In dw974/MyCoV: Predict classifications of partial RdRp Coronavirus sequences

knitr::opts_chunk$set(
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MyCoV installation:

The ‘MyCoV’ package will currently only work in a linux environment. Essentially it is a wrapper script for nucleotide BLAST, and therefore requires a local installation of BLAST+.

On Ubuntu, this can be installed via terminal as such:

sudo apt-get install ncbi-blast+

In R, you will need to install ‘MyCoV’ via github using the ‘devtools’ package.

There are a lot of dependencies for devtools and the other packages used by MyCoV.

From a fresh install of ubuntu (tested in Ubuntu 19, and assuming you already have R), the following series of commands works to install MyCoV.

1) Install dependencies in terminal (takes a while):

sudo apt-get install libxml2-dev

sudo apt-get install libssl-dev

sudo apt-get install libcurl4-openssl-dev

sudo apt-get install libmagick++-dev

2) In R, install the devtools package (takes a while):

install.packages(“devtools”)

3) In R, install MyCoV (again, this takes a long time on a fresh install):

devtools::install_github(“dw974/MyCoV”)

library(MyCoV)

Using MyCoV

To run MyCoV, you just need a fasta file containing the sequences you wish to query. The file can contain any number of sequences.

res=MyCoV(fasta=”query_sequences.fas”)

This generates a dataframe (res) containing the results of blast searches against the Coronavirus reference data set.

then you can tabulate the results through the 'formattable' package:

tabulate_CoV(df=res)

This will give you something that looks like this:

{#id .class width=100%}

To interpret this:

"query": the name of the sequence in the query fasta file "best_hit": the sequence that showed the closed identity in the reference database "predicted_subgenus": the subgenus to which the best_hit sequence was assigned in the phylogenetic analyses in our paper. "predicted_genus": the genus to which the best_hit sequence was assigned in the phylogenetic analyses in our paper. "posterior_probability": the proportion of trees in our bayesian inference in which the best_hit sequence clustered in the same group as the corresponding subgenus holotype sequence. "paiwise_identity": pairwise identity between the query sequence and the bet_hit sequence in the reference database.

Alternatively, you can visualise the results in terms of best-hit pairwise distances to the query sequence(s):

visualise_CoV(df=res)

This will generate something like this:

{#id .class width=100%}

MyCoV also allows you to visualise the relationship between a single query sequence and all sequences in the reference database, whilst also providing a summary of the host and geographical origin of the metadata associated with the reference database sequences.

plot_similarity(fasta="query_sequence.fasta")

This will give you something that looks like this:

{#id .class width=100%}

Remember to cite BOTH our paper and ggtree if you use this.

The tree presented here is a subtree of the main phylogenetic analysis comparing more than 3000 unique coronavirus sequences that is presented in the paper. The metadata are compiled from more than 7000 isolates.

The subtree presented is the lowest branch of the main tree that contains all of the isolates that return blast hits to the queried sequence.

Pairwise identities to each sequence are shown on the tips, and the tips are coloured with respect to identity. The best hit is highlighted by a large diamond tip. Predicted subgenus classifications of all tips are shown.

To the right, a presence/absence summary of the metadata associated with each tip is displayed. The idea of this is that it is possible to rapidly assess the associations made by sequences that are similar to the one you are querying.

I hope you enjoy using the package.

Remember to cite our paper: Analysis of partial sequences of the RNA-dependent RNA polymerase gene as a tool for genus and subgenus classification of coronaviruses Wilkinson DA, Joffrin L, Lebarbenchon C, Mavingui P. Journal of General Virology (2020) https://doi.org/10.1099/jgv.0.001494

dw974/MyCoV documentation built on Oct. 9, 2020, 7:29 p.m.