Comparative study of RNA-seq- and Microarray-derived coexpression networks in Arabidopsis thaliana
Open accessAbstract
Motivation: Coexpression networks are data-derived representations of genes behaving in a similar way across tissues and experimental conditions. They have been used for hypothesis generation and guilt-by-association approaches for inferring functions of previously unknown genes. So far, the main platform for expression data has been DNA microarrays; however, the recent development of RNA-seq allows for higher accuracy and coverage of transcript populations. It is therefore important to assess the potential for biological investigation of coexpression networks derived from this novel technique in a condition-independent dataset. Results: We collected 65 publicly available Illumina RNA-seq high quality Arabidopsis thaliana samples and generated Pearson correlation coexpression networks. These networks were then compared with those derived from analogous microarray data. We show how Variance-Stabilizing Transformed (VST) RNA-seq data samples are the most similar to microarray ones, with respect to inter-sample variation, correlation coefficient distribution and network topological architecture. Microarray networks show a slightly higher score in biology-derived quality assessments such as overlap with the known protein–protein interaction network and edge ontological agreement. Different coexpression network centralities are investigated; in particular, we show how betweenness centrality is generally a positive marker for essential genes in A.thaliana, regardless of the platform originating the data. In the end, we focus on a specific gene network case, showing that although microarray data seem more suited for gene network reverse engineering, RNA-seq offers the great advantage of extending coexpression analyses to the entire transcriptome. Contact: fgiorgi@appliedgenomics.org Supplementary information: Supplementary data are available at Bioinformatics online.
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Differential expression analysis for sequence count data
Anders, S., & Huber, W. (2010) · Genome Biology · doi:10.1186/gb-2010-11-10-r106 · Cited by 15,581 · see citing works
Trapnell, C., Williams, B. A., Pertea, G., Mortazavi, A., Kwan, G., van Baren, M. J., Salzberg, S. L., Wold, B. J., & Pachter, L. (2010) · Nature Biotechnology · doi:10.1038/nbt.1621 · Cited by 15,556 · see citing works
Mapping and quantifying mammalian transcriptomes by RNA-Seq
Mortazavi, A., Williams, B. A., McCue, K., Schaeffer, L., & Wold, B. (2008) · Nature Methods · doi:10.1038/nmeth.1226 · Cited by 13,696 · see citing works
Gene Expression Omnibus: NCBI gene expression and hybridization array data repository
Edgar, R. (2002) · Nucleic Acids Research · doi:10.1093/nar/30.1.207 · Cited by 13,288 · see citing works
RNA-Seq: a revolutionary tool for transcriptomics
Wang, Z., Gerstein, M., & Snyder, M. (2009) · Nature Reviews Genetics · doi:10.1038/nrg2484 · Cited by 13,174 · see citing works
TopHat: discovering splice junctions with RNA-Seq
Trapnell, C., Pachter, L. S., & Salzberg, S. L. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp120 · Cited by 11,477 · see citing works
Network biology: understanding the cell's functional organization
Barabási, A.-L., & Oltvai, Z. N. (2004) · Nature Reviews Genetics · doi:10.1038/nrg1272 · Cited by 7,903 · see citing works
Lethality and centrality in protein networks
Jeong, H., Mason, S., Barabási, A., & Oltvai, Z. (2001) · Nature · doi:10.1038/35075138 · Cited by 5,327 · see citing works
Cytoscape 2.8: new features for data integration and network visualization
Smoot, M. E., Ono, K., Ruscheinski, J., Wang, P., & Ideker, T. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq675 · Cited by 4,770 · see citing works
Systems biology: a brief overview
Kitano, H. (2002) · Science · doi:10.1126/science.1069492 · Cited by 4,265 · see citing works
Caspi, R., Billington, R., Ferrer, L., Foerster, H., Fulcher, C. A., Keseler, I. M., Kothari, A., Krummenacker, M., Latendresse, M., Mueller, L. A., et al. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkv1164 · Cited by 3,395 · see citing works
Thimm, O., Bläsing, O. E., Gibon, Y., Nagel, A., Meyer, S., Krüger, P., Selbig, J., Müller, L. A., Rhee, S. Y., & Stitt, M. (2004) · The Plant Journal · doi:10.1111/j.1365-313x.2004.02016.x · Cited by 3,356 · see citing works
