scholarly journals An Efficient Network Motif Discovery Approach for Co-Regulatory Networks

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 14151-14158 ◽  
Author(s):  
Jiawei Luo ◽  
Lv Ding ◽  
Cheng Liang ◽  
Nguyen Hoang Tu
Keyword(s):  
Regulatory Networks ◽  
Motif Discovery ◽  
Network Motif

scholarly journals IndeCut evaluates performance of network motif discovery algorithms

2017 ◽  
Vol 34 (9) ◽  
pp. 1514-1521 ◽  
Author(s):  
Mitra Ansariola ◽  
Molly Megraw ◽  
David Koslicki
Keyword(s):  
Motif Discovery ◽  
Network Motif ◽  
Discovery Algorithms

scholarly journals MODA: An efficient algorithm for network motif discovery in biological networks

2009 ◽  
Vol 84 (5) ◽  
pp. 385-395 ◽  
Author(s):  
Saeed Omidi ◽  
Falk Schreiber ◽  
Ali Masoudi-Nejad
Keyword(s):  
Biological Networks ◽  
Efficient Algorithm ◽  
Motif Discovery ◽  
Network Motif

scholarly journals A novel k-mer set memory (KSM) motif representation improves regulatory variant prediction

2017 ◽  
Author(s):  
Yuchun Guo ◽  
Kevin Tian ◽  
Haoyang Zeng ◽  
Xiaoyun Guo ◽  
David Kenneth Gifford
Keyword(s):  
Binding Sites ◽  
Regulatory Networks ◽  
Motif Discovery ◽  
De Novo ◽  
Binding Motif ◽  
Large Set ◽  
Public Resource ◽  
Regulatory Variant ◽  
The Impact

ABSTRACTThe representation and discovery of transcription factor (TF) sequence binding specificities is critical for understanding gene regulatory networks and interpreting the impact of disease-associated non-coding genetic variants. We present a novel TF binding motif representation, the K-mer Set Memory (KSM), which consists of a set of aligned k-mers that are over-represented at TF binding sites, and a new method called KMAC for de novo discovery of KSMs. We find that KSMs more accurately predict in vivo binding sites than position weight matrix models (PWMs) and other more complex motif models across a large set of ChIP-seq experiments. KMAC also identifies correct motifs in more experiments than four state-of-the-art motif discovery methods. In addition, KSM derived features outperform both PWM and deep learning model derived sequence features in predicting differential regulatory activities of expression quantitative trait loci (eQTL) alleles. Finally, we have applied KMAC to 1488 ENCODE TF ChIP-seq datasets and created a public resource of KSM and PWM motifs. We expect that the KSM representation and KMAC method will be valuable in characterizing TF binding specificities and in interpreting the effects of non-coding genetic variations.

Motif discovery in biological network using expansion tree

2018 ◽  
Vol 16 (06) ◽  
pp. 1850024 ◽  
Author(s):  
Sabyasachi Patra ◽  
Anjali Mohapatra
Keyword(s):  
Complex Networks ◽  
Protein Interaction ◽  
Motif Discovery ◽  
Statistical Significance ◽  
Network Motif ◽  
Graph Isomorphism ◽  
Building Blocks ◽  
Network Motifs ◽  
Topological Features ◽  
Scalable Network

Networks are powerful representation of topological features in biological systems like protein interaction and gene regulation. In order to understand the design principles of such complex networks, the concept of network motifs emerged. Network motifs are recurrent patterns with statistical significance that can be seen as basic building blocks of complex networks. Identification of 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, protein function annotation, etc. However, identification of network motifs is challenging as it involves graph isomorphism which is computationally hard. Though this problem has been studied extensively in the literature using different computational approaches, we are far from satisfactory results. Motivated by the challenges involved in this field, an efficient and scalable network Motif Discovery algorithm based on Expansion Tree (MODET) is proposed. Pattern growth approach is used in this proposed motif-centric algorithm. Each node of the expansion tree represents a non-isomorphic pattern. The embeddings corresponding to a child node of the expansion tree are obtained from the embeddings of the parent node through vertex addition and edge addition. Further, the proposed algorithm does not involve any graph isomorphism check and the time complexities of these processes are [Formula: see text] and [Formula: see text], respectively. The proposed algorithm has been tested on Protein–Protein Interaction (PPI) network obtained from the MINT database. The computational efficiency of the proposed algorithm outperforms most of the existing network motif discovery algorithms.

Parallelization of network motif discovery using star contraction

2021 ◽  
Vol 101 ◽  
pp. 102734
Author(s):  
Esra Ruzgar Ateskan ◽  
Kayhan Erciyes ◽  
Mehmet Emin Dalkilic
Keyword(s):  
Motif Discovery ◽  
Network Motif
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