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An Introductory Guide to Aligning Networks Using SANA, the Simulated Annealing Network Aligner

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Protein-Protein Interaction Networks

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2074))

Abstract

Sequence alignment has had an enormous impact on our understanding of biology, evolution, and disease. The alignment of biological 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 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 two 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.

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Authors and Affiliations

  1. Department of Computer Science, University of California, Irvine, CA, USA

    Wayne B. Hayes

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  1. Wayne B. Hayes
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Correspondence to Wayne B. Hayes .

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Editors and Affiliations

  1. Gene Center, Ludwig-Maximilians-Universität München, München, Germany

    Stefan Canzar

  2. Gene Center, Ludwig-Maximilians-Universität München, München, Germany

    Francisca Rojas Ringeling

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Hayes, W.B. (2020). An Introductory Guide to Aligning Networks Using SANA, the Simulated Annealing Network Aligner. In: Canzar, S., Ringeling, F. (eds) Protein-Protein Interaction Networks. Methods in Molecular Biology, vol 2074. Humana, New York, NY. https://doi.org/10.1007/978-1-4939-9873-9_18

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  • DOI: https://doi.org/10.1007/978-1-4939-9873-9_18

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  • Publisher Name: Humana, New York, NY

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