BLANT: Basic Local Alignment of Network Topology, Part 1: Seeding local alignments with unambiguous 8-node graphlets

TL;DR

BLANT introduces a seed-based algorithm for local network alignment using unambiguous 8-node graphlets, inspired by bioinformatics sequence alignment methods, to identify significant topological similarities between networks.

Contribution

This paper presents BLANT-seed, a novel topology-only method for creating high-specificity network indexes to facilitate local network alignment, analogous to sequence seed-and-extend techniques.

Findings

BLANT-seed effectively identifies common network topologies.

The method uses 8-node graphlets for high specificity.

Preliminary results show promising alignment seed detection.

Abstract

BLAST is a standard tool in bioinformatics for creating local sequence alignments using a "seed-and-extend" approach. Here we introduce an analogous seed-and-extend algorithm that produces local network alignments: BLANT, for Basic Local Alignment of Network Topology. This paper introduces BLANT-seed: given an input graph, BLANT-seed uses network topology alone to create a limited, high-specificity index of k-node induced subgraphs called k-graphlets (analogous to BLASTS's k-mers). The index is constructed so that, if significant common network topology exists between two graphs, their indexes are likely to overlap. BLANT-seed then queries the indexes of two networks to generate a list of common k-graphlets which, when paired, form a seed pair. Our companion paper (submitted elsewhere) describes BLANT-extend, which "grows" these seeds to larger local alignments, again using only topological information.