Leinonen, R., Sugawara, H., & Shumway, M. F. (2010) · Nucleic Acids Research · doi:10.1093/nar/gkq1019 · Cited by 2,760 · see citing works
Margolin, A. A., Nemenman, I., Basso, K., Wiggins, C., Stolovitzky, G., Favera, R. D., & Califano, A. (2006) · BMC Bioinformatics · doi:10.1186/1471-2105-7-s1-s7 · Cited by 2,420 · see citing works
Margolin, A. A., Nemenman, I. M., Basso, K., Wiggins, C. H., Stolovitzky, G. A., Favera, R. D., & Califano, A. (2006) · BMC Bioinformatics · doi:10.7916/d8g15zbp · Cited by 2,260 · see citing works
Fisher, R. A. (1915) · Biometrika · doi:10.1093/biomet/10.4.507 · Cited by 2,159 · see citing works
Assessing the accuracy of prediction algorithms for classification: an overview
Baldi, P., Brunak, S., Chauvin, Y., Andersen, C. A. F., & Nielsen, H. (2000) · Bioinformatics · doi:10.1093/bioinformatics/16.5.412 · Cited by 2,111 · see citing works
Evolving gene/transcript definitions significantly alter the interpretation of GeneChip data
Dai, M. (2005) · Nucleic Acids Research · doi:10.1093/nar/gni179 · Cited by 1,797 · see citing works
Empirical Bayes analysis of a microarray experiment
Efron, B., Tibshirani, R., Storey, J. D., & Tusher, V. (2001) · Journal of the American Statistical Association · doi:10.1198/016214501753382129 · Cited by 1,733 · see citing works
Reverse engineering of regulatory networks in human B cells
Basso, K., Margolin, A. A., Stolovitzky, G. A., Klein, U., Dalla‐Favera, R., & Califano, A. (2005) · Nature Genetics · doi:10.1038/ng1532 · Cited by 1,406 · see citing works
Fisher, R. A. (1915) · Biometrika · doi:10.2307/2331838 · Cited by 1,334 · see citing works
The Arabidopsis Information Resource (TAIR): gene structure and function annotation
Swarbreck, D., Wilks, C., Lamesch, P., Berardini, T., Garcia-Hernandez, M., Foerster, H., Li, D., Meyer, T., Muller, R., Ploetz, L., et al. (2007) · Nucleic Acids Research · doi:10.1093/nar/gkm965 · Cited by 1,066 · see citing works
The BioGRID interaction database: 2013 update
Chatr‐aryamontri, A., Breitkreutz, B., Heinicke, S., Boucher, L., Winter, A. G., Stark, C., Nixon, J., Ramage, L., Kolas, N. K., O’Donnell, L., et al. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks1158 · Cited by 1,010 · see citing works
Identification of novel transcripts in annotated genomes using RNA-Seq
Roberts, A., Pimentel, H., Trapnell, C., & Pachter, L. S. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btr355 · Cited by 963 · see citing works
Genetic network inference: from co-expression clustering to reverse engineering
D’haeseleer, P., Liang, S., & Somogyi, R. (2000) · Bioinformatics · doi:10.1093/bioinformatics/16.8.707 · Cited by 943 · see citing works
Cytoscape 2.8: new features for data integration and network
Smoot, E., Ono, K., Ruscheinski, J., Wang, P., & Ideker, T. E. (2011) · Cited by 791 · see citing works
The BioGRID Interaction Database: 2008 update
Breitkreutz, B., Stark, C., Reguly, T., Boucher, L., Breitkreutz, A., Livstone, M. S., Oughtred, R., Lackner, D. H., Bähler, J., Wood, V., et al. (2007) · Nucleic Acids Research · doi:10.1093/nar/gkm1001 · Cited by 698 · see citing works
UPTON, G. J. G. (1992) · Journal of the Royal Statistical Society Series A (Statistics in Society) · doi:10.2307/2982890 · Cited by 636 · see citing works
The use of relative operating characteristic (ROC) curves in test performance evaluation.
Beck, & Shultz, E. K. (1986) · PubMed · Cited by 630 · see citing works
Caspi, R. R., Altman, T., Dreher, K. A., Fulcher, C., Subhraveti, P., Keseler, I. M., Kothari, A., Krummenacker, M., Latendresse, M., Mueller, L. A., et al. (2011) · Nucleic Acids Research · doi:10.1093/nar/gkr1014 · Cited by 603 · see citing works
Robust estimators for expression analysis
Hubbell, E. A., Liu, W., & Mei, R. (2002) · Bioinformatics · doi:10.1093/bioinformatics/18.12.1585 · Cited by 600 · see citing works
Persson, S., Wei, H., Milne, J., Page, G. P., & Somerville, C. R. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0503392102 · Cited by 595 · see citing works
Discovery of meaningful associations in genomic data using partial correlation coefficients
de la Fuente, A., Bing, N., Hoeschele, I., & Mendes, P. (2004) · Bioinformatics · doi:10.1093/bioinformatics/bth445 · Cited by 552 · see citing works
