YACHT

YACHT is a mathematically rigorous hypothesis test for the presence or absence of organisms in a metagenomic sample, based on average nucleotide identity (ANI).

The associated publication can be found here: https://academic.oup.com/bioinformatics/article/40/2/btae047/7588873

And the preprint can be found at: https://doi.org/10.1101/2023.04.18.537298.

Please cite via:

Koslicki, D., White, S., Ma, C., & Novikov, A. (2024). YACHT: an ANI-based statistical test to detect microbial presence/absence in a metagenomic sample. Bioinformatics, 40(2), btae047.

Quick start

We provide a demo to show how to use YACHT. Please follow the command lines below to try it out:

NUM_THREADS=64 # Adjust based on your machine's capabilities

cd demo # the 'demo' folder can be downloaded via command 'yacht download demo' if it doesn't exist

# build k-mer sketches for the query sample and ref genomes
yacht sketch sample --infile ./query_data/query_data.fq --kmer 31 --scaled 1000 --outfile sample.sig.zip
yacht sketch ref --infile ./ref_genomes --kmer 31 --scaled 1000 --outfile ref.sig.zip

# preprocess the reference genomes (training step)
yacht train --ref_file ref.sig.zip --ksize 31 --num_threads ${NUM_THREADS} --ani_thresh 0.95 --prefix 'demo_ani_thresh_0.95' --outdir ./ --force

# run YACHT algorithm to check the presence of reference genomes in the query sample (inference step)
yacht run --json demo_ani_thresh_0.95_config.json --sample_file sample.sig.zip --significance 0.99 --num_threads ${NUM_THREADS} --min_coverage_list 1 0.6 0.2 0.1 --out ./result.xlsx

# convert result to CAMI profile format (Optional)
yacht convert --yacht_output result.xlsx --sheet_name min_coverage0.2 --genome_to_taxid toy_genome_to_taxid.tsv --mode cami --sample_name 'MySample' --outfile_prefix cami_result --outdir ./

There will be an output EXCEL file result.xlsx recoding the presence of reference genomes with different spreadsheets given the minimum coverage of 1 0.6 0.2 0.1.

Contents

• YACHT
  • Quick start
  • Installation
    • Conda Installation
    • Manual installation
      • Using Conda
      • Using Mamba
      • Using Docker
  • Usage
    • YACHT Commands Overview
    • YACHT workflow
    • Creating sketches of your reference database genomes (yacht sketch ref)
      • Automatic download of reference sketches
      • Manual download of reference sketches
    • Creating sketches of your sample (yacht sketch sample)
    • Preprocess the reference genomes (yacht train)
      • Parameters
      • Output
      • Some pre-trained reference databases available on Zenodo
    • Run the YACHT algorithm (yacht run)
      • Parameters
      • Output
    • Convert YACHT result to other popular output formats (yacht convert)
      • Parameters

Installation

Please note YACHT does not currently support MacOS. However, we are actively working on developing compatibility for this operating system and hope to have it available soon. During this time, we provide a docker container (see using docker section below) for those who need to run YACHT on MacOS.

Conda Installation

YACHT is available on Conda can be installed via the steps below to install：

# create conda environment
conda create -n yacht_env

# activiate environment
conda activate yacht_env

# install YACHT
conda install -c conda-forge -c bioconda yacht

Manual installation

YACHT requires Python 3.6 or higher and Conda. We recommend using a virtual environment to ensure a clean and isolated workspace. This can be accomplished using either Conda or Mamba (a faster alternative to Conda).

Using Conda

To create your Conda environment and install YACHT, follow these steps:

# Clone the YACHT repository
git clone https://github.com/KoslickiLab/YACHT.git
cd YACHT

# Create a new virtual environment named 'yacht_env'
conda env create -f env/yacht_env.yml

# Activate the newly created environment
conda activate yacht_env

# Install YACHT within the environment
pip install .

Using Mamba

If you prefer using Mamba instead of Conda, just simply repalce conda with mamba in the above commands.

Using Docker

Using Dockerfile:

docker build --tag 'yacht' .
docker run -it --entrypoint=/bin/bash yacht -i
conda activate yacht_env

Using Act:

Act. To run YACHT on docker, simply execute "act" from the main YACHT folder, or "act --container-architecture linux/amd64" if you are on MacOS system.

Usage

YACHT Commands Overview

YACHT can be run via the command line yacht <module>. Now it has three four main modules: download, sketch, train, run, and convert.

