On The Origin Of A Genetic Constant - 6

DNA    ...    RNA

I. Preface

In the previous post of this series we took a look at the ~(32/35/25/6) genetic constant concerning DNA.

Today we ask and answer the same question concerning the ~(32/33/26/7) genetic constant in RNA.

II. Where Did The ~(32/33/26/7) Originate?

That is a fair question about RNA just as it was with DNA.

So, this post will answer the question again, just as the previous post did with DNA,  and will provide the exact values concerning RNA and where the RNA genetic constant "~(32/33/26/7)" originates deep in the atomic structure of many, if not all, genomes.

Let's start with the nucleotide and its atoms, and their quantities that are in BOTH DNA and RNA (just like we did with DNA):

Nucleotides(ACG), atoms, and counts in both DNA and RNA:

A: (Adenine) 15 atoms (5 carbon, 5 hydrogen, 5 nitrogen, 0 oxygen)

C: (Cytocine) 13 atoms (4 carbon, 5 hydrogen, 3 nitrogen, 1 oxygen)

G: (Guanine) 16 atoms (5 carbon, 5 hydrogen, 5 nitrogen, 1 oxygen)

44 total atoms (14 carbon, 15 hydrogen, 13 nitrogen, 2 oxygen)

Percentages:
(carbon 31.8181, hydrogen 34.0909, nitrogen 29.5455, oxygen 4.5455)

---

Now let's add the missing ingredient ("U") needed to make the nucleotide group complete for RNA:

Additional nucleotide(U), atoms, and count (only in RNA):
U: (Uracil) 12 atoms, (4 carbon, 4 hydrogen, 2 nitrogen, 2 oxygen)
56 total atoms (18 carbon, 19 hydrogen, 15 nitrogen, 4 oxygen)

Percentages:
(carbon 32.1429, hydrogen 33.9286, nitrogen 26.7857, oxygen 7.1429)

That is the source for the RNA (ACGU) ~(32/33/26/7) genetic constant.

It is 18, 19, 15, and 4 divided by 56 (x 100.0) which determines those percentages of those atoms in the genomes of RNA.

Here is an early test on a several hundred RNA genomes (more to come when I build-up my SQL RNA database):

GenBank FASTA Files Genome Analysis Report (RNA)

after processing 100 genomes:

variation count @ <1.0% = 400
variation count @ <2.0% = 0
variation count @ <3.0% = 0
variation count @ <4.0% = 0
variation count @ >=4.0% = 0

after processing 200 genomes::

variation count @ <1.0% = 800
variation count @ <2.0% = 0
variation count @ <3.0% = 0
variation count @ <4.0% = 0
variation count @ >=4.0% = 0

after processing 300 genomes::

variation count @ <1.0% = 1,200
variation count @ <2.0% = 0
variation count @ <3.0% = 0
variation count @ <4.0% = 0
variation count @ >=4.0% = 0

after processing 400 genomes::

variation count @ <1.0% = 1,600
variation count @ <2.0% = 0
variation count @ <3.0% = 0
variation count @ <4.0% = 0
variation count @ >=4.0% = 0

Total processed 405 genomes:

variation count @ <1.0% = 1,620 (100.0000%)
variation count @ <2.0% = 0 (0.0000%)
variation count @ <3.0% = 0 (0.0000%)
variation count @ <4.0% = 0 (0.0000%)
variation count @ >=4.0% = 0 (0.0000%)

III. Update

I added 43,279 SARS-CoV-2 RNA virus genomes to the SQL table (total RNA genomes now is 43,684).

Here are the new results:

GenBank Flat Files Genome Analysis Report

after processing 5,000 genomes:

variation count @ <1.0% = 20,000
variation count @ <2.0% = 0
variation count @ <3.0% = 0
variation count @ <4.0% = 0
variation count @ >=4.0% = 0

after processing 10,000 genomes:

variation count @ <1.0% = 40,000
variation count @ <2.0% = 0
variation count @ <3.0% = 0
variation count @ <4.0% = 0
variation count @ >=4.0% = 0

after processing 15,000 genomes:

variation count @ <1.0% = 60,000
variation count @ <2.0% = 0
variation count @ <3.0% = 0
variation count @ <4.0% = 0
variation count @ >=4.0% = 0

after processing 20,000 genomes:

variation count @ <1.0% = 80,000
variation count @ <2.0% = 0
variation count @ <3.0% = 0
variation count @ <4.0% = 0
variation count @ >=4.0% = 0

after processing 25,000 genomes:

variation count @ <1.0% = 100,000
variation count @ <2.0% = 0
variation count @ <3.0% = 0
variation count @ <4.0% = 0
variation count @ >=4.0% = 0

after processing 30,000 genomes:

variation count @ <1.0% = 120,000
variation count @ <2.0% = 0
variation count @ <3.0% = 0
variation count @ <4.0% = 0
variation count @ >=4.0% = 0

after processing 35,000 genomes:

variation count @ <1.0% = 140,000
variation count @ <2.0% = 0
variation count @ <3.0% = 0
variation count @ <4.0% = 0
variation count @ >=4.0% = 0

after processing 40,000 genomes:

variation count @ <1.0% = 160,000
variation count @ <2.0% = 0
variation count @ <3.0% = 0
variation count @ <4.0% = 0
variation count @ >=4.0% = 0

Total processed 43,684 genomes:

variation count @ <1.0% = 174,736 (100.0000%)
variation count @ <2.0% = 0 (0.0000%)
variation count @ <3.0% = 0 (0.0000%)
variation count @ <4.0% = 0 (0.0000%)
variation count @ >=4.0% = 0 (0.0000%)

IV. Closing Comments

This  may end up being a useful tool for determining the degree of validity of the collecting and processing of RNA samples ("how close does a genome match the constant?" would be the question to ask).

The next post in this series is here, the previous post in this series is here.