IsoRankN: spectral methods for global alignment of multiple protein networks

By Chung-Shou Liao, K. Lu, Michael Baym, Rohit Singh, Bonnie Berger. Bioinform., 2009

433 citations recorded by the source corpus.

Motivation: With the increasing availability of large protein–protein interaction networks, the question of protein network alignment is becoming central to systems biology. Network alignment is further delineated into two sub-problems: local alignment, to find small conserved motifs across networks, and global alignment, which attempts to find a best mapping between all nodes of the two networks. In this article, our aim is to improve upon existing global alignment results. Better network alignment will enable, among other things, more accurate identification of functional orthologs across species. Results: We introduce IsoRankN (IsoRank-Nibble) a global multiple-network alignment tool based on spectral clustering on the induced graph of pairwise alignment scores. IsoRankN outperforms existing algorithms for global network alignment in coverage and consistency on multiple alignments of the five available eukaryotic networks. Being based on spectral methods, IsoRankN is both error tolerant and computationally efficient. Availability: Our software is available freely for non-commercial purposes on request from: http://isorank.csail.mit.edu/ Contact: bab@mit.edu

Subfields: Computational Theory and Mathematics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/6beb58f7547048e1ca4e4f99afdd405dbaea4e1b

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Citations in the corpus

Network propagation: a universal amplifier of genetic associations (2017, methodology): “Similar ideas were used by Isorank 38 and its follow-up, IsoRankN 39 , to predict functional orthology by aligning PPI networks from multiple species.”
Structure and dynamics of molecular networks: A novel paradigm of drug discovery: A comprehensive review (2012, background): “…(IsoRank Nibble) Metabolic networks Uses spectral clustering on the induced graph of pair-wise alignment scores. http://isorank.csail.mit.edu Liao et al., 2009 MetaPathway-Hunter Metabolic networks Finds tree-like pathways in metabolic networks.…”
Computational solutions for omics data (2013, methodology): “It has been used to align and to analyse networks based on protein–protein interaction 134,135 , genetic interaction 140 and metabolic data 141 .”