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Description:

orthoani 0.5.0

OrthoANI
A Python implementation of the OrthoANI algorithm for nucleotide identity measurement.

🗺️ Overview
OrthoANI is a metric proposed by Lee et al.
in 2016 to improve computation of Average Nucleotide Identity. It uses
BLASTn to find orthologous
blocks in a pair of sequences, and then computes the average identity only
considering alignments of reciprocal orthologs.

This project is a reimplementation of the closed-source Java implementation
provided by the authors on ezbiocloud.net.
It relies on Biopython to handle the I/O and the
interaction with the BLAST+ binaries.
🔧 Installing
Installing with pip is the easiest:
$ pip install orthoani

orthoani also requires the BLAST+ binaries to be installed on your machine
and available somewhere in your $PATH.
💡 Example
Use Biopython to load two FASTA files, and then orthoani.orthoani to compute
the OrthoANI metric between them:
import orthoani
from Bio.SeqIO import read

genome_1 = read("sequence1.fa", "fasta")
genome_2 = read("sequence2.fa", "fasta")

ani = orthoani.orthoani(genome_1, genome_2)

orthoani can also be used from the CLI using a very simple command-line
interface:
$ orthoani -q sequence1.fa -r sequence2.fa
0.5725

🐏 Memory
orthoani uses the machine temporary folder to handle BLAST+ input and output
files, which is configurable through
tempfile.tempdir.
On some systems (like ArchLinux), this filesystem can reside in memory, which means
that your computer could have trouble processing very large files. If this
happens, try changing the value of the tempfile.tempdir to a directory that
is actually located on physical storage.
📏 Precision
Values computed by this package and the original Java implementation may differ
slightly because in Java the authors perform rounding of floating-point values
at the sub-percent level, while this library uses the full values.
📜 About
This library is provided under the open-source
MIT license.
This project is in no way not affiliated, sponsored, or otherwise endorsed by
the original OrthoANI authors. It was developed by
Martin Larralde during his PhD project
at the European Molecular Biology Laboratory in
the Zeller team.