AligNet- A New Protein-Protein Interaction Network Aligner

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alignet: ppi network aligner

Protein-protein interactions (PPIs) are essential to study to understand the molecular functions carried out by a group of proteins. Alcala et al., [1] developed a new aligner software to provide biologically relevant alignments of PPI networks.

AligNet is based on a parameter-free pairwise alignment algorithm [1]. It provides biologically and topologically efficient alignments. AligNet algorithm focuses on structural matching and protein function conservation. It is implemented in the R language and is freely available to download from GitHub.

How AligNet works?

  1. AligNet takes two networks as input.
  2. It recognizes all nodes and creates a cluster for every node present in the networks separately.
  3. After that, it aligns both the clusters.
  4. Calculates the local alignment and selects the best-computed alignments.
  5. Later, it selects a well-defined local alignment amongst the rest of the computed alignments.
  6. Further,  it creates a global alignment out of the selected local alignment.
  7. As a result, it provides all aligned nodes.

The developing team has tested the performance of AligNet against the other state-of-the-art tools, namely, PINALOG [2], SPINAL [3], HubAlign [4], and L-GRAAL [5]. As a result, the AligNet performed better than the other aligners.

For further reading, click here.


  1. Alberich, R., Alcala, A., Llabrés, M., Rosselló, F., & Valiente, G. (2019). Alignet: alignment of protein-protein interaction networks. arXiv preprint arXiv:1902.07107.
  2. Phan, H. T., & Sternberg, M. J. (2012). PINALOG: a novel approach to align protein interaction networks—implications for complex detection and function prediction. Bioinformatics28(9), 1239-1245.
  3. Aladag˘,A.E. and Erten,C. (2013) Spinal: scalable protein interaction network alignment. Bioinformatics, 29, 917–924
  4. Hashemifar, S., & Xu, J. (2014). Hubalign: an accurate and efficient method for global alignment of protein–protein interaction networks. Bioinformatics30(17), i438-i444.
  5. Malod-Dognin, N., & Pržulj, N. (2015). L-GRAAL: Lagrangian graphlet-based network aligner. Bioinformatics31(13), 2182-2189.
Muniba is a Bioinformatician based in the South China University of Technology. She has cutting edge knowledge of bioinformatics tools, algorithms, and drug designing. When she is not reading she is found enjoying with the family. Know more about Muniba

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