Autometa documentation
Guide
Getting Started
You will need to specifically configure your compute environment depending on how you would like to run the Autometa workflow.
Choose a workflow
🍏 Nextflow Workflow 🍏
Why nextflow?
Nextflow helps Autometa produce reproducible results while allowing the pipeline to scale across different platforms and hardware.
System Requirements
Currently the nextflow pipeline requires Docker 🐳 so it must be installed on your system. If you don’t have Docker installed you can install it from docs.docker.com/get-docker. We plan on removing this dependency in future versions, so that other dependency managers (e.g. Conda, Singularity, etc) can be used.
Nextflow runs on any Posix compatible system. Detailed system requirements can be found in the nextflow documentation
Nextflow (required) and nf-core tools (optional but highly recommended) installation will be discussed in Installing Nextflow and nf-core tools with Conda.
Data Preparation
Metagenome Assembly
You will first need to assemble your shotgun metagenome, to provide to Autometa as input.
The following is a typical workflow for metagenome assembly:
Trim adapter sequences from the reads
We usually use Trimmomatic.
Quality check the trimmed reads to ensure the adapters have been removed
We usually use FastQC.
Assemble the trimmed reads
We usually use MetaSPAdes which is a part of the SPAdes package.
Check the quality of your assembly (Optional)
We usually use metaQuast for this (use
--min-contig 1option to get an accurate N50).
This tool can compute a variety of assembly statistics one of which is N50. This can often be useful for selecting an appropriate length cutoff value for pre-processing the metagenome.
Preparing a Sample Sheet
An example sample sheet for three possible ways to provide a sample as an input is provided below. The first example provides a metagenome with paired-end read information, such that contig coverages may be determined using a read-based alignment sub-workflow. The second example uses pre-calculated coverage information by providing a coverage table with the input metagenome assembly. The third example retrieves coverage information from the assembly contig headers (Currently, this is only available with metagenomes assembled using SPAdes)
Note
If you have paired-end read information, you can supply these file paths within the sample sheet and the coverage
table will be computed for you (See example_1 in the example sheet below).
If you have used any other assembler, then you may also provide a coverage table (See example_2 in the example sheet below).
Fortunately, Autometa can construct this table for you with: autometa-coverage.
Use --help to get the complete usage or for a few examples see 2. Coverage calculation.
If you use SPAdes then Autometa can use the k-mer coverage information in the contig names (example_3 in the example sample sheet below).
sample |
assembly |
fastq_1 |
fastq_2 |
coverage_tab |
cov_from_assembly |
|---|---|---|---|---|---|
example_1 |
/path/to/example/1/metagenome.fna.gz |
/path/to/paired-end/fwd_reads.fastq.gz |
/path/to/paired-end/rev_reads.fastq.gz |
0 |
|
example_2 |
/path/to/example/2/metagenome.fna.gz |
/path/to/coverage.tsv |
0 |
||
example_3 |
/path/to/example/3/metagenome.fna.gz |
spades |
Note
To retrieve coverage information from a sample’s contig headers, provide the assembler used for the sample value in the row under the cov_from_assembly column.
Using a 0 will designate to the workflow to try to retrieve coverage information from the coverage table (if it is provided)
or coverage information will be calculated by read alignments using the provided paired-end reads. If both paired-end reads and a coverage table are provided,
the pipeline will prioritize the coverage table.
If you are providing a coverage table to coverage_tab with your input metagenome, it must be tab-delimited and contain at least the header columns, contig and coverage.
Supported Assemblers for cov_from_assembly
Assembler |
Supported (Y/N) |
|
|---|---|---|
[meta]SPAdes |
Y |
|
Unicycler |
N |
|
Megahit |
N |
|
You may copy the below table as a csv and paste it into a file to begin your sample sheet. You will need to update your input parameters, accordingly.
Example sample_sheet.csv
sample,assembly,fastq_1,fastq_2,coverage_tab,cov_from_assembly
example_1,/path/to/example/1/metagenome.fna.gz,/path/to/paired-end/fwd_reads.fastq.gz,/path/to/paired-end/rev_reads.fastq.gz,,0
example_2,/path/to/example/2/metagenome.fna.gz,,,/path/to/coverage.tsv,0
example_3,/path/to/example/3/metagenome.fna.gz,,,,spades
Caution
Paths to any of the file inputs must be absolute file paths.
Incorrect |
Correct |
Description |
|---|---|---|
|
|
Replacing any instance of the |
|
|
Using the entire file path of the input |
Quick Start
The following is a condensed summary of steps required to get Autometa installed, configured and running. There are links throughout to the appropriate documentation sections that can provide more detail if required.
Installation
For full installation instructions, please see the Installation section
If you would like to install Autometa via conda (I’d recommend it, its almost foolproof!), you’ll need to first install Miniconda on your system. You can do this in a few easy steps:
Type in the following and then hit enter. This will download the Miniconda installer to your home directory.
wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O $HOME/Miniconda3-latest-Linux-x86_64.sh
Note
$HOME is synonymous with /home/user and in my case is /home/sam
Now let’s run the installer. Type in the following and hit enter:
bash $HOME/Miniconda3-latest-Linux-x86_64.sh
Follow all of the prompts. Keep pressing enter until it asks you to accept. Then type yes and enter. Say yes to everything.
Note
If for whatever reason, you accidentally said no to the initialization, do not fear. We can fix this by running the initialization with the following command:
cd $HOME/miniconda3/bin/
./conda init
Finally, for the changes to take effect, you’ll need to run the following line of code which effectively acts as a “refresh”
source ~/.bashrc
Now that you have conda up and running, its time to install the Autometa conda environment. Run the following code:
conda env create --file=https://raw.githubusercontent.com/KwanLab/Autometa/main/nextflow-env.yml
Attention
You will only need to run the installation (code above) once. The installation does NOT need to be performed every time you wish to use Autometa. Once installation is complete, the conda environment (which holds all the tools that Autometa needs) will live on your server/computer much like any other program you install.
Anytime you would like to run Autometa, you’ll need to activate the conda environment. To activate the environment you’ll need to run the following command:
conda activate autometa-nf
Configuring a scheduler
For full details on how to configure your scheduler, please see the Configuring your process executor section.
If you are using a Slurm scheduler, you will need to create a configuration file. If you do not have a scheduler, skip ahead to Running Autometa
First you will need to know the name of your slurm partition. Run sinfo to find this. In the example below, the partition name is “agrp”.
Next, generate a new file called slurm_nextflow.config via nano:
nano slurm_nextflow.config
Then copy the following code block into that new file (“agrp” is the slurm partition to use in our case):
profiles {
slurm {
process.executor = "slurm"
process.queue = "agrp" // <<-- change this to whatever your partition is called
docker.enabled = true
docker.userEmulation = true
singularity.enabled = false
podman.enabled = false
shifter.enabled = false
charliecloud.enabled = false
executor {
queueSize = 8
}
}
}
Keep this file somewhere central to you. For the sake of this example I will be keeping it in a folder called “Useful scripts” in my home directory
because that is a central point for me where I know I can easily find the file and it won’t be moved e.g.
/home/sam/Useful_scripts/slurm_nextflow.config
Save your new file with Ctrl+O and then exit nano with Ctrl+O.
Installation and set up is now complete. 🎉 🥳
Running Autometa
For a comprehensive list of features and options and how to use them please see Running the pipeline
Autometa can bin one or several metagenomic datasets in one run. Regardless of the number of metagenomes you want to process, you will need to provide a sample sheet which specifies the name of your sample, the full path to where that data is found and how to retrieve the sample’s contig coverage information.
If the metagenome was assembled via SPAdes, Autometa can extract coverage and contig length information from the sequence headers.
If you used a different assembler you will need to provide either raw reads or a table containing contig/scaffold coverage information. Full details for data preparation may be found under Preparing a Sample Sheet
First ensure that your Autometa conda environment is activated. You can activate your environment by running:
conda activate autometa-nf
Run the following code to launch Autometa:
nf-core launch KwanLab/Autometa
Note
You may want to note where you have saved your input sample sheet prior to running the launch command. It is much easier (and less error prone) to copy/paste the sample sheet file path when specifying the input (We’ll get to this later in Input and Output).
You will now use the arrow keys to move up and down between your options and hit your “Enter” or “Return” key to make your choice.
KwanLab/Autometa nf-core parameter settings:
Choose a version
The double, right-handed arrows should already indicate the latest release of Autometa (in our case 2.0.0).
The latest version of the tool will always be at the top of the list with older versions descending below.
To select the latest version, ensure that the double, right-handed arrows are next to 2.0.0, then hit “Enter”.
Choose nf-core interface
Pick the Command line option.
Note
Unless you’ve done some fancy server networking (i.e. tunneling and port-forwarding),
or are using Autometa locally, Command line is your only option.
General nextflow parameters
If you are using a scheduler (Slurm in this example), -profile is the only option you’ll need to change.
If you are not using a scheduler, you may skip this step.
Input and Output
Now we need to give Autometa the full paths to our input sample sheet, output results folder and output logs folder (aka where trace files are stored).
Note
A new folder, named by its respective sample value, will be created within the output results folder for each metagenome listed in the sample sheet.
Binning parameters
If you’re not sure what you’re doing I would recommend only changing length_cutoff.
The default cutoff is 3000bp, which means that any contigs/scaffolds smaller than 3000bp will not be considered for binning.
Note
This cutoff will depend on how good your assembly is: e.g. if your N50 is 1200bp, I would choose a cutoff of 1000. If your N50 is more along the lines of 5000, I would leave the cutoff at the default 3000. I would strongly recommend against choosing a number below 900 here. In the example below, I have chosen a cutoff of 1000bp as my assembly was not particularly great (the N50 is 1100bp).
Additional Autometa options
Here you have a choice to make:
By enabling taxonomy aware mode, Autometa will attempt to use taxonomic data to make your bins more accurate.
However, this is a more computationally expensive step and will make the process take longer.
By leaving this option as the default
Falseoption, Autometa will bin according to coverage and kmer patterns.
Despite your choice, you will need to provide a path to the necessary databases using the single_db_dir option.
In the example below, I have enabled the taxonomy aware mode and provided the path to where the databases are stored
(in my case this is /home/sam/Databases).
For additional details on required databases, see the Databases section.
Computational parameters
This will depend on the computational resources you have available. You could start with the default values and see how the binning goes. If you have particularly complex datasets you may want to bump this up a bit. For your average metagenome, you won’t need more than 150Gb of memory. I’ve opted to use 75 Gb as a starting point for a few biocrust (somewhat diverse) metagenomes.
Note
These options correspond to the resources provided to each process of Autometa, not the entire workflow itself.
Also, for TB worth of assembled data you may want to try the 🐚 Bash Workflow 🐚 using the autometa-large-data-mode.sh template
Do you want to run the nextflow command now?
You will now be presented with a choice. If you are NOT using a scheduler, you can go ahead and type y to launch the workflow.
If you are using a scheduler, type n - we have one more step to go. In the example below, I am using the slurm scheduler so I have typed n
to prevent immediately performing the nextflow run command.
If you recall, we created a file called slurm_nextflow.config that contains the information Autometa will need to communicate with the Slurm scheduler.
We need to include that file using the -c flag (or configuration flag). Therefore to launch the Autometa workflow, run the following command:
Note
You will need to change the /home/sam/Useful_scripts/slurm_nextflow.config file path to what is appropriate for your system.
nextflow run KwanLab/Autometa -r 2.0.0 -profile "slurm" -params-file "nf-params.json" -c "/home/sam/Useful_scripts/slurm_nextflow.config"
Once you have hit the “Enter” key to submit the command, nextflow will display the progress of your binning run, such as the one below:
When the run is complete, output will be stored in your designated output folder, in my case /home/same/Trial/Autometa_output (See Input and Output).
Basic
While the Autometa Nextflow pipeline can be run using Nextflow directly, we designed it using nf-core standards and templating to provide an easier user experience through use of the nf-core “tools” python library. The directions below demonstrate using a minimal Conda environment to install Nextflow and nf-core tools and then running the Autometa pipeline.
Installing Nextflow and nf-core tools with Conda
If you have not previously installed/used Conda, you can get it using the Miniconda installer appropriate to your system, here: https://docs.conda.io/en/latest/miniconda.html
After installing conda, running the following command will create a minimal Conda environment named “autometa-nf”, and install Nextflow and nf-core tools.
conda env create --file=https://raw.githubusercontent.com/KwanLab/Autometa/main/nextflow-env.yml
If you receive the message…
CondaValueError: prefix already exists:
…it means you have already created the environment. If you want to overwrite/update
the environment then add the --force flag to the end of the command.
conda env create --file=https://raw.githubusercontent.com/KwanLab/Autometa/main/nextflow-env.yml --force
Once Conda has finished creating the environment be sure to activate it:
conda activate autometa-nf
Using nf-core
Download/Launch the Autometa Nextflow pipeline using nf-core tools. The stable version of Autometa will always be the “main” git branch. To use an in-development git branch switch “main” in the command with the name of the desired branch. After the pipeline downloads, nf-core will start the pipeline launch process.
nf-core launch KwanLab/Autometa
Caution
nf-core will give a list of revisions to use following the above command. Any of the version 1.* revisions are NOT supported.
Attention
If you receive an error about schema parameters you may be able to resolve this
by first removing the existing project and pulling the desired KwanLab/Autometa
project using nextflow.
If a local project exists (you can check with nextflow list), first drop this project:
nextflow drop KwanLab/Autometa
Now pull the desired revision:
nextflow pull KwanLab/Autometa -r 2.0.0
# or
nextflow pull KwanLab/Autometa -r main
# or
nextflow pull KwanLab/Autometa -r dev
# Now run nf-core with selected revision from above
nf-core launch KwanLab/Autometa -r <2.0.0|main|dev>
Now after re-running nf-core launch ... select the revision that you downloaded from above.
You will then be asked to choose “Web based” or “Command line” for selecting/providing options. While it is possible to use the command line version, it is preferred and easier to use the web-based GUI. Use the arrow keys to select one or the other and then press return/enter.
Setting parameters with a web-based GUI
The GUI will present all available parameters, though some extra parameters may be hidden (these can be revealed by selecting “Show hidden params” on the right side of the page).
