Kavosh: a new algorithm for finding network motifs

Kashani, Zahra Razaghi Moghadam; Ahrabian, H.; Elahi, Elahe; Nowzari-Dalini, Abbas; Ansari, Elnaz Saberi; Asadi, Shahrokh; Mohammadi, Shahin; Schreiber, Falk; Masoudi‐Nejad, Ali

doi:10.1186/1471-2105-10-318

Cited by 210 publications

(162 citation statements)

References 16 publications

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“…We used the Kavosh program with default parameters to both enumerate subgraphs and calculate motifs, using the random rewiring model, chosen for its speed, command line interface, and accessible output format (30). Significance scores for the random rewiring model used 1,000 random networks.…”

Section: Methodsmentioning

confidence: 99%

Neutral forces acting on intragenomic variability shape the Escherichia coli regulatory network topology

Ruths

Nakhleh

2013

Proc. Natl. Acad. Sci. U.S.A.

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Cis-regulatory networks (CRNs) play a central role in cellular decision making. Like every other biological system, CRNs undergo evolution, which shapes their properties by a combination of adaptive and nonadaptive evolutionary forces. Teasing apart these forces is an important step toward functional analyses of the different components of CRNs, designing regulatory perturbation experiments, and constructing synthetic networks. Although tests of neutrality and selection based on molecular sequence data exist, no such tests are currently available based on CRNs. In this work, we present a unique genotype model of CRNs that is grounded in a genomic context and demonstrate its use in identifying portions of the CRN with properties explainable by neutral evolutionary forces at the system, subsystem, and operon levels. We leverage our model against experimentally derived data from Escherichia coli. The results of this analysis show statistically significant and substantial neutral trends in properties previously identified as adaptive in origin-degree distribution, clustering coefficient, and motifswithin the E. coli CRN. Our model captures the tightly coupled genome-interactome of an organism and enables analyses of how evolutionary events acting at the genome level, such as mutation, and at the population level, such as genetic drift, give rise to neutral patterns that we can quantify in CRNs. Reconstructing a CRN from experimental data, elucidating its dynamic and topological properties, and understanding how these properties emerge during development and evolution are major endeavors in experimental and computational biology (1-5).The complexity of CRNs, coupled with observed "unexpected" trends in their properties, such as scale-freeness (6), high degree of clustering (7), and overrepresented subgraphs (3,(8)(9)(10), has led to several hypotheses of adaptive origins and explanations of CRNs and their properties. Central to most of these studies was the use of simplistic graph-theoretic models, such as randomly rewiring the connectivity of a biological network, to serve as a null model for CRN connectivity maps and their properties (11). However, it has been shown that when subjecting CRNs to the various neutral evolutionary forces and tracing their trajectory in time, many of these topological patterns may simply arise spontaneously due to the forces of mutation, recombination, gene duplication, and genetic drift (10, 12). These studies call into question arguments that were made in favor of adaptive explanations for the emergence and conservation of CRN properties (8,(13)(14)(15) and identify important parameters that may significantly affect the evolution of CRNs from a neutral perspective. Specifically (12), they highlighted the role that promoter length, binding-site size, and population size may play in forming certain topological patterns known as motifs. Nonetheless, a lingering question remains: Which specific parts of a CRN arise due to nonadaptive forces and, moreover, can we quantify these patterns to...

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Section: Methodsmentioning

confidence: 99%

Neutral forces acting on intragenomic variability shape the Escherichia coli regulatory network topology

Ruths

Nakhleh

2013

Proc. Natl. Acad. Sci. U.S.A.

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“…Among the existing network-centric algorithms, Kavosh [20] is one of the best one. However, the algorithm focuses on induced occurrences.…”

Section: Enumerationmentioning

confidence: 99%

“…Hence, subtree-centric methods have to be run once for each possible k-size subtrees, and all the different non-isomorphic k-size subtrees should be generated first. Although there are many motif-finding algorithms which exploit network-centric approaches, such as ESU [19] or Kavosh [20], there is no such study, to the best of our knowledge, that attempt to apply network-centric approaches to subtree counting problem. Network-centric approaches enumerate all subgraphs of a certain size, and then classify each enumerated subgraph into isomorphic classes.…”

Section: Introductionmentioning

confidence: 99%

MTMO: an efficient network‐centric algorithm for subtree counting and enumeration

Luo

Xiao

et al. 2018

Quant. Biol.

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Background: The frequency of small subtrees in biological, social, and other types of networks could shed light into the structure, function, and evolution of such networks. However, counting all possible subtrees of a prescribed size can be computationally expensive because of their potentially large number even in small, sparse networks. Moreover, most of the existing algorithms for subtree counting belong to the subtree-centric approaches, which search for a specific single subtree type at a time, potentially taking more time by searching again on the same network. Methods: In this paper, we propose a network-centric algorithm (MTMO) to efficiently count k-size subtrees. Our algorithm is based on the enumeration of all connected sets of k-1 edges, incorporates a labeled rooted tree data structure in the enumeration process to reduce the number of isomorphism tests required, and uses an array-based indexing scheme to simplify the subtree counting method. Results: The experiments on three representative undirected complex networks show that our algorithm is roughly an order of magnitude faster than existing subtree-centric approaches and base network-centric algorithm which does not use rooted tree, allowing for counting larger subtrees in larger networks than previously possible. We also show major differences between unicellular and multicellular organisms. In addition, our algorithm is applied to find network motifs based on pattern growth approach. Conclusions: A network-centric algorithm which allows for a faster counting of non-induced subtrees is proposed. This enables us to count larger motif in larger networks than previously.Keywords: complex network; evolutionary systems biology; network motif discovery; subtree counting; subtree isomorphism Author summary: Network motifs are considered as simple building blocks of biological networks, which may help in network's function and evolution. However, discovering motifs could be computationally demanding because of the involvement of graph isomorphic detection. As any connected subgraph that is non-treelike can be generated by expanding a corresponding subtree through adding edges, this study proposes a network-centric algorithm (MTMO) to efficiently count tree-structured subgraphs. This enables us to discover larger network motif in larger networks than previously.

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“…Thus, in this field there are many open questions that need to be analyzed in the following years. Once a suitable random graph model is chosen as a null-model, the number of occurrences of all subgraphs of interest is computed for the original graph G. This is of course a costly task [39] and still a matter of active research. Other approaches try to estimate the number of subgraphs in the original graph by different sampling methods [34,40,63].…”

Section: Network Motifsmentioning

confidence: 99%

Good versus optimal: Why network analytic methods need more systematic evaluation

Zweig

2011

Open Computer Science

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Network analytic method designed for the analysis of static networks promise to identify significant relational patterns that correlate with important structures in the complex system the network is derived from. In this mini review, three groups of network analytic methods are discussed: centrality indices, network motifs, and clustering algorithms. We show that so far these methods have mainly been used in a descriptive way, but that they show promising possibilities to be used for prediction and classification. We thus conclude the article with a discussion of how benchmark sets and evaluation criteria could look like to realize this promise.

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Kavosh: a new algorithm for finding network motifs

Cited by 210 publications

References 16 publications

Neutral forces acting on intragenomic variability shape the Escherichia coli regulatory network topology

Neutral forces acting on intragenomic variability shape the Escherichia coli regulatory network topology

MTMO: an efficient network‐centric algorithm for subtree counting and enumeration

Good versus optimal: Why network analytic methods need more systematic evaluation

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