KOGAL (KnOwledge Graph ALignment) is a tool for local network alignment.

This page describes the usage of the command line interface of KOGAL. The executable KOGAL is compiled for Linux x86_64 and Windows 64bit platform. The KOGAL python source code and the datasets used to generate such results are publicly accessible online https://perso.isima.fr/~enmephun/FILES/KOGAL/KOGAL.zip

KOGAL aims to predict conserved protein complexes across species’ PPI networks. It employs two strategies for seed discovery and initial alignment: the first computes the cosine similarity between embedding vectors derived from knowledge graph models like TransE or DistMult to generate an alignment matrix. The second strategy enhances alignment by calculating the degree of centrality of nodes within each network, capturing the importance of each protein in the network structure. Protein similarities are quantified by combining protein sequence similarities with knowledge graph embeddings, ensuring biologically meaningful structural alignments.

A) KOGAL workflow

B) How to execute the source code

(1) Using the IPCA method during the alignment process:

python3 KOGAL.py --clstm ipca
    

(2) Using the MCODE method during the alignment process:

python3 KOGAL.py --clstm mcode
    

The options are as follows (you can also use the "-h" or "--help" flag):

usage: KOGAL.py [] [-h | --help]
Local network alignment using Knowledge Graph Embedding Models
options:
  -h, --help            show this help message and exit
  --policy.txt          Paths for BLAST files and networks
  --strategy            (strategy == 1) the alignment process starts by computes the cosine similarity
                        between embedding vectors derived from knowledge graph models. (strategy == 2) the alignment process starts by calculating the centrality degree of nodes within each network, highlighting the importance of each protein in the network structure.
  --SEED_DC             Applying a threshold to filter the pertinent seed node pairs of (strategy ==1) in order to detect the pairs of initial clusters
  --entity_emb_path     Entity embedding file
  --relation_emb_path   Relation embedding file
  --entity_idmap_path   Entity mapping file
  --relation_idmap_path Relation mapping file
  --gamma GAMMA
  --SCORE_THRESHOLD SCORE_THRESHOLD
                        Minimum score needed for cluster detection. Any
                        cluster whose score is less than a given threshold is
                        abandoned
  --SEED_THRESHOLD SEED_THRESHOLD
                        Applying a threshold to filter the pertinent seed node
                        pairs in order to detect the pairs of initial clusters
  --alpha ALPHA
                        Tuning the contribution between the local and global
                        edge score computed from the knowledge graph embedding
  -save SAVE_PATH, --save_path SAVE_PATH
  --clstm CLSTM         Choosing graph clustering techniques (i.e. ipca,
                        mocode or coach)