Calculate semantic distance for sets of Gene Ontology terms.

These instructions will get you a copy of the project up and running on your local machine.

Scripts are written in python 3. One easy way to get started is installing miniconda 3.

On linux:

wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
bash Miniconda3-latest-Linux-x86_64.sh

Clone the repository:

git clone https://github.com/MEGA-GO/Mega-Go.git

Install package:

cd Mega-Go
pip install -U .

Execute example analysis:

megago sample7.txt sample8.txt

These files can be found here:

• sample7.txt
• sample8.txt

MegaGO calculates the similarity between GO terms with the Lin semantic similarity (sim_Lin) metric ¹.

where:

• MICA: most informative common ancestor.
• IC(go_i): information content of the term go_i.

The information content of a go term is calculated as follows:

The frequency p of a term go is defined as:

where:

• c: children of go.
• N: total number of terms in GO corpus.
• n_go': number of occurences of a term go' in a reference data set.

To calculate the similarity of two sets of terms, the best match average (BMA)¹ is used.

where:

• m,n: number of terms in set g_i and g_j, respectively
• sim(go_1i,go_2j): similarity between two GO terms

1: Lin, Dekang. 1998. “An Information-Theoretic Definition of Similarity.” In Proceedings of the 15th International Conference on Machine Learning, 296—304.

The relative similarity ranges between 0 and 1.

This project is licensed under the MIT License - see the LICENSE file for details