An introductory guide to aligning networks using SANA, the Simulated Annealing Network Aligner

TL;DR

SANA is a fast, memory-efficient, and flexible heuristic algorithm for biological network alignment using simulated annealing, outperforming many existing methods in speed and quality.

Contribution

Introduction of SANA, a novel simulated annealing-based network aligner that is faster, more memory-efficient, and easier to use than many existing algorithms.

Findings

SANA produces better alignments in minutes on a laptop.

SANA outperforms other algorithms in speed and alignment quality.

SANA is flexible for different types of biomolecular networks.

Abstract

Sequence alignment has had an enormous impact on our understanding of biology, evolution, and disease. The alignment of biological {\em networks} holds similar promise. Biological networks generally model interactions between biomolecules such as proteins, genes, metabolites, or mRNAs. There is strong evidence that the network topology -- the "structure" of the network -- is correlated with the functions performed, so that network topology can be used to help predict or understand function. However, unlike sequence comparison and alignment -- which is an essentially solved problem -- network comparison and alignment is an NP-complete problem for which heuristic algorithms must be used. Here we introduce SANA, the {\it Simulated Annealing Network Aligner}. SANA is one of many algorithms proposed for the arena of biological network alignment. In the context of global network alignment, SANA stands out for its speed, memory efficiency, ease-of-use, and flexibility in the arena of producing alignments between 2 or more networks. SANA produces better alignments in minutes on a laptop than most other algorithms can produce in hours or days of CPU time on large server-class machines. We walk the user through how to use SANA for several types of biomolecular networks. Availability: https://github.com/waynebhayes/SANA