Required parameters
The first required parameter is the input sample sheet for the Autometa workflow, specified using --input. This is the path to your input sample sheet.
See Preparing a Sample Sheet for additional details.
The other parameter is a nextflow argument, specified with -profile. This configures nextflow and the Autometa workflow as outlined in the respective
“profiles” section in the pipeline’s nextflow.config file.
standard(default): runs all process jobs locally, (currently this requires Docker, i.e. docker is enabled for all processes the default profile).
slurm: submits all process jobs into the slurm queue. See SLURM before using
docker: enables docker for all processes
Caution
Additional profiles exists in the nextflow.config file, however these have not yet been tested. If you
are able to successfully configure these profiles, please get in touch or submit a pull request and we will add these configurations
to the repository.
conda: Enables running all processes using condasingularity: Enables running all processes using singularitypodman: Enables running all processes using podmanshifter: Enables running all processes using shiftercharliecloud: Enables running all processes using charliecloud
Caution
Notice the number of hyphens used between --input and -profile. --input is an Autometa workflow parameter
where as -profile is a nextflow argument. This difference in hyphens is true for passing in all arguments to the Autometa
workflow and nextflow, respectively.
Running the pipeline
After you are finished double-checking your parameter settings, click “Launch”
at the top right of web based GUI page, or “Launch workflow” at the bottom of
the page. After returning to the terminal you should be provided the option
Do you want to run this command now? [y/n] enter y to begin the pipeline.
This process will lead to nf-core tools creating a file named nf-params.json.
This file contains your specified parameters that differed from the pipeline’s defaults.
This file can also be manually modified and/or shared to allow reproducible configuration
of settings (e.g. among members within a lab sharing the same server).
Additionally all Autometa specific pipeline parameters can be used as command line arguments
using the nextflow run ... command by prepending the parameter name with two hyphens
(e.g. --outdir /path/to/output/workflow/results)
Caution
If you are restarting from a previous run, DO NOT FORGET to also add the -resume flag to the nextflow run command.
Notice only 1 hyphen is used with the -resume nextflow parameter!
Note
You can run the KwanLab/Autometa project without using nf-core if you already have a correctly
formatted parameters file. (like the one generated from nf-core launch ..., i.e. nf-params.json)
nextflow run KwanLab/Autometa -params-file nf-params.json -profile slurm -resume
Advanced
Parallel computing and computer resource allotment
While you might want to provide Autometa all the compute resources available in order to get results faster, that may or may not actually achieve the fastest run time.
Within the Autometa pipeline, parallelization happens by providing all the assemblies at once to software that internally handles parallelization.
The Autometa pipeline will try and use all resources available to individual pipeline modules. Each module/process has been pre-assigned resource allotments via a low/medium/high tag. This means that even if you don’t select for the pipeline to run in parallel some modules (e.g. DIAMOND BLAST) may still use multiple cores.
The maximum number of CPUs that any single module can use is defined with the
--max_cpusoption (default: 4).You can also set
--max_memory(default: 16GB)--max_time(default: 240h).--max_timerefers to the maximum time each process is allowed to run, not the execution time for the the entire pipeline.
Databases
Autometa uses the following NCBI databases throughout its pipeline:
- Non-redundant nr database
- prot.accession2taxid.gz
- nodes.dmp, names.dmp and merged.dmp - Found within
If you are running autometa for the first time you’ll have to download these databases.
You may use autometa-update-databases --update-ncbi. This will download the databases to the default path. You can check
the default paths using autometa-config --print. If you need to change the default download directory you can use
autometa-config --section databases --option ncbi --value <path/to/new/ncbi_database_directory>.
See autometa-update-databases -h and autometa-config -h for full list of options.
In your nf-params.json file you also need to specify the directory where the different databases are present.
Make sure that the directory path contains the following databases:
Diamond formatted nr file => nr.dmnd
Extracted files from tarball taxdump.tar.gz
prot.accession2taxid.gz
{
"single_db_dir" = "$HOME/Autometa/autometa/databases/ncbi"
}
Note
Find the above section of code in nf-params.json and update this path to the folder
with all of the downloaded/formatted NCBI databases.
CPUs, Memory, Disk
Note
Like nf-core pipelines, we have set some automatic defaults for Autometa’s processes. These are dynamic and each process will try a second attempt using more resources if the first fails due to resources. Resources are always capped by the parameters (show with defaults):
--max_cpus = 2--max_memory = 6.GB--max_time = 48.h
The best practice to change the resources is to create a new config file and point to it at runtime by adding the
flag -c path/to/custom/file.config
For example, to give all resource-intensive (i.e. having label process_high) jobs additional memory and cpus, create a file called process_high_mem.config and insert
process {
withLabel:process_high {
memory = 200.GB
cpus = 32
}
}
Then your command to run the pipeline (assuming you’ve already run nf-core launch KwanLab/Autometa which created
a nf-params.json file) would look something like:
nextflow run KwanLab/Autometa -params-file nf-params.json -c process_high_mem.config
Caution
If you are restarting from a previous run, DO NOT FORGET to also add the -resume flag to the nextflow run command.
Notice only 1 hyphen is used with the -resume nextflow parameter!
For additional information and examples see Tuning workflow resources
Additional Autometa parameters
Up to date descriptions and default values of Autometa’s nextflow parameters can be viewed using the following command:
nextflow run KwanLab/Autometa -r main --help
You can also adjust other pipeline parameters that ultimately control how binning is performed.
params.length_cutoff : Smallest contig you want binned (default is 3000bp)
params.kmer_size : kmer size to use
params.norm_method : Which kmer frequency normalization method to use. See
Advanced Usage section for details
params.pca_dimensions : Number of dimensions of which to reduce the initial k-mer frequencies
matrix (default is 50). See Advanced Usage section for details
params.embedding_method : Choices are sksne, bhsne, umap, densmap, trimap
(default is bhsne) See Advanced Usage section for details
params.embedding_dimensions : Final dimensions of the kmer frequencies matrix (default is 2).
See Advanced Usage section for details
params.kingdom : Bin contigs belonging to this kingdom. Choices are bacteria and archaea
(default is bacteria).
params.clustering_method : Cluster contigs using which clustering method. Choices are “dbscan” and “hdbscan”
(default is “dbscan”). See Advanced Usage section for details
params.binning_starting_rank : Which taxonomic rank to start the binning from. Choices are superkingdom, phylum,
class, order, family, genus, species (default is superkingdom). See Advanced Usage section for details
params.classification_method : Which clustering method to use for unclustered recruitment step.
Choices are decision_tree and random_forest (default is decision_tree). See Advanced Usage section for details
params.completeness : Minimum completeness needed to keep a cluster (default is at least 20% complete, e.g. 20).
See Advanced Usage section for details
params.purity : Minimum purity needed to keep a cluster (default is at least 95% pure, e.g. 95).
See Advanced Usage section for details
params.cov_stddev_limit : Which clusters to keep depending on the coverage std.dev (default is 25%, e.g. 25).
See Advanced Usage section for details
params.gc_stddev_limit : Which clusters to keep depending on the GC% std.dev (default is 5%, e.g. 5).
See Advanced Usage section for details
Customizing Autometa’s Scripts
In case you want to tweak some of the scripts, run on your own scheduling system or modify the pipeline you can clone the repository and then run nextflow directly from the scripts as below:
# Clone the autometa repository into current directory
git clone git@github.com:KwanLab/Autometa.git
# Modify some code
# e.g. one of the local modules
code $HOME/Autometa/modules/local/align_reads.nf
# Generate nf-params.json file using nf-core
nf-core launch $HOME/Autometa
# Then run nextflow
nextflow run $HOME/Autometa -params-file nf-params.json -profile slurm
Note
If you only have a few metagenomes to process and you would like to customize Autometa’s behavior, it may be easier to first try customization of the 🐚 Bash Workflow 🐚
Useful options
-c : In case you have configured nextflow with your executor (see Configuring your process executor)
and have made other modifications on how to run nextflow using your nexflow.config file, you can specify that file
using the -c flag
To see all of the command line options available you can refer to nexflow CLI documentation
Resuming the workflow
One of the most powerful features of nextflow is resuming the workflow from the last completed process. If your pipeline
was interrupted for some reason you can resume it from the last completed process using the resume flag (-resume).
Eg, nextflow run KwanLab/Autometa -params-file nf-params.json -c my_other_parameters.config -resume
Execution Report
After running nextflow you can see the execution statistics of your autometa run, including the time taken, CPUs used,
RAM used, etc separately for each process. Nextflow will generate summary, timeline and trace reports automatically for
you in the ${params.outdir}/trace directory. You can read more about this in the
nextflow docs on execution reports.
Visualizing the Workflow
You can visualize the entire workflow ie. create the directed acyclic graph (DAG) of processes from the written DOT file. First install
Graphviz (conda install -c anaconda graphviz) then do dot -Tpng < pipeline_info/autometa-dot > autometa-dag.png to get the
in the png format.
Configuring your process executor
For nextflow to run the Autometa pipeline through a job scheduler you will need to update the respective profile
section in nextflow’s config file. Each profile may be configured with any available scheduler as noted in the
nextflow executors docs. By default nextflow will use your
local computer as the ‘executor’. The next section briefly walks through nextflow executor configuration to run
with the slurm job scheduler.
We have prepared a template for nextflow.config which you can access from the KwanLab/Autometa GitHub repository using this
nextflow.config template. Go ahead
and copy this file to your desired location and open it in your favorite text editor (eg. Vim, nano, VSCode, etc).
SLURM
This allows you to run the pipeline using the SLURM resource manager. To do this you’ll first needed to identify the
slurm partition to use. You can find the available slurm partitions by running sinfo. Example: On running sinfo
on our cluster we get the following:
The slurm partition available on our cluster is queue. You’ll need to update this in nextflow.config.
profiles {
// Find this section of code in nextflow.config
slurm {
process.executor = "slurm"
// NOTE: You can determine your slurm partition (e.g. process.queue) with the `sinfo` command
// Set SLURM partition with queue directive.
process.queue = "queue" // <<-- change this to whatever your partition is called
// queue is the slurm partition to use in our case
docker.enabled = true
docker.userEmulation = true
singularity.enabled = false
podman.enabled = false
shifter.enabled = false
charliecloud.enabled = false
executor {
queueSize = 8
}
}
}
More parameters that are available for the slurm executor are listed in the nextflow executor docs for slurm.
Docker image selection
Especially when developing new features it may be necessary to run the pipeline with a custom docker image.
Create a new image by navigating to the top Autometa directory and running make image. This will create a new
Autometa Docker image, tagged with the name of the current Git branch.
To use this tagged version (or any other Autometa image tag) add the argument --autometa_image tag_name to the nextflow run command
🐚 Bash Workflow 🐚
Getting Started
Compute Environment Setup
If you have not previously installed/used Conda, you can get it using the Miniconda installer appropriate to your system, here: https://docs.conda.io/en/latest/miniconda.html
After installing conda, running the following command will create a minimal Conda environment named “autometa”.
conda env create --file=https://raw.githubusercontent.com/KwanLab/Autometa/main/autometa-env.yml
If you receive the message…
CondaValueError: prefix already exists:
…it means you have already created the environment. If you want to overwrite/update
the environment then add the --force flag to the end of the command.
conda env create --file=https://raw.githubusercontent.com/KwanLab/Autometa/main/autometa-env.yml --force
Once Conda has finished creating the environment be sure to activate it:
conda activate autometa
Download Workflow Template
To run Autometa using the bash workflow you will simply need to download and configure the workflow template to your metagenomes specifications.
Here are a few download commands if you do not want to navigate to the workflow on GitHub
via curl
curl -o autometa.sh https://raw.githubusercontent.com/KwanLab/Autometa/main/workflows/autometa.sh
via wget
wget https://raw.githubusercontent.com/KwanLab/Autometa/main/workflows/autometa.sh
Note
The autometa-large-data-mode workflow is also available and is configured similarly to the autometa bash workflow.
Configure Required Inputs
The Autometa bash workflow requires the following input file and directory paths. To see how to prepare each input, see Data preparation
Assembly (
assembly)Alignments (
bam)ORFs (
orfs)Diamond blastp results table (
blast)NCBI database directory (
ncbi)Input sample name (
simpleName)Output directory (
outdir)
Data preparation
Metagenome Assembly (
assembly)Alignments Preparation (
bam)ORFs (
orfs)Diamond blastp Preparation (
blast)NCBI Preparation (
ncbi)
Metagenome Assembly
You will first need to assemble your shotgun metagenome, to provide to Autometa as input.
The following is a typical workflow for metagenome assembly:
Trim adapter sequences from the reads
We usually use Trimmomatic.
Quality check the trimmed reads to ensure the adapters have been removed
We usually use FastQC.
Assemble the trimmed reads
We usually use MetaSPAdes which is a part of the SPAdes package.
Check the quality of your assembly (Optional)
We usually use metaQuast for this (use
--min-contig 1option to get an accurate N50). This tool can compute a variety of assembly statistics one of which is N50. This can often be useful for selecting an appropriate length cutoff value for pre-processing the metagenome.
Alignments Preparation
Note
The following example requires bwa, kart and samtools
conda install -c bioconda bwa kart samtools
# First index metagenome assembly
bwa index \\
-b 550000000000 \\ # block size for the bwtsw algorithm (effective with -a bwtsw) [default=10000000]
metagenome.fna # Path to input metagenome
# Now perform alignments (we are using kart, but you can use another alignment tool if you'd like)
kart \\
-i metagenome.fna \\ # Path to input metagenome
-t 20 \\ # Number of cpus to use
-f /path/to/forward_reads.fastq.gz \\ # Path to forward paired-end reads
-f2 /path/to/reverse_reads.fastq.gz \\ # Path to reverse paired-end reads
-o alignments.sam # Path to alignments output
# Now sort alignments and convert to bam format
samtools sort \\
-@ 40 \\ # Number of cpus to use
-m 10G \\ # Amount of memory to use
alignments.sam \\ # Input alignments file path
-o alignments.bam # Output alignments file path
ORFs
Note
The following example requires prodigal. e.g. conda install -c bioconda prodigal
prodigal -i metagenome.fna \\
-f "gbk" \\
-d "metagenome.orfs.fna" \\
-o "metagenome.orfs.gbk" \\
-a "metagenome.orfs.faa" \\ # This generated file is required as input to the bash workflow
-s "metagenome.all_orfs.txt"
Diamond blastp Preparation
Note
The following example requires diamond. e.g. conda install -c bioconda diamond
diamond blastp \\
--query "metagenome.orfs.faa" \\ # See prodigal output from above
--db /path/to/nr.dmnd \\ # See NCBI section
--threads <num cpus to use> \\
--out blastp.tsv # This generated file is required as input to the bash workflow
NCBI Preparation
If you are running Autometa for the first time you’ll have to download the NCBI databases.
