Login Register Cart 0
Generic placeholder image

Current Bioinformatics

Editor-in-Chief

ISSN (Print): 1574-8936
ISSN (Online): 2212-392X

Back
Journal Home About Journal Editorial Board Journal Insight Current Issue Volumes/Issues
Subscribe
Research Article

A Comparison Analysis for Protein-Protein Interaction Network-Based Methods in Prioritizing Arabidopsis Functional Genes

Author(s): Chun-Jing Si, Si-Min Deng, Yuan Quan* and Hong-Yu Zhang

Volume 17, Issue 8, 2022

Published on: 23 August, 2022

Page: [775 - 785] Pages: 11

DOI: 10.2174/1574893616666210806100011

Abstract

Background: Connecting genes to phenotypes is still a great challenge in genetics. Research related to gene-phenotype associations has made remarkable progress recently due to high-throughput sequencing technology and genome-wide association study (GWAS). However, these genes, which are considered to be significantly associated with a target phenotype according to traditional GWAS, are less precise or subject to greater confounding.

Objective: The present study is an attempt to prioritize functional genes for complex phenotypes employing protein-protein interaction (PPI) network-based systems genetics methods on available GWAS results.

Methods: In this paper, we calculated the functional gene enrichment ratios of the trait ontology of A. thaliana for three common systems genetics methods (i.e. GeneRank, K-shell and HotNet2). Then, comparison of gene enrichment ratios obtained by PPI network-based methods was performed. Finally, a hybrid model was proposed, integrating GeneRank, comprehensive score algorithm and HotNet diffusion- oriented subnetworks (HotNet2) to prioritize functional genes.

Results: These PPI network-based systems genetics methods were indeed useful for prioritizing 775henoltype-associated genes. And functional gene enrichment ratios calculated from the top 20% of GeneRank-identified genes were higher than these ratios of K-shell and these ratios of HotNet2 for most phenotypes. However, the hybrid model can improve the efficiency of functional gene enrichment for A. thaliana (up to 40%).

Conclusion: The present study provides a hybrid method integrating GeneRank, comprehensive score algorithm and HotNet2 to prioritize functional genes. The method will contribute to functional genomics in plants. The source data and codes are freely available at http://47.242.161.60/Plant/.

