Visualizing Covid-19's RNA
I thought it might be interesting to visualize the code in the Covid-19 virus based on the fact it has sugar proteins stitched into it's outer coating to try to fool immune systems. I found the virus' genome decoded here:
https://www.ncbi.nlm.nih.gov/nuccore/MN988668
I wrote code to count the number of codons of a particular length in the virus' genomic dataset. In search, we call such strings "words" and the data set a "document". A "facet" is a count of strings appearing in a set of documents with words being broken by a given character, like a white space.
In the case of this virus, I'm breaking the words along fixed lengths of nucleotides with a sliding frame that increments by 1 as it slides. I've graphed various length words, some of which appear below.
How
I used vim to remove all line numbers and whitespace and placed it in covid19_rna.txt. I then created a random file of the same length and placed it in random_rna.txt. I also dumped the code for SARS.
I use a variety of word lengths, I'll call them codons here. Codon lengths of 5, 4 and 3 result in graphs that I consider to be regular (ordered) in nature, but not necessarly odd looking. The expecation would be to see some codons have more popularity than others, such as words like "the" and "and" appear more frequently in text.
However, when I run with codon lengths of 6, 7 and 8 this results in distribution graphs which appear to follow the golden ratio (phi). When the codons are sorted by count occurance, it becomes obvious that there are groups of codons which share a similar count of occurance in the genomes of Covid-19 and SARS, and which decrease over the entire data set by something that appears to be related to a numerical sequence.
At this time I'm assuming this graph might be a result in frames of a given size sliding by 1, instead of the frame length, as we would do with normal word breaking in search.
When graphing random numbers, this sequencing is not observed. I intend to graph other ordered document (such as text and software code) to see if my code is somehow introducing the sequence.
Running
You'll need to hack around a bit to run this. I'll work on making better later. For now you can see the output in the output folder.
Graphs
These graphs were generated from the output of the code for various codon lengths using Google Docs (spreadsheets).
For codon length 5 and below, the graphs seem to show a non-smooth curve in distribution. Please note that not all nucleotide sequences are shown in the labeling, but the counts are all there.
In codon lengths 6 and above, the genome of the virus appears to produce curves (from the counts of occurances of a given string) that follow a fibonacci sequence. The number of the items in each unique counts appear to be increasing in size by a ratio, while the counts themselves are decreasing. Again, this appears for codon lengths 6 and up.
Here's the unsorted graph for a codon length of 6:
Here's what random data looks like when you graph it and sort by string occurance:
More Thoughts
I've found some information about how DNA uses phi.
https://www.goldennumber.net/dna/ (a bit fluffy) https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2863758/ https://www.researchgate.net/publication/339331507_Wuhan_nCoV-2019_SARS_Coronaviruses_Genomics_Fractal_Metastructures_Evolution_and_Origins
Here's stuff I found using the odd flat groupings of sequences in the 5 length words of the virus:
66 occurances each
AAAAG https://www.ncbi.nlm.nih.gov/pubmed/264240
AATTG https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5334452/
ACATT https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5778463/
ACTTA https://www.ncbi.nlm.nih.gov/pmc/articles/PMC323787/
ATGCT https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2977869/
CAATT https://www.ncbi.nlm.nih.gov/pubmed/9786894
CTATT https://www.microbiologyresearch.org/docserver/fulltext/jgv/74/12/JV0740122539.pdf?expires=1585808280&id=id&accname=guest&checksum=568B9B26882A4A88F1355CEE1716DE5E
TACTA https://jvi.asm.org/content/jvi/79/11/6918.full.pdf and https://www.sciencedirect.com/science/article/pii/S004268221730048X
65 each
TGCTA https://jvi.asm.org/content/jvi/84/19/10266.full.pdf
TTGAA https://www.microbiologyresearch.org/docserver/fulltext/jgv/98/5/946_vir000758.pdf?expires=1585808671&id=id&accname=guest&checksum=2C2374257B060DA42469D9A1A838A096
64 each
AATGG https://www.nature.com/articles/s41467-018-07944-x
TAAAG https://jvi.asm.org/content/jvi/51/3/754.full.pdf
TAAAT https://pdf.sciencedirectassets.com/272412/1-s2.0-S0042682200X06691/1-s2.0-004268229090513Q/main.pdf
TGACA https://www.researchgate.net/publication/6399009_Plant_lectins_are_potent_inhibitors_of_coronaviruses_by_interfering_with_two_targets_in_the_viral_replication_cycle#pf3
TGTAG https://journals.plos.org/plospathogens/article?id=10.1371/journal.ppat.0030005
TTCAA https://link.springer.com/content/pdf/10.1007%2F978-1-4684-1280-2_6.pdf
TTGAT https://patents.google.com/patent/CA2953677A1/en
56 each
AAACT https://patentimages.storage.googleapis.com/48/4b/34/400b8f64987e55/US7888102.pdf
AGACA https://virologyj.biomedcentral.com/articles/10.1186/s12985-017-0747-z
AGATG https://www.researchgate.net/publication/7225941_High_expression_level_of_soluble_SARS_spike_protein_mediated_by_adenovirus_in_HEK293_cells
CTAAT https://patents.google.com/patent/CN1815234B/en
CTTCT https://pdf.sciencedirectassets.com/272412/1-s2.0-S0042682200X00530/1-s2.0-S0042682200902186/main.pdf
GAAAA https://www.researchgate.net/publication/232220254_Detection_of_a_novel_human_coronavirus_by_real-time_reverse-transcription_polymerase_chain_reaction
GAAGA https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7087027/
GTTAC https://jvi.asm.org/content/jvi/82/3/1465.full.pdf
TAAAC https://en.wikipedia.org/wiki/Expression_cassette and https://jvi.asm.org/content/jvi/69/12/7851.full.pdf
TATGT https://www.tandfonline.com/doi/pdf/10.1080/03079450120066368
TCAAA https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4763971/
TGATT sciencedirect.com/science/article/pii/S0042682208005230 (ACE2 warning)
TGTCT https://www.microbiologyresearch.org/docserver/fulltext/jgv/68/10/JV0680102639.pdf
TTTGA https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6178081/
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