High‐Betweenness Proteins in the Yeast Protein Interaction Network
Joy, M. P., Brock, A., Ingber, D. E., & Huang, S. (2005) · BioMed Research International · doi:10.1155/jbb.2005.96 · Cited by 486 · see citing works
Co‐expression tools for plant biology: opportunities for hypothesis generation and caveats
Usadel, B., Obayashi, T., Mutwil, M., Giorgi, F. M., Bassel, G. W., Tanimoto, M., CHOW, A., Steinhauser, D., Persson, S., & Provart, N. J. (2009) · Plant Cell & Environment · doi:10.1111/j.1365-3040.2009.02040.x · Cited by 476 · see citing works
Self-citationIngle, K. A. (1994) · Cited by 429 · see citing works
Wolfe, C. J., Kohane, I. S., & Butte, A. J. (2005) · BMC Bioinformatics · doi:10.1186/1471-2105-6-227 · Cited by 422 · see citing works
Fu, F., & Xue, H. (2010) · PLANT PHYSIOLOGY · doi:10.1104/pp.110.159517 · Cited by 407 · see citing works
Opgen-Rhein, R., & Strimmer, K. (2007) · BMC Systems Biology · doi:10.1186/1752-0509-1-37 · Cited by 381 · see citing works
Reverter, A., & Chan, E. K. F. (2008) · Bioinformatics · doi:10.1093/bioinformatics/btn482 · Cited by 361 · see citing works
Signaling through MAP kinase networks in plants
Mishra, N. S., Tuteja, R., & Tuteja, N. K. (2006) · Archives of Biochemistry and Biophysics · doi:10.1016/j.abb.2006.05.001 · Cited by 352 · see citing works
Dirk Koschützki, A., & Schreiber, F. (2008) · Gene Regulation and Systems Biology · doi:10.4137/grsb.s702 · Cited by 344 · see citing works
AGRIS: the Arabidopsis Gene Regulatory Information Server, an update
Yılmaz, A., Mejía‐Guerra, M. K., Kurz, K., Liang, X., Welch, L. R., & Erich Grotewold, A. (2010) · Nucleic Acids Research · doi:10.1093/nar/gkq1120 · Cited by 330 · see citing works
Luo, F., Yang, Y., Zhong, J., Gao, H., Khan, L. R., Thompson, D. K., & Zhou, J. Z. (2007) · BMC Bioinformatics · doi:10.1186/1471-2105-8-299 · Cited by 299 · see citing works
Sterling, J. D., Atmodjo, M. A., Inwood, S. E. E., Kolli, V. S. K., Quigley, H. F., Hahn, M. G., & Mohnen, D. (2006) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0600120103 · Cited by 289 · see citing works
Daub, C. O., Steuer, R., Selbig, J., & Kloska, S. (2004) · BMC Bioinformatics · doi:10.1186/1471-2105-5-118 · Cited by 284 · see citing works
Evolution of biomolecular networks — lessons from metabolic and protein interactions
Yamada, T., & Peer Bork, B. (2009) · Nature Reviews Molecular Cell Biology · doi:10.1038/nrm2787 · Cited by 253 · see citing works
Bassel, G. W., Lan, H., Glaab, E., Gibbs, D. J., Gerjets, T., Krasnogor, N., Bonner, A. J., Holdsworth, M. J., & Provart, N. J. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1100958108 · Cited by 238 · see citing works
Vandepoele, K., Quimbaya, M., Casneuf, T., Veylder, L. D., & de Peer, Y. V. (2009) · PLANT PHYSIOLOGY · doi:10.1104/pp.109.136028 · Cited by 225 · see citing works
Mutwil, M., Usadel, B., Schuݶtte, M., Loraine, A. E., Ebenhöh, O., & Persson, S. (2009) · PLANT PHYSIOLOGY · doi:10.1104/pp.109.145318 · Cited by 202 · see citing works
Conservation and Coevolution in the Scale-Free Human Gene Coexpression Network
Jordan, I. K., Mariño‐Ramírez, L., Wolf, Y. I., & Koonin, E. V. (2004) · Molecular Biology and Evolution · doi:10.1093/molbev/msh222 · Cited by 193 · see citing works
Lim, W. K., Wang, K., Lefèbvre, C., & Califano, A. (2007) · Bioinformatics · doi:10.1093/bioinformatics/btm201 · Cited by 190 · see citing works
A local average connectivity-based method for identifying essential proteins from the network level
Li, M., Wang, J., Chen, X. '., Wang, H., & Pan, Y. F. (2011) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2011.04.002 · Cited by 184 · see citing works
Prediction of alternative isoforms from exon expression levels in RNA-Seq experiments
Richard, H., Schulz, M. H., Sultan, M., Nürnberger, A., Schrinner, S., Balzereit, D., Dagand, E., Rasche, A., Lehrach, H., Vingron, M., et al. (2010) · Nucleic Acids Research · doi:10.1093/nar/gkq041 · Cited by 160 · see citing works
Digital RNA allelotyping reveals tissue-specific and allele-specific gene expression in human
Zhang, K., Li, J. B., Gao, Y., Egli, D. M., Xie, B., Deng, J., Li, Z., Lee, J., Aach, J., LeProust, E. M., et al. (2009) · Nature Methods · doi:10.1038/nmeth.1357 · Cited by 156 · see citing works
Reverse engineering gene regulatory networks
