viral-mcorr

viral-mcorr is a method for inferring recombination rates from large-scale sequencing data in (+)ssRNA viruses using correlation profiles of synonymous substitutions.

The viral-mcorr method is described in the following paper:

@article {doi:10.1073/pnas.2206945119,
    author = {Asher Preska Steinberg  and Olin K. Silander  and Edo Kussell },
    title = {Correlated substitutions reveal SARS-like coronaviruses recombine frequently with a diverse set of structured gene pools},
    journal = {Proceedings of the National Academy of Sciences},
    volume = {120},
    number = {5},
    pages = {e2206945119},
    year = {2023},
    doi = {10.1073/pnas.2206945119},
    URL = {https://www.pnas.org/doi/full/10.1073/pnas.2206945119}
}

https://www.pnas.org/doi/full/10.1073/pnas.2206945119

Requirements

• Install git from https://git-scm.com;
• Install go from https://golang.org/doc/install;
• Install python3 from https://www.python.org/ (we found running issues using the default Python in MacOS);
• Install pip3 from https://pip.pypa.io/en/stable/installing/.

Installation

1. For basic usage, install mcorr-gene-aln, mcorrViralGenome, mcorrLDGenome from your terminal:

go install github.com/kussell-lab/viral-mcorr/cmd/mcorr-gene-aln@latest
go install github.com/kussell-lab/viral-mcorr/cmd/mcorrViralGenome@latest
go install github.com/kussell-lab/viral-mcorr/cmd/mcorrLDGenome@latest


cd $HOME/go/src/github.com/kussell-lab/mcorr/cmd/mcorr-viral-fit
pip install $HOME/go/src/github.com/kussell-lab/mcorr/cmd/mcorr-viral-fit

Install mcorr-viral-fit by cloning this github repository and then using pip to install the program locally:

git clone [email protected]:kussell-lab/viral-mcorr.git
pip install ./

2. Add $HOME/go/bin and $HOME/.local/bin to your $PATH environment. In Linux, you can do it in your terminal:

export PATH=$PATH:$HOME/go/bin:$HOME/.local/bin

In MacOS, you can do it as follows:

export PATH=$PATH:$HOME/go/bin:$HOME/Library/Python/3.6/bin

We have tested installation in MacOS Monterey (w/ an M1 chip), using Python 3 and Go 1.15 and 1.16.

Basic usage for inferring recombination parameters

The inference of recombination parameters requires two steps:

1. Calculate Correlation Profile

   1. For multi-fasta alignments of single genes or whole genomes in which there is a single CDS region, use mcorr-gene-aln :

      mcorr-gene-aln <input MFA file> <output prefix>

   2. To calculate correlation profiles across the CDS region of whole-genome alignments (multiple gene alignments), use mcorrViralGenome:

      mcorrViralGenome <input XMFA file> <output prefix>

      The flag --mate-aln allows for inclusion of a second XMFA file of viral genomes. The flag --between-clades can be used when you have two XMFA files to calculate correlation profiles exclusively across sequence pairs in which neither sequence is from the same XMFA file.

      The XMFA files should contain only coding sequences and should not include any redundant CDS regions (i.e., CDS regions which code for a subregion of another CDS region should be removed from the XMFA). Gapped regions should be denoted by dashes or Ns. The description of XMFA file can be found in http://darlinglab.org/mauve/user-guide/files.html. We provide two useful pipelines to generate whole-genome alignments:

      • from multiple assemblies: https://github.com/kussell-lab/AssemblyAlignmentGenerator;
      • from raw reads: https://github.com/kussell-lab/ReferenceAlignmentGenerator

   All programs will produce two files:

   • a .csv file stores the calculated Correlation Profile, which will be used for fitting in the next step;
   • a .json file stores the (intermediate) Correlation Profile for each gene.

2. Fit the Correlation Profile using mcorr-viral-fit:

   1. For fitting correlation profiles as described in our paper [link will go here] use mcorr-viral-fit:

      mcorr-viral-fit <.csv file> <output_prefix>

      This will produce several files:

      • <output_prefix>_template-switch_best_fit.svg and <output_prefix>_zero-recombo_best_fit.svg show the plots of the Correlation Profile, fitting, and residuals for the template-switching recombination model and for the zero recombination case;
      • <output_prefix>_comparemodels.csv shows the table of fitted parameters for all recombination models (template-switching, fragment-incorporation, and zero-recombination) and AIC values;
      • <output_prefix>_template-switch_residuals.csv and <output_prefix>_zero-recombo_residuals.csv includes residuals for the model with template-switching and the zero-recombination case
      • <output_prefix>_template-switch_fit_results.csv shows fit results for data and bootstrap replicates to template-switching model (if correlations were analyzed w/ mcorr-gene-aln)
      • <output_prefix>_template-switch_fit_report.txt shows fit results and bootstrap CIs if correlations were analyzed w/ mcorr-gene-aln

Basic usage for measuring correlation coefficients for sites across the genome or genes

To measure correlations at individual codons across the genome, you can use mcorrLDGenome as described in our paper [link will go here]:

      mcorrLDGenome <input XMFA file> <output prefix>

XMFA files must be formatted in the same way as described for mcorrViralGenome, above. Alternatively, multi-fasta alignments of single CDS regions.

Examples

1. How to create alignments of viral genomes for use with viral-mcorr.
2. Example workflows for using mcorr-gene-aln, mcorrViralGenome, and mcorrLDGenome