@gut_health
@bkoskella
@RSocPublishing
Check out this special feature of #ProcB edited by @bkoskella @gut_health and me. Thanks to the authors for their excellent article contributions - and all articles are #freetoread. Hopefully there is something of interest for all #microbiome researchers here @RSocPublishing
#ProcB Application of ecological and evolutionary theory to #microbiome community dynamics @jamesemcdonald @bkoskella https://t.co/fqOH0kVkaE. All Special Feature papers Free to Read - https://t.co/S02w5ODyHQ pic.twitter.com/jLk3eai1eO
— Royal Society Publishing (@RSocPublishing) January 1, 2021
@gut_health
@bkoskella
@RSocPublishing
@PhilipLeftwich
Matthew P. Edgington
and Tracey Chapman
@clydeandforth1
Martin Broberg
Sandra Denman
@jamesemcdonald
Inga Leena Angell
Knut Rudi
@CristinaHerren
@WillPerryMEFGL
Elle Lindsay, Christopher James Payne, Christopher Brodie and Raminta Kazlauskaite
@BrianTrevelline Jahree Sosa, Barry K. Hartup and
@KevinDKohl
Shen Jean Lim and @Symbionticism
Adam Ossowicki, Vittorio Tracanna, Marloes Petrus, @GillesvanWezel Jos Raaijmakers, Marnix Medema @PaolinaGarbeva
@cgreysongaito, Timothy Bartley, Karl Cottenie, Will Jarvis, @Newman_lab and @M_Stothart
@PaulineDScanlan
@M_Stothart
Rupert Palme
@Newman_lab
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The new answer to a 77-year-old problem in data analysis, published today in @naturemethods. Instead of significance tests, use estimation graphics. Our software suite DABEST makes it easy for everyone to visualize effect sizes.https://t.co/UzwXJ7EUC5 pic.twitter.com/VtxyY0xaRM
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Hard agree. And if this is useful, let me share something that often gets omitted (not by @kakape).
Variants always emerge, & are not good or bad, but expected. The challenge is figuring out which variants are bad, and that can't be done with sequence alone.
You can't just look at a sequence and say, "Aha! A mutation in spike. This must be more transmissible or can evade antibody neutralization." Sure, we can use computational models to try and predict the functional consequence of a given mutation, but models are often wrong.
The virus acquires mutations randomly every time it replicates. Many mutations don't change the virus at all. Others may change it in a way that have no consequences for human transmission or disease. But you can't tell just looking at sequence alone.
In order to determine the functional impact of a mutation, you need to actually do experiments. You can look at some effects in cell culture, but to address questions relating to transmission or disease, you have to use animal models.
The reason people were concerned initially about B.1.1.7 is because of epidemiological evidence showing that it rapidly became dominant in one area. More rapidly that could be explained unless it had some kind of advantage that allowed it to outcompete other circulating variants.
Variants always emerge, & are not good or bad, but expected. The challenge is figuring out which variants are bad, and that can't be done with sequence alone.
Feels like the next thing we're going to need is a ranking system for how concerning "variants of concern\u201d actually are.
— Kai Kupferschmidt (@kakape) January 15, 2021
A lot of constellations of mutations are concerning, but people are lumping together variants with vastly different levels of evidence that we need to worry.
You can't just look at a sequence and say, "Aha! A mutation in spike. This must be more transmissible or can evade antibody neutralization." Sure, we can use computational models to try and predict the functional consequence of a given mutation, but models are often wrong.
The virus acquires mutations randomly every time it replicates. Many mutations don't change the virus at all. Others may change it in a way that have no consequences for human transmission or disease. But you can't tell just looking at sequence alone.
In order to determine the functional impact of a mutation, you need to actually do experiments. You can look at some effects in cell culture, but to address questions relating to transmission or disease, you have to use animal models.
The reason people were concerned initially about B.1.1.7 is because of epidemiological evidence showing that it rapidly became dominant in one area. More rapidly that could be explained unless it had some kind of advantage that allowed it to outcompete other circulating variants.
