Strain Pure Gene Content (StrainPGC)

Strain-informed gene content inference from shotgun metagenomes

Summary

Pangenome profiling methods harness shotgun metagenomics to identify gene families encoded in the genomes of individual strains. By using a reference database of genes or genomes from a given species, genes with sufficiently high mapping depth in a single sample are inferred to be encoded by that species in that sample.

This approach has proven fruitful, but has three major shortcomings when strain-specific gene content is desired:

1. In samples with multiple, strains, genes may not be assigned when they are missing from one or more of these genomes. due to lower depth relative to core genes.
2. Gene assignment error is elevated in low-abundance species due to a lower signal-to-noise ratio in mapping depth.
3. Assignments to individual species are ambiguous when gene families are found in the pangenomes of more than one species.

StrainPGC tackles these challenges by combining depth information across multiple samples. Specifically, StrainPGC:

1. Combines depth across multiple samples to achieve a higher signal-to-noise ratio
2. Also considers the correlation between gene depth and species depth, reducing the impact of cross-mapping of reads across multiple species
3. Considers, one at a time, subsets of samples where a single strain is believed to be present in order to overcome the obscuring effects of strain mixing

Quick-start

Installation

Canonically, StrainPGC can be installed directly from this code repository using setuptools/pip:

git clone https://github.com/bsmith89/StrainPGC StrainPGC
cd StrainPGC
pip install .

This will also install all Python software dependencies, which currently include:

• pandas
• xarray
• netcdf4
• scipy

Basic Usage

Example input data are provided for testing and demonstration purposes:

With correctly formatted input data, StrainPGC method is run as follows:

spgc run \
    example_data/100035.gene_depth.tsv.gz \
    example_data/100035.core_gene.list \
    example_data/100035.strain_map.tsv \
    example_data/100035.spgc.tsv

where example_data/100035.spgc.tsv is the desired output path.

Additional options are described in the help:

spgc run --help

Inputs

The core StrainPGC method takes three inputs for each species:

1. Pangenome profiles: a sample-by-gene matrix of mapping depths
2. Species core genes: a list of genes believed to be found in single copy in every strain of the species
3. Strain-pure samples: a mapping of samples to individual strains

A suggested protocol for obtaining each of these inputs directly from raw metagenomic data as part of the larger StrainPGC workflow is described below and implemented as a Snakemake pipeline.

Outputs

The key result provided by StrainPGC is a strain-by-gene matrix assigning gene families to the genomes of each of the strains.

The StrainPGC Workflow

A complete workflow can be divided into four phases:

1. Metagenomic profiling, which includes both:
   • SNP profiling for strain tracking
   • Pangenome profiling
2. Strain tracking / identification of strain-pure sets
3. Running the StrainPGC algorithm
4. Quality assessment / control

SNP Profiling

The StrainPGC workflow uses GT-Pro for SNP profiling, which captures metagenotypes across polymorphic positions found in the Unified Human Gut Genome reference database.

Pangenome Profiling

The StrainPGC workflow implements pangenome profiling against the (MIDAS2 UHGG reference database)[https://github.com/czbiohub-sf/MIDAS2] gene clusters using using Bowtie2-based read mapping.

While other profiling tools may be used, excessive post-hoc filtering of mapping depth can be detrimental and we find competitive mapping to a reference index that includes multiple species reduces the issue of cross mapping of reads from other species.

This step is by far the most computationally intensive, dwarfing by far the runtime and memory requirements of all other steps.

Strain tracking

The StrainPGC workflow estimates strain compositions in each sample based on SNP profiles using StrainFacts.

Sets of samples that are pure (or nearly pure) for a single strain are selected based on these estimates.

Quality assessment

The quality of gene content assignments for each strain can be assessed post-hoc based on

1. The fraction of species core genes assigned
2. Detecting outliers with an anomalous total numbers of genes assigned

Strains failing these two checks should be removed from downstream analyses.

Additional utilities

Tools are included with StrainPGC for several auxiliary purposes:

• Summarizing statistics about strains and genes (TODO)
• Identifying species core genes based on a reference genome-by-gene occurrence table (TODO)
• Visualizing the distribution of depth ratios and correlations (TODO)
• TODO: What else is needed?

Citation

For now, please cite this repository directly. A Zenodo DOI will be available in the near future as well as a BioRxiv preprint.