# First configure where you want to download the NCBI databases
autometa-config \\
--section databases --option ncbi \\
--value <path/to/your/ncbi/database/directory>
# Now download and format the NCBI databases
autometa-update-databases --update-ncbi
Note
You can check the default config paths using autometa-config --print.
See autometa-update-databases -h and autometa-config -h for full list of options.
The previous command will download the following NCBI databases:
- Non-redundant nr database
- prot.accession2taxid.gz
- nodes.dmp, names.dmp and merged.dmp - Found within
Input Sample Name
A crucial step prior to running the Autometa bash workflow is specifying the metagenome sample name and where to output Autometa’s results.
# Default
simpleName="TemplateAssemblyName"
# Replace with your sample name
simpleName="MySample"
Note
The simpleName that is provided will be used as a prefix to all of the resulting autometa output files.
Output directory
Immediately following the simpleName parameter, you will need to specify where to write all results.
# Default
outdir="AutometaOutdir"
# Replace with your output directory...
outdir="MySampleAutometaResults"
Running the pipeline
After you are finished configuring/double-checking your parameter settings..
You may run the pipeline via bash:
bash autometa.sh
or submit the pipeline into a queue:
For example, with slurm:
sbatch autometa.sh
Caution
Make sure your conda autometa environment is activated or the autometa entrypoints will not be available.
Additional parameters
You can also adjust other pipeline parameters that ultimately control how binning is performed. These are located at the top of the workflow just under the required inputs.
length_cutoff : Smallest contig you want binned (default is 3000bp)
kmer_size : kmer size to use
norm_method : Which kmer frequency normalization method to use. See
Advanced Usage section for details
pca_dimensions : Number of dimensions of which to reduce the initial k-mer frequencies
matrix (default is 50). See Advanced Usage section for details
embed_method : Choices are sksne, bhsne, umap, densmap, trimap
(default is bhsne) See Advanced Usage section for details
embed_dimensions : Final dimensions of the kmer frequencies matrix (default is 2).
See Advanced Usage section for details
cluster_method : Cluster contigs using which clustering method. Choices are “dbscan” and “hdbscan”
(default is “dbscan”). See Advanced Usage section for details
binning_starting_rank : Which taxonomic rank to start the binning from. Choices are superkingdom, phylum,
class, order, family, genus, species (default is superkingdom). See Advanced Usage section for details
classification_method : Which clustering method to use for unclustered recruitment step.
Choices are decision_tree and random_forest (default is decision_tree). See Advanced Usage section for details
completeness : Minimum completeness needed to keep a cluster (default is at least 20% complete, e.g. 20).
See Advanced Usage section for details
purity : Minimum purity needed to keep a cluster (default is at least 95% pure, e.g. 95).
See Advanced Usage section for details
cov_stddev_limit : Which clusters to keep depending on the coverage std.dev (default is 25%, e.g. 25).
See Advanced Usage section for details
gc_stddev_limit : Which clusters to keep depending on the GC% std.dev (default is 5%, e.g. 5).
See Advanced Usage section for details
Note
If you are configuring an autometa job using the autometa-large-data-mode.sh template,
there will be an additional parameter called, max_partition_size (default=10,000). This is the maximum size
partition the Autometa clustering algorithm will consider. Any taxon partitions larger than this setting
will be skipped.
📓 Step by Step Tutorial 📓
Here is the step by step tutorial of the entire pipeline. This is helpful in case you have your own files or just want to run a specific step.
Before running anything make sure you have activated the conda environment using
conda activate autometa.
See the Autometa Package Installation page for details on setting up your conda environment.
I will be going through this tutorial using the 78Mbp test dataset which can be found here https://drive.google.com/drive/u/2/folders/1McxKviIzkPyr8ovj8BG7n_IYk-QfHAgG.
You only need to download metagenome.fna.gz from the above link and save it at a directory as per your liking. I’m saving it in $HOME/tutorial/test_data/.
For instructions on how to download the dataset using command-line see the “Using command-line” section on Benchmarking page.
1. Length filter
The first step when running Autometa is the length filtering. This would remove any contigs that are below the length cutoff. This is useful in removing the noise from the data, as small contigs may have ambiguous kmer frequencies. The default cutoff if 3,000bp, ie. any contig that is smaller than 3,000bp would be removed.
Note
It is important that you alter the cutoff based on your N50. If your N50 is really small, e.g. 500bp (pretty common for soil assemblies), then you might want to lower your cutoff to somewhere near N50. The tradeoff with lowering the length cutoff, however, is a greater number of contigs which may make it more difficult for the dataset to be binned. As was shown in the Autometa paper, as assembly quality degrades so does the binning performance.
Use the following command to run the length-filter step:
autometa-length-filter \
--assembly $HOME/tutorial/test_data/78mbp_metagenome.fna \
--cutoff 3000 \
--output-fasta $HOME/tutorial/78mbp_metagenome.filtered.fna \
--output-stats $HOME/tutorial/78mbp_metagenome.stats.tsv \
--output-gc-content $HOME/tutorial/78mbp_metagenome.gc_content.tsv
Let us dissect the above command:
Flag |
Input arguments |
Requirement |
|---|---|---|
|
Path to metagenome assembly (nucleotide fasta) file |
Required |
|
Length cutoff for the filtered assembly. Default is 3,000bp |
Optional |
|
Path to filtered metagenomic assembly that would be used for binning |
Required |
|
Path to assembly statistics table |
Optional |
|
Path to assembly contigs’ GC content and length stats table |
Optional |
You can view the complete command-line options using autometa-length-filter -h
The above command generates the following files:
File |
Description |
|---|---|
78mbp_metagenome.filtered.fna |
Length filtered metagenomic assembly to be used for binning |
78mbp_metagenome.stats.tsv |
Table describing the filtered metagenome assembly statistics |
78mbp_metagenome.gc_content.tsv |
Table of GC content and length of each contig in the filtered assembly |
2. Coverage calculation
Coverage calculation for each contig is done to provide another parameter to use while clustering contigs.
from SPAdes
If you have used SPAdes to assemble your metagenome, you can use the following command to generate the coverage table:
autometa-coverage \
--assembly $HOME/tutorial/78mbp_metagenome.fna \
--out $HOME/tutorial/78mbp_metagenome.coverages.tsv \
--from-spades
from alignments.bed
If you have assembled your metagenome using some other assembler you can use one of the following commands to generate the coverage table.
# If you have already made a bed file
autometa-coverage \
--assembly $HOME/tutorial/78mbp_metagenome.filtered.fna \
--bed 78mbp_metagenome.bed \
--out $HOME/tutorial/78mbp_metagenome.coverages.tsv \
--cpus 40
from alignments.bam
# If you have already made an alignment (bam file)
autometa-coverage \
--assembly $HOME/tutorial/78mbp_metagenome.filtered.fna \
--bam 78mbp_metagenome.bam \
--out $HOME/tutorial/78mbp_metagenome.coverages.tsv \
--cpus 40
from alignments.sam
# If you have already made an alignment (sam file)
autometa-coverage \
--assembly $HOME/tutorial/78mbp_metagenome.filtered.fna \
--sam 78mbp_metagenome.sam \
--out $HOME/tutorial/78mbp_metagenome.coverages.tsv \
--cpus 40
from paired-end reads
You may calculate coverage using forward and reverse reads with the assembled metagenome.
autometa-coverage \
--assembly $HOME/tutorial/78mbp_metagenome.filtered.fna \
--fwd-reads fwd_reads_1.fastq \
--rev-reads rev_reads_1.fastq \
--out $HOME/tutorial/78mbp_metagenome.coverages.tsv \
--cpus 40
In case you have multiple forward and reverse read pairs supply a comma-delimited list.
autometa-coverage \
--assembly $HOME/tutorial/78mbp_metagenome.filtered.fna \
--fwd-reads fwd_reads_1.fastq,fwd_reads_2.fastq \
--rev-reads rev_reads_1.fastq,rev_reads_2.fastq \
--out $HOME/tutorial/78mbp_metagenome.coverages.tsv \
--cpus 40
Note
No spaces should be used when providing the forward and reverse reads.
The lists of forward and reverse reads should be in the order corresponding to their respective reads pair.
Let us dissect the above commands:
Flag |
Function |
|---|---|
|
Path to length filtered metagenome assembly |
|
If the input assembly is generated using SPades then extract k-mer coverages from contig IDs |
|
Path to alignments BED file |
|
Path to alignments BAM file |
|
Path to alignments SAM file |
|
Path to forward reads |
|
Path to reverse reads |
|
Number of CPUs to use (default is to use all available CPUs) |
|
Path to coverage table of each contig |
You can view the complete command-line options using autometa-coverage -h
The above command would generate the following files:
File |
Description |
|---|---|
78mbp_metagenome.coverages.tsv |
Table with read or k-mer coverage of each contig in the metagenome |
3. Generate Open Reading Frames (ORFs)
ORF calling using prodigal is performed here. The ORFs are needed for single copy marker gene detection and for taxonomic assignment.
Use the following command to run the ORF calling step:
autometa-orfs \
--assembly $HOME/tutorial/78mbp_metagenome.filtered.fna \
--output-nucls $HOME/tutorial/78mbp_metagenome.orfs.fna \
--output-prots $HOME/tutorial/a78mbp_metagenome.orfs.faa \
--cpus 40
Let us dissect the above command:
Flag |
Function |
|---|---|
|
Path to length filtered metagenome assembly |
|
Path to nucleic acid sequence of ORFs |
|
Path to amino acid sequence of ORFs |
|
Number of CPUs to use (default is to use all available CPUs) |
You can view the complete command-line options using autometa-orfs -h
The above command would generate the following files:
File |
Description |
|---|---|
78mbp_metagenome.orfs.fna |
Nucleic acid fasta file of ORFs |
78mbp_metagenome.orfs.faa |
Amino acid fasta file of ORFs |
4. Single copy markers
Autometa uses single-copy markers to guide clustering, and does not assume that recoverable genomes will necessarily be “complete”. You first need to download the single-copy markers.
# Create a markers directory to hold the marker genes
mkdir -p $HOME/Autometa/autometa/databases/markers
# Change the default download path to the directory created above
autometa-config \
--section databases \
--option markers \
--value $HOME/Autometa/autometa/databases/markers
# Download single-copy marker genes
autometa-update-databases --update-markers
# hmmpress the marker genes
hmmpress -f $HOME/Autometa/autometa/databases/markers/bacteria.single_copy.hmm
hmmpress -f $HOME/Autometa/autometa/databases/markers/archaea.single_copy.hmm
Use the following command to annotate contigs containing single-copy marker genes:
autometa-markers \
--orfs $HOME/tutorial/78mbp_metagenome.orfs.faa \
--kingdom bacteria \
--hmmscan $HOME/tutorial/78mbp_metagenome.hmmscan.tsv \
--out $HOME/tutorial/78mbp_metagenome.markers.tsv \
--parallel \
--cpus 4 \
--seed 42
Let us dissect the above command:
Flag |
Function |
Requirement |
|---|---|---|
|
Path to fasta file containing amino acid sequences of ORFS |
Required |
|
Kingdom to search for markers. Choices bacteria (default) and archaea |
Optional |
|
Path to hmmscan output table containing the respective kingdom single-copy marker annotations |
Required |
|
Path to write filtered annotated markers corresponding to kingdom |
Required |
|
Use hmmscan parallel option (default: False) |
Optional |
|
Number of CPUs to use (default is to use all available CPUs) |
Optional |
|
Seed to set random state for hmmscan. (default: 42) |
Optional |
You can view the complete command-line options using autometa-markers -h
The above command would generate the following files:
File |
Description |
|---|---|
78mbp_metagenome.hmmscan.tsv |
hmmscan output table containing the respective kingdom single-copy marker annotations |
78mbp_metagenome.markers.tsv |
Annotated marker table corresponding to the particular kingdom |
5. Taxonomy assignment
5.1 BLASTP
Autometa assigns a taxonomic rank to each contig and then takes only the contig belonging to the specified kingdom (either bacteria or archaea) for binning. We found that in host-associated metagenomes, this step vastly improves the binning performance of Autometa (and other pipelines) because less eukaryotic or viral contigs will be placed into bacterial bins.
The first step for contig taxonomy assignment is a local alignment search of the ORFs against a reference database. This can be accelerated using diamond.
Create a diamond formatted database of the NCBI non-redundant (nr.gz) protein database.
diamond makedb \
--in $HOME/Autometa/autometa/databases/ncbi/nr.gz \
--db $HOME/Autometa/autometa/databases/ncbi/nr \
--threads 40
Breaking down the above command:
Flag |
Function |
|---|---|
–in |
Path to nr database |
–db |
Path to diamond formated nr database |
-p |
Number of processors to use |
Note
diamond makedb will append .dmnd to the provided path of --db.
i.e. --db /path/to/nr will become /path/to/nr.dmnd
Run diamond blastp using the following command:
diamond blastp \
--query $HOME/tutorial/78mbp_metagenome.orfs.faa \
--db $HOME/Autometa/autometa/databases/ncbi/nr.dmnd \
--evalue 1e-5 \
--max-target-seqs 200 \
--threads 40 \
--outfmt 6 \
--out $HOME/tutorial/78mbp_metagenome.blastp.tsv
Breaking down the above command:
Flag |
Function |
|---|---|
–query |
Path to query sequence. Here, amino acid sequence of ORFs |
–db |
Path to diamond formatted nr database |
–evalue |
Maximum expected value to report an alignment |
–max-target-seqs |
Maximum number of target sequences per query to report alignments for |
–threads |
Number of processors to use |
–outfmt |
Output format of BLASTP results |
–out |
Path to BLASTP results |
To see the complete list of acceptable output formats see Diamond GitHub Wiki. A complete list of all command-line options for Diamond can be found on its GitHub Wiki.