Hartemink, A. J. (2005) · Nature Biotechnology · doi:10.1038/nbt0505-554 · Cited by 144 · see citing works
Haughn, G. W., & Western, T. L. (2012) · Frontiers in Plant Science · doi:10.3389/fpls.2012.00064 · Cited by 144 · see citing works
Usadel, B., Kuschinsky, A. M., Rosso, M. G., Eckermann, N., & Pauly, M. (2004) · PLANT PHYSIOLOGY · doi:10.1104/pp.103.034314 · Cited by 143 · see citing works
The Arabidopsis SeedGenes Project
Tzafrir, I. (2003) · Nucleic Acids Research · doi:10.1093/nar/gkg028 · Cited by 136 · see citing works
AtPIN: Arabidopsis thaliana Protein Interaction Network
Brandão, M. M., Dantas, L. L. D. B., & Silva-Filho, M. C. (2009) · BMC Bioinformatics · doi:10.1186/1471-2105-10-454 · Cited by 119 · see citing works
Conservation of gene co-regulation in prokaryotes and eukaryotes
Teichmann, S. A., & Babu, M. M. (2002) · Trends in biotechnology · doi:10.1016/s0167-7799(02)02032-2 · Cited by 115 · see citing works
Utilizing RNA-Seq data for <i>de novo</i> coexpression network inference
Iancu, O. D., Kawane, S., Bottomly, D., Searles, R., Hitzemann, R., & McWeeney, S. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts245 · Cited by 114 · see citing works
Lohse, M., Nunes‐Nesi, A., Krüger, P., Nagel, A., Hannemann, J., Giorgi, F. M., Childs, L. H., Osorio, S., Walther, D., Selbig, J., et al. (2010) · PLANT PHYSIOLOGY · doi:10.1104/pp.109.152553 · Cited by 109 · see citing works
Self-citationNetwork Analysis Tools: from biological networks to clusters and pathways
Brohée, S., Faust, K., Lima‐Mendez, G., Vanderstocken, G., & van Helden, J. (2008) · Nature Protocols · doi:10.1038/nprot.2008.100 · Cited by 107 · see citing works
UDP-Glucose Pyrophosphorylase is not Rate Limiting, but is Essential in Arabidopsis
Meng, M., Geisler, M. J., Johansson, H., Harholt, J., Scheller, H. V., Mellerowicz, E. J., & Kleczkowski, L. A. (2009) · Plant and Cell Physiology · doi:10.1093/pcp/pcp052 · Cited by 102 · see citing works
Comparative co‐expression analysis in plant biology
Movahedi, S., Bel, M. V., Heyndrickx, K. S., & Vandepoele, K. (2012) · Plant Cell & Environment · doi:10.1111/j.1365-3040.2012.02517.x · Cited by 94 · see citing works
Comparative mycobacterial genomics as a tool for drug target and antigen discovery
Cole, S. T. (2002) · European Respiratory Journal · doi:10.1183/09031936.02.00400202 · Cited by 86 · see citing works
The Choice between MapMan and Gene Ontology for Automated Gene Function Prediction in Plant Science
Klie, S., & Nikoloski, Z. (2012) · Frontiers in Genetics · doi:10.3389/fgene.2012.00115 · Cited by 84 · see citing works
COXPRESdb: a database of comparative gene coexpression networks of eleven species for mammals
Obayashi, T., Okamura, Y., Ito, S., Tadaka, S., Motoike, I. N., & Kinoshita, K. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks1014 · Cited by 81 · see citing works
Chalker, A. F., & Lunsford, R. D. (2002) · Pharmacology & Therapeutics · doi:10.1016/s0163-7258(02)00222-x · Cited by 67 · see citing works
Low duplicability and network fragility of cancer genes
Rambaldi, D., Giorgi, F. M., Capuani, F., Ciliberto, A., & Ciccarelli, F. D. (2008) · Trends in Genetics · doi:10.1016/j.tig.2008.06.003 · Cited by 66 · see citing works
Self-citationWang, S., Yin, Y., Ma, Q., Tang, X., Hao, D., & Xu, Y. (2012) · BMC Plant Biology · doi:10.1186/1471-2229-12-138 · Cited by 60 · see citing works
Integrating Global Gene Expression Analysis and Genetics
Farber, C. R., & Lusis, A. J. (2008) · Advances in genetics · doi:10.1016/s0065-2660(07)00420-8 · Cited by 52 · see citing works
Algorithm-driven Artifacts in median polish summarization of Microarray data
Giorgi, F. M., Bolger, A., Lohse, M., & Usadel, B. (2010) · BMC Bioinformatics · doi:10.1186/1471-2105-11-553 · Cited by 52 · see citing works
Self-citationBernert, G., Fountoulakis, M., & Lübec, G. (2002) · PROTEOMICS · doi:10.1002/1615-9861(200212)2:12 · Cited by 42 · see citing works
SLocX: predicting subcellular localization of Arabidopsis proteins leveraging gene expression data
Ryngajłło, M. (2011) · Frontiers in Plant Science · doi:10.3389/fpls.2011.00043 · Cited by 36 · see citing works
Discerning static and causal interactions in genome-wide reverse engineering problems
Zampieri, M., Soranzo, N., & Altafini, C. (2008) · Bioinformatics · doi:10.1093/bioinformatics/btn220 · Cited by 34 · see citing works
