Diffing coronaviruses

We can use standard UNIX tools to investigate the origins of the Wuhan coronavirus! I read on Wikipedia that “2019-nCoV has been reported to have a genome sequence 75% to 80% identical to the SARS-CoV and to have more similarities to several bat coronaviruses.” We can use diff to see those similarities:

$ ./genome_diff MG772933.1 MN988713.1
MG772933.1: 29802 words  26618 89% common  856 3% deleted  2328 8% changed
MN988713.1: 29882 words  26618 89% common  897 3% inserted  2367 8% changed

This says that there’s an 89% similarity between bat CoV (MG772933.1) and human nCoV (MN988713.1). More precisely, they share a subsequence of 26618 bases, in a total genome of only ~29800 bases.

That genome_diff script looks like this:

#!/bin/bash
fetch_genome() {
  curl -s "https://www.ncbi.nlm.nih.gov/sviewer/viewer.cgi?report=fasta&id=$1" \
  | grep -v '^>' | tr -d '\n' | sed 's/\(.\)/\1 /g' > $1
}
fetch_genome $1
fetch_genome $2
wdiff -s -123 $1 $2

This script works by fetching the genome from the NCBI database. The strings “MG772933.1” and “MN988713.1” are accession numbers. The API returns the RNA sequence in FASTA format, which looks like:

$ curl -s 'https://www.ncbi.nlm.nih.gov/sviewer/viewer.cgi?report=fasta&id=MN988713.1'
>MN988713.1 Wuhan seafood market pneumonia virus isolate 2019-nCoV/USA-IL1/2020, complete genome
ATTAAAGGTTTATACCTTCCCAGGTAACAAACCAACCAACTTTCGATCTCTTGTAGATCTGTTCTCTAAA
CGAACTTTAAAATCTGTGTGGCTGTCACTCGGCTGCATGCTTAGTGCACTCACGCAGTATAATTAATAAC
TAATTACTGTCGTTGACAGGACACGAGTAACTCGTCTATCTTCTGCAGGCTGCTTACGGTTTCGTCCGTG
TTGCAGCCGATCATCAGCACATCTAGGTTTCGTCCGGGTGTGACCGAAAGGTAAGATGGAGAGCCTTGTC
...

The FASTA format needs some “massaging” before we can diff it. The first line, starting with >, describes the sequence that follows. We don’t need this metadata, so we strip it with grep -v '^>'. Next, we don’t need those newline characters, so we strip them with tr -d '\n'. Finally, because diff works on lines rather than characters, we’ll instead use wdiff, after separating the characters into separate words using sed 's/\(.\)/\1 /g'. This gives us genomes that look like A T A T T A G G ....

Finally, we can call wdiff -s -123 on these genomes, which gives us some statistics about their similarity. If we omit -s -123, we get the actual base differences between the sequences. For example, check out the end of the sequences:

$ ./genome_diff MG772933.1 MN988713.1 | fold | tail -2
[-A A C C A C-] T [-C G A C A-] {+T+} A G {+G+} A {+G+} A A {+T G+} A [-A A A A
A A A A A A-] {+C+} A A A A A A A A A A A A

We can see that the sequences both have a long sequence of As at the end, but the Bat CoV’s tail is significantly longer. This is known as a “poly(A) tail”.

A different way to see similarities is to use NCBI’s BLAST tool. Enter the accession number MN988713.1, and you’ll get a list of other sequences, ranked by “percent identity”. The most similar are several recent sequences of 2019-nCoV, followed by the “Bat SARS-like coronavirus”, followed by many SARS coronavirus sequences.

Correction 2020-02-11: My script used tr -d -C 'ATGC' to strip newlines, but it should use tr -d '\n'. It’s important, because the full set of FASTA characters in this file also includes S, W, and Y! For the meaning of these, see FASTA format.

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Tagged #programming, #bioinformatics. All content copyright James Fisher 2020. This post is not associated with my employer. Found an error? Edit this page.