Caution
Autometa only parses output format 6 provided above as: --outfmt 6
The above command would generate the blastP table (78mbp_metagenome.blastp.tsv) in output format 6
5.2 Lowest Common Ancestor (LCA)
The second step in taxon assignment is determining each ORF’s lowest common ancestor (LCA).
This step uses the blastp results generated in the previous step to generate a table having the LCA of each ORF. As a default only
the blastp hits (subject accessions) which are within 10% of the top bitscore are used. These subject accessions are translated to
their respective taxids (prot.accession2taxid.gz) to be looked up in NCBI’s taxonomy database (nodes.dmp). Each ORFs’ list of taxids
are then reduced to its lowest common ancestor via a range minimum query.
Note
For more details on the range minimum query algorithm, see the closed issue (#170) on Github and a walkthrough on topcoder
Use the following command to get the LCA of each ORF:
autometa-taxonomy-lca \
--blast $HOME/tutorial/78mbp_metagenome.blastp.tsv \
--dbdir $HOME/Autometa/autometa/databases/ncbi/ \
--lca-output $HOME/tutorial/78mbp_metagenome.lca.tsv \
--sseqid2taxid-output $HOME/tutorial/78mbp_metagenome.lca.sseqid2taxid.tsv \
--lca-error-taxids $HOME/tutorial/78mbp_metagenome.lca.errorTaxids.tsv
Let us dissect the above command:
Parameter |
Function |
Required (Y/N) |
|---|---|---|
|
Path to diamond blastp output |
Y |
|
Path to NCBI databases directory |
Y |
|
Path to write lca output |
Y |
|
Path to write qseqids sseqids to taxids translations table |
N |
|
Path to write table of blast table qseqids that were assigned root due to a missing taxid |
N |
You can view the complete command-line options using autometa-taxonomy-lca -h
The above command would generate a table (78mbp_metagenome.lca.tsv) having the name, rank and taxid of the LCA for each ORF.
5.3 Majority vote
The next step in taxon assignment is doing a modified majority vote to decide the taxonomy of each contig. This was developed to help minimize the effect of horizontal gene transfer (HGT). Briefly, the voting system helps assign the correct taxonomy to the contig from its component ORF classification. Even with highly divergent ORFs this allows for accurate kingdom level classification, enabling us to remove any eukaryotic contaminants or host DNA.
You can run the majority vote step using the following command:
autometa-taxonomy-majority-vote \
--lca $HOME/tutorial/78mbp_metagenome.lca.tsv \
--output $HOME/tutorial/78mbp_metagenome.votes.tsv \
--dbdir $HOME/Autometa/autometa/databases/ncbi/
Let us dissect the above command:
Flag |
Function |
|---|---|
–lca |
Path to LCA table |
–output |
Path to write majority vote table |
–dbdir |
Path to ncbi database directory |
You can view the complete command-line options using autometa-taxonomy-majority-vote -h
The above command would generate a table (78mbp_metagenome.votes.tsv) having the taxid of each contig identified as per majority vote.
5.4 Split kingdoms
In this final step of taxon assignment we use the voted taxid of each contig to split the contigs into different kingdoms and write them as per the provided canonical rank.
autometa-taxonomy \
--votes $HOME/tutorial/78mbp_metagenome.votes.tsv \
--output $HOME/tutorial/ \
--assembly $HOME/tutorial/78mbp_metagenome.filtered.fna \
--prefix 78mbp_metagenome \
--split-rank-and-write superkingdom \
--ncbi $HOME/Autometa/autometa/databases/ncbi/
Let us dissect the above command:
Flag |
Function |
Requirement |
|---|---|---|
|
Path to voted taxids table |
Required |
|
Directory to output fasta files of split canonical ranks and taxonomy.tsv |
Required |
|
Path to filtered metagenome assembly |
Required |
|
prefix to use for each file written |
Optional |
|
Split contigs by provided canonical-rank column then write to output directory |
Optional |
|
Path to ncbi database directory |
Optional |
Other options available for --split-rank-and-write are phylum, class, order, family, genus and species
If --split-rank-and-write is specified then it will split contigs by provided canonical-rank column then write a file corresponding that rank. Eg. Bacteria.fasta, Archaea.fasta, etc for superkingdom.
You can view the complete command-line options using autometa-taxonomy -h
File |
Description |
|---|---|
78mbp_metagenome.taxonomy.tsv |
Table with taxonomic classification of each contig |
78mbp_metagenome.bacteria.fna |
Fasta file having the nucleic acid sequence of all bacterial contigs |
78mbp_metagenome.unclassified.fna |
Fasta file having the nucleic acid sequence of all contigs unclassified at kingdom level |
In my case there are no non-bacterial contigs. For other datasets, autometa-taxonomy may produce other fasta files, for example Eukaryota.fasta and Viruses.fasta.
6. K-mer counting
A k-mer (ref) is just a sequence of k characters in a string (or nucleotides in a DNA sequence). It is known that contigs that belong to the same genome have similar k-mer composition (ref1 and ref2) . Here, we compute k-mer frequencies of only the bacterial contigs.
This step does the following:
Create a k-mer count matrix of \(k^4/2\) dimensions using the specified k-mer length
Normalization of the k-mer count matrix to a normalized k-mer frequency matrix
Reduce the dimensions of k-mer frequencies using principal component analysis (PCA).
Embed the PCA dimensions into two dimensions to allow the ease of visualization and manual binning of the contigs (see ViZBin paper).
Use the following command to run the k-mer counting step:
autometa-kmers \
--fasta $HOME/tutorial/78mbp_metagenome.bacteria.fna \
--kmers $HOME/tutorial/78mbp_metagenome.bacteria.kmers.tsv \
--size 5 \
--norm-method am_clr \
--norm-output $HOME/tutorial/78mbp_metagenome.bacteria.kmers.normalized.tsv \
--pca-dimensions 50 \
--embedding-method bhsne \
--embedding-output $HOME/tutorial/78mbp_metagenome.bacteria.kmers.embedded.tsv \
--cpus 40 \
--seed 42
Let us dissect the above command:
Flag |
Input arguments |
Requirement |
|---|---|---|
|
Path to length filtered metagenome assembly |
Required |
|
Path to k-mer frequency table |
Required |
|
k-mer size in bp (default 5bp) |
Optional |
|
Path to normalized k-mer table |
Required |
|
Normalization method to transform kmer counts prior to PCA and embedding (default am_clr). Choices : ilr, clr and am_clr |
Optional |
|
Number of dimensions to reduce to PCA feature space after normalization and prior to embedding (default: 50) |
Optional |
|
Path to embedded k-mer table |
Required |
|
Embedding method to reduce the k-mer frequencies. Choices: sksne, bhsne (default), umap, densmap and trimap. |
Optional |
|
Number of CPUs to use (default is to use all available CPUs) |
Optional |
|
Set random seed for dimension reduction determinism (default 42). Useful in replicating the results |
Optional |
You can view the complete command-line options using autometa-kmers -h
The above command generates the following files:
File |
Description |
|---|---|
78mbp_metagenome.kmers.tsv |
Table with raw k-mer counts of each contig |
78mbp_metagenome.kmers.normalized.tsv |
Table with normalized k-mer frequencies of each contig |
78mbp_metagenome.kmers.embedded.tsv |
Table with embedded k-mer frequencies of each contig |
Advanced Usage
In the command used above k-mer normalization is being done using Autometa’s implementation of
the center log-ratio transform (am_clr). Other available normalization methods are isometric
log-ratio transform (ilr, scikit-bio implementation) and center log-ratio transform (clr, scikit-bio implementation).
Normalization method can be altered using the --norm-method flag.
In the above command k-mer embedding is being done using Barnes-Hut t-distributed Stochastic Neighbor Embedding (BH-tSNE).
Other embedding methods that are available are Uniform Manifold Approximation and Projection (UMAP), densMAP (a density-preserving tool based
on UMAP) and TriMap, a method that uses triplet constraints to form a low-dimensional embedding of a set of points.
Two implementations of BH-tSNE are available, bhsne and sksne corresponding to the tsne and scikit-learn libraries, respectively.
Embedding method can be altered using the --embedding-method flag.
Autometa uses a k-mer size of 5 and then embeds the resulting k-mer frequency table
into 50 PCA dimensions which are then reduced to two dimentions. k-mer size can be
altered using the --size flag, number of dimensions to reduce to PCA feature
space after normalization and prior to embedding can be altered using the --pca-dimensions
flag and the number of dimensions of which to reduce k-mer frequencies can be altered using the --embedding-dimensions flag.
Note
1. Even though bhsne and sksne are the same embedding method (but different implementations)
they appear to give very different results. We recommend using the former.
Providing a
0to--pca-dimensionswill skip the PCA step.
7. Binning
This is the step where contigs are binned into genomes via clustering. Autometa assesses genome bins by examining their completeness, purity, GC content std.dev. and coverage std.dev. A taxonomy table may also be used to selectively iterate through contigs based on their profiled taxon.
This step does the following:
Optionally iterate through contigs based on taxonomy
Bin contigs based on embedded k-mer coordinates and coverage
- Accept genome bins that pass the following metrics:
Above completeness threshold (
default=20.0)Above purity threshold (
default=95.0)Below GC content standard deviation threshold (
default=5.0)Below coverage standard deviation threshold (
default=25.0)
Unbinned contigs will be re-binned until no more acceptable genome bins are yielded
If you include a taxonomy table Autometa will attempt to further partition the data based on ascending taxonomic specificity (i.e. in the order superkingdom, phylum, class, order, family, genus, species) when binning unclustered contigs from a previous attempt. We found that this is mainly useful if you have a highly complex metagenome (lots of species), or you have several related species at similar coverage level.
Use the following command to perform binning:
autometa-binning \
--kmers $HOME/tutorial/78mbp_metagenome.bacteria.kmers.embedded.tsv \
--coverages $HOME/tutorial/78mbp_metagenome.coverages.tsv \
--gc-content $HOME/tutorial/78mbp_metagenome.gc_content.tsv \
--markers $HOME/tutorial/78mbp_metagenome.markers.tsv \
--clustering-method dbscan \
--completeness 20 \
--purity 90 \
--cov-stddev-limit 25 \
--gc-stddev-limit 5 \
--taxonomy $HOME/tutorial/78mbp_metagenome.taxonomy.tsv \
--output-binning $HOME/tutorial/78mbp_metagenome.binning.tsv \
--output-main $HOME/tutorial/78mbp_metagenome.main.tsv \
--starting-rank superkingdom \
--rank-filter superkingdom
--rank-name-filter bacteria
Let us dissect the above command:
Flag |
Function |
Requirement |
|---|---|---|
|
Path to embedded k-mer frequencies table |
Required |
|
Path to metagenome coverages table |
Required |
|
Path to metagenome GC contents table |
Required |
|
Path to Autometa annotated markers table |
Required |
|
Path to write Autometa binning results |
Required |
|
Path to write Autometa main table |
Required |
|
Clustering algorithm to use for recursive binning. Choices dbscan (default) and hdbscan |
Optional |
|
completeness cutoff to retain cluster (default 20) |
Optional |
|
purity cutoff to retain cluster (default 95) |
Optional |
|
coverage standard deviation limit to retain cluster (default 25) |
Optional |
|
GC content standard deviation limit to retain cluster (default 5) |
Optional |
|
Path to Autometa assigned taxonomies table |
Required |
|
Canonical rank at which to begin subsetting taxonomy (default: superkingdom) |
Optional |
|
Kingdom to consider. Choices bacteria (default) and archaea |
Optional |
You can view the complete command-line options using autometa-binning -h
The above command generates the following files:
78mbp_metagenome.binning.tsvcontains the final binning results along with a few more metrics regarding each genome bin.78mbp_metagenome.main.tsvwhich contains the feature table that was utilized during the genome binning process as well as the corresponding output predictions.
The following table describes each column for the resulting binning outputs. We’ll start with the columns present in 78mbp_metagenome.binning.tsv
then describe the additional columns that are present in 78mbp_metagenome.main.tsv.
Column |
Description |
|---|---|
Contig |
Name of the contig in the input fasta file |
Cluster |
Genome bin assigned by autometa to the contig |
Completeness |
Estimated completeness of the Genome bin, based on single-copy marker genes |
Purity |
Estimated purity of the Genome bin, based on the number of single-copy marker genes that are duplicated in the cluster |
coverage_stddev |
Coverage standard deviation of the Genome bin |
gc_content_stddev |
GC content standard deviation of the Genome bin |
In addition to the above columns 78mbp_metagenome.main.tsv file has the following additional columns:
Column |
Description |
|---|---|
Coverage |
Estimated coverage of the contig |
gc_content |
Estimated GC content of the contig |
length |
Estimated length of the contig |
species |
Assigned taxonomic species for the contig |
genus |
Assigned taxonomic genus for the contig |
family |
Assigned taxonomic family for the contig |
order |
Assigned taxonomic order for the contig |
class |
Assigned taxonomic class for the contig |
phylum |
Assigned taxonomic phylum for the contig |
superkingdom |
Assigned taxonomic superkingdom for the contig |
taxid |
Assigned NCBI taxonomy ID number for the contig |
x_1 |
The first coordinate after dimension reduction |
x_2 |
The second coordinate after dimension reduction |
You can attempt to improve your genome bins with an unclustered recruitment step which uses features from existing genome bins to recruit unbinned contigs. Alternatively you can use these initial genome bin predictions and continue to the Examining Results section.
Advanced Usage
Completeness = Number of single copy marker genes present just once / Total number of single copy marker genes
Purity = Number of single copy marker genes present more than once / Total number of single copy marker genes
These are default parameters that autometa uses to accept clusters are 20% complete, 95% pure, below 25% coverage standard deviation
and below 5% GC content standard deviation. These parameters can be altered using the flags, --completeness, --purity, --cov-stddev-limit and --gc-stddev-limit.
