BLANT: Basic Local Alignment of Network Topology, Part 2: Topology-only Extension Beyond Graphlet Seeds

TL;DR

BLANT introduces a seed-and-extend algorithm for local network alignment based solely on topology, capable of identifying high-quality alignments in similar networks and adaptable with user-defined constraints.

Contribution

This paper presents BLANT-extend, a novel topology-only local network alignment method that grows seeds into alignments with customizable bounds, extending the initial seed generation approach from Part 1.

Findings

Effective in high topological similarity networks

Recovers hundreds of orthologs in PPI networks

Less effective in noisy, dissimilar networks

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 (Basic Local Alignment of Network Topology). In Part 1, we introduced BLANT-seed, which generates graphlet-based seeds using only topological information. Here, in Part 2, we describe BLANT-extend, which "grows" seeds to larger local alignments using only topological information. We allow the user to specify bounds on several measures an alignment must satisfy, including the edge density, edge commonality (i.e., aligned edges), and node-pair similarity if such a measure is used; the latter allows the inclusion of sequence-based similarity, if desired, as well as local topological constraints. BLANT-extend is able to enumerate all possible alignments satisfying the bounds that can be grown from each seed, within a specified CPU time or number of generated alignments. While topology-driven local network alignment has a wide variety of potential applications outside bioinformatics, here we focus on the alignment of Protein-Protein Interaction (PPI) networks. We show that BLANT is capable of finding large, high-quality local alignments when the networks are known to have high topological similarity -- for example recovering hundreds of orthologs between networks of the recent Integrated Interaction Database (IID). Predictably, however, it performs less well when true topological similarity is absent, as is the case in most current experimental PPI networks that are noisy and have wide disparity in edge density which results in low common coverage.