pyani 0.2.12
pyani provides a package and script for calculation of genome-scale average nucleotide identity.
License: MIT
Keywords: genome bioinformatics sequence

# `pyani`

Whole-genome classification using Average Nucleotide Identity

-----

[![pyani PyPi version](https://img.shields.io/pypi/v/pyani.svg "PyPi version")](https://pypi.python.org/pypi/pyani)
[![pyani licence](https://img.shields.io/pypi/l/pyani.svg "PyPi licence")](https://github.com/widdowquinn/pyani/blob/master/LICENSE)
[![pyani TravisCI build status](https://api.travis-ci.org/widdowquinn/pyani.svg?branch=master)](https://travis-ci.org/widdowquinn/pyani/branches)
[![pyani codecov.io coverage](https://img.shields.io/codecov/c/github/widdowquinn/pyani/master.svg)](https://codecov.io/github/widdowquinn/pyani)
[![pyani Docker Pulls](https://img.shields.io/docker/pulls/leightonpritchard/average_nucleotide_identity.svg)](https://hub.docker.com/r/leightonpritchard/average_nucleotide_identity)

-----

`pyani` is maintained by:

- [Leighton Pritchard](https://pureportal.strath.ac.uk/en/persons/leighton-pritchard)
- [Bailey Harrington](https://pureportal.strath.ac.uk/en/persons/bailey-ann-harrington)

and we are grateful to all who have contributed to this software:

- [Balázs Brankovics](https://github.com/b-brankovics)
- [Peter Cock](https://github.com/peterjc)
- [Robert Davey](https://github.com/froggleston)
- [Özcan Esen](https://github.com/ozcan)
- [Nick Waters](https://github.com/nickp60)
- [@ytanzaw](https://github.com/ytanizaw)

-----

## Table of Contents

<!-- TOC -->

- [`pyani`](#pyani)
  - [Table of Contents](#table-of-contents)
  - [Overview](#overview)
  - [Installation](#installation)
    - [`conda`](#conda)
    - [`pip`](#pip)
    - [Docker images](#docker-images)
      - [NOTE](#note)
    - [Installing from the repository/source code](#installing-from-the-repositorysource-code)
      - [IMPORTANT NOTICE](#important-notice)
      - [Obtain source code](#obtain-source-code)
        - [Direct download](#direct-download)
        - [Clone the repository using `git`](#clone-the-repository-using-git)
      - [Installation From Source](#installation-from-source)
      - [Third-party tools](#third-party-tools)
  - [How To Run `pyani`](#how-to-run-pyani)
    - [Script: <a name="average_nucleotide_identity.py">`average_nucleotide_identity.py`</a>](#script-average_nucleotide_identitypy)
    - [Script: <a name="genbank_get_genomes_by_taxon.py">`genbank_get_genomes_by_taxon.py`</a>](#script-genbank_get_genomes_by_taxonpy)
  - [Testing `pyani`](#testing-pyani)
  - [Method and Output Description](#method-and-output-description)
    - [Average Nucleotide Identity (ANI)](#average-nucleotide-identity-ani)
  - [Developer notes](#developer-notes)
    - [Code Style and Pre-Commit Hooks](#code-style-and-pre-commit-hooks)
  - [Licensing](#licensing)

<!-- /TOC -->

-----

## Overview

`pyani` is a program that calculates average nucleotide identity (ANI) and related measures for whole genome comparisons, and renders graphical summary output. Where available, it takes advantage of multicore systems, and can integrate with [SGE/OGE](http://gridscheduler.sourceforge.net/)-type job schedulers for the sequence comparisons.

**This `README.md` applies only to v0.2 of `pyani`.**

The following scripts should be visible in your `$PATH` after installation:

- `average_nucleotide_identity.py` that enables command-line ANI analysis.
- `genbank_get_genomes_by_taxon.py` that downloads publicly-available genomes from NCBI.
- `delta_filter_wrapper.py` is a helper script required to run delta-filter on SGE/OGE systems.

-----

## Installation

The easiest way to install `pyani` v0.2 is to use `conda` or  `pip`. `conda` is recommended for the simplest installation of third-party tool dependencies (`mummer` and `BLAST`/`BLAST+`).

### `conda`

You will need to install the `bioconda` channel, following instructions at [https://bioconda.github.io/user/install.html](https://bioconda.github.io/user/install.html). Then, to create a new environment for `pyani` and install the program, issue the following command:

```bash
conda create --name pyani_env python=3.8 -y
conda activate pyani_env
conda install pyani
```

### `pip`

`pip` will install `pyani` and its Python dependencies, but not the third-party tools.

```bash
pip3 install pyani
```

### Docker images

`pyani` v0.2 scripts are also provided as [Docker](https://www.docker.com/) images that can be run locally as containers on any operating system which supports Docker. To use these images, first install [Docker](https://www.docker.com/). Then, to run the corresponding scripts, issue either:

```bash
docker run -v ${PWD}:/host_dir leightonpritchard/average_nucleotide_identity
```

or

```bash
docker run -v ${PWD}:/host_dir leightonpritchard/genbank_get_genomes_by_taxon
```

#### NOTE

The `-v ${PWD}:/host_dir` argument enables the Docker container to see the current directory. Without this argument, the container will not be able to see your input files, or write output data.

