Evidence map›Paper›PMID 25161230›Full record

ArticleBioinformatics (Oxford, England)2014

Alignment-free protein interaction network comparison.

Waqar Ali, Tiago Rito, Gesine Reinert, Fengzhu Sun, Charlotte M Deane

Abstract read
In one paragraph

Article in Bioinformatics (Oxford, England), 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

17 citing papers in PubMed.

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  15. The post-genomic era of biological network alignment.EURASIP journal on bioinformatics & systems biology · 2015
    Article
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  17. mSystems
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors.

Waqar AliDepartment of Statistics, University of Oxford, Oxford OX1 3TG, UK and Molecular and Computational Biology Program, Department of Biological Sciences, University of Southern California, CA 90089-2910, USA.
Tiago RitoDepartment of Statistics, University of Oxford, Oxford OX1 3TG, UK and Molecular and Computational Biology Program, Department of Biological Sciences, University of Southern California, CA 90089-2910, USA.
Gesine ReinertDepartment of Statistics, University of Oxford, Oxford OX1 3TG, UK and Molecular and Computational Biology Program, Department of Biological Sciences, University of Southern California, CA 90089-2910, USA.
Fengzhu SunDepartment of Statistics, University of Oxford, Oxford OX1 3TG, UK and Molecular and Computational Biology Program, Department of Biological Sciences, University of Southern California, CA 90089-2910, USA.
Charlotte M DeaneDepartment of Statistics, University of Oxford, Oxford OX1 3TG, UK and Molecular and Computational Biology Program, Department of Biological Sciences, University of Southern California, CA 90089-2910, USA.

Funding

Biotechnology and Biological Sciences Research Council
6 · The paper itself

Abstract

motivationBiological 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.

resultsWe 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. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Indexed as

AlgorithmsAnimalsBiological EvolutionHumansPhylogenyProtein Interaction Mapping

Identifiers

PMID25161230
PMCPMC4147900

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.