UCL Discovery
UCL home » Library Services » Electronic resources » UCL Discovery

Unified Alignment of Protein-Protein Interaction Networks

Malod-Dognin, N; Ban, K; Przulj, N; (2017) Unified Alignment of Protein-Protein Interaction Networks. Scientific Reports , 7 , Article 953. 10.1038/s41598-017-01085-9. Green open access

[img]
Preview
Text
Malod-Dognin_s41598-017-01085-9.pdf - Published version

Download (1MB) | Preview

Abstract

Paralleling the increasing availability of protein-protein interaction (PPI) network data, several network alignment methods have been proposed. Network alignments have been used to uncover functionally conserved network parts and to transfer annotations. However, due to the computational intractability of the network alignment problem, aligners are heuristics providing divergent solutions and no consensus exists on a gold standard, or which scoring scheme should be used to evaluate them. We comprehensively evaluate the alignment scoring schemes and global network aligners on large scale PPI data and observe that three methods, HUBALIGN, L-GRAAL and NATALIE, regularly produce the most topologically and biologically coherent alignments. We study the collective behaviour of network aligners and observe that PPI networks are almost entirely aligned with a handful of aligners that we unify into a new tool, Ulign. Ulign enables complete alignment of two networks, which traditional global and local aligners fail to do. Also, multiple mappings of Ulign define biologically relevant soft clusterings of proteins in PPI networks, which may be used for refining the transfer of annotations across networks. Hence, PPI networks are already well investigated by current aligners, so to gain additional biological insights, a paradigm shift is needed. We propose such a shift come from aligning all available data types collectively rather than any particular data type in isolation from others.

Type: Article
Title: Unified Alignment of Protein-Protein Interaction Networks
Open access status: An open access version is available from UCL Discovery
DOI: 10.1038/s41598-017-01085-9
Publisher version: http://dx.doi.org/10.1038/s41598-017-01085-9
Language: English
Additional information: This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
Keywords: GLOBAL ALIGNMENT, SYSTEMATIC IDENTIFICATION, MAXIMIZING ACCURACY, BIOLOGICAL NETWORKS, GENE ONTOLOGY, CONSERVATION, DISCOVERY, TOPOLOGY, MODULES, YEAST
UCL classification: UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/1557414
Downloads since deposit
57Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item