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Recently I learned something about DNA that blew my mind, and in this thread, I'll attempt to blow your mind as well. Behold: Chargaff's 2nd Parity Rule for DNA N-Grams.
If you are into cryptography or reverse engineering, you should love this.
Thread:
DNA consists of four different 'bases', A, C, G and T. These bases have specific meaning within our biology. Specifically, within the 'coding part' of a gene, a triplet of bases encodes for an amino acid
Most DNA is stored redundantly, in two connected strands. Wherever there is an A on one strand, you'll find a T on the other one. And similarly for C and G:
T G T C A G T
A C A G T C A
(note how the other strand is upside down - this matters!)
If you take all the DNA of an organism (both strands), you will find equal numbers of A's and T's, as well as equal numbers of C's and G's. This is true by definition.
This is called Chargaff's 1st parity rule.
https://t.co/jD4cMt0PJ0
Strangely enough, this rule also holds per strand! So even if you take away the redundancy, there are 99% equal numbers of A/T and C/G * on each strand *. And we don't really know why.
This is called Chargaff's 2nd parity rule.
If you are into cryptography or reverse engineering, you should love this.
Thread:
DNA consists of four different 'bases', A, C, G and T. These bases have specific meaning within our biology. Specifically, within the 'coding part' of a gene, a triplet of bases encodes for an amino acid
Most DNA is stored redundantly, in two connected strands. Wherever there is an A on one strand, you'll find a T on the other one. And similarly for C and G:
T G T C A G T
A C A G T C A
(note how the other strand is upside down - this matters!)
If you take all the DNA of an organism (both strands), you will find equal numbers of A's and T's, as well as equal numbers of C's and G's. This is true by definition.
This is called Chargaff's 1st parity rule.
https://t.co/jD4cMt0PJ0
Strangely enough, this rule also holds per strand! So even if you take away the redundancy, there are 99% equal numbers of A/T and C/G * on each strand *. And we don't really know why.
This is called Chargaff's 2nd parity rule.
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.
This is a thread on statistics in science: 1/7 (via @LogicofScience)
Basic Statistics Part 1: The Law of Large Numbers https://t.co/wUH8eAAIak
#Science #Statistics
Basic Statistics Part 2: Correlation vs. Causation
https://t.co/Azhyl8pDsX (2/7)
Basic Statistics Part 3: The Dangers of Large Data Sets: A Tale of P values, Error Rates, and Bonferroni Corrections
https://t.co/LetN6aEBRM (3/7)
Basic statistics part 4: understanding P values
https://t.co/K8MMMgTCOf (4/7)
Basic Statistics Part 5: Means vs Medians, Is the “Average”
Basic Statistics Part 1: The Law of Large Numbers https://t.co/wUH8eAAIak
#Science #Statistics
Basic Statistics Part 2: Correlation vs. Causation
https://t.co/Azhyl8pDsX (2/7)
Basic Statistics Part 3: The Dangers of Large Data Sets: A Tale of P values, Error Rates, and Bonferroni Corrections
https://t.co/LetN6aEBRM (3/7)
Basic statistics part 4: understanding P values
https://t.co/K8MMMgTCOf (4/7)
Basic Statistics Part 5: Means vs Medians, Is the “Average”
1/ Automobiles and Intake Fraction. Since cars are back in the news I thought I would retweet this model result I offered in early April 2020. I focused only on 1 micron particles & accounted for windows completely closed & cracked slightly open.
2/ Related air exchange rates were based on experimental results in literature for mid-sized sedans. Particle deposition to indoor surfaces were accounted for, as the surface to volume ratio in a 3 m3 cab is large. An important outcome was the intake fraction (IF)
3/ Here, IF is the number of particles (or virions in collective particles) inhaled by a receptor DIVIDED BY the number or particles (or virions in collective particles) emitted by an infector.
4/ Integrated over the two hour drive (in this example) the IF for all windows closed & a receptor at rest is 0.08 (8% of what comes out of the infectors respiratory system ends up in the respiratory system of the receptor). 8%! That is a very high intake factor.
5/ With additional ventilation from cracking a window open drops the IF to 0.012 (1.2%) still relatively high. Can get lower by opening more windows.
Simulation: Riding in car for 120 min w/ infected passenger who seems fine other than a cough every few mins. (1) a lot of SARS-CoV-2 virus (in fine aerosol particles) accumulation in car cabin w/ windows closed; (2) cracking window open slightly = dramatic reduction. #COVID19 pic.twitter.com/bCmrmnLUPG
— Dr. Richard Corsi (@CorsIAQ) April 4, 2020
2/ Related air exchange rates were based on experimental results in literature for mid-sized sedans. Particle deposition to indoor surfaces were accounted for, as the surface to volume ratio in a 3 m3 cab is large. An important outcome was the intake fraction (IF)
3/ Here, IF is the number of particles (or virions in collective particles) inhaled by a receptor DIVIDED BY the number or particles (or virions in collective particles) emitted by an infector.
4/ Integrated over the two hour drive (in this example) the IF for all windows closed & a receptor at rest is 0.08 (8% of what comes out of the infectors respiratory system ends up in the respiratory system of the receptor). 8%! That is a very high intake factor.
5/ With additional ventilation from cracking a window open drops the IF to 0.012 (1.2%) still relatively high. Can get lower by opening more windows.
It's another stunning Malagasy #dartfrog/#poisonfrog for today's #FrogOfTheDay, #42 Mantella cowani Boulenger, 1882! A highly threatened, actively conserved and managed frog from the highlands of central #Madagascar
#MadagascarFrogs
📸D.Edmonds/CalPhotos
This thread will cover only a tiny fraction of the work on Mantella cowanii because, being so charismatic and threatened, it has received quite a bit of attention.
#MadagascarFrogs
We start at the very beginning: the first specimens, two females, were collected by Reverend Deans Cowan in East Betsileo, Madagascar, and sent to London, where George Albert Boulenger described the species in 1882.
#MadagascarFrogs
Boulenger placed the species in his new genus, Mantella, along with ebenaui, betsileo, and madagascariensis. He recognised that the other Malagasy poison frogs were distinct from the Dendrobates of the Americas, although he did keep them in the Dendrobatidae.
#MadagascarFrogs
As more specimens were collected, it became clear that the species was highly variable. In 1978, Jean Guibé wrote with interest about this variability, describing a new subspecies, M. cowani nigricans—today a full species. #MadagascarFrogs
https://t.co/dwaHMbrYbj
#MadagascarFrogs
📸D.Edmonds/CalPhotos
This thread will cover only a tiny fraction of the work on Mantella cowanii because, being so charismatic and threatened, it has received quite a bit of attention.
#MadagascarFrogs
We start at the very beginning: the first specimens, two females, were collected by Reverend Deans Cowan in East Betsileo, Madagascar, and sent to London, where George Albert Boulenger described the species in 1882.
#MadagascarFrogs
Boulenger placed the species in his new genus, Mantella, along with ebenaui, betsileo, and madagascariensis. He recognised that the other Malagasy poison frogs were distinct from the Dendrobates of the Americas, although he did keep them in the Dendrobatidae.
#MadagascarFrogs
As more specimens were collected, it became clear that the species was highly variable. In 1978, Jean Guibé wrote with interest about this variability, describing a new subspecies, M. cowani nigricans—today a full species. #MadagascarFrogs
https://t.co/dwaHMbrYbj