There are two binning algorithms to choose from Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN). The default is DBSCAN.
It is important to note that if recursively binning with taxonomy, only contigs at the specific taxonomic rank are analyzed and once the binning algorithm has moved on to the next rank, these
are not considered until they fall under another taxonomic rank under consideration. I.e. Iterate through phyla. Contig of one phylum is only considered for that phylum then not
for the rest of the phyla. If it is still unbinned at the Class rank, then it will be considered only during its respective Class’s iteration. The canonical rank from which to start
binning can be changed using the --starting-rank flag. The default is superkingdom.
8. Unclustered recruitment (Optional)
An unclustered recruitment step which uses features from existing genome bins is used to classify the unbinned contigs to the genome bins that were produced in the previous step. This step is optional and the results should be verified before proceeding with these results.
Note
The machine learning step has been observed to bin contigs that do not necessarily belong to the predicted genome. Careful inspection of coverage and taxonomy should be done before proceeding with these results.
Use the following command to run the unclustered recruitment step:
autometa-unclustered-recruitment \
--kmers $HOME/tutorial/78mbp_metagenome.bacteria.kmers.normalized.tsv \
--coverage $HOME/tutorial/78mbp_metagenome.coverages.tsv \
--binning $HOME/tutorial/78mbp_metagenome.binning.tsv \
--markers $HOME/tutorial/78mbp_metagenome.markers.tsv \
--taxonomy $HOME/tutorial/78mbp_metagenome.taxonomy.tsv \
--output-binning $HOME/tutorial/78mbp_metagenome.recruitment.binning.tsv \
--output-features $HOME/tutorial/78mbp_metagenome.recruitment.features.tsv \
--output-main $HOME/tutorial/78mbp_metagenome.recruitment.main.tsv \
--classifier decision_tree \
--seed 42
Let us dissect the above command:
Flag |
Function |
Required (Y/N) |
|---|---|---|
|
Path to normalized k-mer frequencies table |
Y |
|
Path to metagenome coverages table |
Y |
|
Path to autometa binning output |
Y |
|
Path to Autometa annotated markers table |
Y |
|
Path to write Autometa unclustered recruitment table |
Y |
|
Path to taxonomy table |
N |
|
Path to write Autometa main table used during/after unclustered recruitment |
N |
|
Path to write Autometa main table used during/after unclustered recruitment |
N |
|
classifier to use for recruitment of contigs. Choices decision_tree (default) and random_forest |
N |
|
Seed to use for RandomState when initializing classifiers (default: 42) |
N |
You can view the complete command-line options using autometa-unclustered-recruitment -h
The above command would generate 78mbp_metagenome.recruitment.binning.tsv and 78mbp_metagenome.recruitment.main.tsv.
78mbp_metagenome.recruitment.binning.tsv contains the final predictions of autometa-unclustered-recruitment. 78mbp_metagenome.recruitment.features.tsv
is the feature table utilized during/after the unclustered recruitment algorithm. This represents unbinned contigs with their respective annotations and output predictions of their recruitment into a genome bin.
The taxonomic features have been encoded using “one-hot encoding” or a presence/absence matrix where each column is a canonical taxonomic rank and its respective value for each row represents its presence or absence.
Presence and absence are denoted with 1 and 0, respectively. Hence “one-hot” encoding being an encoding of presence and absence of the respective annotation type. In our case taxonomic designation.
The 78mbp_metagenome.recruitment.binning.tsv file contains the following columns:
Column |
Description |
|---|---|
contig |
Name of the contig in the input fasta file |
cluster |
Genome bin assigned by autometa to the contig |
recruited_cluster |
Genome bin assigned by autometa to the contig after unclustered recruitment step |
Advanced Usage
The clustering method for the unclustered recruitment step can be performed either using a decision tree classifier (default) or using a random forst algorithm. The choice of method can be selected using the --classifier flag.
Databases
Markers
Autometa comes packaged with the necessary markers files. Links to these markers files and their associated cutoff values are below:
NCBI
If you are running Autometa for the first time you will need to download the NCBI databases. You may do this manually or using a few Autometa helper scripts. If you would like to use Autometa’s scripts for this, you will first need to download Autometa (See Installation).
# First configure where you want to download the NCBI databases
autometa-config \\
--section databases --option ncbi \\
--value <path/to/your/ncbi/database/directory>
# Now download and format the NCBI databases
autometa-update-databases --update-ncbi
Note
You can check the default config paths using autometa-config --print.
See autometa-update-databases -h and autometa-config -h for full list of options.
The previous command will download the following NCBI databases:
- Non-redundant nr database
- prot.accession2taxid.gz
- nodes.dmp, names.dmp and merged.dmp - Found within
After these files are downloaded, the taxdump.tar.gz tarball’s files are extracted and the non-redundant protein database (nr.gz)
is formatted as a diamond database (i.e. nr.dmnd). This will significantly speed-up the diamond blastp searches.
Examining Results
Automappa
An interactive interface for exploration and refinement of metagenomes Automappa is a tool built to interface with Autometa output to help you explore your binning results.
For details, see the Automappa page
Note
The performance of Automappa may slow down when trying to visualize highly complex communities.
Visualize bins
To run the following commands you’ll need to install R, Rstudio and ggplot2 package in R.
You can now run the following R scripts (preferably in RStudio) to examine your results.
# Load packages
library("ggplot2")
# Read the main binning table
filepath="/Users/sidd/Research/simulated/78mbp_metagenome.main.tsv"
data = read.table(filepath, header=TRUE, sep='\t')
# Fill empty cells as unclustered
data$cluster <- sub("^$", "Unclustered", data$cluster)
ggplot(data, aes(x=x_1, y=x_2, color=cluster, group=cluster)) +
geom_point(size=(sqrt(data$length))/100, shape=20, alpha=0.5) +
theme_classic() + xlab('BH-tSNE X') + ylab('BH-tSNE Y') +
guides( color = guide_legend( title = 'Genome Bin' ))
In the above chart each point represents a contig. They are plotted on two axes using results from dimension-reduction of k-mer frequencies. Rough differences between K-mer frequencies are utilized to guide Autometa’s density-based binning algorithm. Points are also scaled in size according to their respective contig’s length and colored by their assigned genome bin. You can see that there are some bins which are well-separated from others, while other bins are closer together. The latter cases may be worth investigating manually as multiple Autometa bins close together could actually be different parts of the same genome.
In addition to using nucleotide composition, Autometa uses coverage and can also use taxonomy to distinguish contigs with similar composition. We can also visualize these differences with R.
ggplot(data, aes(x=x_1, y=x_2, color=phylum, group=phylum)) +
geom_point(size=(sqrt(data$length))/100, shape=20, alpha=0.5) +
theme_classic() + xlab('BH-tSNE X') + ylab('BH-tSNE Y') +
guides( color = guide_legend( title = 'Phylum' ))
In the above plot, we have now colored the points by taxonomic phylum, and this reveals that several clusters that are close together in BH-tSNE space are in fact quite divergent from one another (like bottom left). This is probably the basis for Autometa’s assignment of separate bins in these cases.
In some cases, the contigs in a bin may in fact look divergent. You may want to manually examine cases such as these, but they could well be real if, for example, some contigs have few protein coding genes, or the organism is highly divergent from known sequences (see our paper here for some examples).
In this particular dataset, the coverages of all genomes are fairly similar, as revealed in the next plot:
ggplot(data, aes(x=coverage, y=gc_content, color=cluster, group=cluster)) +
geom_point(size=(sqrt(data$length))/100, shape=20, alpha=0.5) +
theme_classic() + xlab('Coverage') + ylab('GC content') +
guides( color = guide_legend( title = 'Genome Bin' ))
In the above plot, the points are colored by genome bin again, and you can see that in this case, coverage is not much of a distinguishing feature. In other datasets, you may see closely related genomes at different coverages, which will be separable by Autometa.
Benchmarking
Note
The most recent Autometa benchmarking results covering multiple modules and input parameters are hosted on our
KwanLab/metaBenchmarks Github repository and provide a range of
analyses covering multiple stages and parameter sets. These benchmarks are available with their own respective
modules so that the community may easily assess how Autometa’s novel (taxon-profiling, clustering,
binning, refinement) algorithms perform compared to current state-of-the-art methods. Tools were selected for
benchmarking based on their relevance to environmental, single-assembly, reference-free binning pipelines.
Benchmarking with the autometa-benchmark module
Autometa includes the autometa-benchmark entrypoint, a script to benchmark Autometa taxon-profiling, clustering
and binning-classification prediction results using clustering and classification evaluation metrics. To select the
appropriate benchmarking method, supply the --benchmark parameter with the respective choice. The three benchmarking
methods are detailed below.
Note
If you’d like to follow along with the benchmarking commands, you may download the test datasets using:
autometa-download-dataset \
--community-type simulated \
--community-sizes 78Mbp \
--file-names reference_assignments.tsv.gz binning.tsv.gz taxonomy.tsv.gz \
--dir-path $HOME/Autometa/autometa/datasets/simulated
This will download three files:
reference_assignments: tab-delimited file containing contigs with their reference genome assignments.cols: [contig, reference_genome, taxid, organism_name, ftp_path, length]binning.tsv.gz: tab-delimited file containing contigs with Autometa binning predictions,cols: [contig, cluster]taxonomy.tsv.gz: tab-delimited file containing contigs with Autometa taxon-profiling predictionscols: [contig, kingdom, phylum, class, order, family, genus, species, taxid]
Taxon-profiling
Example benchmarking with simulated communities
# Set community size (see above for selection/download of other community types)
community_size=78Mbp
# Inputs
## NOTE: predictions and reference were downloaded using autometa-download-dataset
predictions="$HOME/Autometa/autometa/datasets/simulated/${community_size}/taxonomy.tsv.gz" # required columns -> contig, taxid
reference="$HOME/Autometa/autometa/datasets/simulated/${community_size}/reference_assignments.tsv.gz"
ncbi=$HOME/Autometa/autometa/databases/ncbi
# Outputs
output_wide="${community_size}.taxon_profiling_benchmarks.wide.tsv.gz" # file path
output_long="${community_size}.taxon_profiling_benchmarks.long.tsv.gz" # file path
reports="${community_size}_taxon_profiling_reports" # directory path
autometa-benchmark \
--benchmark classification \
--predictions $predictions \
--reference $reference \
--ncbi $ncbi \
--output-wide $output_wide \
--output-long $output_long \
--output-classification-reports $reports
Note
Using --benchmark=classification requires the path to a directory containing files (nodes.dmp, names.dmp, merged.dmp)
from NCBI’s taxdump tarball. This should be supplied using the --ncbi parameter.
Clustering
Example benchmarking with simulated communities
# Set community size (see above for selection/download of other community types)
community_size=78Mbp
# Inputs
## NOTE: predictions and reference were downloaded using autometa-download-dataset
predictions="$HOME/Autometa/autometa/datasets/simulated/${community_size}/binning.tsv.gz" # required columns -> contig, cluster
reference="$HOME/Autometa/autometa/datasets/simulated/${community_size}/reference_assignments.tsv.gz"
# Outputs
output_wide="${community_size}.clustering_benchmarks.wide.tsv.gz"
output_long="${community_size}.clustering_benchmarks.long.tsv.gz"
autometa-benchmark \
--benchmark clustering \
--predictions $predictions \
--reference $reference \
--output-wide $output_wide \
--output-long $output_long
Binning
Example benchmarking with simulated communities
# Set community size (see above for selection/download of other community types)
community_size=78Mbp
# Inputs
## NOTE: predictions and reference were downloaded using autometa-download-dataset
predictions="$HOME/Autometa/autometa/datasets/simulated/${community_size}/binning.tsv.gz" # required columns -> contig, cluster
reference="$HOME/Autometa/autometa/datasets/simulated/${community_size}/reference_assignments.tsv.gz"
# Outputs
output_wide="${community_size}.binning_benchmarks.wide.tsv.gz"
output_long="${community_size}.binning_benchmarks.long.tsv.gz"
autometa-benchmark \
--benchmark binning-classification \
--predictions $predictions \
--reference $reference \
--output-wide $output_wide \
--output-long $output_long
Autometa Test Datasets
Descriptions
Simulated Communities
Community |
Num. Genomes |
Num. Control Sequences |
|---|---|---|
21 |
4,044 |
|
38 |
3,573 |
|
85 |
7,708 |
|
166 |
17,590 |
|
319 |
41,507 |
|
656 |
67,702 |
|
1,288 |
140,529 |
|
2,638 |
285,262 |
You can download all the Simulated communities using this link. Individual communities can be downloaded using the links in the above table.
For more information on simulated communities,
check the README.md
located in the simulated_communities directory.
Synthetic Communities
51 bacterial isolates were assembled into synthetic communities which we’ve titled MIX51.
The initial synthetic community was prepared using a mixture of fifty-one bacterial isolates. The synthetic community’s DNA was extracted for sequencing, assembly and binning.
You can download the MIX51 community using this link.
Download
Using autometa-download-dataset
Autometa is packaged with a built-in module that allows any user to download any of the available test datasets.
To use retrieve these datasets one simply needs to run the autometa-download-dataset command.
For example, to download the reference assignments for a simulated community as well as the most recent Autometa binning and taxon-profiling predictions for this community, provide the following parameters:
# choices for simulated: 78Mbp,156Mbp,312Mbp,625Mbp,1250Mbp,2500Mbp,5000Mbp,10000Mbp
autometa-download-dataset \
--community-type simulated \
--community-sizes 78Mbp \
--file-names reference_assignments.tsv.gz binning.tsv.gz taxonomy.tsv.gz \
--dir-path simulated
This will download reference_assignments.tsv.gz, binning.tsv.gz, taxonomy.tsv.gz to the simulated/78Mbp directory.
reference_assignments: tab-delimited file containing contigs with their reference genome assignments.cols: [contig, reference_genome, taxid, organism_name, ftp_path, length]binning.tsv.gz: tab-delimited file containing contigs with Autometa binning predictions,cols: [contig, cluster]taxonomy.tsv.gz: tab-delimited file containing contigs with Autometa taxon-profiling predictionscols: [contig, kingdom, phylum, class, order, family, genus, species, taxid]
Using gdrive
You can download the individual assemblies of different datasests with the help of gdown using command line
(This is what autometa-download-dataset is using behind the scenes). If you have installed autometa using
conda then gdown should already be installed. If not, you can install it using
conda install -c conda-forge gdown or pip install gdown.