### Installing from the repository/source code

If you wish to install `pyani` v0.2 from source code, you will need to download this code from GitHub either directly, or by cloning the repository.

Both methods will create a directory called `pyani` which contains the source code for v0.2.

#### IMPORTANT NOTICE

Please note that v0.2 is a **MAINTENANCE-ONLY RELEASE** and is found on the branch called `version_0_2`. Bugs and security issues will be fixed as we become aware of them, but no new features will be added. All new feature development now takes place under v0.3 (the `master` branch on the repository).

#### Obtain source code

You can obtain the source for `pyani` eithe by downloading the latest release as a compressed archive, or cloning the `version_0_2` branch.

##### Direct download

Download the source as `.zip` or `.tar.gz` from:

- [https://github.com/widdowquinn/pyani/releases/tag/v0.2.10](https://github.com/widdowquinn/pyani/releases/tag/v0.2.10)

Extract the archived file using the appropriate commands (or by double-clicking in your file explorer/finder):

```bash
unzip v0.2.10.zip
```

or

```bash
tar -zxvf v0.2.10.tar.gz
```

##### Clone the repository using `git`

Download the current `pyani` repository with `git clone`, and change branch to `version_0_2`:

```bash
git clone git@github.com:widdowquinn/pyani.git
git checkout version_0_2
```

#### Installation From Source

Change directory to `pyani`, and use the `python setup.py` setuptools command, to install the package and scripts. This will **not** install the third-party tools `BLAST`/`BLAST+` and `mummer`.

```bash
cd pyani
python setup.py build
python setup.py install
```

#### Third-party tools

Three alignment packages are required if you wish to use all of `pyani`'s methods: `mummer`, `BLAST+`, and legacy `BLAST`.

**NOTE: the legacy BLAST executables provided by NCBI will not run on macOS Big Sur; we do not provide executables for this tool.***

The simplest route to obtaining these tools is to use `conda`/`bioconda`:

```bash
conda install mummer blast legacy-blast -y
```

But they can also be installed by following instructions from the tools' own websites.

- **BLAST+** (for `anib`) [ftp://ftp.ncbi.nlm.nih.gov/blast/executables/blast+/LATEST/](ftp://ftp.ncbi.nlm.nih.gov/blast/executables/blast+/LATEST/)
- **legacy BLAST** (for `aniblastall`) [ftp://ftp.ncbi.nlm.nih.gov/blast/executables/release/LATEST/](ftp://ftp.ncbi.nlm.nih.gov/blast/executables/release/LATEST/)
- **MUMmer** (for `anim`) [http://mummer.sourceforge.net/](ftp://ftp.ncbi.nlm.nih.gov/blast/executables/release/LATEST/)

-----

## How To Run `pyani`

### Script: <a name="average_nucleotide_identity.py">`average_nucleotide_identity.py`</a>

The `average_nucleotide_identity.py` script - installed as part of this package - enables straightforward ANI analysis at the command-line, and uses the `pyani` module behind the scenes.

You can get a summary of available command-line options with `average_nucleotide_identity.py -h`

```bash
$ average_nucleotide_identity.py -h
usage: average_nucleotide_identity.py [-h] [-o OUTDIRNAME] [-i INDIRNAME] [-v]
                                      [-f] [-s FRAGSIZE] [-l LOGFILE]
                                      [--skip_nucmer] [--skip_blastn]
                                      [--noclobber] [--nocompress] [-g]
                                      [--gformat GFORMAT] [--gmethod GMETHOD]
                                      [--labels LABELS] [--classes CLASSES]
                                      [-m METHOD] [--scheduler SCHEDULER]
                                      [--workers WORKERS]
                                      [--SGEgroupsize SGEGROUPSIZE]
                                      [--maxmatch] [--nucmer_exe NUCMER_EXE]
                                      [--blastn_exe BLASTN_EXE]
                                      [--makeblastdb_exe MAKEBLASTDB_EXE]
                                      [--blastall_exe BLASTALL_EXE]
                                      [--formatdb_exe FORMATDB_EXE]
                                      [--write_excel] [--subsample SUBSAMPLE]
                                      [--seed SEED] [--jobprefix JOBPREFIX]


[…]
```

