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annopro

Feature map and function annotation of Proteins

https://github.com/idrblab/annopro

Science Score: 67.0%

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

  • ✓
    CITATION.cff file
    Found CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ✓
    .zenodo.json file
    Found .zenodo.json file
  • ✓
    DOI references
    Found 3 DOI reference(s) in README
  • ○
    Academic publication links
  • ✓
    Committers with academic emails
    1 of 3 committers (33.3%) from academic institutions
  • ○
    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (14.4%) to scientific vocabulary

Keywords

blastp deep-learning feature-map protein-function-prediction
Last synced: 6 months ago · JSON representation ·

Repository

Feature map and function annotation of Proteins

Basic Info
  • Host: GitHub
  • Owner: idrblab
  • License: mit
  • Language: Python
  • Default Branch: main
  • Homepage:
  • Size: 4.01 MB
Statistics
  • Stars: 33
  • Watchers: 0
  • Forks: 7
  • Open Issues: 13
  • Releases: 3
Topics
blastp deep-learning feature-map protein-function-prediction
Created over 3 years ago · Last pushed over 1 year ago
Metadata Files
Readme License Citation

README.md

AnnoPRO

AUR python pypi keras DOI

AnnoPRO generation

  • step 1: input proteins sequeces
  • step 2: features extraction by Profeat
  • step 3: Feature pairwise distance calculation --> cosine, correlation, jaccard
  • Step4: Feature 2D embedding --> umap, tsne, mds
  • Step5: Feature grid arrangement --> grid, scatter
  • Step5: Transform --> minmax, standard

image

AnnoPRO architecture

  • Encoding layers: Protein features was learned by CNNs and Protein similarity was learned by FCs.
  • Decoding layers: LSTMs

image

Installation

You can install it directly by pip install annopro or install from source code as following steps. bash git clone https://github.com/idrblab/AnnoPRO.git cd AnnoPRO conda create -n annopro python=3.8 conda activate annopro pip install .

Usage

  • Use it as a terminal command. For all parameters, type annopro -h. bash annopro -i test_proteins.fasta -o output
  • Use it as a python executable package

bash python -m annopro -i test_proteins.fasta -o output

  • Use it as a library to integrated with your project. python from annopro import main main("test_proteins.fasta", "output")

The result is displayed in the ./output/bp(cc,mf)_result.csv.

Notice: if you use annopro for the first time, annopro will automatically download required resources when they are used (lazy download mechanism)

Possible problems

  1. pip is looking at multiple versions of XXX to determine which version is compatible with other requirements. this could take a while.

Your pip is latest, back to old version such as 20.2, or just add --use-deprecated=legacy-resolver param.

Contact

If any questions, please create an issue on this repo, we will deal with it as soon as possible.

Owner

  • Name: idrblab
  • Login: idrblab
  • Kind: organization
  • Email: idrblab@zju.edu.cn
  • Location: Zhejiang University

Innovative Drug Research and Bioinformatics Group

Citation (CITATION.cff) 

# This CITATION.cff file was generated with cffinit.
# Visit https://bit.ly/cffinit to generate yours today!

cff-version: 1.2.0
title: >-
  AnnoPRO: an innovative strategy for protein function
  annotation based on image-like protein representation and
  multimodal deep learnin
message: >-
  If you use this software, please cite it using the
  metadata from this file.
type: software
authors:
  - given-names: Lingyan
    family-names: Zheng
    affiliation: Zhejiang University
    email: zhenglingyan@zju.edu.cn
    orcid: 'https://orcid.org/0000-0001-7533-2649'
  - given-names: Hongning
    family-names: Zhang
    affiliation: Zhejiang University
    orcid: 'https://orcid.org/0000-0002-7818-7915'
    email: zhanghn@zju.edu.cn
repository-code: 'https://github.com/idrblab/AnnoPRO'
url: 'https://idrblab.org/annopro/'
keywords:
  - protein functional annotation
  - deep learning
  - feature embedding
license: MIT
commit: 1743925a08c7171f071c735e017b0019b758c460
version: '0.2'
date-released: '2023-03-13'

GitHub Events

Total
  • Issues event: 2
  • Watch event: 4
  • Issue comment event: 2
Last Year
  • Issues event: 2
  • Watch event: 4
  • Issue comment event: 2

Committers

Last synced: almost 3 years ago

All Time
  • Total Commits: 101
  • Total Committers: 3
  • Avg Commits per committer: 33.667
  • Development Distribution Score (DDS): 0.297
Top Committers
Name Email Commits
Zhang.H.N z****n@f****m 71
lingyan z****n@z****n 22
GCS-ZHN 7****N@u****m 8
Committer Domains (Top 20 + Academic)
zju.edu.cn: 1 foxmail.com: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time
  • Total issues: 12
  • Total pull requests: 14
  • Average time to close issues: 2 days
  • Average time to close pull requests: 4 days
  • Total issue authors: 11
  • Total pull request authors: 2
  • Average comments per issue: 0.75
  • Average comments per pull request: 0.0
  • Merged pull requests: 10
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 6
  • Pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 6
  • Pull request authors: 0
  • Average comments per issue: 0.33
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
  • Maxim-Karpov (2)
  • smoothyly (1)
  • a-ill (1)
  • huang-zeyu (1)
  • emilpaulitz (1)
  • aswiniitkgp (1)
  • smilenaderi (1)
  • gilles-20 (1)
  • Sourdoc (1)
  • jov131 (1)
Pull Request Authors
  • swallow-design (11)
  • GCS-ZHN (4)
Top Labels
Issue Labels
Pull Request Labels

Packages

  • Total packages: 1
  • Total downloads:
    • pypi 67 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 0
  • Total versions: 4
  • Total maintainers: 2
pypi.org: annopro

A simple python package for annotating protein sequences

  • Versions: 4
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 67 Last month
Rankings
Dependent packages count: 6.6%
Downloads: 9.3%
Forks count: 17.3%
Average: 18.4%
Stargazers count: 28.2%
Dependent repos count: 30.6%
Maintainers (2)
GCS-ZHN swallow
Last synced: 6 months ago

Dependencies

.github/workflows/pypi.yml actions
  • GCS-ZHN/python-wheels-manylinux-build v1.1 composite
  • actions/checkout v3 composite
  • actions/download-artifact v3 composite
  • actions/setup-python v4 composite
  • actions/upload-artifact v3 composite
  • awvwgk/setup-fortran main composite
  • frabert/replace-string-action v2 composite
  • microsoft/setup-msbuild v1.1 composite
  • pypa/gh-action-pypi-publish release/v1 composite
  • softprops/action-gh-release v1 composite
requirements.txt pypi
  • diamond4py >=0.0.2rc2
  • fasta >=2.3.2
  • llvmlite >=0.38.1
  • numpy <=1.19.5
  • pandas >=1.2.4,<=1.4.8
  • scikit-learn >=1.0.2
  • scipy >=1.4.1,<=1.9.5
  • tensorflow >=2.5.0,<=2.6.5
  • threadpoolctl >=3.1.0
  • wget *