Epilepsi ile ilgili GWAS veri kümesinde alt ağ arama programlarının değerlendirmesi

Yıl 2022, Cilt: 28 Sayı: 2, 292 - 298, 30.04.2022

Beyhan Adanur Dedeturk Burcu Bakir Gungor

Öz

Aktif alt ağ tespiti, bir protein-protein etkileşim ağında hastalıkla ilgili genlerin birbirine bağlı bir grup genini bulmayı amaçlamaktadır. Son yıllarda bu problem için çeşitli algoritmalar geliştirilmiştir. Bu çalışmada, hastalığa özgü alt ağ tanımlama programlarının analizleri epilepsi veri seti kullanılarak değerlendirilmiştir. Aynı koşullar altında ve aynı veri seti ile 9 farklı program çalıştırılmış ve bu programların Greedy algoritması, Genetik algoritma, Simüle Tavlama Algoritması, MCC (Maximal Clique Centrality) algoritması, MCODE (Molecular Complex Detection) algoritması ve PEWCC (Protein Complex) Ağırlıklı Kümeleme Katsayısı) algoritması sonuçları gösterilmiştir. Her programın en yüksek puan alan 5 modülü, kat zenginleştirme analizi ve normalleştirilmiş karşılıklı bilgi kullanılarak karşılaştırılmıştır. Aynı zamanda tanımlanan alt ağlar, hipergeometrik test kullanılarak fonksiyonel olarak zenginleştirilmiş ve hastalıkla ilişkili biyolojik yollar belirlenmeye çalışılmıştır. Ayrıca programların çalışma süreleri ve özellikleri karşılaştırmalı olarak değerlendirilmiştir.

Anahtar Kelimeler

Protein-Protein etkileşim ağları, Aktif alt-ağ araması, Foksiyonel zenginleştirme analizi, Kat zenginleştirme, Normalleştirilmiş karşılıklı bilgi

Evaluation of Sub-Network search programs in epilepsy-related GWAS dataset

Öz

The active sub-network detection aims to find a group of interconnected genes of disease-related genes in a protein-protein interaction network. In recent years, several algorithms have been developed for this problem. In this study, the analysis of disease-specific sub-network identification programs is evaluated using epilepsy data set. Under the same conditions and with the same data set, 9 different programs are run and results of their Greedy algorithm, Genetic algorithm, Simulated Annealing Algorithm, MCC (Maximal Clique Centrality) algorithm, MCODE (Molecular Complex Detection) algorithm, and PEWCC (Protein Complex Detection using Weighted Clustering Coefficient) algorithm are shown. The top-scoring 5 modules of each program, are compared using fold enrichment analysis and normalized mutual information. Also, the identified subnetworks are functionally enriched using a hypergeometric test, and hence, disease-associated biological pathways are identified. In addition, running times and features of the programs are comparatively evaluated.

Anahtar Kelimeler

Protein-Protein interaction networks, Active subnetwork search, Functional enrichment analysis, Fold enrichment, Normalized mutual information

