distillerycats

Distill sourmash with spacegraphcats!

Find disease associations across metagenomes with k-mers using sourmash, and then recover pangenomic accessory elements using spacegraphcats.

distillerycats currently takes as input metagenome sequencing reads from multiple studies, the study from which each sample originated, and a classification variable of interest.

Warning: This repository is under active development. The code in the master branch is not guaranteed to be completely functional yet, and our documentation is still being written. We will update this message as we make progress :)

Assumptions made by the pipeline

Some or all of the assumptions may be removed in future iterations

• Assumes paired-end shotgun metagenome reads
• Assumes data are already downloaded and in inputs/raw
• Currently focused on human microbiome samples
  • Removes human reads as contaminant host
  • Classifies sequences against human microbiome MAG databases
• Requires samples from at least two studies.
• Reads must be > 31 bp in length.
• Requires snakemake-minimal, sourmash, feather, and pandas.

Getting started

This pipeline uses conda for software and environment management. To get started, install miniconda. If you're new to miniconda, see this tutorial.

To get started, clone this repository. All computation will take place within the cloned github directory.:

git clone https://github.com/dib-lab/distillerycats.git

cd into the directory and create the environment:

cd distillerycats
conda env create -f environment.yml
conda activate dcats

Now, do a development-ready install:

pip install -e '.'

Make a configuration file

Put the following in a configuration file named conf-tutorial.yml:

metadata_file: inputs/test_metadata.csv

Note: configuration is under development

Run distillerycats!

Execute:

distillerycats run conf-tutorial.yml

Note, to show the full configuration first, you can run distillerycats showconf conf-tutorial.yml

Other configuration info (to be modified)

Make sure your input data is in the inputs/raw directory. This pipeline assumes that all input data paths follow this format:

inputs/raw/{sample}_R1.fq.gz
inputs/raw/{sample}_R2.fq.gz

Where all input samples are paired-end reads with and R1 and R2 file, reads are gzipped, and files end with .fq.gz.`

The {sample} root of the input files should match sample column in the metadata file:

sample,study,var
PSM7J199,iHMP,CD
PSM7J1BJ,iHMP,CD
PSM7J177,iHMP,CD

The metadata file must be located in the inputs directory, and should be called test_metadata.csv. It needs to be in csv format, and should have the sample columns sample, study, and var. The names must match exactly in capitalization/spelling.

If you would like to try distillerycats on a (large) test data set, we have uploaded one to OSF.

To run the full pipeline including read preprocessing, including adapter trimming, human DNA removal, and k-mer abundance trimming, download this following dataset, untar it, and make sure the fastq files are in inputs/raw as below. Note that k-mer trimming and human DNA removal both take ~64GB of RAM.

mkdir -p inputs/raw
wget -O inputs/test_data.tar.gz https:...
tar xf inputs/test_data.tar.gz -C inputs/raw

If you would like to skip read preprocessing but would still like to try the pipeline, you can either download the preprocessed reads, or you can download the signatures. These files should be placed in ouputs/abundtrim and outputs/sigs, respectively. The first step in the pipeline after preprocessing is to calculate signatures, and signatures are much lighter weight than preprocessed fastq files.

preprocessed reads:

mkdir -p outputs
wget -O outputs/abundtrim.tar.gz ...
tar xf outputs/abundtrim.tar.gz -C outputs/

signatures:

mkdir -p outputs
wget -O outputs/sigs.tar.gz https://osf.io/scbk8/
tar xf outputs/sigs.tar.gz -C outputs/

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@taylorreiter @ctb