BLANT: Basic Local Alignment of Network Topology, Part 2: Topology-only Extension Beyond Graphlet Seeds
Tingyin Ding, Utsav Jain, Wayne B. Hayes

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…
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Taxonomy
TopicsBioinformatics and Genomic Networks · Machine Learning in Bioinformatics · Gene expression and cancer classification
