exabiome

This package contains executable models for each of the main steps in the neural network training process. There exist an executable for each of the following steps:

1. Converting data
2. Training networks
3. Network inference
4. Summarizing network outputs

Installation

To ensure proper functioning of this package, it should be installed in its own conda environment, cloned from the specification file in the env subdirectory.

git clone git@github.com:exabiome/deep-taxon.git
cd deep-taxon
conda create --name myclone --file `env/python_38.txt`
conda activate myclone
python setup.py install

Commands

All commands can be accessed with the deep-taxon executable. Below is the deep-taxon usage statement, which lists the available commands.

Usage: deep-taxon <command> [options]
Available commands are:

    train           Run training with PyTorch Lightning
    lr-find         Run Lightning Learning Rate finder
    cuda-sum        Summarize what Torch sees in CUDA land
    infer           Run inference using PyTorch
    summarize       Summarize training/inference results
    sample-gtdb     Sample taxa from a tree
    make-fof        Run function make_fof from exabiome.gtdb.make_fof
    prepare-data    Aggregate sequence data GTDB using a file-of-files
    ncbi-path       Print path at NCBI FTP site to stdout
    ncbi-fetch      Retrieve sequence data from NCBI FTP site using rsync

Sampling taxa to train with

This command will sample taxa from a GTDB tree.

deep-taxon sample-gtdb

Downloading data from NCBI

This command will retrieve sequence files from NCBI.

deep-taxon ncbi-fetch

Converting Data

This command can be used to convert sequence data into an aggregated file with data prepared for training.

deep-taxon prepare-data

Training neural networks

To train neural networks, we use PyTorch Lightning. This code can be executed with the following command.

deep-taxon train

This command will split up the input dataset into training, validation, and testing data. The seed used to do this will be saved in the checkpoint, so subsequent use, such as for testing, will have the same split.

Doing inference with neural networks

This command will compute network outputs for each sample from all sub-datasets. To run, you must provide this command with the checkpoint produced during training. When it is finished, it will save the results in the same directory that the input checkpoint file was saved.

deep-taxon infer

Network output summary

After computing model outputs, the outputs can be summarized using the follwoing command. This will produce a PNG figure with a scatter plot of a 2D UMAP embedding if the model outputs. It will also build a simple random forest classifier and plot a classification report

deep-taxon summarize

Example workflow

Before preparing an input file for training a network, you will need to download the necessary input files from the Genome Taxonomy Database (GTDB). Files can be downloaded here. You will need to download the metadata file (i.e. *_metadata*) and the tree file (i.e. *.tree)

Step 1 - Sample the GTDB tree

Once you have a metadata file and a tree file, you can run sample-gtdb to generate a list of NCBI accessions.

$ deep-taxon sample-gtdb ar122_metadata_r89.tsv ar122_r89.tree > my_accessions.txt

Step 2 - Download files from NCBI

Next, pass my_accessions.txt into ncbi-fetch to obtain sequence files for the accessions you have chosen. If you already have files downloaded, you can skip this step. This command calls rsync, so if you already have the files downloaded, it will not re-download them.

$ deep-taxon ncbi-fetch -f my_accessions.txt ncbi_sequences

Note that you will need to use the -f flag to indication that first arguemnt is a file containing a list of accessions. The second argument is where sequence files get downloaded to. ncbi-fetch will preserve the directory structure from the NCBI FTP site. Do not modify this, as the following command, prepare-data will expect this directory structure. If you are downloading many files and would like to speed things up, use -p to run downloads in parallel.

Step 3 - Converting to training input file

Now that sequence files are downloaded, sequence data can be converted into a input file for training.

$ deep-taxon prepare-data -V -G my_accessions.txt ncbi_sequences ar122_metadata_r89.tsv ar122_r89.tree my_input.h5

This will convert genomic sequence (i.e. -G flag) for the accessions you stored in my_accessions.txt. Data will be read from the directory ncbi_sequences.

Getting non-representative genomes

The previous workflow will generate an input file for representative genomes. You may want to use non-representatives. To do this, you can use the command deep-taxon sample-nonrep

$ deep-taxon sample-nonrep my_accessions.txt ar122_metadata_r89.tsv > nonrep_accessions.txt

This will print the accessions of non-representative genomes to the file nonrep_accessions.txt. You can also get the paths to the sequence files these for these strains by supplying a directory with the NCBI files. You can use the flags -G, -C, or -P to get the genomes, gene coding sequences, or protein sequences, respectively. By default, genome paths will be printed if you only provide the path to the NCBI Fasta directory.

Once you have a list of accessions, you can run Steps 2 and 3 from above to finish building an input file for inference on held-out genomes.

LICENSE

deep-taxon Copyright (c) 2022, The Regents of the University of California, through Lawrence Berkeley National Laboratory (subject to receipt of any required approvals from the U.S. Dept. of Energy). All rights reserved.

Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:

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(3) Neither the name of the University of California, Lawrence Berkeley National Laboratory, U.S. Dept. of Energy nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.

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COPYRIGHT

deep-taxon Copyright (c) 2022, The Regents of the University of California, through Lawrence Berkeley National Laboratory (subject to receipt of any required approvals from the U.S. Dept. of Energy). All rights reserved.

If you have questions about your rights to use or distribute this software, please contact Berkeley Lab's Intellectual Property Office at IPO@lbl.gov.

NOTICE. This Software was developed under funding from the U.S. Department of Energy and the U.S. Government consequently retains certain rights. As such, the U.S. Government has been granted for itself and others acting on its behalf a paid-up, nonexclusive, irrevocable, worldwide license in the Software to reproduce, distribute copies to the public, prepare derivative works, and perform publicly and display publicly, and to permit others to do so.