Navigate to the 78Mbp community dataset using the link mentioned above.
- Get the file ID by navigating to any of the files and right clicking, then selecting the
get linkoption. This will have a
copy linkbutton that you should use. The link for the metagenome assembly (ie.metagenome.fna.gz) should look like this :https://drive.google.com/file/d/15CB8rmQaHTGy7gWtZedfBJkrwr51bb2y/view?usp=sharing
- Get the file ID by navigating to any of the files and right clicking, then selecting the
The file ID is within the
/forward slashes betweenfile/d/and/, e.g:
# Pasted from copy link button:
https://drive.google.com/file/d/15CB8rmQaHTGy7gWtZedfBJkrwr51bb2y/view?usp=sharing
# begin file ID ^ ------------------------------^ end file ID
Copy the file ID
Now that we have the File ID, you can specify the ID or use the
drive.google.comprefix. Both should work.
file_id="15CB8rmQaHTGy7gWtZedfBJkrwr51bb2y"
gdown --id ${file_id} -O metagenome.fna.gz
# or
gdown https://drive.google.com/uc?id=${file_id} -O metagenome.fna.gz
Note
Unfortunately, at the moment gdown doesn’t support downloading entire directories from Google drive.
There is an open Pull request on the gdown repository
addressing this specific issue which we are keeping a close eye on and will update this documentation when it is merged.
Advanced
Data Handling
Aggregating benchmarking results
import pandas as pd
import glob
df = pd.concat([
pd.read_csv(fp, sep="\t", index_col="dataset")
for fp in glob.glob("*.clustering_benchmarks.long.tsv.gz")
])
df.to_csv("benchmarks.tsv", sep='\t', index=True, header=True)
import pandas as pd
import os
import glob
dfs = []
for fp in glob.glob("*.clustering_benchmarks.long.tsv.gz"):
df = pd.read_csv(fp, sep="\t", index_col="dataset")
df.index = df.index.map(lambda fpath: os.path.basename(fpath))
dfs.append(df)
df = pd.concat(dfs)
df.to_csv("benchmarks.tsv", sep='\t', index=True, header=True)
Downloading multiple test datasets at once
To download all of the simulated communities reference binning/taxonomy assignments as well as the Autometa
v2.0 binning/taxonomy predictions all at once, you can provide the multiple arguments to --community-sizes.
e.g. --community-sizes 78Mbp 156Mbp 312Mbp 625Mbp 1250Mbp 2500Mbp 5000Mbp 10000Mbp
An example of this is shown in the bash script below:
# choices: 78Mbp,156Mbp,312Mbp,625Mbp,1250Mbp,2500Mbp,5000Mbp,10000Mbp
community_sizes=(78Mbp 156Mbp 312Mbp 625Mbp 1250Mbp 2500Mbp 5000Mbp 10000Mbp)
autometa-download-dataset \
--community-type simulated \
--community-sizes ${community_sizes[@]} \
--file-names reference_assignments.tsv.gz binning.tsv.gz taxonomy.tsv.gz \
--dir-path simulated
Generating new simulated communities
Communities were simulated using ART, a sequencing read simulator, with a collection of 3000 bacteria randomly retrieved. Genomes were retrieved until the provided total length was reached.
e.g. -l 1250 would translate to 1250Mbp as the sum of total lengths for all bacterial genomes retrieved.
# Work out coverage level for art_illumina
# C = [(LN)/G]/2
# C = coverage
# L = read length (total of paired reads)
# G = genome size in bp
# -p : indicate a paired-end read simulation or to generate reads from both ends of amplicons
# -ss : HS25 -> HiSeq 2500 (125bp, 150bp)
# -f : fold of read coverage simulated or number of reads/read pairs generated for each amplicon
# -m : the mean size of DNA/RNA fragments for paired-end simulations
# -s : the standard deviation of DNA/RNA fragment size for paired-end simulations.
# -l : the length of reads to be simulated
$ coverage = ((250 * reads) / (length * 1000000))
$ art_illumina -p -ss HS25 -l 125 -f $coverage -o simulated_reads -m 275 -s 90 -i asm_path
Installation
Currently Autometa package installation is supported by conda and docker. For installation using conda, we suggest downloading miniconda.
Attention
If you are only trying to run the Autometa workflow, you should start at Getting Started before proceeding.
Direct installation (Quickest)
Install miniconda
Create a new environment with autometa installed:
conda create -c bioconda -n autometa autometaActivate autometa environment
conda activate autometa
Install from source (using make)
Download and install miniconda. Now run the following commands:
# Navigate to the directory where you would like to clone Autometa
cd $HOME
# Clone the Autometa repository
git clone https://github.com/KwanLab/Autometa.git
# Navigate into the cloned repository
cd Autometa
# create autometa conda environment
make create_environment
# activate autometa conda environment
conda activate autometa
# install autometa source code in autometa environment
make install
Note
You can see a list of all available make commands by running make without any other arguments.
Install from source (full commands)
Download and install miniconda. Now run the following commands:
# Construct the autometa environment from autometa-env.yml
conda env create --file=https://raw.githubusercontent.com/KwanLab/Autometa/main/autometa-env.yml
# Activate environment
conda activate autometa
# Install the autometa code base from source
python setup.py install
Building the Docker image
You can build a docker image for your clone of the Autometa repository.
Install Docker
Run the following commands
# Navigate to the directory where you need to clone Autometa
cd $HOME
# Clone the Autometa repository
git clone https://github.com/KwanLab/Autometa.git
# Navigate into the cloned repository
cd Autometa
# This will tag the image as jasonkwan/autometa:<your current branch>
make image
# (or the full command from within the Autometa repo)
docker build . -t jasonkwan/autometa:`git branch --show-current`
Testing Autometa
You can also check the installation using autometa’s built-in unit tests. This is not at all necessary and is primarily meant for development and debugging purposes. To run the tests, however, you’ll first need to install the following packages and download the test dataset.
# Activate your autometa conda environment
conda activate autometa
# List all make options
make
# Install dependencies for test environment
make test_environment
# Download test_data.json for unit testing to tests/data/
make unit_test_data_download
You can now run different unit tests using the following commands:
# Run all unit tests
make unit_test
# Run unit tests marked with entrypoint
make unit_test_entrypoints
# Run unit tests marked with WIP
make unit_test_wip
Note
As a shortcut you can also create the test environment and run all the unit tests using make unit_test command.
For more information about the above commands see the Contributing Guidelines page. Additional unit tests are provided in the test directory. These are designed to aid in future development of autometa.
Autometa Python API
Running modules
Many of the Autometa modules may be run standalone.
Simply pass in the -m flag when calling a script to signify to python you are
running the script as an Autometa module.
I.e. python -m autometa.common.kmers -h
Note
Autometa has many entrypoints available that are utilized by the 🍏 Nextflow Workflow 🍏 and 🐚 Bash Workflow 🐚. If you have installed autometa, all of these entrypoints will be available to you.
If you would like to get a better understanding of each entrypoint, we recommend reading the 📓 Step by Step Tutorial 📓 section.
Using Autometa’s Python API
Autometa’s classes and functions are available after installation. To access these, do the same as importing any other python library.
Examples
Samtools wrapper
To incorporate a call to samtools sort inside of your python code using the Autometa samtools wrapper.
from autometa.common.external import samtools
# To see samtools.sort parameters try the commented command below:
# samtools.sort?
# Run samtools sort command in ipython interpreter
samtools.sort(sam="<path/to/alignment.sam>", out="<path/to/output/alignment.bam>", cpus=4)
Metagenome Description
Here is an example to easily assess your metagenome’s characteristics using Autometa’s Metagenome class
from autometa.common.metagenome import Metagenome
# To see input parameters, instance attributes and methods
# Metagenome?
# Create a metagenome instance
mg = Metagenome(assembly="/path/to/metagenome.fasta")
# To see available methods (ignore any elements in the list with a double underscore)
dir(mg)
# Get pandas dataframe of metagenome details.
metagenome_df = mg.describe()
metagenome_df.to_csv("path/to/metagenome_description.tsv", sep='\t', index=True, header=True)
k-mer frequency counting, normalization, embedding
To quickly perform a k-mer frequency counting, normalization and embedding pipeline…
from autometa.common import kmers
# Count kmers
counts = kmers.count(
assembly="/path/to/metagenome.fasta",
size=5
)
# Normalize kmers
norm_df = kmers.normalize(
df=counts,
method="ilr"
)
# Embed kmers
embed_df = kmers.embed(
norm_df,
pca_dimensions=50,
embed_dimensions=3,
method="densmap"
)
Usage
common
external
bedtools.py
usage: bedtools.py
Compute genome coverage from sorted BAM file
optional arguments:
-h, --help show this help message and exit
--ibam filepath Path to sorted alignment.bam
--bed filepath Path to write alignment.bed; tab-delimited
cols=[contig,length]
--output filepath Path to output coverage.tsv
--force-bed force overwrite `bed`
--force-cov force overwrite `--output`
hmmsearch.py
usage: hmmsearch.py
Filters domtblout generated from hmmsearch using provided cutoffs
optional arguments:
-h, --help show this help message and exit
--domtblout DOMTBLOUT
Path to domtblout generated from hmmsearch -domtblout
<domtblout> ... <hmmfile> <seqdb>
--cutoffs CUTOFFS Path to cutoffs corresponding to hmmfile used with
hmmsearch <hmmfile> <seqdb>
--seqdb SEQDB Path to orfs seqdb used as input to hmmsearch ...
<hmmfile> <seqdb>
--out OUT Path to write table of markers passing provided
cutoffs
hmmscan.py
usage: hmmscan.py
Retrieves markers with provided input assembly
positional arguments:
orfs </path/to/assembly.orfs.faa>
hmmdb </path/to/hmmpressed/hmmdb>
cutoffs </path/to/hmm/cutoffs.tsv>
hmmscan </path/to/hmmscan.tblout>
markers </path/to/markers.tsv>
optional arguments:
-h, --help show this help message and exit
--force force overwrite of out filepath
--cpus CPUS num cpus to use
--parallel enable hmmer multithreaded parallelization
--gnu-parallel enable GNU parallelization
bowtie.py
usage: bowtie.py
Align provided reads to metagenome `assembly` and write alignments to
`sam`.NOTE: At least one reads file is required.
positional arguments:
assembly </path/to/assembly.fasta>
database </path/to/alignment.database>. Will construct database
at provided path if not found.
sam </path/to/alignment.sam>
optional arguments:
-h, --help show this help message and exit
-1 [FWD_READS ...], --fwd-reads [FWD_READS ...]
</path/to/forward-reads.fastq>
-2 [REV_READS ...], --rev-reads [REV_READS ...]
</path/to/reverse-reads.fastq>
-U [SE_READS ...], --se-reads [SE_READS ...]
</path/to/single-end-reads.fastq>
--cpus CPUS Num processors to use.
prodigal.py
usage: prodigal.py
Calls ORFs with provided input assembly
optional arguments:
-h, --help show this help message and exit
--assembly filepath Path to metagenome assembly (default: None)
--output-nucls filepath
Path to output nucleotide ORFs (default: None)
--output-prots filepath
Path to output amino-acid ORFs (default: None)
--cpus int Number of processors to use. (If more than one this
will parallelize prodigal using GNU parallel)
(default: 1)
--force Overwrite existing output ORF filepaths (default:
False)
diamond.py
samtools.py
usage: samtools.py
Takes a sam file, sorts it and returns the output to a bam file
positional arguments:
sam </path/to/alignment.sam>
bam </path/to/output/alignment.bam>
optional arguments:
-h, --help show this help message and exit
--cpus CPUS Number of processors to use
exceptions.py
coverage.py
usage: coverage.py
Construct contig coverage table given an input `assembly` and provided files.
Provided files may include one from the list below:
1. `fwd_reads` and/or `rev_reads` and/or `se_reads`
2. `sam` - alignment of `assembly` and `reads` in SAM format
3. `bam` - alignment of `assembly` and `reads` in BAM format
4. `bed` - alignment of `assembly` and `reads` in BED format
optional arguments:
-h, --help show this help message and exit
-f ASSEMBLY, --assembly ASSEMBLY
</path/to/metagenome.fasta>
-1 [FWD_READS ...], --fwd-reads [FWD_READS ...]
</path/to/forwards-reads.fastq>
-2 [REV_READS ...], --rev-reads [REV_READS ...]
</path/to/reverse-reads.fastq>
-U [SE_READS ...], --se-reads [SE_READS ...]
</path/to/single-end-reads.fastq>
--sam SAM </path/to/alignments.sam>
--bam BAM </path/to/alignments.bam>
--bed BED </path/to/alignments.bed>
--cpus CPUS Num processors to use. (default: 2)
--from-spades Extract k-mer coverages from contig IDs. (Input
assembly is output from SPAdes)
--out OUT Path to write a table of coverages
markers.py
usage: markers.py
Annotate ORFs with kingdom-specific marker information
optional arguments:
-h, --help show this help message and exit
--orfs ORFS Path to a fasta file containing amino acid sequences
of open reading frames (default: None)
--kingdom {bacteria,archaea}
kingdom to search for markers (default: bacteria)
--hmmscan HMMSCAN Path to hmmscan output table containing the respective
`kingdom` single-copy marker annotations. (default:
None)
--out OUT Path to write filtered annotated markers corresponding
to `kingdom`. (default: None)
--dbdir DBDIR Path to directory containing the single-copy marker
HMM databases. (default: MARKERS_DIR)
--hmmdb HMMDB Path to single-copy marker HMM databases. (default:
None)
--cutoffs CUTOFFS Path to single-copy marker cutoff tsv. (default: None)
--force Whether to overwrite existing provided annotations.