• The download module has three submodules: demo, default_ref_db, and pretrained_ref_db:

  • demo can automatically download the demo files to a specified folder:

  # Example
  yacht download demo --outfolder ./demo

  • default_ref_db can automatically download pre-generated sketches of reference genomes from GTDB or GenBank as our input reference databases.

  # Example for downloading the k31 sketches of representative genomes of GTDB rs214 version 
  yacht download default_ref_db --database gtdb --db_version rs214 --gtdb_type reps --k 31 --outfolder ./

  Parameter	Explanation
  database	two options for default reference databases: 'genbank' or 'gtdb'
  db_version	the version of database, options: "genbank-2022.03", "rs202", "rs207", "rs214"
  ncbi_organism	the NCBI organism for the NCBI reference genome, options: "archaea", "bacteria", "fungi", "virus", "protozoa"
  gtdb_type	for GTDB database, chooses "representative" genome version or "full" genome version
  k	the length of k-mer
  outfolder	the path to a folder where the downloaded file is expected to locate

  • pretrained_ref_db can automatically download our pre-trained reference genome database that can be directly used as input for yacht run module.

  # Example for downloading the pretrained reference database that was trained from GTDB rs214 representative genomes with k=31 and ani_threshold=0.9995
  yacht download pretrained_ref_db --database gtdb --db_version rs214 --k 31 --ani_thresh 0.9995 --outfolder ./

  Parameter	Explanation
  database	two options for default reference databases: 'genbank' or 'gtdb'
  db_version	the version of database, options: "genbank-2022.03", "rs214"
  ncbi_organism	the NCBI organism for the NCBI reference genome, options: "archaea", "bacteria", "fungi", "virus", "protozoa"
  ani_thresh	the cutoff by which two organisms are considered indistinguishable (default: 0.95)
  k	the length of k-mer
  outfolder	the path to a folder where the downloaded file is expected to locate

• The sketch module (note that it is a simple wrapper to sourmash) has two submodules: ref and sample:

  • ref is used to sketch fasta files and make them as a reference database

  # Example for sketching multiple fasta files as reference genomes in a given folder
  yacht sketch ref --infile ./demo/ref_genomes --kmer 31 --scaled 1000 --outfile ref.sig.zip
  

  Parameter	Explanation
  infile	the path to a input FASTQ file or a folder containing multiple FASTQ files
  kmer	the length of k-mer
  scaled	the scaled factor
  outfile	the path to a output file

  • sample is used to sketch the single-end or paired-end fasta file(s) and make it/them as a query sample.

  # Example for sketching a FASTA/Q file as a metagenomic example
  yacht sketch sample --infile ./query_data/query_data.fq --kmer 31 --scaled 1000 --outfile sample.sig.zip

  Parameter	Explanation
  infile	the input FASTA/Q file(s). For paired-end reads, provide two files
  kmer	the length of k-mer
  scaled	the scaled factor
  outfile	the path to a output file

• The train module pre-reprocesses the given sketches of reference genomes (the .zip file) to identify and merge the "identical' genomes based on the given ANI threshold (e.g., --ani_threshold 0.95). For an example, please refer to the yacht train command in the "Quick start" section.

• The run module runs the YACHT algorithm to detect the presence of reference genomes in a given sample. For an example, please refer to the yacht run command in the "Quick start" section.

• The convert module can covert YACHT result to other popular output formats (e.g., CAMI profiling format, BIOM format, GraphPlAn). For an example, please refer to the yacht convert command in the "Quick start" section.

YACHT workflow

This section simply introduces the analysis workflow for YACHT:

1. Create Sketches of Your Reference Database Genomes and Your Sample:
   • This step involves generating compact representations (sketches) of genomic data for efficient comparison and analysis.
2. Preprocess the Reference Genomes:
   • This is the training step of YACHT, aiming to identify and merge the "identical" genomes based on Average Nucleotide Identity (ANI) using the ani_thresh parameter.
3. Run YACHT algorithm:
   • This step involves running the YACHT algorithm to detect the presence of reference genomes in your sample.
4. Convert YACHT result to other output formats
   • This step is optional if you prefer other output formats (e.g., CAMI profiling format, BIOM format) for the downstream analysis.

For each step of this workflow, please see more detailed description in the sections below.

