LCS

Lineage deComposition for Sars-cov-2 pooled samples.

Supporting material for the paper "A mixture model for determining SARS-Cov-2 variant composition in pooled samples".

Running the pipeline

The pipeline was written with snakemake: https://snakemake.readthedocs.io/en/stable/.

To get started, clone this repository and use it as a template:

git clone https://github.com/rvalieris/LCS.git
cd LCS

1. Create the conda environment

All the software used on the pipeline can be installed with conda.

If you don't already have conda installed in your machine, you can follow this guide for instalation according to your operational system.

On Linux, you can execute the command below to create an environment, install all dependencies to run LCS and activate the new environment:

conda env create -n lcs -f conda.env.yaml
conda activate lcs

On MacOS, you can use another environment file to install all required dependencies:

conda env create -n lcs -f conda.env.macosx.yaml
conda activate lcs

We have successfully tested LCS on a MacOS version 11.5.2 with python 3.8 and ray 1.9.0.

2. Markers source choice

The markers table contains the list of all mutation markers found in each of the variant-groups defined in data/variant-groups.tsv.

You can either generate a new table or use a pre-generated one.

You will need to generate a new table if you want to change the variant-groups definition.

Choose one of these 3 options:

1. Use a pre-generated table:

   Pre-generated tables are provided to shorten the time required to run the pipeline, simply choose which table you want to use and copy it to the appropriate place:

   1. pango-designation:

      mkdir -p outputs/variants_table &&
      zcat data/pre-generated-marker-tables/pango-designation-markers-v1.2.60.tsv.gz > outputs/variants_table/pango-markers-table.tsv

   2. ucsc:

      mkdir -p outputs/variants_table &&
      zcat data/pre-generated-marker-tables/ucsc-markers-2021-08-19.tsv.gz > outputs/variants_table/ucsc-markers-table.tsv

2. Generate a new table using pango-designation as a source:

   To do this you need to have a fasta file in data/gisaid.fa.gz containing all GISAID genomes listed in the lineages.csv file from pango-designation repository.

   You must register on the GISAID website to gain access to these sequences.

   The variable PANGO_DESIGNATIONS_VERSION on rules/config.py controls which version of pango-designation to use.

   You can run snakemake --config markers=pango dataset=x -j1 repo to download the appropriate pango-designation repository to data/pango-designation.

3. Generate a new table using sequences tree generated by UCSC as a source:

   This data, gathered by the UShER team, includes only public sequences, as such they are downloaded by the pipeline automatically.

   These data trees are updated daily, the variable PB_VERSION on rules/config.py controls which version of UCSC data to use, make sure to change it to use the most recent data, the download of the data is done automatically by the pipeline so no intermediary step is needed.

3. Prepare your pooled sample dataset

Place your raw-fastq files pooled samples in data/fastq, and create a tags file listing your samples name. It should look like this:

$ ls data/fastq/
sample1_1.fastq.gz
sample1_2.fastq.gz
sample2_1.fastq.gz
sample2_2.fastq.gz
sample3_1.fastq.gz
sample3_2.fastq.gz

$ cat data/tags_pool_mypool
sample1
sample2
sample3

If your sequencing data is amplicon, create a fasta file of the primers used and set the variable PRIMERS_FA, on rules/config.py, the primers will be trimmed from the sequences if you set the primer_trimming=True option.

4. Run the pipeline

To execute the pipeline run the command:

snakemake --config markers=pango dataset=mypool --cores <C> --resources mem_gb=<M>

The markers config indicates which markers table you are using (pango or ucsc) and the dataset config should match your tags file data/tags_pool_mypool describing your samples.

You also need to indicate how many cores and memory you have available to run the analysis, snakemake will parallelize the pipeline accordingly.

If you want to trim primers, also set the option primer_trimming=True.

5. View the results

After the pipeline completes, the results should be in outputs/decompose.

Generate plots

Plots can be generated by running the notebook:

• results.ipynb

Citing

If you use this software please consider citing:

@article{10.1093/bioinformatics/btac047,
    author = {Valieris, Renan and Drummond, Rodrigo D and Defelicibus, Alexandre and Dias-Neto, Emmanuel and Rosales, Rafael A and Tojal da Silva, Israel},
    title = "{A mixture model for determining SARS-Cov-2 variant composition in pooled samples}",
    journal = {Bioinformatics},
    year = {2022},
    month = {02},
    issn = {1367-4803},
    doi = {10.1093/bioinformatics/btac047},
    url = {https://doi.org/10.1093/bioinformatics/btac047},
    note = {btac047},
    eprint = {https://academic.oup.com/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/btac047/42357424/btac047.pdf},
}