MAGNA: Maximizing Accuracy in Global Network Alignment

Saraph, Vikram; Milenković, Tijana

doi:10.1093/bioinformatics/btu409

Cited by 170 publications

(248 citation statements)

References 45 publications

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“…In the biological network domain, work over the past few years has included IsoRank (Singh et al, 2008), the family of GRAAL algorithms (GRAAL (Kuchaiev et al, 2010), H-GRAAL , C-GRAAL (Memisevic and Pržulj, 2012), MI-GRAAL Pržulj, 2011), L-GRAAL (Malod-Dognin and), NATALIE (Klau, 2009;El-Kebir et al, 2011), GHOST (Patro and Kingsford, 2012), NETAL (Neyshabur et al, 2013), SPINAL (Aladag and Erten, 2013), PISwap (Chindelevitch et al, 2013) MAGNA (Saraph and Milenković, 2014) and its successor MAGNA++ (Vijayan et al, 2015), GREAT , WAVE , HubAlign (Hashemifar and Xu, 2014), OptNetalign , SPINAL (Aladag and Erten, 2013), GEDEVO (Ibragimov et al, 2013) and CytoGEDEVO (Malek et al, 2016). Several recent surveys exist (Clark and Kalita, 2014;Elmsallati et al, 2015;Faisal et al, 2015).…”

Section: Previous Workmentioning

confidence: 99%

“…A good objective function should guide the search algorithm to alignments that score well in all the target measures. Sometimes the objective function can be one of the target measures (Saraph and Milenković, 2014). However, since target measures are usually global measures, the search algorithms for local measures described above are in general not applicable.…”

Section: Previous Workmentioning

confidence: 99%

“…It shares many characteristics with OptNetAlign , MAGNA and MAGNA++ (Saraph and Milenković, 2014;Vijayan et al, 2015). All are random search algorithms that can be used to directly optimize any objective function, and all can start with any alignment and then improve it.…”

Section: Our Contributionsmentioning

confidence: 99%

See 2 more Smart Citations

SANA: simulated annealing far outperforms many other search algorithms for biological network alignment

Mamano

Hayes

2017

Bioinformatics

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Every alignment algorithm consists of two orthogonal components: an objective function M measuring the quality an alignment, and a search algorithm that explores the space of alignments looking for ones scoring well according to M . We introduce a new search algorithm called SANA (Simulated Annealing Network Aligner) and apply it to protein-protein interaction networks using S 3 as the the topological measure. Compared against 12 recent algorithms, SANA produces 5-10 times as many correct node mappings as the others when the correct answer is known. We expose an anti-correlation in many existing aligners between their ability to produce good topological vs. functional similarity scores, whereas SANA usually outscores other methods in both measures. If given the perfect objective function encoding the identity mapping, SANA quickly converges to the perfect solution while many other algorithms falter. We observe that when aligning networks with a known mapping and optimizing only S 3 , SANA creates alignments that are not perfect and yet whose S 3 scores match that of the perfect alignment. We call this phenomenon saturation of the topological score. Saturation implies that a measure's correlation with alignment correctness falters before the perfect alignment is reached. This, combined with SANA's ability to produce the perfect alignment if given the perfect objective function, suggests that better objective functions may lead to dramatically better alignments. We conclude that future work should focus on finding better objective functions, and offer SANA as the search algorithm of choice.

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Section: Previous Workmentioning

confidence: 99%

Section: Previous Workmentioning

confidence: 99%

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SANA: simulated annealing far outperforms many other search algorithms for biological network alignment

Mamano

Hayes

2017

Bioinformatics

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“…There has also been an approach to directly optimize edge conservation while the alignment is constructed, known as MAGNA (Saraph and Milenković, 2013). While pairwise alignment is a great tool for comparing two networks, multiple alignment is more appropriate at highlighting highly conserved interaction patterns across networks.…”

Section: Introduction Dmentioning

confidence: 99%

“…This makes the evaluation of alignments, and the comparison of alignment algorithms very difficult. The accuracy measures for pairwise alignment such as edge correctness (EC) (Kuchaiev et al, 2010), induced conserved structure (ICS) (Patro and Kingsford, 2012), and symmetric substructure Score (S 3 ) (Saraph and Milenković, 2013), demonstrate a substantial reliance on the number of conserved interactions across all networks. Therefore, they do not scale well when applied to multiple networks, where the conservation or absence of interactions across a subset of networks is highly informative.…”

Section: Introduction Dmentioning

confidence: 99%

Node Handprinting: A Scalable and Accurate Algorithm for Aligning Multiple Biological Networks

Radu

Charleston

2015

Journal of Computational Biology

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Due to recent advancements in high-throughput sequencing technologies, progressively more protein-protein interactions have been identified for a growing number of species. Subsequently, the protein-protein interaction networks for these species have been further refined. The increase in the quality and availability of these networks has in turn brought a demand for efficient methods to analyze such networks. The pairwise alignment of these networks has been moderately investigated, with numerous algorithms available, but there is very little progress in the field of multiple network alignment. Multiple alignment of networks from different organisms is ideal at finding abnormally conserved or disparate subnetworks. We present a fast and accurate algorithmic approach, Node Handprinting (NH), based on our previous work with Node Fingerprinting, which enables quick and accurate alignment of multiple networks. We also propose two new metrics for the analysis of multiple alignments, as the current metrics are not as sophisticated as their pairwise alignment counterparts. To assess the performance of NH, we use previously aligned datasets as well as protein interaction networks generated from the public database BioGRID. Our results indicate that NH compares favorably with current methodologies and is the only algorithm capable of performing the more complex alignments.

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Broad Applications of Network Embeddings in Computational Biology, Genomics, Medicine, and Health

Jaiswar

Arora

Malhotra

et al. 2022

Bioinformatics and Medical Applications

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MAGNA: Maximizing Accuracy in Global Network Alignment

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 170 publications

References 45 publications

SANA: simulated annealing far outperforms many other search algorithms for biological network alignment

SANA: simulated annealing far outperforms many other search algorithms for biological network alignment

Node Handprinting: A Scalable and Accurate Algorithm for Aligning Multiple Biological Networks

Broad Applications of Network Embeddings in Computational Biology, Genomics, Medicine, and Health

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