\(GO_3\)

Table of Contents

• GO3

• 🖥️ Examples

GO3

\(GO_3\). is a Python library to work with the Gene Ontology (GO). It can calculate similarities between individual terms or between sets of terms. It also can calculate similarities between genes directly with the associated GO Terms from any given sub-ontology: MF, BP or CC.

Main features

Installation

go3 is provided as binary wheels for most platforms on PyPI (Linux, Windows and MacOS). You can run

pip install go3

go3 does not ship with any prebuilt GO Ontology by default. If you don’t provide any .obo, when you try to load the ontology into memory it automatically downloads the last version of go-basic.obo.

Quick Start

import go3
go3.load_go_terms()
annots = go3.load_gaf("goa_human.gaf")
counter = go3.build_term_counter(annots)
sim = go3.semantic_similarity("GO:0006397", "GO:0008380", "resnik", counter)
print("Resnik similarity:", sim)

Main Features

• Ontology loading: Download or load any GO OBO file.

• Annotation parsing: Read GAF files for any organism.

• Term and gene similarity: Compute Resnik, Lin, Wang, TopoICSim, and more.

• Batch and parallel computation: Fast, scalable, and memory-efficient.

• Rich Python API: All results are Python objects, easy to inspect and use.

See the Examples for more!

Benchmark

This library is built as fast, scalable and memory-efficient as possible. Comparing with Goatools, which is the de facto library for manipulating GO in Python

We compare the time and peak memory consumption of go3 vs goatools while loading the ontology and the annotation (.GAF) file, and building the TermCounter.

We also compare the speed of the libraries calculating the similarities between batches of GO Terms of different sizes.

Finally, we compare the gene similarity calculation times. Goatools does not implement natively the groupwise algorithms to compare genes, so we built it for a fair comparison in top of the GO term semantic similarities of the library.