« Previous
[1]
Casacuberta JM, Jackson S, Panaud O, Purugganan M, Wendel J. Evolution of plant phenotypes, from genomes to traits. G3 (Bethesda) 2016; 6(4): 775-8.
[http://dx.doi.org/10.1534/g3.115.025502] [PMID: 26869620]
[2]
Vanhaelen Q. Web-based tools for drug repurposing: successful examples of collaborative research. Curr Med Chem 2021; 28(1): 181-95.
[http://dx.doi.org/10.2174/0929867327666200128111925] [PMID: 32003659]
[3]
Canduri F, Silva RG, dos Santos DM, et al. Structure of human PNP complexed with ligands. Acta Crystallogr D Biol Crystallogr 2005; 61(Pt 7): 856-62.
[http://dx.doi.org/10.1107/S0907444905005421] [PMID: 15983407]
[4]
Dias MV, Ely F, Palma MS, de Azevedo WF Jr, Basso LA, Santos DS. Chorismate synthase: an attractive target for drug development against orphan diseases. Curr Drug Targets 2007; 8(3): 437-44.
[http://dx.doi.org/10.2174/138945007780058924] [PMID: 17348836]
[5]
Pereira JH, Vasconcelos IB, Oliveira JS, et al. Shikimate kinase: a potential target for development of novel antitubercular agents. Curr Drug Targets 2007; 8(3): 459-68.
[http://dx.doi.org/10.2174/138945007780059013] [PMID: 17348838]
[6]
Filgueira de Azevedo W Jr, Canduri F, Simões de Oliveira J, et al. Molecular model of shikimate kinase from Mycobacterium tuberculosis. Biochem Biophys Res Commun 2002; 295(1): 142-8.
[http://dx.doi.org/10.1016/S0006-291X(02)00632-0] [PMID: 12083781]
[7]
Filgueira de Azevedo W Jr, Canduri F, Marangoni dos Santos D, et al. Structural basis for inhibition of human PNP by immucillin-H. Biochem Biophys Res Commun 2003; 309(4): 917-22.
[http://dx.doi.org/10.1016/j.bbrc.2003.08.094] [PMID: 13679061]
[8]
Seren Ü, Vilhjálmsson BJ, Horton MW, et al. GWAPP: a web application for genome-wide association mapping in Arabidopsis. Plant Cell 2012; 24(12): 4793-805.
[http://dx.doi.org/10.1105/tpc.112.108068] [PMID: 23277364]
[9]
Grimm DG, Roqueiro D, Salomé PA, et al. easyGWAS: A cloud-based platform for comparing the results of genome-wide association studies. Plant Cell 2017; 29(1): 5-19.
[http://dx.doi.org/10.1105/tpc.16.00551] [PMID: 27986896]
[10]
MacRae CA, Vasan RS. Next-generation genome-wide association studies: time to focus on phenotype? Circ Cardiovasc Genet 2011; 4(4): 334-6.
[http://dx.doi.org/10.1161/CIRCGENETICS.111.960765] [PMID: 21846867]
[11]
Huang X, Han B. Natural variations and genome-wide association studies in crop plants. Annu Rev Plant Biol 2014; 65: 531-51.
[http://dx.doi.org/10.1146/annurev-arplant-050213-035715] [PMID: 24274033]
[12]
Song JM, Lei Y, Shu CC, et al. Rice information gateway: a comprehensive bioinformatics platform for indica rice genomes. Mol Plant 2018; 11(3): 505-7.
[http://dx.doi.org/10.1016/j.molp.2017.10.003] [PMID: 29042268]
[13]
Atwell S, Huang YS, Vilhjálmsson BJ, et al. Genome-wide association study of 107 phenotypes in Arabidopsis thaliana inbred lines. Nature 2010; 465(7298): 627-31.
[http://dx.doi.org/10.1038/nature08800] [PMID: 20336072]
[14]
Seren Ü, Grimm D, Fitz J, et al. AraPheno: a public database for Arabidopsis thaliana phenotypes. Nucleic Acids Res 2017; 45(D1): D1054-9.
[http://dx.doi.org/10.1093/nar/gkw986] [PMID: 27924043]
[15]
Van Norman JM, Benfey PN. Arabidopsis thaliana as a model organism in systems biology. Wiley Interdiscip Rev Syst Biol Med 2009; 1(3): 372-9.
[http://dx.doi.org/10.1002/wsbm.25] [PMID: 20228888]
[16]
Togninalli M, Seren Ü, Meng D, et al. The AraGWAS catalog: a curated and standardized Arabidopsis thaliana GWAS catalog. Nucleic Acids Res 2018; 46(D1): D1150-6.
[http://dx.doi.org/10.1093/nar/gkx954] [PMID: 29059333]
[17]
Togninalli M, Seren Ü, Freudenthal JA, et al. AraPheno and the AraGWAS Catalog 2020: a major database update including RNA-Seq and knockout mutation data for Arabidopsis thaliana. Nucleic Acids Res 2020; 48(D1): D1063-8.
[PMID: 31642487]
[18]
Lee T, Lee I. araGWAB: Network-based boosting of genome-wide association studies in Arabidopsis thaliana. Sci Rep 2018; 8(1): 2925.
[http://dx.doi.org/10.1038/s41598-018-21301-4] [PMID: 29440686]
[19]
Shaffer JR, Feingold E, Marazita ML. Genome-wide association studies: prospects and challenges for oral health. J Dent Res 2012; 91(7): 637-41.
[http://dx.doi.org/10.1177/0022034512446968] [PMID: 22562461]
[20]
Ioannidis JP, Tarone R, McLaughlin JK. The false-positive to false-negative ratio in epidemiologic studies. Epidemiology 2011; 22(4): 450-6.
[http://dx.doi.org/10.1097/EDE.0b013e31821b506e] [PMID: 21490505]
[21]
Johnson EC, Border R, Melroy-Greif WE, de Leeuw CA, Ehringer MA, Keller MC. No evidence that schizophrenia candidate genes are more associated with schizophrenia than noncandidate genes. Biol Psychiatry 2017; 82(10): 702-8.
[http://dx.doi.org/10.1016/j.biopsych.2017.06.033] [PMID: 28823710]
[22]
Brachi B, Morris GP, Borevitz JO. Genome-wide association studies in plants: the missing heritability is in the field. Genome Biol 2011; 12(10): 232.
[http://dx.doi.org/10.1186/gb-2011-12-10-232] [PMID: 22035733]
