Identification of large disjoint motifs in biological networks

Elhesha, Rasha; Kahveci, Tamer

doi:10.1186/s12859-016-1271-7

Cited by 25 publications

(32 citation statements)

References 38 publications

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“…This is a well studied problem in the literature. We use the method developed by Elhesha et al [12] for this step as it is one of the most recent and efficient methods. One can however replace this step with another method for F 1 count without affecting the rest of our algorithm.…”

Section: Counting Partial Overlapping Motifsmentioning

confidence: 99%

Characterizing Building Blocks of Resource Constrained Biological Networks

Ren

Dobra

et al. 2018

Preprint

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Identification of motifs-recurrent and statistically significant patternsin biological networks is the key to understand the design principles, and to infer governing mechanisms of biological systems. This, however, is a computationally challenging task. This task is further complicated as biological interactions depend on limited resources, i.e., a reaction takes place if the reactant molecule concentrations are above a certain threshold level. This biochemical property implies that network edges can participate in a limited number of motifs simultaneously. Existing motif counting methods ignore this problem. This simplification often leads to inaccurate motif counts (over-or under-estimates), and thus, wrong biological interpretations. In this paper, we develop a novel motif counting algorithm, Partially Overlapping MOtif Counting (POMOC), that considers capacity levels for all interactions in counting motifs. Our experiments on real and synthetic networks demonstrate that motif count using the POMOC method significantly differs from the existing motif counting approaches, and our method extends to large-scale biological networks in practical time. Our results also show that our method makes it possible to characterize the impact of different stress factors on cell's organization of network. In this regard, analysis of a S. cerevisiae transcriptional regulatory network using our method shows that oxidative stress is more disruptive to organization and abundance of motifs in this network than mutations of individual genes. Our analysis also suggests that by focusing on the edges that lead to variation in motif counts, our method can be used to find important genes, and to reveal subtle topological and functional differences of the biological networks under different cell states.

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Section: Counting Partial Overlapping Motifsmentioning

confidence: 99%

Characterizing Building Blocks of Resource Constrained Biological Networks

Ren

Dobra

et al. 2018

Preprint

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“…CoMoFinder is developed based on a parallel subgraph enumeration strategy to efficiently and accurately identify composite motifs in large TF-miRNA co-regulatory networks. Rasha Elhesha and Tamer Kahveci [27] in 2016, proposed a motif centric algorithm for finding motifs in a target network. The core idea of this method is to build a set of basic building patterns and find instances of these patterns.…”

Section: Literature Reviewmentioning

confidence: 99%

Discovery of Large Disjoint Motif in Biological Network using Dynamic Expansion Tree

Patra

Mohapatra

2018

Preprint

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Network motifs play an important role in structural analysis of biological networks. Identification of such network motifs leads to many important applications, such as: understanding the modularity and the large-scale structure of biological networks, classification of networks into super-families etc. However, identification of network motifs is challenging as it involved graph isomorphism which is computationally hard problem. Though this problem has been studied extensively in the literature using different computational approaches, we are far from encouraging results. Motivated by the challenges involved in this field we have proposed an efficient and scalable Motif discovery algorithm using a Dynamic Expansion Tree (MDET). In this algorithm embeddings corresponding to child node of expansion tree are obtained from the embeddings of parent node, either by adding a vertex with time complexity O(n) or by adding an edge with time complexity O(1) without involving any isomorphic check. The growth of Dynamic Expansion Tree (DET) depends on availability of patterns in the target network. DET reduces space complexity significantly and the memory limitation of static expansion tree can overcome. The proposed algorithm has been tested on Protein Protein Interaction (PPI) network obtained from MINT database. It is able to identify large motifs faster than most of the existing motif * Corresponding Author: Sabyasachi Patra; Email: sabyasachi@iiit-bh.ac.in discovery algorithms.

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“…The analysis was limited to motifs of ≤ 4 nodes. First, because it has been shown that the fundamental regulatory 555 sub-network patterns consist of a small number of nodes networks (Baiser et al, 2015;Elhesha and Kahveci, 2016;Milo et al, 2002;Yeger-Lotem et al, 2004). Second, because the number of possible motif topologies grow exponentially with number of nodes making it impossible to test larger motif sizes.…”

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confidence: 99%

“…We also created multiple 560 shuffled networks that have the same number of nodes and edges with the RT network and set a z-score as 2.54 for a sub-graph to be considered as a motif present in the network. Is important to note that the motif frequency (i.e., the number of times a given motif appears in a given network) do not monotonically decrease or increase with the motif size and the statistical significance of the motif abundance is independent of the motif size and topology (Elhesha and Kahveci, 2016). This 565 is because motif count does not have downward closure property and a motif is considered as abundant in a given network only if its frequency is significantly higher than the number of times the same motif appears in randomized networks (p-value <0.01).…”

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confidence: 99%

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Replication Timing Networks: a novel class of gene regulatory networks

Rivera-Mulia¹,

Kim

Gabr

et al. 2017

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SummaryDNA replication occurs in a defined temporal order known as the replication-timing (RT) program and is regulated during development, coordinated with 3D genome organization and transcriptional activity. Here, we exploit genome-wide RT profiles from 15 human cell types and intermediate differentiation stages derived from human embryonic stem cells to construct 5 different types of RT regulatory networks. First, we constructed networks based on the coordinated RT changes during cell fate commitment to create RT networks composed of specific functional sub-network communities. We also constructed directional regulatory networks based on the order of RT changes within cell lineages and identified master regulators of differentiation pathways. Finally, we explored relationships between RT networks and 10 transcriptional regulatory networks (TRNs), by combining them into more complex circuitries of composite and bipartite networks. Our findings show that RT networks can be exploited to dissect the cellular mechanisms that regulate lineage specification and cellular identity maintenance. 15 Highlights• DNA replication timing (RT) programs were used to construct gene regulatory networks.• RT networks revealed functional organization of sub-network communities. 20• RT networks identified master regulators of cell fate commitment.

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Identification of large disjoint motifs in biological networks

Cited by 25 publications

References 38 publications

Characterizing Building Blocks of Resource Constrained Biological Networks

Characterizing Building Blocks of Resource Constrained Biological Networks

Discovery of Large Disjoint Motif in Biological Network using Dynamic Expansion Tree

Replication Timing Networks: a novel class of gene regulatory networks

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