MiNAA: Microbiome Network Alignment Algorithm

TL;DR

MiNAA is a novel algorithm for aligning microbiome networks that combines topological and customizable biological similarity measures, enabling flexible and applicable comparisons across microbiome datasets.

Contribution

It introduces a flexible microbiome network alignment method that allows any biological similarity measure and is tailored for microbiome networks, unlike existing methods.

Findings

Supports any biological similarity input by the user

First network alignment method specifically designed for microbiome networks

Combines GRAAL and Hungarian algorithms for improved alignment

Abstract

Our Microbiome Network Alignment Algorithm (MiNAA) aligns two microbial networks using a combination of the GRAph ALigner (GRAAL) algorithm and the Hungarian algorithm. Network alignment algorithms find pairs of nodes (one node from the first network and the other node from the second network) that have the highest similarity. Traditionally, similarity has been defined as topological similarity such that the neighborhoods around the two nodes are similar. Recent implementations of network alignment methods such as NETAL and L-GRAAL also include measures of biological similarity, yet these methods are restricted to one specific type of biological similarity (e.g. sequence similarity in L-GRAAL). Our work extends existing network alignment implementations by allowing any type of biological similarity to be input by the user. This flexibility allows the user to choose whatever measure of biological similarity is suitable for the study at hand. In addition, unlike most existing network alignment methods that are tailored for protein or gene interaction networks, our work is the first one suited for microbiome networks.