman on pan-genomes, linkage, and recombination in phage genomes.
We analyzed data collected by the fantastic SEA-PHAGES program in phagesdb.org.
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www.biorxiv.org/content/10.1...
An image showing the ARTIC and Pathoplexus logos connected by blue lines, with the text "Hiring: post-doc/software engineer" and "Global open-source pathogen tools" and "Join Swiss TPH, Work on ARTIC2 and Pathoplexus, Basel, Switzerland"
📢New job posting!📢
Are you excited by the idea of building global infrastructure to make pathogen sequencing more accessible, interpretable, and equitable? 🧑🏻💻🧬
My group at @swisstph.ch has an opening working with ARTIC2, @pathoplexus.org, & Loculus - read on!
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Bonsai is out! Finally, we can visually explore high-dimensional data in a distortion-free way. Try it out! It works from scRNA-seq data (bonsai.unibas.ch/bonsai-scout...) up to football statistics (bonsai.unibas.ch/bonsai-scout...). Ever wondered why low-dimensional embeddings like t-SNE
A card reads: "These Words Are Disappearing in the New Trump Administration." A long list of words follows, including: biologically female; biologically male; BIPOC; Black; breastfeed + people; clean energy; climate crisis; climate science; commercial sex worker; community diversity; community equity; confirmation bias; cultural competence; cultural differences; cultural heritage; cultural sensitivity; culturally appropriate; culturally responsive; DEI; equity; feminism; genders.
Agencies within the Trump administration have flagged hundreds of words to limit or avoid, according to a compilation of government documents. These terms appeared in government memos, in official and unofficial agency guidance and in other documents. www.nytimes.com/interactive/...
Transformation, Recombination and horizontal transfer
The contribution of natural transformation for the acquisition of novel genes has been notoriously difficult to quantify because it relies on recombination (which is affected by other processes). Here's a first estimate : doi.org/10.1101/2025... (for the very busy: 1-6% of gene gains) #MicroSky
Building on the UShER tree of millions of SARS-CoV-2 genomes maintained by Angie Hinrichs, Hugh Haddox and Georg Angehrn (and others in @matsen.bsky.social lab and @jbloomlab.bsky.social) have looked into how the neutral mutation rate varies along the genome:
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www.biorxiv.org/content/10.1...
Thank you Will! 🙏 Coming from you who know the tool means a lot. Always inspiring to see it used!
Thank you so much! 🙏 Very very honored!
Thank you! 🙏 That’s so nice to hear!
Thank you Alice! 🙏
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We are curious to check in follow-up works whether these rates and patterns are specific to ST131, or are more general and can be found in other sequence types or even microbial species.
Hope you'll find the work interesting! Let us know if you have any observations or comments!
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To explain the total structural diversity of the dataset, more than 2000 distinct structural variations must have happened in its short evolutionary history, corresponding to an average rate of one every 3 mutations on the core-genome. This is a remarkably high rate!
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Most of the IS integrations disrupt genes, and such structural gains would be interpreted as loss events in gene-based analyses. However, this happens less than expected by chance, indicating that roughly 2/3 of these integrations have already been removed by purifying selection.
Most events are gain events, associated to the movement of an Insertion sequence.
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In binary junctions the vast majority of events are gains, often corresponding to an insertion sequence (IS) or prophage integrating in an otherwise conserved region of the genome. This corresponds to a rough rate of one of these events every 20 mutations on the core-genome.
By comparing presence / absence of sequence with the phylogenetic tree, we can associate junction with gain/loss events.
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For binary junctions we can go even further: they can be associated with gain or loss events.
In particular singleton junctions correspond to events on terminal branches of the tree, while non-singleton junctions can in principle be associated also to events on internal branches.
In the previous scatter-plot we highlight junctions containing insertion sequences, prophages and defense systems. Insertion sequences are present in the majority of junctions, and in particular they explain the high number of 1 kbp junctions. Prophages are present in many hotspots, and also explain the enrichment in 30 kbp junctions. Defense systems are mainly present in hotspots.
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By looking at the content of the junctions, we find that the two peaks in binary junctions are explained by the movement of insertion sequences and prophages respectively, while hotspots are very flexible regions, rich in mobile genetic elements and defense systems.
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On the other end of the spectrum we find hotspots, regions with tens to hundreds of different distinct paths, and a total accessory genome content of tens to hundreds of kbp in length.
scatter-plot of number of different paths and total accessory length for all junctions in the dataset. It shows an enrichment in binary junctions of length 700 bp and 30-40 kbp.
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If we scatter-plot these two quantities for all of the 519 junctions in the dataset, we find that the majority are binary, i.e. they contain only two possible distinct paths, of which one is often empty. Their length distribution is bimodal, with a peak around 1 kbp and another around 30 kbp.
the portion of graph between two subsequent core-blocks is called a junction graph. One can quantify the number of different paths and the amount of accessory sequence in the junction.
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We look at the local graph between two adjacent core blocks, that we call a junction graph. In this graph the diversity can be quantified in terms of number of distinct paths and total accessory sequence content.
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Next we investigate the structural diversity in the accessory genome. The fact that the order of core-genes is mostly conserved provides a well-defined frame of reference in which to study accessory variation.
Phylogenetic tree with marks on the leaves that show core-genome rearrangements. Most of the events occur on terminal branches, possibly as a consequence of purifying selection.
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However, the fact that synteny is largely conserved across big evolutionary distances, and the fact that many of these changes happen on terminal branches of the tree, indicate that these changes are likely removed by purifying selection.
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This suggests that changes in synteny are relatively frequent, occurring at a rough rate of one every ~250 clonally-inherited mutations on the (filtered) core-genome.
schematic representation of all observed changes in order of genes in the core genome for the dataset considered.
[8/N]
Using the graph we can survey all possible changes of synteny in the dataset. Out of 222 isolates, we find only 26 with any change in synteny. Most of these changes are inversions, often around the origin or terminus of replication.
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Core-genome synteny, i.e. the order of core genes, has been shown to be strongly conserved across large evolutionary distances. But what happens on short timescales?
Schematic representation for a pangenome graph. Edges in the graph represent alignments of homologous portions of the sequences in different genomes. Each genome is a particular walk through the graph.
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Once selected the dataset we tackle a second challenge: detecting structural changes. For this we encode all genomes in a pangenome graph. The fundamental units of this representation are blocks, encoding alignments of homologous sequences, and paths, encoding genomes as sequences of blocks.
A core-genome phylogenetic tree for our ST131 dataset.
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Thanks to its recent evolution, we can filter out the effects of recombination from the core-genome alignment of this dataset, and recover a reliable phylogeny.
