Feels very timely today to be on the way to the #Nobel Symposium in Stockholm: The First Great Pandemic of the 21st Century.
Though I do not believe the #monkeypox outbreak will be a pandemic, it's a reminder there are many pathogens that can rear their heads in different ways.
Tomorrow, I'll talk about how phylogenetics played a critical role in #COVID19#SARSCoV2 - but also outline what challenges we face when we look to tomorrow's pandemics. In particular we have to ensure: sequencing, sharing, software & sufficient background.
A lot of pandemic preparedness focuses on detecting a new pathogen, or what to do once we know it's bad news. But a lot of the critical work is a little more boring: encouraging #openscience, ensuring credit, maintaining & advancing software & building up our virus knowledge.
Even as I wrote out the framework for the talk, I feel like #monkeypox is highlighting exactly why these things are so critical:
Through available sequencing tech in affected countries & truly open sharing, we've got the first genomes fast & a community pouring over them.
We have software that's ready to handle big genomes (#monkeypox is 200,000 bases compared to #SARSCoV2 30,000) & (mostly ready) to do tricky alignments for a DNA virus with lots of repeats. We have great viz for trees & geography ready to go.
Critically, with truly open genomes scientists are able to share alignments with each other (so everyone doesn't have to figure out how to do those tricky bits) & feed these genomes into high-powered platforms that will spit out incredible stats (more on this later!).
And finally, sequences from previous outbreaks are allowing us to gain critically important insight into how this #monkeypox outbreak relates to previous ones. Still, how much is unsampled that might be giving us more clues now? Background is important!
Chances are, we won't perfectly identify in advance the next pathogen we have to tackle. But if we ensure the community, technology, sequencing, personnel, & background is in place widely, we'll be better prepared to tackle _whatever_ the next pathogen is that causes us alarm.
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This morning's session at the #Nobel Symposium starts with Michael Farzan explaining ACE2 binding in #SARSCoV2 & #SARSCoV1, including a great timeline comparison between how long steps took between the two (much faster in SC2) & asking if we can improve vaccines.
@MichaelWorobey follows, speaking to the importance of digging into the evidence of #SARSCoV2 outbreak sources & Hunan market.
He opens with a remark about how this beautiful location with so many great people to talk to is pretty close to heaven - he's not wrong!
Hyeryun Choe closes the first session with a great talk on how #SARSCoV2 adapted to humans, and the importance of the D615G mutation, to stabilizing the protein. With a great graphic reminder of how quickly G614 dominated.
🎉VERY excited🎊 to share news of the shiny new #monkeypox@GenSpectrum (previously CoV-Spectrum) website & API - allowing lightning-fast ⚡️ queries to get info on metadata & sequence information.
As more #opendata sequences come in, this will be incredibly powerful!
I hinted at this coming a little earlier today 😉 - but want to really emphasize: this is what truly #opendata looks like.
Enabling development of tools that are able to benefit everyone to do fast & effective research to underpin response & understanding
You can find these links in a dedicated box, just above the gif protein structures - just click on any of the available proteins (any with mutations) to go straight through to Aquaria.app!
2/4
For details on how this works, check out @aSchaFu's fantastic thread, including some of the amazing things you can investigate using Aquaria.app:
In line with the recent addition of 22A (BA.4), 22B (BA.5), & 22C (BA.2.12.1) as @nextstrain clades, you can now find these three variants on CoVariants.org!
As usual, each new variant has their own page, where you can see defining mutations (left), a per-variant plot (bottom) and read about the variant, its mutations, & studies done so far!
Note that these are *estimates* of the proportion of cases by variant. Only a small % of cases are sequenced - we're extrapolating, so interpret with care!!
2/7
Currently we only show countries that have sequenced at least 3% cases for >=50% of the 2-week period CoVariants tracks (since May 2020). This tries to avoid plotting data that may not represent cases well.
If you think a country has good data & is missing - let me know!
3/7
CoVariants now filters out more data that can lead to misleading or confusing variant trends! Thanks to eagle-eyed users 🔎 & @MoiraZuber!
1/5
1 - CoVariants now only displays variant data from sequences that have come from humans.
A variant may circulate longer in animals than in people, & focused sampling of those animals can thus give a misleading picture. See mink EU1 samples in Latvia, before & after!
2/5
2 - Many people had flagged strange pockets of recent 'other' sequences in countries like Italy, India, Chile & others.
Was this a new variant?
No: most of these are just bad sequences where a clade can't be assigned! CoVariants now filters these out entirely!