sourmash-plugin-pangenomics 0.3.0

sourmash plugin to do pangenomics.

sourmash_plugin_pangenomics: tools for sourmash-based pangenome analyses

Installation

pip install sourmash_plugin_pangenomics

Quickstart

You can run all of these commands in the test_workflow directory of the git repository.

Build a pangenome database using lineages

(CTB: explain contents!)

The following command builds a pangenome database for the species present in the lineages file gtdb-rs214-agatha.lineages.csv.gz (currently only s__Agathobacter faecis), using the sketches present in the gtdb-rs214-agatha-k21.zip.

sourmash scripts pangenome_createdb \
    gtdb-rs214-agatha-k21.zip \
    -t gtdb-rs214-agatha.lineages.csv.gz \
    -o agatha-merged.sig.zip --abund -k 21

The output file is agatha-merged.sig.zip and contains the following:

% sourmash sig summarize agatha-merged.sig.zip

...
num signatures: 1
** examining manifest...
total hashes: 27398
summary of sketches:
   1 sketches with DNA, k=21, scaled=1000, abund      27398 total hashes

Note: the command pangenome_merge (see below) will construct a pangenome sketch by merging all provided signatures.

Build a pangenome "ranktable"

A "ranktable" is our name for a database that assigns hashes a pangenomic "rank" - central core, external core, shell, inner cloud, or surface cloud.

The following command builds a ranktable for the species s__Agathobacter faecis, selected from the pangenome database created above:

sourmash scripts pangenome_ranktable \
    agatha-merged.sig.zip \
    -o test_output/agathobacter_faecis.csv \
    -k 21 -l 'GCF_020557615 s__Agathobacter faecis'

The output file is test_output/agathobacter_faecis.csv, and it contains two columns:

hashval,pangenome_classification
96834755571756,1
119187685848053,1
129679169912030,1
...
18440589591308259,4
18443409651295626,4
18446214016691046,4

where the first column is the hash value, and the second column is the pangenome rank for that hash.

Summarize the ranks of the hashes in a sketch

We can now use our ranktable to summarize any sketch, including a metagenome. Here we use a human gut metagenome, SRR5650070:

sourmash scripts pangenome_classify \
    SRR5650070.trim.sig.zip \
    test_output/agathobacter_faecis.csv \
    -k 21

This will yield the following output:

For 'test_output/agathobacter_faecis.csv', signature 'SRR5650070' contains:
         497 (12.5%) hashes are classified as central core
         427 (10.8%) hashes are classified as external core
         1791 (45.2%) hashes are classified as shell
         1251 (31.5%) hashes are classified as inner cloud
         0 (0.0%) hashes are classified as surface cloud
         ...and 262716 hashes are NOT IN the csv file

Build a pangenome sketch without using lineages

(CTB: explain contents!)

The following command builds a pangenome sketch by combining all provided sketches. Here we use the sketches present in the gtdb-rs214-agatha-k21.zip file:

sourmash scripts pangenome_merge \
    gtdb-rs214-agatha-k21.zip \
    -o agatha-merged-2.sig.zip-k 21

The output file is agatha-merged-2.sig.zip and is identical (via e.g. sourmash compare) to the agatha-merged.sig.zip file.

Support

We suggest filing issues in the main sourmash issue tracker as that receives more attention (and is monitored by the same people anyway)!

Dev docs

sourmash_plugin_pangenomics is developed at https://github.com/sourmash-bio/sourmash_plugin_pangenomics.

Testing

The current tests are implemented as Snakemake workflow in test_workflow/. To run them, execute the following command in the main directory:

make cleanrun

Generating a release

Bump version number in pyproject.toml and push.

Make a new release on github.

Then pull, and:

python -m build

followed by twine upload dist/....