Glad to see someone else really have a proper look at the data, and to see our raw analysis be replicated. After looking briefly at Jesse's results, I agree, it largely in agreement!
That said there are some enormous issues in interpretation and presentation here to address (1/7) Image
Most inaccurate to me is table 1. It relies on an arbitrary 20% cutoff of all chordates to dismiss many samples with wildlife DNA/RNA. I've put it below, with a corrected version on the right without this cutoff. Multiple viral positive samples had raccoon dog DNA. (2/7) Image
This cutoff is wrong for a few reasons. #1: wildlife samples have naturally more species diversity, so fewer hits >20%. #2: the virus is generally rare in the samples, and the host likely is too. #3: mammalian DNA% is really what is relevant for guessing who shed the virus. (3/7)
Second, here was exactly what we wrote about correlation analyses. I've put it next to Bloom's correlation with the species labeled. Inferring the meaning of this juxtaposition is left as an exercise to the reader. (4/7) Image
Third, the most lacking part of the somewhat silly correlation figures made to date (I do think we can all agree!) is how they do not show their error bars. Here I've remade Bloom Fig 5b (linear axes) with some basic 95% confidence intervals. These don't say anything... (5/7) Image
Finally, correlations, abundances... meh, never going to "prove" much, either way. I'd prefer to do science that makes predictions:
The market was identified as the epidemiological center of the virus in the city, and a stall as the viral hotspot in the market last year.
After, the data came out: the exact right species were in the exact spot they were predicted to be, the very center of all known epidemiological links within the city, and the hot spot within the market. And the 2 viral lineages found nearby.
Look for predictions that work! (7/7)

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More from @acritschristoph

Apr 3
(A weekend SARS2 origins thread)
Last week Liu et al. provided an extensive update to their preprint on sampling the Huanan market:…

Their supplementary notes locations of all samples in the market, so we can map Jan 2020 positivity rate across the market:
The above plots the significance of relative sample positivity (comparing + vs - samples) across the market, and confirms a prediction from Worobey et al. 2022 Science: samples from the southwest corner of the market where wildlife was sold were more likely to test positive.
72 stalls were sampled 3+ times in first half of Jan 2020.

Only 6 had a positivity rate >30%: 4/6 were in the southwest corner. Wildlife stall #6-29 had the single highest positivity rate of these. This stall had DNA/RNA from raccoon dogs and other wildlife at the epicenter.
Read 20 tweets
Mar 22
Hoped to not have to, but briefly before I logout to get work done:
1. Now known that the team did not violate GISAID ToS. Shocked at how people who were supposedly advocates of open data on this issue attacked for that: the hypocrisy of that contingent has never been clearer
2. The main finding was that susceptible animals were in fact exactly where they were predicted to be, in two wildlife stalls on the side of the market where the earliest cases were, and the area where most positive swabs were taken. That conclusion is robust.
3. There were 6 swabs positive for raccoon dog from these stalls, not 1. Yes, one swab had very high abundance, and likely RNA, strongly indicating animal recency. That swab was not SARS2 PCR-positive, which indicates viral load was low, but was adjacent to other positive swabs.
Read 6 tweets
Nov 22, 2022
[CW: politics]
Another conspiracy bites the dust, and now there are a great number of people who owe Anthony Fauci and others an enormous apology. Here Fauci and Collins asked for honest evaluation of the evidence, alerting authorities, and pushing for a WHO investigation Image
And here Fauci first pushes for what would become the WHO investigations into the origins of COVID-19, at the suggestion of Jeremy Farrar. All of the new emails here *should* be bigger news than the original conspiracies were - especially with any upcoming drama from House Rs ImageImage
No response here but sadness and fury at how many conspiratorial lies here have been repeated about this over and over- by @aychan, @WashburneAlex, @emilyakopp, @Biol4Ever, @R_H_Ebright, Rand Paul, etc- and how many scientists have been duped by these lies
Read 6 tweets
Nov 13, 2022
I know he was a climate change denier... but presumably at one point he understood basic principles of evolution, given his biology books... But now he has no clue how recombination works or how to read a phylogeny. How consistently does this conspiracy turn brains to mush?
RaTG13 is not the most similar virus to SARS-CoV-2 at the whole genome level: BANAL-20-52 is. (96.8% for BANAL-52 and 96.1% for RaTG13) At the whole genome level, RaTG13 and SARS-CoV-2 diverged several decades ago.
But prolific natural recombination between betacoronaviruses means that some parts of the genome have different ancestry (and thus closest relatives) than others. That is what the work these pundits (Ridley, Ebright, Kopp, etc) are commenting on was about:
Read 17 tweets
Oct 21, 2022
Alright, I had a look last night and will quickly do a thread on it.
There are many kinds of 'wrong' in science, but this preprint is False. There are many reasons (links at end), but the main one: the “unusual” sites are all *exactly* found in natural bat coronaviruses. 1/n
The authors focus on cut sites of BsaI and BsmIB. Why these two REs? The simple answer is there is no good reason- they were just cherry-picked as the “most unusual”. Here's a plot of just a few RE cut sites possibilities across coronavirus genomes (SARS-CoV-2 at top)
And here are the two they happened to choose. Not because they are the most commonly used - or the best choice - but post-hoc rationalized because one of them was used previously in an old coronavirus paper from UNC.
Read 22 tweets
Oct 20, 2022
I am personally negative on this. Problem #1: There are two kinds of "gate-keeping" in publication: editors and peer reviewers. This model entirely retains editorial power and removes peer power. I think we actually want the exact opposite: democratizing the desk decision.
Problem #2: the idea here is that readers will read the paper, read the reviews, read the response, and reach a conclusion. I think that rarely works in practice except for field experts- we've been trying this on the rest of the internet for decades without a lot of success
Here I'm reminded of an infamous internet discussion as to whether or not we actually landed on the moon. After 14,000 replies and 5 years there was little consensus. Is that a model for science?…
Read 6 tweets

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