Vasilevski, A., Giorgi, F. M., Bertinetti, L., & Usadel, B. (2012) · Molecular BioSystems · doi:10.1039/c2mb25096a · Cited by 33 · see citing works
Self-citationRNA-seq transcriptome analysis of male and female zebra finch cell lines
Balakrishnan, C. N., Lin, Y., London, S. E., & Clayton, D. F. (2012) · Genomics · doi:10.1016/j.ygeno.2012.08.002 · Cited by 27 · see citing works
17th International Conference on Arabidopsis Research
Bender, J. (2006) · University of North Texas Digital Library (University of North Texas) · Cited by 25 · see citing works
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Caspi, R., Altman, T., Billington, R., Dreher, K., Foerster, H., Fulcher, C. A., Holland, T. A., Keseler, I. M., Kothari, A., Kubo, A., et al. (2014) · Nucleic Acids Research · doi:10.1093/nar/gkt1103 · Cited by 1,204 · see citing works
Gene co-expression analysis for functional classification and gene–disease predictions
van Dam, S., Võsa, U., van der Graaf, A., Franke, L., & de Magalhães, J. P. (2017) · Briefings in Bioinformatics · doi:10.1093/bib/bbw139 · Cited by 1,086 · see citing works
Wang, C., Gong, B., Bushel, P. R., Thierry-Mieg, J., Thierry-Mieg, D., Xu, J., Fang, H., Hong, H., Shen, J., Su, Z., et al. (2014) · Nature Biotechnology · doi:10.1038/nbt.3001 · Cited by 512 · see citing works
An Extensive Evaluation of Read Trimming Effects on Illumina NGS Data Analysis
Fabbro, C. D., Scalabrin, S., Morgante, M., & Giorgi, F. M. (2013) · PLoS ONE · doi:10.1371/journal.pone.0085024 · Cited by 478 · see citing works
Self-citationA Comparative Study of Techniques for Differential Expression Analysis on RNA-Seq Data
Zhang, Z. H., Jhaveri, D. J., Marshall, V. M., Bauer, D. C., Edson, J., Narayanan, R. K., Robinson, G. J., Lundberg, A. E., Bartlett, P. F., Wray, N. R., et al. (2014) · PLoS ONE · doi:10.1371/journal.pone.0103207 · Cited by 357 · see citing works
Advanced Applications of RNA Sequencing and Challenges
Han, Y., Gao, S., Muegge, K., Zhang, W., & Zhou, B. (2015) · Bioinformatics and Biology Insights · doi:10.4137/bbi.s28991 · Cited by 277 · see citing works
Towards revealing the functions of all genes in plants
Rhee, S. Y., & Mutwil, M. (2013) · Trends in Plant Science · doi:10.1016/j.tplants.2013.10.006 · Cited by 240 · see citing works
Gene regulatory network inference resources: A practical overview
Mercatelli, D., Scalambra, L., Triboli, L., Ray, F., & Giorgi, F. M. (2019) · Biochimica et Biophysica Acta (BBA) - Gene Regulatory Mechanisms · doi:10.1016/j.bbagrm.2019.194430 · Cited by 157 · see citing works
Self-citationVannozzi, A., Wong, D. C. J., ll, J. H. ⁄., Hmmam, I., Matus, J. T., Bogs, J., Ziegler, T., Dry, I. B., Barcaccia, G., & Lucchin, M. (2018) · Plant and Cell Physiology · doi:10.1093/pcp/pcy045 · Cited by 158 · see citing works
Okamura, Y., Aoki, Y., Obayashi, T., Tadaka, S., Ito, S., Narise, T., & Kinoshita, K. (2014) · Nucleic Acids Research · doi:10.1093/nar/gku1163 · Cited by 145 · see citing works
Ramaswami, G., Won, H., Gandal, M. J., Haney, J., Wang, J. C., Wong, C. C. Y., Sun, W., Prabhakar, S., Mill, J., & Geschwind, D. H. (2020) · Nature Communications · doi:10.1038/s41467-020-18526-1 · Cited by 113 · see citing works
Ariani, A., & Gepts, P. (2015) · Molecular Genetics and Genomics · doi:10.1007/s00438-015-1038-2 · Cited by 94 · see citing works
Clercq, I. D., de Velde, J. V., Luo, X., Liu, L., Storme, V., Bel, M. V., Pottie, R., Vaneechoutte, D., Breusegem, F. V., & Vandepoele, K. (2021) · Nature Plants · doi:10.1038/s41477-021-00894-1 · Cited by 90 · see citing works
Distance correlation application to gene co-expression network analysis
Hou, J., Ye, X., Feng, W., Zhang, Q. E., Han, Y., Liu, Y., Li, Y., & Wei, Y. (2022) · BMC Bioinformatics · doi:10.1186/s12859-022-04609-x · Cited by 88 · see citing works
corto : a lightweight R package for gene network inference and master regulator analysis
Mercatelli, D., López, G., & Giorgi, F. M. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa223 · Cited by 77 · see citing works
Self-citation(Differential) Co-Expression Analysis of Gene Expression: A Survey of Best Practices
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Xiang, R., Oddy, V. H., Archibald, A. L., Vercoe, P. E., & Dalrymple, B. P. (2016) · PeerJ · doi:10.7717/peerj.1762 · Cited by 71 · see citing works