Creating sketches of your reference database genomes (yacht sketch ref)

You will need a reference database in the form of sourmash sketches of a collection of microbial genomes. There are a variety of pre-created databases available at: https://sourmash.readthedocs.io/en/latest/databases.html. Our code uses the "Zipfile collection" format, and we suggest using the GTDB genomic representatives database:

Automatic download of reference sketches

yacht download default_ref_db --database gtdb --db_version rs214 --gtdb_type reps --k 31 --outfolder ./

Manual download of reference sketches

wget https://farm.cse.ucdavis.edu/~ctbrown/sourmash-db/gtdb-rs214/gtdb-rs214-reps.k31.zip

If you want to use a custom database, you will need to create a Sourmash sketch Zipfile collection from the FASTA/FASTQ files of your reference database genomes (see Sourmash documentation for details). In brief, this can be accomplished via the following commands:

If you have a single FASTA file with one genome per record:

# the command below is equivalent to: sourmash sketch dna -f -p k=31,scaled=1000,abund --singleton <path to your multi-FASTA file> -o training_database.sig.zip
yacht sketch ref --infile <path to your multi-FASTA file> --kmer 31 --scaled 1000 --outfile training_database.sig.zip

If you have a directory of FASTA files, one per genome:

# the command below is equivalent to: find <path of foler containg FASTA/FASTQ files> > dataset.csv; sourmash sketch fromfile dataset.csv -p dna,k=31,scaled=1000,abund -o training_database.sig.zip
yacht sketch ref --infile <path of foler containg FASTA/FASTQ files> --kmer 31 --scaled 1000 --outfile training_database.sig.zip

Creating sketches of your sample (yacht sketch sample)

Creating a sketch of your sample metagenome is an essential step in the YACHT workflow. This process involves using the same k-mer size and scale factor that were used for the reference database. You can use the following commands to implement this step:

# For a single-end FASTA/Q file
# the command below is equivalent to: sourmash sketch dna -f -p k=31,scaled=1000,abund -o sample.sig.zip <input FASTA/Q file>
yacht sketch sample --infile <input FASTA/Q file> --kmer 31 --scaled 1000 --outfile sample.sig.zip

# For pair-end FASTA/Q files, you need to separately specify two FASTA/Q files
# the command below is equivalent to: cat <FASTA/Q file 1> <FASTA/Q file 2> > combine.fastq (or combine.fasta); sourmash sketch dna -f -p k=31,scaled=1000,abund -o sample.sig.zip combine.fastq (or combine.fasta)
yacht sketch sample --infile <FASTA/Q file 1> <FASTA/Q file 2> --kmer 31 --scaled 1000 --outfile sample.sig.zip

Note: Sourmash database offers three available k values (21, 31, and 51), allowing you to select the one that best suits your particular analytical needs. The scale factor serves as an indicator of data compression, and if your dataset is small, you might consider using a smaller value (corresponding to a higher portion of genomes retained in the sketch).

Preprocess the reference genomes (yacht train)

The yacht train module utilizes a fast algorithm written by C++ to preprocess the reference genomes. In our test with the GTDB representative genomes (r214) including 85,205 species-level genomes, YACHT takes around 12 minutes and 52 GB of RAM to preprocess them and generate the reference files for the yacht run on a Ubuntu 22.04.5 system using 64 threads. You can also use the pre-trained databases we built (see here) to skip this step.

The command yacht train extracts the sketches from the Zipfile-format reference database, and then turns them into a form usable by YACHT. In particular, it removes one of any two organisms that have ANI greater than the user-specified threshold as these two organisms are too close to be "distinguishable".

yacht train --ref_file gtdb-rs214-reps.k31.zip --ksize 31 --num_threads 64 --ani_thresh 0.95 --prefix 'gtdb_ani_thresh_0.95' --outdir ./

Parameters

The most important parameter of this script is --ani_thresh: this is average nucleotide identity (ANI) value equal to or below which two organisms are considered distinct. For example, if --ani_thresh is set to 0.95, then two organisms with ANI > 0.95 will be considered indistinguishable. For the organisms with ANI > 0.95, only the one with the largest number of unique kmers will be kept. If there is a tie in the number of unique kmers, one organism will be randomly selected. The default value of --ani_thresh is 0.95. The --ani_thresh value chosen here must match the one chosen for the YACHT algorithm (see below).

Parameter	Explanation
--ref_file	the path to the sourmash signature database zip file
--ksize	the length of k-mer, must match the k size used in previous sketching steps (default: 31)
--num_threads	the number of threads to use for parallelization (default: 16)
--ani_thresh	the cutoff by which two organisms are considered indistinguishable (default: 0.95)
--prefix	the prefix for output folders and files (see details below)
--outdir	the path to output directory where the results and intermediate files will be genreated

Output

File (names starting with prefix)	Content
_config.json	A JSON file stores the required information needed to run the next YACHT algorithm
_manifest.tsv	A TSV file contains organisms and their relevant info after removing the similar ones

Some pre-trained reference databases available on Zenodo

For convenience, we have provided some pre-trained reference database for the GenBank and GTDB genomes on Zenodo. If any of them is suitable for your study, you can simply run the following command to download it and skip the training step below. Note: download of pre-trained data is provided in the yacht download feature, please see here for more details about yacht download.