(default: False)
--parallel Whether to use hmmscan parallel option. (default:
False)
--gnu-parallel Whether to run hmmscan using GNU parallel. (default:
False)
--cpus CPUS Number of cores to use for parallel execution.
(default: 8)
--seed SEED Seed to set random state for hmmscan. (default: 42)
utilities.py
kmers.py
usage: kmers.py
Count k-mer frequencies of given `fasta`
optional arguments:
-h, --help show this help message and exit
--fasta filepath Metagenomic assembly fasta file (default: None)
--kmers filepath K-mers frequency tab-delimited table (will skip if
file exists) (default: None)
--size int k-mer size in bp (default: 5)
--norm-output filepath
Path to normalized kmers table (will skip if file
exists) (default: None)
--norm-method {ilr,clr,am_clr}
Normalization method to transform kmer counts prior to
PCA and embedding. ilr: isometric log-ratio transform
(scikit-bio implementation). clr: center log-ratio
transform (scikit-bio implementation). am_clr: center
log-ratio transform (Autometa implementation).
(default: am_clr)
--pca-dimensions int Number of dimensions to reduce to PCA feature space
after normalization and prior to embedding (NOTE:
Setting to zero will skip PCA step) (default: 50)
--embedding-output filepath
Path to write embedded kmers table (will skip if file
exists) (default: None)
--embedding-method {sksne,bhsne,umap,densmap,trimap}
embedding method [sk,bh]sne are corresponding
implementations from scikit-learn and tsne,
respectively. (default: bhsne)
--embedding-dimensions int
Number of dimensions of which to reduce k-mer
frequencies (default: 2)
--force Whether to overwrite existing annotations (default:
False)
--cpus int num. processors to use. (default: 2)
--seed int Seed to set random state for dimension reduction
determinism. (default: 42)
metagenome.py
usage: metagenome.py
This script handles filtering by length and can calculate various metagenome
statistics.
optional arguments:
-h, --help show this help message and exit
--assembly filepath Path to metagenome assembly (nucleotide fasta).
(default: None)
--output-fasta filepath
Path to output length-filtered assembly fasta file.
(default: None)
--output-stats filepath
Path to output assembly stats table. (default: None)
--output-gc-content filepath
Path to output assembly contigs' GC content and
length. (default: None)
--cutoff int Cutoff to apply to length filter (default: 3000)
--force Overwrite existing files (default: False)
--verbose Log more information to terminal. (default: False)
binning
large_data_mode.py
usage: large_data_mode.py
Autometa Large-data-mode binning by contig set selection using max-partition-
size
optional arguments:
-h, --help show this help message and exit
--kmers filepath Path to k-mer counts table (default: None)
--coverages filepath Path to metagenome coverages table (default: None)
--gc-content filepath
Path to metagenome GC contents table (default: None)
--markers filepath Path to Autometa annotated markers table (default:
None)
--taxonomy filepath Path to Autometa assigned taxonomies table (default:
None)
--output-binning filepath
Path to write Autometa binning results (default: None)
--output-main filepath
Path to write Autometa main table used during/after
binning (default: None)
--clustering-method {dbscan,hdbscan}
Clustering algorithm to use for recursive binning.
(default: dbscan)
--completeness 0 < float <= 100
completeness cutoff to retain cluster. e.g. cluster
completeness >= `completeness` (default: 20.0)
--purity 0 < float <= 100
purity cutoff to retain cluster. e.g. cluster purity
>= `purity` (default: 95.0)
--cov-stddev-limit float
coverage standard deviation limit to retain cluster
e.g. cluster coverage standard deviation <= `cov-
stddev-limit` (default: 25.0)
--gc-stddev-limit float
GC content standard deviation limit to retain cluster
e.g. cluster GC content standard deviation <= `gc-
content-stddev-limit` (default: 5.0)
--norm-method {am_clr,ilr,clr}
kmer normalization method to use on kmer counts
(default: am_clr)
--pca-dims int PCA dimensions to reduce normalized kmer frequencies
prior to embedding (default: 50)
--embed-method {bhsne,umap,sksne,trimap}
kmer embedding method to use on normalized kmer
frequencies (default: bhsne)
--embed-dims int Embedding dimensions to reduce normalized kmers table
after PCA. (default: 2)
--max-partition-size int
Maximum number of contigs to consider for a recursive
binning batch. (default: 10000)
--starting-rank {superkingdom,phylum,class,order,family,genus,species}
Canonical rank at which to begin subsetting taxonomy
(default: superkingdom)
--reverse-ranks Reverse order at which to split taxonomy by canonical-
rank. When `--reverse-ranks` is given, contigs will be
split in order of species, genus, family, order,
class, phylum, superkingdom. (default: False)
--cache dirpath Directory to store itermediate checkpoint files during
binning (If this is provided and the job fails, the
script will attempt to begin from the checkpoints in
this cache directory). (default: None)
--binning-checkpoints filepath
File path to store itermediate contig binning results
(The `--cache` argument is required for this feature).
If `--cache` is provided without this argument, a
binning checkpoints file will be created. (default:
None)
--rank-filter {superkingdom,phylum,class,order,family,genus,species}
Taxonomy column canonical rank to subset by provided
value of `--rank-name-filter` (default: superkingdom)
--rank-name-filter RANK_NAME_FILTER
Only retrieve contigs with this name corresponding to
`--rank-filter` column (default: bacteria)
--verbose log debug information (default: False)
--cpus int Number of cores to use by clustering method (default
will try to use as many as are available) (default:
-1)
utilities.py
summary.py
usage: summary.py
Summarize Autometa results writing genome fastas and their respective
taxonomies/assembly metrics for respective metagenomes
optional arguments:
-h, --help show this help message and exit
--binning-main filepath
Path to Autometa binning main table (output from
--binning-main argument) (default: None)
--markers filepath Path to annotated markers respective to domain
(bacteria or archaea) binned (default: None)
--metagenome filepath
Path to metagenome assembly (default: None)
--ncbi dirpath Path to user NCBI databases directory (Required for
retrieving metabin taxonomies) (default: None)
--binning-column str Binning column to use for grouping metabins (default:
cluster)
--output-stats filepath
Path to write metabins stats table (default: None)
--output-taxonomy filepath
Path to write metabins taxonomies table (default:
None)
--output-metabins dirpath
Path to output directory. (Directory must not exist.
This directory will be created.) (default: None)
recursive_dbscan.py
usage: recursive_dbscan.py
Perform marker gene guided binning of metagenome contigs using annotations
(when available) of sequence composition, coverage and homology.
optional arguments:
-h, --help show this help message and exit
--kmers filepath Path to embedded k-mers table (default: None)
--coverages filepath Path to metagenome coverages table (default: None)
--gc-content filepath
Path to metagenome GC contents table (default: None)
--markers filepath Path to Autometa annotated markers table (default:
None)
--output-binning filepath
Path to write Autometa binning results (default: None)
--output-main filepath
Path to write Autometa main table used during/after
binning (default: None)
--clustering-method {dbscan,hdbscan}
Clustering algorithm to use for recursive binning.
(default: dbscan)
--completeness 0 < float <= 100
completeness cutoff to retain cluster. e.g. cluster
completeness >= `completeness` (default: 20.0)
--purity 0 < float <= 100
purity cutoff to retain cluster. e.g. cluster purity
>= `purity` (default: 95.0)
--cov-stddev-limit float
coverage standard deviation limit to retain cluster
e.g. cluster coverage standard deviation <= `cov-
stddev-limit` (default: 25.0)
--gc-stddev-limit float
GC content standard deviation limit to retain cluster
e.g. cluster GC content standard deviation <= `gc-
content-stddev-limit` (default: 5.0)
--taxonomy filepath Path to Autometa assigned taxonomies table (default:
None)
--starting-rank {superkingdom,phylum,class,order,family,genus,species}
Canonical rank at which to begin subsetting taxonomy
(default: superkingdom)
--reverse-ranks Reverse order at which to split taxonomy by canonical-
rank. When `--reverse-ranks` is given, contigs will be
split in order of species, genus, family, order,
class, phylum, superkingdom. (default: False)
--rank-filter {superkingdom,phylum,class,order,family,genus,species}
Taxonomy column canonical rank to subset by provided
value of `--rank-name-filter` (default: superkingdom)
--rank-name-filter RANK_NAME_FILTER
Only retrieve contigs with this name corresponding to
`--rank-filter` column (default: bacteria)
--verbose log debug information (default: False)
--cpus int Number of cores to use by clustering method (default
will try to use as many as are available) (default:
-1)
large_data_mode_loginfo.py
usage: large_data_mode_loginfo.py
Retrieve clustering time stats from autometa.binning.recursive_dbscan err log
optional arguments:
-h, --help show this help message and exit
--log LOG Path to binning log file (If using slurm, this is typically
stderr output path) (default: None)
--outdir OUTDIR Directory to write runtime information tables (default: .)
--prefix PREFIX Prefix to prepend to runtime information tables (Do not use
a directory path as a prefix) (default: None)
--overwrite Overwrite existing log info table if it already exists
(default: False)
unclustered_recruitment.py
usage: unclustered_recruitment.py
Recruit unclustered contigs given metagenome annotations and Autometa binning
results. Note: All tables must contain a 'contig' column to be used as the
unique table index
optional arguments:
-h, --help show this help message and exit
--kmers KMERS Path to normalized kmer frequencies table. (default:
None)
--coverage COVERAGE Path to coverage table. (default: None)
--binning BINNING Path to autometa binning output [will look for
col='cluster'] (default: None)
--markers MARKERS Path to domain-specific markers table. (default: None)
--output-binning OUTPUT_BINNING
Path to output unclustered recruitment table.
(default: None)
--output-main OUTPUT_MAIN
Path to write Autometa main table used during/after
unclustered recruitment. (default: None)
--output-features OUTPUT_FEATURES
Path to write Autometa features table used during
unclustered recruitment. (default: None)
--taxonomy TAXONOMY Path to taxonomy table. (default: None)
--taxa-dimensions TAXA_DIMENSIONS
Num of dimensions to reduce taxonomy encodings
(default: None)
--additional-features [ADDITIONAL_FEATURES ...]
Path to additional features with which to add to
classifier training data. (default: [])
--confidence CONFIDENCE
Percent confidence to allow classification (confidence
= num. consistent predictions/num. classifications)
(default: 1.0)
--num-classifications NUM_CLASSIFICATIONS
Num classifications for predicting/validating contig
cluster recruitment (default: 10)
--classifier {decision_tree,random_forest}
classifier to use for recruitment of contigs (default:
decision_tree)
--kmer-dimensions KMER_DIMENSIONS
Num of dimensions to reduce normalized k-mer
frequencies (default: 50)
--seed SEED Seed to use for RandomState when initializing
classifiers. (default: 42)
taxonomy
ncbi.py
majority_vote.py
usage: majority_vote.py
Script to assign taxonomy via a modified majority voting algorithm.
optional arguments:
-h, --help show this help message and exit
--lca LCA Path to LCA results table. (default: None)
--output OUTPUT Path to write voted taxid results table. (default: None)
--dbdir DBDIR Path to NCBI databases directory. (default: NCBI_DIR)
--orfs ORFS Path to ORFs fasta containing amino-acid sequences to be
annotated. (Only required for prodigal version < 2.6)
(default: None)
--verbose Add verbosity to logging stream. (default: False)
lca.py
usage: lca.py
Script to determine Lowest Common Ancestor
optional arguments:
-h, --help show this help message and exit
--blast filepath Path to BLAST results table respective to `orfs`.
(Note: The table provided must be in outfmt=6)
(default: None)
--dbdir dirpath Path to NCBI databases directory. (default: NCBI_DIR)
--lca-output filepath
Path to write LCA results. (default: None)
--sseqid2taxid-output filepath
Path to write qseqids sseqids to taxids translations
table (default: None)
--lca-error-taxids filepath
Path to write table of blast table qseqids that were
assigned root due to a missing taxid (default: None)
--verbose Add verbosity to logging stream. (default: False)
--force Force overwrite if results already exist. (default:
False)
--cache dirpath Path to cache pickled LCA database objects. (default:
None)
--only-prepare-cache Only prepare the LCA database objects and write to
provided --cache parameter (default: False)
--force-cache-overwrite
Force overwrite if results already exist. (default:
False)
vote.py
usage: vote.py
Filter metagenome by taxonomy.
optional arguments:
-h, --help show this help message and exit
--votes filepath Input path to voted taxids table. should contain (at
least) 'contig' and 'taxid' columns (default: None)
--assembly filepath Path to metagenome assembly (nucleotide fasta).
(default: None)
--output dirpath Directory to output fasta files of split canonical
ranks and taxonomy.tsv. (default: None)
--prefix str prefix to use for each file written e.g.
`prefix`.taxonomy.tsv. Note: Do not use a directory
prefix. (default: None)
--split-rank-and-write {superkingdom,phylum,class,order,family,genus,species}
If specified, will split contigs by provided
canonical-rank column then write to `output` directory
(default: None)
--ncbi dirpath Path to NCBI databases directory. (default: NCBI_DIR)
config
utilities.py
usage: utilities.py
Update Autometa configuration using provided arguments
optional arguments:
-h, --help show this help message and exit
Logging:
--print Print configuration without updating
Updating:
--section {environ,databases,ncbi,markers}
config section to update
--option OPTION option in `--section` to update
--value VALUE Value to update `--option`
environ.py
databases.py
usage: databases.py
Main script to configure Autometa database dependencies.
optional arguments:
-h, --help show this help message and exit
--config CONFIG </path/to/input/database.config> (default:
DEFAULT_FPATH)
--dryrun Log configuration actions but do not perform them.
(default: False)
--update-all Update all out-of-date databases. (default: False)
--update-markers Update out-of-date markers databases. (default: False)
--update-ncbi Update out-of-date ncbi databases. (default: False)
--check-dependencies Check database dependencies are satisfied. (default:
False)
--no-checksum Do not perform remote checksum comparisons to validate
databases are up-to-date. (default: False)
--nproc NPROC num. cpus to use for DB formatting. (default: 2)
--out OUT </path/to/output/database.config> (default: None)
By default, with no arguments, will download/format databases into default
databases directory.
