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Alignment-free protein interaction network comparison

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TLDR
Netdis is described, a topology-based distance measure between networks, which offers the possibility of network phylogeny reconstruction and the biological applicability of the method is shown by its ability to build the correct phylogenetic tree of species based solely on the topology of current protein interaction networks.
Abstract
Motivation: Biological network comparison software largely relies on the concept of alignment where close matches between the nodes of two or more networks are sought. These node matches are based on sequence similarity and/or interaction patterns. However, because of the incomplete and error-prone datasets currently available, such methods have had limited success. Moreover, the results of network alignment are in general not amenable for distance-based evolutionary analysis of sets of networks. In this article, we describe Netdis, a topology-based distance measure between networks, which offers the possibility of network phylogeny reconstruction. Results: We first demonstrate that Netdis is able to correctly separate different random graph model types independent of network size and density. The biological applicability of the method is then shown by its ability to build the correct phylogenetic tree of species based solely on the topology of current protein interaction networks. Our results provide new evidence that the topology of protein interaction networks contains information about evolutionary processes, despite the lack of conservation of individual interactions. As Netdis is applicable to all networks because of its speed and simplicity, we apply it to a large collection of biological and non-biological networks where it clusters diverse networks by type. Availability and implementation: The source code of the program is freely available at http://www.stats.ox.ac.uk/research/proteins/resources. Contact: ku.ca.xo.stats@ila.w Supplementary information: Supplementary data are available at Bioinformatics online.

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Fifty years of graph matching, network alignment and network comparison

TL;DR: A novel classification scheme is introduced by distinguishing between methods for deterministic and random graphs for a better understanding of the methods, their challenges and, finally, for applying the methods efficiently in an interdisciplinary setting of data science to solve a particular problem involving comparative network analysis.
Journal ArticleDOI

NetCoMi: network construction and comparison for microbiome data in R

TL;DR: NetCoMi (Network Construction and comparison for Microbiome data), an R package that integrates existing methods for each analysis step in a single reproducible computational workflow, is introduced, enabling insights into whether single taxa, groups of taxa or the overall network structure change between groups.
Journal ArticleDOI

Comparing methods for comparing networks

TL;DR: This work reviews and classify a collection of network comparison methods, highlighting the criteria they are based on and their advantages and drawbacks, and applies the methods to two real-world datasets, the European Air Transportation Network and the FAO Trade Network, to discuss the results that can be drawn from this type of analysis.
Journal ArticleDOI

The post-genomic era of biological network alignment

TL;DR: Computational challenges behind the network alignment problem, existing approaches for solving the problem, ways of evaluating their alignment quality, and the approaches’ biomedical applications are reviewed.
Posted ContentDOI

NetCoMi: Network Construction and Comparison for Microbiome Data in R

TL;DR: NetCoMi (Network Construction and comparison for Microbiome data), an R package that integrates existing methods for each analysis step in a single reproducible computational workflow, is introduced, offering functionality for constructing and analyzing single microbial association networks as well as quantifying network differences.
References
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Journal Article

R: A language and environment for statistical computing.

R Core Team
- 01 Jan 2014 - 
TL;DR: Copyright (©) 1999–2012 R Foundation for Statistical Computing; permission is granted to make and distribute verbatim copies of this manual provided the copyright notice and permission notice are preserved on all copies.
Journal ArticleDOI

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TL;DR: A new criterion for triggering the extension of word hits, combined with a new heuristic for generating gapped alignments, yields a gapped BLAST program that runs at approximately three times the speed of the original.
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