Liesecke, F., Daudu, D., de Bernonville, R. D., Besseau, S., Clastre, M., Courdavault, V., Craene, J. D., Crèche, J., Giglioli‐Guivarc’h, N., Glévarec, G., et al. (2018) · Scientific Reports · doi:10.1038/s41598-018-29077-3 · Cited by 70 · see citing works
Cosplicing network analysis of mammalian brain RNA-Seq data utilizing WGCNA and Mantel correlations
Iancu, O. D., Colville, A., Oberbeck, D., Darakjian, P., McWeeney, S. K., & Hitzemann, R. (2015) · Frontiers in Genetics · doi:10.3389/fgene.2015.00174 · Cited by 57 · see citing works
Mizzotti, C., Rotasperti, L., Moretto, M., Tadini, L., Resentini, F., Galliani, B. M., Galbiati, M., Engelen, K., Pesaresi, P., & Masiero, S. (2018) · PLANT PHYSIOLOGY · doi:10.1104/pp.18.00727 · Cited by 61 · see citing works
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Wong, D. C. J., Sweetman, C., Drew, D. P., & Ford, C. M. (2013) · BMC Genomics · doi:10.1186/1471-2164-14-882 · Cited by 61 · see citing works
Fukushima, A., & Kusano, M. (2014) · Journal of Experimental Botany · doi:10.1093/jxb/eru322 · Cited by 61 · see citing works
Construction and Optimization of a Large Gene Coexpression Network in Maize Using RNA-Seq Data
Huang, J., Vendramin, S., Shi, L., & McGinnis, K. M. (2017) · PLANT PHYSIOLOGY · doi:10.1104/pp.17.00825 · Cited by 51 · see citing works
Mistry, D., Wise, R. P., & Dickerson, J. A. (2017) · PLoS ONE · doi:10.1371/journal.pone.0187091 · Cited by 51 · see citing works
Ca2+/Calmodulin-Dependent AtSR1/CAMTA3 Plays Critical Roles in Balancing Plant Growth and Immunity
Yuan, P., Du, L., & Poovaiah, B. W. (2018) · International Journal of Molecular Sciences · doi:10.3390/ijms19061764 · Cited by 51 · see citing works
RNA-Seq Reveals Novel Transcriptional Reorganization in Human Alcoholic Brain
Farris, S. P., & Dayne Mayfield, R. (2014) · International Review of Neurobiology · doi:10.1016/b978-0-12-801105-8.00011-4 · Cited by 49 · see citing works
A Collection of Conserved Noncoding Sequences to Study Gene Regulation in Flowering Plants
de Velde, J. V., Bel, M. V., Vaneechoutte, D., & Vandepoele, K. (2016) · PLANT PHYSIOLOGY · doi:10.1104/pp.16.00821 · Cited by 48 · see citing works
Liu, L., Zinkgraf, M. S., Petzold, H. E., Beers, E. P., Filkov, V., & Groover, A. T. (2014) · New Phytologist · doi:10.1111/nph.13151 · Cited by 44 · see citing works
Network-assisted crop systems genetics: network inference and integrative analysis
Lee, T. H., Kim, H., & Lee, I. (2015) · Current Opinion in Plant Biology · doi:10.1016/j.pbi.2015.02.001 · Cited by 40 · see citing works
petal: Co-expression network modelling in R
Petereit, J., Smith, S., Harris, F. C., & Schlauch, K. A. (2016) · BMC Systems Biology · doi:10.1186/s12918-016-0298-8 · Cited by 39 · see citing works
Emamjomeh, A., Robat, E. S., Zahiri, J., Solouki, M., & Khosravi, P. (2017) · Plant Biotechnology Reports · doi:10.1007/s11816-017-0433-z · Cited by 38 · see citing works
LSTrAP: efficiently combining RNA sequencing data into co-expression networks
Proost, S., Krawczyk, A., & Mutwil, M. (2017) · BMC Bioinformatics · doi:10.1186/s12859-017-1861-z · Cited by 38 · see citing works
Azaman, S. N. A., Wong, D. C. J., Tan, S. W., Yusoff, F. M., Nagao, N., & Yeap, S. K. (2020) · Scientific Reports · doi:10.1038/s41598-020-74410-4 · Cited by 38 · see citing works
Hansen, B. O., Meyer, E. H., Ferrari, C., Vaid, N., Movahedi, S., Vandepoele, K., Nikoloski, Z., & Mutwil, M. (2017) · New Phytologist · doi:10.1111/nph.14921 · Cited by 37 · see citing works
Poursani, E. M., Mercatelli, D., Raninga, P. V., Bell, J. L., Saletta, F., Kohane, F. V., Neumann, D., Ye, Z., Rouaen, J. R. C., Jue, T. R., et al. (2023) · Cell & Bioscience · doi:10.1186/s13578-023-01083-7 · Cited by 38 · see citing works
Self-citationGene co-expression network analysis identifies trait-related modules in Arabidopsis thaliana
Liu, W., Lin, L., Zhang, Z., Liu, S., Gao, K., Lv, Y., Tao, H., & He, H. (2019) · Planta · doi:10.1007/s00425-019-03102-9 · Cited by 32 · see citing works
Identification and function annotation of long intervening noncoding RNAs
Luo, H., Bu, D., Sun, L. D., Fang, S., Liu, Z., & Zhao, Y. C. (2016) · Briefings in Bioinformatics · doi:10.1093/bib/bbw046 · Cited by 33 · see citing works
Effects of TiO2 nanoparticles on nutrition metabolism in silkworm fat body