Contributing Guidelines
Autometa is an open-source project developed on GitHub. If you would like to help develop Autometa or have ideas for new features please see our contributing guidelines
Some good first issues are available on the KwanLab Autometa GitHub repository good first issues 🥇💡
If you are wanting to help develop Autometa, you will need these additional dependencies:
Documentation
Autometa builds documentation using readthedocs. You have to install the following to be able to build the docs
# Activate your autometa conda environment
conda activate autometa
# Install dependencies
conda install -n autometa -c conda-forge \
sphinx sphinx_rtd_theme
# List all make options
make
# Build documentation for autometa.readthedocs.io
make docs
make docs
This command runs sphinx and generates autometa documentation for autometa.readthedocs.io.
Unit tests
You will have to install certain dependencies as well as test data to be able to run and develop unit tests.
# Activate your autometa conda environment
conda activate autometa
# List all make options
make
# Install dependencies for test environment
make test_environment
# Download test_data.json for unit testing to tests/data/
make unit_test_data_download
You can now run different unit tests using the following commands:
# Run all unit tests
make unit_test
# Run unit tests marked with entrypoint
make unit_test_entrypoints
# Run unit tests marked with WIP
make unit_test_wip
make test_environment
This command installs all the dependencies that you need to successfully run the unit tests.
make unit_test_data_download
This command downloads the test_data.json object that you need to run the Unit tests. This is a necessary step when wanting to run unit tests as the test_data.json file will hold many of the variables necessary to conduct these tests.
make unit_test_data_build
This is used to create your own test_data.json object locally. This step is NOT required for running unit tests, you can directly download the test_data.json object using the previous command. This command is needed in case you are changing file formats or adding more objects into the test suite. To do this you first need to download all the files from here in tests/data/ and then run make unit_test_data_build. This would generate a similar test_data.json object that you get by running the previous command.
The above command is used to manually build the test_data.json file for unit testing. I.e. it will run the script make_test_data.py which will aggregate all of the files in the tests/data folder that have been downloaded from here. This is the first or perhaps 0th step when it comes to running the tests without an already generated test_data.json object as it generates the test_data.json file that is parsed to retrieve all of the pre-generated variables used for intermediate stages of the pipeline. This is done to reduce the test time and computational workload when running through the test suite.
make unit_test
This command runs all unit tests under the tests directory. This includes all tests marked as WIP or as entrypoints. However this will skip tests marked with the following decorator:
@pytest.mark.skip
def test_some_function(...):
...
make unit_test_entrypoints
This command runs the tests marked as entrypoints. This is denoted in pytest with the decorator:
@pytest.mark.entrypoint
def test_some_function_that_is_an_entrypoint(...):
...
Entrypoints correspond to the entry point functions listed out by ‘console scripts’ in setup.py. These entry point functions are aliased to provide more intuitive commands for the end user. These are important and sometimes referred to as “happy” tests because if one of these fail for the end-user, they will probably be quite unhappy and likely distrust the functionality of the rest of the codebase.
make unit_test_wip
This command runs the tests marked as work-in-progress (WIP). This is denoted in pytest with the decorator:
@pytest.mark.wip
def test_some_function_that_is_wip(...):
...
Autometa modules
autometa package
Subpackages
autometa.binning package
autometa.common package
# License: GNU Affero General Public License v3 or later # A copy of GNU AGPL v3 should have been included in this software package in LICENSE.txt. Script containing wrapper functions for bowtie2.
- autometa.common.external.bowtie.align(db: str, sam: str, fwd_reads: Optional[List[str]] = None, rev_reads: Optional[List[str]] = None, se_reads: Optional[List[str]] = None, cpus: int = 0, **kwargs) str
Align reads to bowtie2-index db (at least one *_reads argument is required).
- Parameters
db (str) – </path/to/prefix/bowtie2/database>. I.e. db.{#}.bt2
sam (str) – </path/to/out.sam>
fwd_reads (list, optional) – [</path/to/forward_reads.fastq>, …]
rev_reads (list, optional) – [</path/to/reverse_reads.fastq>, …]
se_reads (list, optional) – [</path/to/single_end_reads.fastq>, …]
cpus (int, optional) – Num. processors to use (the default is 0).
**kwargs (dict, optional) – Additional optional args to supply to bowtie2. Must be in format: key = flag value = flag-value
- Returns
</path/to/out.sam>
- Return type
str
- Raises
ChildProcessError – bowtie2 failed
- autometa.common.external.bowtie.build(assembly: str, out: str) str
Build bowtie2 index.
- Parameters
assembly (str) – </path/to/assembly.fasta>
out (str) – </path/to/output/database> Note: Indices written will resemble </path/to/output/database.{#}.bt2>
- Returns
</path/to/output/database>
- Return type
str
- Raises
ChildProcessError – bowtie2-build failed
- autometa.common.external.bowtie.main()
- autometa.common.external.bowtie.run(cmd: str) bool
Run cmd via subprocess.
- Parameters
cmd (str) – Executable input str
- Returns
True if no returncode from subprocess.call else False
- Return type
bool
Script containing wrapper functions for samtools
- autometa.common.external.samtools.main()
- autometa.common.external.samtools.sort(sam, bam, cpus=2)
Views then sorts sam file by leftmost coordinates and outputs to bam.
- Parameters
sam (str) – </path/to/alignment.sam>
bam (str) – </path/to/output/alignment.bam>
cpus (int, optional) – Number of processors to be used. By default uses all the processors of the system
- Raises
TypeError – cpus must be an integer greater than zero
FileNotFoundError – Specified path is incorrect or the file is empty
ExternalToolError – Samtools did not run successfully, returns subprocess traceback and command run
# License: GNU Affero General Public License v3 or later # A copy of GNU AGPL v3 should have been included in this software package in LICENSE.txt.
File containing customized AutometaErrors for more specific exception handling
- exception autometa.common.exceptions.AutometaError
Bases:
ExceptionBase class for Autometa Errors.
- exception autometa.common.exceptions.BinningError
Bases:
autometa.common.exceptions.AutometaErrorBinningError exception class.
Exception called when issues arise during or after the binning process.
This is usually a result of no clusters being recovered.
- exception autometa.common.exceptions.ChecksumMismatchError
Bases:
autometa.common.exceptions.AutometaErrorChecksumMismatchError exception class
Exception called when checksums do not match.
- exception autometa.common.exceptions.DatabaseOutOfSyncError(value)
Bases:
autometa.common.exceptions.AutometaErrorRaised when NCBI databases nodes.dmp, names.dmp and merged.dmp are out of sync with each other :param AutometaError: Base class for other exceptions :type AutometaError: class
- __str__()
Operator overloading to return the text message written while raising the error, rather than the message of __str__ by base exception :returns: Message written alongside raising the exception :rtype: str
- exception autometa.common.exceptions.ExternalToolError(cmd, err)
Bases:
autometa.common.exceptions.AutometaErrorRaised when samtools sort is not executed properly.
- Parameters
AutometaError (class) – Base class for other exceptions
- exception autometa.common.exceptions.TableFormatError
Bases:
autometa.common.exceptions.AutometaErrorTableFormatError exception class.
Exception called when Table format is incorrect.
This is usually a result of a table missing the ‘contig’ column as this is often used as the index.
autometa.config package
# License: GNU Affero General Public License v3 or later # A copy of GNU AGPL v3 should have been included in this software package in LICENSE.txt.
Configuration handling for Autometa environment.
- autometa.config.environ.bedtools()
Get bedtools version.
- Returns
version of bedtools
- Return type
str
- autometa.config.environ.bowtie2()
Get bowtie2 version.
- Returns
version of bowtie2
- Return type
str
- autometa.config.environ.configure(config: configparser.ConfigParser) Tuple[configparser.ConfigParser, bool]
Checks executable dependencies necessary to run autometa. Will update config with executable dependencies with details: 1. presence/absence of dependency and its location 2. versions
- Parameters
config (configparser.ConfigParser) – Description of parameter config.
- Returns
(config, satisfied) config updated with executables details Details: 1. location of executable 2. version of executable config : configparser.ConfigParser satisfied : bool
- Return type
2-tuple
- autometa.config.environ.diamond()
Get diamond version.
- Returns
version of diamond
- Return type
str
- autometa.config.environ.find_executables()
Retrieves executable file paths by looking in Autometa dependent executables.
- Returns
{executable:</path/to/executable>, …}
- Return type
dict
- autometa.config.environ.get_versions(program: Optional[str] = None) Union[Dict[str, str], str]
Retrieve versions from all required executable dependencies. If program is provided will only return version for program.
See: https://stackoverflow.com/a/834451/12671809
- Parameters
program (str, optional) – the program to retrieve the version, by default None
- Returns
if program is None: dict - {program:version, …} if program: str - version
- Return type
dict or str
- Raises
ValueError – program is not a string
KeyError – program is not an executable dependency.
- autometa.config.environ.hmmpress()
Get hmmpress version.
- Returns
version of hmmpress
- Return type
str
- autometa.config.environ.hmmscan()
Get hmmscan version.
- Returns
version of hmmscan
- Return type
str
- autometa.config.environ.hmmsearch()
Get hmmsearch version.
- Returns
version of hmmsearch
- Return type
str
- autometa.config.environ.prodigal()
Get prodigal version.
- Returns
version of prodigal
- Return type
str
- autometa.config.environ.samtools()
Get samtools version.
- Returns
version of samtools
- Return type
str
- autometa.config.utilities.get_config(fpath: str) configparser.ConfigParser
Load the config provided at fpath.
- Parameters
fpath (str) – </path/to/file.config>
- Returns
interpolated config object parsed from fpath.
- Return type
config.ConfigParser
- Raises
FileNotFoundError – Provided fpath does not exist.
- autometa.config.utilities.main()
- autometa.config.utilities.parse_args(fpath: Optional[str] = None) argparse.Namespace
Generate argparse namespace (args) from config file.
- Parameters
fpath (str) – </path/to/file.config> (default is DEFAULT_CONFIG in autometa.config)
- Returns
namespace typical to parser.parse_args() method from argparse
- Return type
argparse.Namespace
- Raises
FileNotFoundError – provided fpath does not exist.
- autometa.config.utilities.put_config(config: configparser.ConfigParser, out: str) None
Writes config to out and updates checkpoints checksum.
- Parameters
config (config.ConfigParser) – configuration containing user provided parameters and files information.
out (str) – </path/to/output/file.config>
- Returns
- Return type
NoneType
- autometa.config.utilities.set_home_dir() str
Set the home_dir in autometa’s default configuration (default.config) based on autometa’s current location. If the home_dir variable is already set, then this will be used as the home_dir location.
- Returns
</path/to/package/autometa>
- Return type
str
- autometa.config.utilities.update_config(section: str, option: str, value: str, fpath: str = '/home/docs/checkouts/readthedocs.org/user_builds/autometa/checkouts/2.0.3/autometa/config/default.config') None
Update fpath in section for option with value.
- Parameters
fpath (str) – </path/to/file.config>
section (str) – section header to update within fpath.
option (str) – option to update within section.
value (str) – value to update option.
- Returns
- Return type
NoneType
autometa.datasets package
autometa.taxonomy package
autometa.validation package
Module contents
Legacy Autometa
To run Autometa version 1, you will need to download the latest Autometa version 1 release or navigate to the latest Autometa version 1 commit on the GitHub repository.
The latest Autometa version 1 release may be found here: https://github.com/KwanLab/Autometa/releases
You may find the commit associated with any of the Autometa releases by selecting the release and looking under the release tag on the left.
License
- GNU AFFERO GENERAL PUBLIC LICENSE
Version 3, 19 November 2007
Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/> Everyone is permitted to copy and distribute verbatim copies of this license document, but changing it is not allowed.
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The Free Software Foundation may publish revised and/or new versions of the GNU Affero General Public License from time to time. Such new versions will be similar in spirit to the present version, but may differ in detail to address new problems or concerns.
Each version is given a distinguishing version number. If the Program specifies that a certain numbered version of the GNU Affero General Public License “or any later version” applies to it, you have the option of following the terms and conditions either of that numbered version or of any later version published by the Free Software Foundation. If the Program does not specify a version number of the GNU Affero General Public License, you may choose any version ever published by the Free Software Foundation.
If the Program specifies that a proxy can decide which future versions of the GNU Affero General Public License can be used, that proxy’s public statement of acceptance of a version permanently authorizes you to choose that version for the Program.
Later license versions may give you additional or different permissions. However, no additional obligations are imposed on any author or copyright holder as a result of your choosing to follow a later version.
Disclaimer of Warranty.
THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM “AS IS” WITHOUT WARRANTY OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
Limitation of Liability.
IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS), EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES.
Interpretation of Sections 15 and 16.
If the disclaimer of warranty and limitation of liability provided above cannot be given local legal effect according to their terms, reviewing courts shall apply local law that most closely approximates an absolute waiver of all civil liability in connection with the Program, unless a warranty or assumption of liability accompanies a copy of the Program in return for a fee.
END OF TERMS AND CONDITIONS
How to Apply These Terms to Your New Programs
If you develop a new program, and you want it to be of the greatest possible use to the public, the best way to achieve this is to make it free software which everyone can redistribute and change under these terms.
To do so, attach the following notices to the program. It is safest to attach them to the start of each source file to most effectively state the exclusion of warranty; and each file should have at least the “copyright” line and a pointer to where the full notice is found.
<one line to give the program’s name and a brief idea of what it does.> Copyright (C) <year> <name of author>
This program is free software: you can redistribute it and/or modify it under the terms of the GNU Affero General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Affero General Public License for more details.
You should have received a copy of the GNU Affero General Public License along with this program. If not, see <https://www.gnu.org/licenses/>.
Also add information on how to contact you by electronic and paper mail.
If your software can interact with users remotely through a computer network, you should also make sure that it provides a way for users to get its source. For example, if your program is a web application, its interface could display a “Source” link that leads users to an archive of the code. There are many ways you could offer source, and different solutions will be better for different programs; see section 13 for the specific requirements.
You should also get your employer (if you work as a programmer) or school, if any, to sign a “copyright disclaimer” for the program, if necessary. For more information on this, and how to apply and follow the GNU AGPL, see <https://www.gnu.org/licenses/>.