Example data and output can be found in the directory `test_ani_data`. The data are chromosomes of four isolates of *Caulobacter*. Basic analyses can be performed with the command lines:

```bash
average_nucleotide_identity.py -i tests/test_ani_data/ -o tests/test_ANIm_output -m ANIm -g

average_nucleotide_identity.py -i tests/test_ani_data/ -o tests/test_ANIb_output -m ANIb -g

average_nucleotide_identity.py -i tests/test_ani_data/ -o tests/test_ANIblastall_output -m ANIblastall -g

average_nucleotide_identity.py -i tests/test_ani_data/ -o tests/test_TETRA_output -m TETRA -g
```

The graphical output below, supporting assignment of `NC_002696` and `NC_011916` to the same species (*C.crescentus*), and the other two isolates to distinct species (`NC_014100`:*C.segnis*; `NC_010338`:*C.* sp K31), was generated with the command-line:

```bash
average_nucleotide_identity.py -v -i tests/test_ani_data/ \
    -o tests/test_ANIm_output/ -g --gformat png,pdf,eps \
    --classes tests/test_ani_data/classes.tab \
    --labels tests/test_ani_data/labels.tab
```

![ANIm percentage identity for *Caulobacter* test data](tests/test_ani_data/ANIm_percentage_identity.png "ANIm percentage identity")
![ANIm alignment coverage for *Caulobacter* test data](tests/test_ani_data/ANIm_alignment_coverage.png "ANIm alignment coverage")
![ANIm alignment length for *Caulobacter* test data](tests/test_ani_data/ANIm_alignment_lengths.png "ANIm alignment length")
![ANIm alignment similarity errors for *Caulobacter* test data](tests/test_ani_data/ANIm_similarity_errors.png "ANIm alignment similarity")

### Script: <a name="genbank_get_genomes_by_taxon.py">`genbank_get_genomes_by_taxon.py`</a>

The script `genbank_get_genomes_by_taxon.py`, installed by this package, enables download of genomes from NCBI, specified by taxon ID. The script will download all available assemblies for taxa at or below the specified node in the NCBI taxonomy tree.

Command-line options can be viewed using:

```bash
$ genbank_get_genomes_by_taxon.py -h
usage: genbacnk_get_genomes_by_taxon.py [-h] [-o OUTDIRNAME] [-t TAXON] [-v]
                                        [-f] [--noclobber] [-l LOGFILE]
                                        [--format FORMAT] [--email EMAIL]
                                        [--retries RETRIES]
                                        [--batchsize BATCHSIZE]
[…]
```

For example, the NCBI taxonomy ID for *Caulobacter* is 75, so all publicly-available *Caulobacter* sequences can be obtained using the command-line:

```bash
$ genbank_get_genomes_by_taxon.py -o Caulobacter_downloads -v -t 75 -l Caulobacter_downloads.log --email me@my.email.domain
INFO: genbank_get_genomes_by_taxon.py: Mon Apr 18 17:22:54 2016
INFO: command-line: /Users/lpritc/Virtualenvs/pyani3/bin/genbank_get_genomes_by_taxon.py -o Caulobacter_downloads -v -t 75 -l Caulobacter_downloads.log --email me@my.email.domain
INFO: Namespace(batchsize=10000, email='me@my.email.domain', force=False, format='gbk,fasta', logfile='Caulobacter_downloads.log', noclobber=False, outdirname='Caulobacter_downloads', retries=20, taxon='75', verbose=True)
INFO: Set NCBI contact email to me@my.email.domain
INFO: Creating directory Caulobacter_downloads
INFO: Output directory: Caulobacter_downloads
INFO: Passed taxon IDs: 75
INFO: Entrez ESearch with query: txid75[Organism:exp]
INFO: Entrez ESearch returns 29 assembly IDs
INFO: Identified 29 unique assemblies
INFO: Taxon 75: 29 assemblies
[…]
INFO: Assembly 639581: 271 contigs
INFO: Assembly 233261: 17 contigs
INFO: Assembly 575291: 48 contigs
INFO: Mon Apr 18 17:25:46 2016
INFO: Done.
```

**NOTE:** You must provide a valid email to identify yourself to NCBI for troubleshooting.

The number of attempted retries for each download, and the size of a batch download can be modified. By default, the script will attempt 20 download retries, and obtain sequences in batches of 10000.

-----

## Testing `pyani`

The `pyani` repository includes tests that can be run with `pytest` (including coverage measurement using `pytest-cov`) with the following command, executed from the repository root directory:

```bash
pytest -v
```

-----

## Method and Output Description

### Average Nucleotide Identity (ANI)

This module calculates Average Nucleotide Identity (ANI) according to one of a number of alternative methods described in, e.g.