Tian, J., Hu, J., Li, F. C., Ni, M., Li, Y. Y., Wang, B. B., Xu, K., Shen, W., & Li, B. (2016) · Biology Open · doi:10.1242/bio.015610 · Cited by 29 · see citing works
Tosetti, R., Tardelli, F., Tadiello, A., Zaffalon, V., Giorgi, F. M., Guidi, L., Trainotti, L., Bonghi, C., & Tonutti, P. (2014) · Postharvest Biology and Technology · doi:10.1016/j.postharvbio.2013.12.001 · Cited by 28 · see citing works
Self-citationMeng, G., & Mei, H. (2019) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2019.00101 · Cited by 28 · see citing works
Network aggregation improves gene function prediction of grapevine gene co-expression networks
Wong, D. C. J. (2020) · Plant Molecular Biology · doi:10.1007/s11103-020-01001-2 · Cited by 28 · see citing works
A Hierarchical Poisson Log-Normal Model for Network Inference from RNA Sequencing Data
Gallopin, M., Rau, A., & Jaffrézic, F. (2013) · PLoS ONE · doi:10.1371/journal.pone.0077503 · Cited by 28 · see citing works
The nephrologist of tomorrow: towards a kidney-omic future
Hanna, M. H., Gassa, A. D., Mayer, G. J., Zaza, G., Brophy, P. D., Gesualdo, L., & Pesce, F. (2016) · Pediatric Nephrology · doi:10.1007/s00467-016-3357-x · Cited by 26 · see citing works
Inferring Protein Modulation from Gene Expression Data Using Conditional Mutual Information
Giorgi, F. M., López, G., Woo, J. H., Bisikirska, B. C., Califano, A., & Bansal, M. (2014) · PLoS ONE · doi:10.1371/journal.pone.0109569 · Cited by 25 · see citing works
Self-citationPan-Cancer and Single-Cell Modeling of Genomic Alterations Through Gene Expression
Mercatelli, D., Ray, F., & Giorgi, F. M. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2019.00671 · Cited by 23 · see citing works
Self-citationEscorcia-Rodríguez, J. M., Gaytan-Nuñez, E., Hernandez-Benitez, E. M., Zorro-Aranda, A., Tello‐Palencia, M. A., & Freyre-González, J. A. (2023) · Frontiers in Genetics · doi:10.3389/fgene.2023.1143382 · Cited by 22 · see citing works
Samarth, & Jameson, P. E. (2019) · Scientific Reports · doi:10.1038/s41598-019-45780-1 · Cited by 20 · see citing works
Yu, H., Jiao, B., Lu, L., Wang, P., Chen, S., Liang, C., & Liu, W. (2018) · PLoS ONE · doi:10.1371/journal.pone.0192613 · Cited by 20 · see citing works
Santaniello, A., Giorgi, F. M., Tommaso, D. D., Tommaso, G. D., Piaggesi, A., & Pierdomenico Perata, A. (2013) · Acta Horticulturae · doi:10.17660/actahortic.2013.1009.20 · Cited by 19 · see citing works
Self-citationZhong, L., Mu, H., Wen, B., Zhang, W., Wei, Q., Gao, G., Han, J., & Cao, S. (2018) · Scientific Reports · doi:10.1038/s41598-018-24863-5 · Cited by 19 · see citing works
Piya, S., Hawk, T. E., Patel, B., Baldwin, L., Rice, J. H., Stewart, C. N., & Hewezi, T. (2021) · Molecular Plant Pathology · doi:10.1111/mpp.13168 · Cited by 16 · see citing works
Wang, N., Cao, P., Xia, W., Fang, L., & Yu, H. (2017) · Tree Genetics & Genomes · doi:10.1007/s11295-017-1116-1 · Cited by 18 · see citing works
The application and future prospect of RNA-Seq technology in Chinese medicinal plants
Liu, H., Shi, J., Wu, M., & Xu, D. (2021) · Journal of Applied Research on Medicinal and Aromatic Plants · doi:10.1016/j.jarmap.2021.100318 · Cited by 16 · see citing works
Prediction of Metabolic Profiles from Transcriptomics Data in Human Cancer Cell Lines
Cavicchioli, M. V., Santorsola, M., Balboni, N., Mercatelli, D., & Giorgi, F. M. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms23073867 · Cited by 16 · see citing works
Self-citationAn application of the Shapley value to the analysis of co-expression networks
Cesari, G., Algaba, E., Moretti, S., & Nepomuceno, J. A. (2018) · Applied Network Science · doi:10.1007/s41109-018-0095-y · Cited by 16 · see citing works
Omics approaches to study gene regulatory networks for development in echinoderms
Lowe, E. K., Cuomo, C., & Arnone, M. I. (2017) · Briefings in Functional Genomics · doi:10.1093/bfgp/elx012 · Cited by 15 · see citing works
Choi, D., Choi, J., Kang, B., Lee, S., Cho, Y., Hwang, I., & Hwang, D. (2014) · Molecular Plant · doi:10.1093/mp/sst173 · Cited by 14 · see citing works
Mercatelli, D., Formaggio, F., Caprini, M., Holding, A. N., & Giorgi, F. M. (2021) · Bioscience Reports · doi:10.1042/bsr20212218 · Cited by 11 · see citing works
Self-citationNetwork types and their application in natural variation studies in plants