# remember to replace <zendo_id> and <file_name> for your case before running it
curl --cookie zenodo-cookies.txt "https://zenodo.org/records/<zendo_id>/files/<file_name>?download=1" --output <file_name>

# Example
# curl --cookie zenodo-cookies.txt "https://zenodo.org/records/10113534/files/genbank-2022.03-archaea-k31_0.80_pretrained.zip?download=1" --output genbank-2022.03-archaea-k31_0.80_pretrained.zip

Run the YACHT algorithm (yacht run)

After this, you are ready to perform the hypothesis test via yacht run for each organism in your reference database. This can be accomplished with something like:

yacht run --json 'gtdb_ani_thresh_0.95_config.json' --sample_file 'sample.sig.zip' --num_threads 64 --keep_raw --significance 0.99 --min_coverage_list 1 0.5 0.1 0.05 0.01 --out ./result.xlsx

Parameters

The --significance parameter is basically akin to your confidence level: how sure do you want to be that the organism is present? Higher leads to more false negatives, lower leads to more false positives.

The --min_coverage_list parameter dictates a list of min_coverage which indicates what percentage (value in [0,1]) of the distinct k-mers (think: whole genome) must have been sequenced and present in my sample to qualify as that organism as being "present." Setting this to 1 is usually safe, but if you have a very low coverage sample, you may want to lower this value. Setting it higher will lead to more false negatives, setting it lower will lead to more false positives (pretty rapidly).

Parameter	Explanation
--json	the path to a json file generated by the make_training_data_from_sketches.py script (see above)
--significance	minimum probability of individual true negative (default: 0.99)
--num_threads	the number of threads to use for parallelization (default: 16)
--keep_raw	keep the raw result (i.e. min_coverage=1) no matter if the user specifies it
--show_all	Show all organisms (no matter if present)
--min_coverage_list	a list of min_coverage values, see more detailed description above (default: 1, 0.5, 0.1, 0.05, 0.01)
--out	path to output excel result (default: './result.xlsx')

Output

The output file will be an EXCEL file; column descriptions can be found here. The most important are the following:

• organism_name: The name of the organism
• in_sample_est: A boolean value either False or True: if False, there was not enough evidence to claim this organism is present in the sample.
• p_vals: Probability of observing this or more extreme result at the given ANI threshold, assuming the null hypothesis.

Other interesting columns include:

• num_exclusive_kmers_to_genome: How many k-mers were found in this organism and no others
• num_matches: How many k-mers were found in this organism and the sample
• acceptance_threshold_*: How many k-mers must be found in this organism to be considered "present" at the given ANI threshold. Hence, in_sample_est is True if num_matches >= acceptance_threshold_* (adjusting by coverage if desired).
• alt_confidence_mut_rate_*: What the mutation rate (1-ANI) would need to be to get your false positive to match the false negative rate of 1-significance (adjusting by coverage if desired).

Convert YACHT result to other popular output formats (yacht convert)

When we get the EXCEL result file from run_YACHT.py, you can run yacht convert to covert the YACHT result to other popular output formats (Currently, only cami, biom, graphplan are supported).

Note: Before you run yacht convert, you need to prepare a TSV file genome_to_taxid.tsv containing two columns: genome ID (genome_id) and its corresponding taxid (taxid). An example can be found here. You need to prepare it according to the reference database genomes you used.

Then you are ready to run yacht convert with something like:

yacht convert --yacht_output 'result.xlsx' --sheet_name 'min_coverage0.01' --genome_to_taxid 'genome_to_taxid.tsv' --mode 'cami' --sample_name 'MySample' --outfile_prefix 'cami_result' --outdir ./

Parameters

Parameter	Explanation
--yacht_output	the path to the output excel file generated by run_YACHT.py
--sheet_name	specify which spreadsheet result you want to covert from
--genome_to_taxid	the path to the location of genome_to_taxid.tsv you prepared
--mode	specify to which output format you want to convert (e.g., 'cami', 'biom', 'graphplan')
--sample_name	A random name you would like to show in header of the cami file. Default: Sample1.'
--outfile_prefix	the prefix of the output file. Default: result
--outdir	the path to output directory where the results will be genreated