- Richter M, Rossello-Mora R (2009) Shifting the genomic gold standard for the prokaryotic species definition. Proc Natl Acad Sci USA 106: 19126-19131. doi:10.1073/pnas.0906412106. (ANI1020, ANIm, ANIb)
- Goris J, Konstantinidis KT, Klappenbach JA, Coenye T, Vandamme P, et al. (2007) DNA-DNA hybridization values and their relationship to whole-genome sequence similarities. Int J Syst Evol Micr 57: 81-91. doi:10.1099/ijs.0.64483-0.

ANI is proposed to be the appropriate *in silico* substitute for DNA-DNA
hybridisation (DDH), and so useful for delineating species boundaries. A
typical percentage threshold for species boundary in the literature is 95%
ANI (e.g. Richter et al. 2009).

All ANI methods follow the basic algorithm:

- Align the genome of organism 1 against that of organism 2, and identify the matching regions
- Calculate the percentage nucleotide identity of the matching regions, as an average for all matching regions

Methods differ on: (1) what alignment algorithm is used, and the choice of parameters (this affects the aligned region boundaries); (2) what the input is for alignment (typically either fragments of fixed size, or the most complete assembly available).

- **ANIm**: uses MUMmer (NUCmer) to align the input sequences.
- **ANIb**: uses BLASTN+ to align 1020nt fragments of the input sequences
- **ANIblastall**: uses legacy BLASTN to align 1020nt fragments of the input sequences
- **TETRA**: calculates tetranucleotide frequencies of each input sequence

The algorithms takes as input correctly-formatted FASTA multiple sequence files. All sequences for a single organism should be contained in only one sequence file. Although it is possible to provide new labels for each input genome, for rendering graphical output, the names of these files are used for identification so it is best to name
them sensibly.

Output is written to a named directory. The output files differ depending on the chosen ANI method.

- **ANIm**: MUMmer/NUCmer .delta files, describing each pairwise sequence alignment. Output as tab-separated plain text format tables describing: alignment coverage; total alignment lengths; similarity errors; and percentage identity (ANIm).
- **ANIb** and **ANIblastall**: FASTA sequences describing 1020nt fragments of each input sequence; BLAST nucleotide databases - one for each set of fragments; and BLASTN output files (tab-separated tabular format plain text) - one for each pairwise comparison of input sequences. Output as tab-separated plain text tables describing: alignment coverage; total alignment lengths; similarity errors; and percentage identity (ANIb or ANIblastall).
- **TETRA**: Tab-separated plain text files describing the Pearson correlations between Z-score distributions for each tetranucleotide in each input sequence (TETRA).

If graphical output is chosen, the output directory will also contain PDF, PNG and EPS files representing the various output measures as a heatmap with row and column dendrograms. Other output formats (e.g. SVG) can be specified with the `--gformat` argument.

## Developer notes

The `pyani` package is presented at [`GitHub`](https://github.com/widdowquinn/pyani) under two main branches:

- `master` is the source code underpinning the most recent/current release of `pyani`. It will (almost) always be in sync with the latest release found at [https://github.com/widdowquinn/pyani/releases](https://github.com/widdowquinn/pyani/releases). The only time this code should not be in sync with the release is when there are modifications to documentation, or immediately preceding a release.
- `development` is the current bleeding-edge version of `pyani`. It should (almost) always be in a working and usable condition, but may not be complete and/or some features may be missing or still under development.

### Code Style and Pre-Commit Hooks

The source code for `pyani` is expected to conform to `flake8` linting, and `black` code styling. These are enforced as pre-commit hooks using the `pre-commit` package (included in `requirements.txt`).

The `black` and `flake8` hooks are defined in `.pre-commit-config.yaml`. Custom settings for `flake8` are held in `.flake8`.

To enable pre-commit checks in the codebase on your local machine, execute the following command in the root directory of this repository:

```bash
pre-commit install
```

-----

## Licensing

Unless otherwise indicated, all code is subject to the following agreement:

>(c) The James Hutton Institute 2014-2019
>(c) The University of Strathclyde 2019-2021
>Author: Leighton Pritchard
>
>Contact: leighton.pritchard@strath.ac.uk
>
>Address:
>Leighton Pritchard
>Strathclyde Institute for Pharmacy and Biomedical Sciences
>University of Strathclyde
>161 Cathedral Street
>Glasgow
>Scotland
>G4 0RE
>UK
>
>The MIT License
>
>Copyright (c) 2014-2019 The James Hutton Institute
>Copyright (c) 2019-2021 The University of Strathclyde
>
>Permission is hereby granted, free of charge, to any person obtaining a copy
>of this software and associated documentation files (the "Software"), to deal
>in the Software without restriction, including without limitation the rights
>to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
>copies of the Software, and to permit persons to whom the Software is
>furnished to do so, subject to the following conditions:
>
>The above copyright notice and this permission notice shall be included in
>all copies or substantial portions of the Software.
>
>THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
>IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
>FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
>AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
>LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
>OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
>THE SOFTWARE.