🔊 ERGA committees are open to all members. Interested in embedding justice, equity, diversity, and inclusion principles in #biodiversitygenomics? Consider joining our Social Justice Committee!

www.erga-biodiversity.eu/team-1/socia... #community #science #socialjustice #diversity

💬 Check out this interview to learn more about our #SocialJustice committee and their plans for promoting positive #change inside our community and beyond: www.erga-biodiversity.eu/post/5-quest... @jamesfvfleming.bsky.social @fghiselli.bsky.social

It's that developing sideline in Texas Hold'em, presumably

Indeed, Czech journalist Saša Uhlová argues that the ‘iron wage curtain’, maintaining stark economic East-West inequalities through the systemic reliance of Western industries on exploited East-European labour, is one of the biggest threats to social cohesion and stability in today’s Europe

I love when you see two of them next to each other and they start boxing like some weird Shrimp-themed Rock 'em Sock 'em Robot set. Adorable.

To detect problematic para and xenologs, I might recommend TreSpex, which has a good workflow.
I am also working on BOSTIn, which combines the DE-Score with others for a more comprehensive picture of a variety of phylogenetic artifacts
github.com/JFFleming/BO...
journals.sagepub.com/doi/10.4137/...

At the same time, the Vieira & Rozas study on Odorant Binding Proteins shows 62% of sequences are saturated. That is probably due to the high-paced adaptation associated with ODPs - but that also makes them challenging to resolve phylogenetically (which they rightly note in that study).

A good example (with paralogs) is in Table 1. I took one of my old studies, which has multiple opsin paralogs (expressed in ciliary and rhabdomeric receptors in the eyes, brains, skin and pineal glands), and the DE-Score is high because the sampling across each paralogous clade is reasonable even.

Topology errors from site saturation are generally more of an interaction between the dataset and the model (it's a violation of the GTR model's assumptions), so how the DE-Score deals with para- and xenologs will depend on the dataset.

This was inspired by a lot of great work that has already been done before me, especially this work by Mattia Giacomelli - www.cell.com/iscience/ful...
and this work by @xelamarie92.bsky.social - academic.oup.com/sysbio/artic...

Which got me thinking about how amino acid recoding could be used. 8/7

The reason that score varies is that the DE-Score normalises itself for the size of your dataset (the bottom bit), letting you compare saturation between datasets regardless of how their size. So it's simple! Input the alignment, look for Scores bigger than 0, then bigger than the Critical! 7/7

However, there are some points when the dataset has information, but is still too noisy. A "yellow card" to the "red card" of DE-Score 0. That's when the within/between ratio < 0.265. The DE-Score varies for each dataset, but our software calculates it, and calls it the Critical Score. 6/7