For those wondering how I generated the charts, here’s the preprint describing #ConvMut:

www.biorxiv.org/content/10.1...

hopefully the ConvMut software will be soon part of the @GISAID ecosystem.

H/t @dslab_polimi
And Happy 2025 to you all😅

@jbloomlab.bsky.social @rajlabn.bsky.social @benjmurrell.bsky.social @systemsvirology.bsky.social @erictopol.bsky.social @tryangregory.bsky.social @corneliusroemer.bsky.social @peacockflu.bsky.social @marionkoopmans.bsky.social @ilariacapua.bsky.social

Here are the growth advantages of the discussed sublineages, sorted by descending low IC, taken from CoV-Spectrum collection #42

In this chart you see NP.1, which has gained mutations at positions 31, 435, 445, and 478

In this chart you see MC.10.1, which has gained mutations at positions 31, 435, 445, and 478

Let’s now see the 3 lineages recently studied by @yunlong_cao in his preprint:

t.co/DUzWFFFSSs

In this chart you see LP.8.1 (the most likely candidate for the winter wave), which has gained mutations at positions 31, 190, 346, 445, and 478

With the exception of LF.7.2.1, all the current SARS-CoV-2 sublineages with highest growth advantages are descendant of JN.1.11.1. Let’s see the commonest convergent evolution in the JN.1.11.1 subtree. 🧵

ConvMut: A Web tool to analyze viral convergent mutations along phylogenies Convergent evolution in protein antigens is common across pathogens and has also been documented in SARS-CoV-2; the most likely reason is the need to evade the selective pressure exerted by previous i...

‼️🚨 #ConvMut is finally ready! Our Christmas 🎁 for the variant seekers community: automated charting and analysis of convergent evolution in SARS-CoV-2, developed by Politecnico University (#1 University in Italy). Preprint here👇

www.biorxiv.org/content/10.1...