Kaynakça

• [1] Zhang L, Li Y, Ye X, Bian L. “Bioinformatics analysis of microarray profiling identifies that the miR-203-3p target Ppp2ca aggravates seizure activity in mice”. Journal of Molecular Neuroscience, 66(1), 146-154, 2018.
• [2] Nguyen H, Shrestha S, Tran D, Sha A, Draghici SmNguyen, et al. “A comprehensive survey of tools and software for active subnetwork identification”. Frontiers in Genetics, 10(155), 1-15, 2019.
• [3] Ozisik O, Bakir-Gungor B, Diri B, Sezerman OU. “A genetic algorithm approach to active subnetwork search applied to GWAS data”. In: 2013 8th International Symposium on Health Informatics and Bioinformatics, Ankara, Turkey, 25-27 September 2013.
• [4] Bakir-Gungor B, Baykan B, I_seri SU, Tuncer FN, Sezerman OU. “Identifying SNP targeted pathways in partial epilepsies with genome-wide association study data”. Epilepsy Research, 105(1-2), 92-102, 2013.
• [5] Mitra K, Carvunis AR, Ramesh SK, Ideker T. “Integrative approaches for finding modular structure in biological networks”. Nature Reviews Genetics, 14(10), 719-732, 2013.
• [6] Nikolayeva I, Pla OG, Schwikowski B. “Network module identification-A widespread theoretical bias and best practices”. Methods, 132, 19-25, 2008.
• [7] Ideker T, Ozier O, Schwikowski B, Siegel AF. “Discovering regulatory and signaling circuits in molecular interaction Networks”. Bioinformatics, 18(1), 233-240, 2002.
• [8] Wang L, Matsushita T, Madireddy L, Mousavi P, Baranzini SE. “PINBPA: Cytoscape app for network analysis of GWAS data”. Bioinformatics, 31(2), 262-264, 2014.
• [9] Su G, Morris JH, Demchak B, Bader GD. “Biological network exploration with Cytoscape 3”. Current Protocols in Bioinformatics, 47(1), 8-13, 2014.
• [10] Zaki N, Emov D, Berengueres J. “Protein complex detection using interaction reliability assessment and weighted clustering coefficient”. BMC Bioinformatics, 14(163), 1-9, 2013.
• [11] Ozisik O, Bakir-Gungor B, Diri B, Sezerman UO. “Active subnetwork GA: A two stage genetic algorithm approach31 to active subnetwork search”. Current Bioinformatics, 12(4), 320-328, 2017.
• [12] Wang J, Zhong J, Chen G, Li M, Wu FX, Pan Y. “clusterviz: a cytoscape APP for cluster analysis of the biological network”. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 12(4), 815-822, 2014.
• [13] Chin CH, Chen SH, Wu HH, Ho CW, Ko MT, Lin CY. “cytoHubba: identifying hub objects and sub-networks from complex interactome”. BMC Systems Biology, 8(4), 4-11, 2014.
• [14] Ayati M, Erten S, Chance MR, Koyutu RK M. “MOBAS: identification of disease-associated protein subnetworks using modularity-based scoring”. EURASIP Journal on Bioinformatics and Systems Biology, 2015(1), 1-14, 2015.
• [15] Ulgen E, Ozisik O, Sezerman OU. “pathfindR: An R Package for pathway enrichment analysis utilizing active subnetworks”. BioRxiv, 2018. https://doi.org/10.1101/272450.
• [16] Maere S, Heymans K, Kuiper M. “BiNGO: a cytoscape plugin to assess overrepresentation of gene ontology categories in biological networks”. Bioinformatics, 21(16), 3448-3449, 2006.
• [17] He H, Lin D, Zhang J, Wang YP, Deng HW. “Comparison of statistical methods for subnetwork detection in the integration of gene expression and protein interaction network”. BMC Bioinformatics, 18(1), 149, 1-6, 2017.
• [18] Tripathi S, Moutari S, Dehmer M, Emmert-Streib F. “Comparison of module detection algorithms in protein networks and investigation of the biological meaning of predicted modules”. BMC Bioinformatics, 17(1), 1-18, 2016.
• [19] Taya F, de Souza J, Thakor NV, Bezerianos A. “Comparison method for community detection on brain networks from neuroimaging data”. Applied Network Science, 1(1), 1-20, 2016.
• [20] Adanur B, Gungor BB. “Comparison of disease-specific sub-network ıdentification programs”. In 2018 3rd International Conference on Computer Science and Engineering, Sarajevo, Bosnia, 20-23 September 2018.
• [21] Zhang Q, Zhang ZD. “SubNet: a Java application for subnetwork extraction”. Bioinformatics, 29(19), 2509-2511, 2013.
• [22] Chen X, Xuan J. “MSIGNET: a Metropolis sampling-based method for global optimal significant network identification”. BioRxiv, 2018. https://doi.org/10.1101/260844.
• [23] Farahmand S, Foroughmand-Araabi MH, Goliaei S, Razaghi-Moghadam Z. “CytoGTA: a cytoscape plugin for identifying discriminative subnetwork markers using a game-theoretic approach”. PloS one, 12(10), 1-12, 2017.
• [24] Shi X, Barnes RO, Chen L, Shajahan-Haq AN, HilakiviClarke L et al. “BMRF-Net: a software tool for identification of protein interaction subnetworks by a bagging Markov random field-based method”. Bioinformatics, 31(14), 2412-2414, 2015.
• [25] Wang Q, Yu H, Zhao Z, Jia P. “EW_dmGWAS: edgeweighted dense module search for genome-wide association studies and gene expression proles”. Bioinformatics, 31(15), 2591-2594, 2015.
• [26] Ma H, Schadt EE, Kaplan LM, Zhao H. “COSINE: COnditionSpecIc sub-NEtwork identification using a global optimization method”. Bioinformatics, 27(9), 1290-1298, 2011.
• [27] Akhmedov M, Kedaigle A, Chong RE, Montemanni R, Bertoni F, Fraenkel E, Kwee I. “PCSF: An R-package for network-based interpretation of high-throughput data”. PLoS Computational Biology, 13(7), 1-7, 2017.
• [28] Segre AV, Groop L, Mootha VK, Daly MJ, Altshuler D et al. “Common inherited variation in mitochondrial genes is not enriched for associations with type 2 diabetes or related glycemic traits”. PLoS Genetics, 6(8), 1-19, 2010.
• [29] Kasperaviciute D, Catarino CB, Heinzen EL, Depondt C, Cavalleri GL et al. “Common genetic variation and susceptibility to partial epilepsies: a genome-wide association study”. Brain, 133(7), 2136-2147, 2010.
• [30] Stelzl U, Worm U, Lalowski M, Haenig C, Brembeck FH, et al. “A human protein-protein interaction network: a resource for annotating the proteome”. Cell, 122(6), 957-968, 2005.
• [31] Rual JF, Venkatesan K, Hao T, Hirozane-Kishikawa T, Dricot A et al. “Towards a proteome-scale map of the human protein-protein interaction network”. Nature, 437(7062), 1173-1178, 2005.
• [32] Novo Nordisk Foundation Center for Protein Research. “DISEASES (Disease-Gene Associations Mined From Literature)”. https://diseases.jensenlab.org (08.03.2021).
• [33] Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT et al. “Cytoscape: a software environment for integrated models of biomolecular interaction networks”. Genome Research 13(11), 2498-2504, 2003.
• [34] Manda S, Michael D, Jadhao S, Nagaraj, SH. “Functional enrichment analysis”. Encyclopedia of Bioinformatics and Computational Biology, 2019. https://doi.org/10.1016/B978-0-12-809633-8.20097-6.
• [35] Pietro H. Guzzi. “Functional Enrichment Analysis Methods”. Encyclopedia of Bioinformatics and Computational Biology, 2019. https://doi.org/10.1016/B978-0-12-809633-8.20404-4.
• [36] Vinh NX, Epps J, Bailey J. “Information theoretic measures for clusterings comparison: Variants, properties, normalization and correction for chance”. Journal of Machine Learning Research, 11, 2837-2854, 2010.