[23]
Gao M, Quan Y, Zhou XH, Zhang HY. PheWAS-based systems genetics methods for anti-breast cancer drug discovery. Genes (Basel) 2019; 10(2): 154.
[http://dx.doi.org/10.3390/genes10020154] [PMID: 30781719]
[24]
Cui ZJ, Zhou XH, Zhang HY. DNA methylation module network-based prognosis and molecular typing of cancer. Genes (Basel) 2019; 10(8): 571.
[http://dx.doi.org/10.3390/genes10080571] [PMID: 31357729]
[25]
Quan Y, Zhang QY, Lv BM, Xu RF, Zhang HY. Genome-wide pathogenesis interpretation using a heat diffusion-based systems genetics method and implications for gene function annotation. Mol Genet Genomic Med 2020; 8(10): e1456.
[http://dx.doi.org/10.1002/mgg3.1456] [PMID: 32869547]
[26]
Morrison JL, Breitling R, Higham DJ, Gilbert DR. GeneRank: using search engine technology for the analysis of microarray experiments. BMC Bioinformatics 2005; 6: 233.
[http://dx.doi.org/10.1186/1471-2105-6-233] [PMID: 16176585]
[27]
Johannes M, Brase JC, Fröhlich H, et al. Integration of pathway knowledge into a reweighted recursive feature elimination approach for risk stratification of cancer patients. Bioinformatics 2010; 26(17): 2136-44.
[http://dx.doi.org/10.1093/bioinformatics/btq345] [PMID: 20591905]
[28]
Salkuyeh DK, Edalatpour V, Hezari D. Polynomial preconditioning for the generank problem Electronic transactions on numerical analysis ETNA 2014; 41: 179-89.
[29]
Zhou XH, Chu XY, Xue G, Xiong JH, Zhang HY. Identifying cancer prognostic modules by module network analysis. BMC Bioinformatics 2019; 20(1): 85.
[http://dx.doi.org/10.1186/s12859-019-2674-z] [PMID: 30777030]
[30]
Kitsak M, Gallos LK, Havlin S, et al. Identification of influential spreaders in complex networks. Nat Phys 2010; 6: 888-93.
[http://dx.doi.org/10.1038/nphys1746]
[31]
Pei S, Muchnik L, Andrade JS Jr, Zheng Z, Makse HA. Searching for superspreaders of information in real-world social media. Sci Rep 2014; 4: 5547.
[http://dx.doi.org/10.1038/srep05547] [PMID: 24989148]
[32]
Ahmed H, Howton TC, Sun Y, Weinberger N, Belkhadir Y, Mukhtar MS. Network biology discovers pathogen contact points in host protein-protein interactomes. Nat Commun 2018; 9(1): 2312.
[http://dx.doi.org/10.1038/s41467-018-04632-8] [PMID: 29899369]
[33]
Garas A, Schweitzer F, Havlin S. A K-shell decomposition method for weighted networks. New J Phys 2012; 14(8): 083030.
[http://dx.doi.org/10.1088/1367-2630/14/8/083030]
[34]
Lawyer G. Understanding the influence of all nodes in a network. Sci Rep 2015; 5: 8665.
[http://dx.doi.org/10.1038/srep08665] [PMID: 25727453]
[35]
Leiserson MD, Vandin F, Wu HT, et al. Pan-cancer network analysis identifies combinations of rare somatic mutations across pathways and protein complexes. Nat Genet 2015; 7(2): 106-14.
[http://dx.doi.org/10.1038/ng.3168]
[36]
Dimitrakopoulos CM, Beerenwinkel N. Computational approaches for the identification of cancer genes and pathways. Wiley Interdiscip Rev Syst Biol Med 2017; 9(1): e1364.
[http://dx.doi.org/10.1002/wsbm.1364] [PMID: 27863091]
[37]
Nakka P, Archer NP, Xu H, et al. Novel gene and network associations found for acute lymphoblastic leukemia using case-control and family-based studies in multiethnic populations. Cancer Epidemiol Biomarkers Prev 2017; 26(10): 1531-9.
[http://dx.doi.org/10.1158/1055-9965.EPI-17-0360] [PMID: 28751478]
[38]
Nakka P, Raphael BJ, Ramachandran S. Gene and network analysis of common variants reveals novel associations in multiple complex diseases. Genetics 2016; 204(2): 783-98.
[http://dx.doi.org/10.1534/genetics.116.188391] [PMID: 27489002]
[39]
Martínez-Romero M, Jonquet C, O’Connor MJ, Graybeal J, Pazos A, Musen MA. NCBO ontology recommender 2.0: an enhanced approach for biomedical ontology recommendation. J Biomed Semantics 2017; 8(1): 21.
[http://dx.doi.org/10.1186/s13326-017-0128-y] [PMID: 28592275]
[40]
Whetzel PL, Noy NF, Shah NH, et al. From the national center for biomedical ontology to access and use ontologies in software applications Nucleic Acids Res 2011; 39(Web Server issue): W541- 5.
[41]
Szklarczyk D, Gable AL, Lyon D, et al. STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res 2019; 47(D1): D607-13.
[http://dx.doi.org/10.1093/nar/gky1131] [PMID: 30476243]
[42]
Bouché F, Lobet G, Tocquin P, Périlleux C. FLOR-ID: an interactive database of flowering-time gene networks in Arabidopsis thaliana. Nucleic Acids Res 2016; 44(D1): D1167-71.
[http://dx.doi.org/10.1093/nar/gkv1054] [PMID: 26476447]
[43]
Kwasniewski M, Nowakowska U, Szumera J, Chwialkowska K, Szarejko I. iRootHair: a comprehensive root hair genomics database. Plant Physiol 2013; 161(1): 28-35.
[http://dx.doi.org/10.1104/pp.112.206441] [PMID: 23129204]
[44]
Garcia-Hernandez M, Berardini TZ, Chen G, et al. TAIR: a resource for integrated Arabidopsis data. Funct Integr Genomics 2002; 2(6): 239-53.
[http://dx.doi.org/10.1007/s10142-002-0077-z] [PMID: 12444417]

Rights & Permissions Print Cite
Article Metrics
1
© 2024 Bentham Science Publishers | Privacy Policy