The final "Early Career Researchers in Speciation"-themed seminar is coming up on the 5th of May!
This session features talks by Aryeh Miller on Anolis lizards 🦎 and @rishidekayne.bsky.social on speciation research in industry 📊
+ we'll hear from Dr Mariana Braga about her career in the Q&A ✋
Looking forward to kicking off the seminar series of our special topic network on structural variants #SV in evolutionary biology. @eseb.bsky.social
Join us in listening to Pr. Rieseberg on April 29
All code and data are available on GitHub: github.com/Arcadia-Scie.... We hope this work helps others think about quality control of input sequences before running structure prediction at scale.
@arcadiascience.com
[9/9]
This matters because protein structure prediction is increasingly being run on sequences from non-model organisms where we have little prior knowledge. Erroneous sequences from genome misassembly can silently percolate through prediction models into structure databases. [8/9]
a phylogenetic tree showing that the sub-domains of dimers we found are split between two clades
Our phylogenetic analysis shows this clearly: split domains from tandem proteins land in the same clusters as their separately annotated counterparts, not together as you'd expect if they shared a single evolutionary origin. [7/9]
Most tandem dimers turned out to comprise two different genes, Jhbp7 and takeout, which sit next to each other in the genome. The domain order in every dimer matched the gene order in the D. melanogaster genome, strongly suggesting misassembly or misannotation rather than real biology. [6/9]
To figure out what was going on, we built a pipeline to split tandem-domain proteins into their constituent parts using HMM profiles, then used phylogenetics and BLASTp to identify what each domain actually was. [5/9]
an example of a single-domain takeout copy, a dimer, and a trimer
Here's what these look like: a normal single-domain Takeout structure alongside tandem dimers and trimers from different Drosophila species. [4/9]
Digging in, we found these long sequences were tandem dimers and trimers of the conserved domain. These structures had been inferred from what appear to be erroneous input sequences. [3/9]
We were exploring structural conservation in Takeout proteins using the TIPS insect structure database (tips.shenxlab.com) when we hit a roadblock: some sequences were 2-3x longer than others, creating huge gaps in our alignments. [2/9]
New pub! We analyzed protein structures across 36 Drosophila species and found something unexpected: tandem dimers and even trimers lurking in protein structure databases that likely aren't real proteins at all. 🧵 thestacks.org/publications... [1/9]
You can see some of the many ways we are leveraging evolutionary biology in our pubs here: thestacks.org
Are you an evolutionary biologist who thinks about the evolution of protein sequences/structure/function across scales? Do you want to implement your skills in industry? We’ve opened a full-time role on our platform team (think computational research group)!
lnkd.in/gVE2NGaV
We have an opportunity to join our team at @arcadiascience.com jobs.lever.co/arcadiascien...
We're looking for an evolutionary biologist who thinks a lot about protein evolution. If you have experience investigating genomic and amino acid sequence evolution and protein structural evolution, apply!
🧬 Join the STRiVE kick-off conference (TiBE-STRiVE) July 8-10, 2026 near Porto! Registration & abstracts open:
🔹 Members: Feb 25-Apr 7
🔹 Non-members: Mar 16-Apr 7
Info: structuralvariantsstn.github.io/porto_about/
#Evolution #Genomics #Biology
🧬We are launching STRiVE, a @eseb.bsky.social Special Topic Network on the evolutionary role of structural genomic variation.
🗓️Std:
29/04: Online seminar w/ L. Rieseberg
8-10/07: Kick-off in Porto
Join us: structuralvariantsstn.github.io #Evolution #Genomics #StructuralVariants #Biology #PopGen
This framework could help us move from serendipitous discovery toward predictive identification of bio-utility across the tree of life. Read the full perspective for the details. [8/8]
Evolution tells us not just what's useful, but where to look next, highlighting lineages that might harbor untapped potential for biotechnology applications. [7/8]
But protein data alone isn't enough. Integrating evolutionary relationships between species with this protein information helps us understand where useful functions evolved, distinguish conservation from convergence, and spot breakthrough novelty in constrained protein families. [6/8]
The key? Identifying indicators of bio-utility from protein variation itself:
• Proteins with extreme properties (like extremophile polymerases)
• Proteins with discrete novel functions (like different proteases)
• Proteins showing conserved or convergent functions (like antifreeze proteins) [5/8]
Schematic of information that can be leveraged for finding and interpreting bio-utility, including a gene tree, the characteristics of proteins simplified into a 2D protein surface, and the evolutionary relationships between the species in which the same proteins evolved, highlighting the wealth of knowledge and broader perspective we gain about proteins when we include their evolutionary information.
We propose an evolution-integrated framework that combines the best of both worlds: expanding sampling breadth while more deeply interrogating existing data through an evolutionary lens. [4/8]
In silico bioprospecting tries to fix this by mining existing databases. But those databases are taxonomically biased (see another one of our pubs dropping today: thestacks.org/publications...), and the methods often require knowing what you're looking for upfront. [3/8]
Bioprospecting has given us incredible discoveries, from life-saving drugs to agricultural innovations. But traditional approaches rely heavily on serendipity and are hard to scale. [2/8]
Really excited to share my first pub from my work at @arcadiascience.com - Strategizing the search for bio-utility: A framework for evolution-integrated in silico bioprospecting thestacks.org/publications... [1/8] 🧵
Interested in whether protein language models can learn about evolution and leverage this to predict protein structures? Check out the pub!
I'm very excited to share that today is my last day working at UC Berkeley before I start my new position at @arcadiascience.com next week! It has been a pleasure working as a postdoc in so many amazing labs along side amazing scientists, and I can't wait to start this new chapter!
I am recruiting PhD students to join my lab at MSU starting Fall 2026! Please spread the word and reach out if you are interested and/or want to learn more!
Ryan, @rishidekayne.bsky.social et al. present six new chromosome-level genome assemblies for Poeciliidae, providing a foundation for studies on convergent evolution, repeat content, and demographic history.
🔗 academic.oup.com/gbe/article/17/6/evaf111/8169767
#genome #evolution
Top: Topology weightings across chr15 showing how the karamu haplotype is related to the klugii and orientis haplotypes. Upper panel shows three possible rooted genealogical topologies. Second panel shows weights for each topology along the chromosome, smoothed with a 20 kb span. Arrows above the plot indicate the locations of inversions. Third panel shows unsmoothed topology weightings across a 1.5 Mb region corresponding to Inversion 2. Bottom: Ancestry painting across a 100 kb region within Inversion 2, showing ancestry tracts for two homozygous karamu individuals compared to two representative individuals homozygous for the orientis and klugii haplotypes. Coding regions are indicated below the plot, with the candidate gene for background colouration yellow indicated. Green triangles represent the top 10 SNPs for background colour in our GWAS. There is evidence for recombination throughout the supergene region, and specifically in the vicinity of yellow, consistent with the hypothesis that orientis ancestry at this locus (i.e., the B allele) is associated with darker colouration in karamu individuals.
Dynamics of a supergene. A study of the BC supergene in wing color morphs of the African monarch #butterfly by @rishidekayne.bsky.social &co reveals dynamic evolution of #supergene haplotypes, fueled by incomplete recombination suppression 🧪 @plosbiology.org plos.io/3DiFhnL
Happy that this paper - www.biorxiv.org/content/10.1... - was accepted at Mol Ecol Res and excited about the promise of this technology to measure pop sizes for such elusive species as snow leopards + learn about at least 1st-degree relationships + beyond? Congrats to Katie Solari and other authors!