AnnoPRO

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

AnnoPRO architecture

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

Installation

You can install it directly by pip install annopro or install from source code as following steps.

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.

annopro -i test_proteins.fasta -o output

• Use it as a python executable package

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

• Use it as a library to integrated with your project.

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.