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megago

Calculate semantic distance for sets of Gene Ontology terms

https://github.com/mega-go/megago

Science Score: 39.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ○
    CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ✓
    .zenodo.json file
    Found .zenodo.json file
  • ✓
    DOI references
    Found 2 DOI reference(s) in README
  • ○
    Academic publication links
  • ○
    Committers with academic emails
  • ○
    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (11.8%) to scientific vocabulary

Keywords

bioinformatics gene-ontology-terms similarity-metric
Last synced: 10 months ago · JSON representation

Repository

Calculate semantic distance for sets of Gene Ontology terms

Basic Info
  • Host: GitHub
  • Owner: MEGA-GO
  • License: mit
  • Language: Python
  • Default Branch: master
  • Homepage:
  • Size: 148 MB
Statistics
  • Stars: 5
  • Watchers: 4
  • Forks: 2
  • Open Issues: 20
  • Releases: 8
Topics
bioinformatics gene-ontology-terms similarity-metric
Created over 6 years ago · Last pushed over 2 years ago
Metadata Files
Readme License Citation

README.md

published in: Journal of Proteome Research

MegaGO

Calculate semantic distance for sets of Gene Ontology terms.

Getting Started

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

Prerequisites

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

On linux:

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

Installing

Clone the repository:

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

Install package:

shell script cd Mega-Go pip install -U .

Execute example analysis:

shell script megago sample7.txt sample8.txt

These files can be found here: * sample7.txt * sample8.txt

How does it work?

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

where:

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

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.
  • ngo': 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 gi and gj, respectively - sim(go1i,go2j): 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, 296304.

Interpretation

The relative similarity ranges between 0 and 1.

| sim(go1i,go2j) value | Interpretation | |---------|--------------------------| | >0.9 | highly similar functions | | 0.3-0.9 | functionally related | | <0.3 | not functionally similar |

License

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

GitHub Events

Total
Last Year

Committers

Last synced: over 3 years ago

All Time
  • Total Commits: 244
  • Total Committers: 10
  • Avg Commits per committer: 24.4
  • Development Distribution Score (DDS): 0.467
Top Committers
Name Email Commits
Pieter Verschaffelt p****t@u****e 130
Henning Schiebenhoefer h****r@p****e 68
WassimG w****l@g****m 17
michbur m****z@g****m 7
Bart Mesuere m****t@g****m 7
github-actions[bot] 4****]@u****m 6
tivdnbos t****s@u****m 5
rababerladuseladim r****m@u****m 2
github-actions[bot] g****]@u****m 1
dependabot[bot] 4****]@u****m 1
Committer Domains (Top 20 + Academic)
posteo.de: 1 ugent.be: 1

Issues and Pull Requests

Last synced: 11 months ago

All Time
  • Total issues: 18
  • Total pull requests: 38
  • Average time to close issues: about 1 month
  • Average time to close pull requests: 6 days
  • Total issue authors: 7
  • Total pull request authors: 5
  • Average comments per issue: 1.06
  • Average comments per pull request: 0.32
  • Merged pull requests: 26
  • Bot issues: 0
  • Bot pull requests: 12
Past Year
  • Issues: 0
  • Pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 0
  • Pull request authors: 0
  • Average comments per issue: 0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
  • rababerladuseladim (6)
  • pverscha (4)
  • tivdnbos (3)
  • Jokendo-collab (2)
  • bmesuere (1)
  • michbur (1)
  • LucvZon (1)
Pull Request Authors
  • pverscha (13)
  • dependabot[bot] (12)
  • rababerladuseladim (10)
  • WassimG (2)
  • bmesuere (1)
Top Labels
Issue Labels
enhancement (2) bug (1) documentation (1)
Pull Request Labels
dependencies (12) enhancement (6) javascript (6) python (5) documentation (2) bug (1)

Packages

  • Total packages: 1
  • Total downloads:
    • pypi 58 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 1
  • Total versions: 20
  • Total maintainers: 1
pypi.org: megago

Calculate semantic distance for sets of Gene Ontology terms

  • Versions: 20
  • Dependent Packages: 0
  • Dependent Repositories: 1
  • Downloads: 58 Last month
Rankings
Dependent packages count: 10.1%
Downloads: 16.9%
Average: 17.8%
Forks count: 19.1%
Stargazers count: 21.5%
Dependent repos count: 21.6%
Maintainers (1)
pverscha
Last synced: 11 months ago

Dependencies

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  • markupsafe ==1.1.1
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  • megago ==0.2.1
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  • xlsxwriter ==1.2.9
  • zipp ==3.1.0
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