Jiménez‐Gómez, J. M. (2014) · Current Opinion in Plant Biology · doi:10.1016/j.pbi.2014.02.010 · Cited by 10 · see citing works
Ruiz, K. A., Pelletier, J. M., Wang, Y., Feng, M. J., Behr, J. S., Ðào, T. Q., Li, B., Kliebenstein, D. J., Harada, J. J., & Jenik, P. D. (2021) · Plant Direct · doi:10.1002/pld3.345 · Cited by 10 · see citing works
Izadi, F., Zarrini, H. N., & Jelodar, N. B. (2016) · Bioinformation · doi:10.6026/97320630012340 · Cited by 7 · see citing works
Mercatelli, D., Cabrelle, C., Veltri, P., Giorgi, F. M., & Guzzi, P. H. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac400 · Cited by 6 · see citing works
Self-citationARNetMiT R Package: association rules based gene co-expression networks of miRNA targets
Cingiz, M. Ö., Biricik, G., & Di̇ri̇, B. (2017) · Cellular and Molecular Biology · doi:10.14715/cmb/2017.63.3.4 · Cited by 7 · see citing works
Modeling and analysis of RNA‐seq data: a review from a statistical perspective
Li, W. V., & Li, J. J. (2018) · Quantitative Biology · doi:10.1007/s40484-018-0144-7 · Cited by 10 · see citing works
RapaNet: A Web Tool for the Co-Expression Analysis of Brassica rapa Genes
Kim, J., Jun, K. M., Kim, J. S., Chae, S., Pahk, Y., Lee, T., Sohn, S.-I., Lee, S. I., Lim, M.-H., Kim, C., et al. (2017) · Evolutionary Bioinformatics · doi:10.1177/1176934317715421 · Cited by 6 · see citing works
A Game Theoretic Neighbourhood-Based Relevance Index
Cesari, G., Algaba, E., Moretti, S., & Nepomuceno, J. A. (2017) · Studies in computational intelligence · doi:10.1007/978-3-319-72150-7_3 · Cited by 6 · see citing works
A Framework for Comparison and Assessment of Synthetic RNA-Seq Data
Shakola, F., Palejev, D., & Ivanov, I. V. (2022) · Genes · doi:10.3390/genes13122362 · Cited by 5 · see citing works
Consistency of biological networks inferred from microarray and sequencing data
Vinciotti, V., Wit, E. C., Jansen, R., de Geus, E. J. C., Penninx, B. W. J. H., Boomsma, D. I., & Hoen, P. A. C. ’. (2016) · BMC Bioinformatics · doi:10.1186/s12859-016-1136-0 · Cited by 6 · see citing works
RNA-Sequencing Muscle Plasticity to Resistance Exercise Training and Disuse in Youth and Older Age
Fernandez‐Gonzalo, R., Willis, C. R. G., Etheridge, T., & Deane, C. S. (2022) · Physiologia · doi:10.3390/physiologia2040014 · Cited by 4 · see citing works
Cingiz, M. Ö., Biricik, G., & Di̇ri̇, B. (2021) · Interdisciplinary Sciences Computational Life Sciences · doi:10.1007/s12539-021-00440-9 · Cited by 4 · see citing works
Challenges Analyzing RNA-Seq Gene Expression Data
López-Kleine, L., & González-Prieto, C. (2016) · Open Journal of Statistics · doi:10.4236/ojs.2016.64053 · Cited by 4 · see citing works
Modern Approaches for Transcriptome Analyses in Plants.
Riaño-Pachón, D., Espitia-Navarro, H. F., Riascos, J. J., & Margarido, G. (2021) · Advances in Experimental Medicine and Biology · doi:10.1007/978-3-030-80352-0_2 · Cited by 3 · see citing works
Liu, W., & Wang, Y. (2020) · Marine Genomics · doi:10.1016/j.margen.2020.100770 · Cited by 3 · see citing works
High-quality rice RNA-seq-based co-expression network for predicting gene function and regulationPreprint
Yu, H., Jiao, B., & Liang, C. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/138040 · Cited by 3 · see citing works
Two-tier combinatorial structure to integrate various gene co-expression networks of prostate cancer
Cingiz, M. Ö., & Di̇ri̇, B. (2019) · Gene · doi:10.1016/j.gene.2019.144102 · Cited by 2 · see citing works
Meng, G., & Mei, H. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/240002 · Cited by 2 · see citing works
Pan-Cancer and Single-Cell modelling of genomic alterations through gene expressionPreprint
Mercatelli, D., Ray, F., & Giorgi, F. M. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/492561 · Cited by 2 · see citing works
Self-citationCingiz, M. Ö., & Di̇ri̇, B. (2017) · Signal Processing and Communications Applications Conference (SIU) · doi:10.1109/siu.2017.7960684 · Cited by 1 · see citing works
Inference of Gene Coexpression Networks from Bulk-Based RNA-Sequencing Data
Lamere, A. T. (2021) · Methods in molecular biology · doi:10.1007/978-1-0716-1534-8_2 · Cited by 1 · see citing works
Analysis of cellulose synthase gene expression strategies in higher plants using RNA-sequencing data
Padvitski, T., Galinousky, D., Анисимова, Н. В., Bayer, G. Y., Pirko, Y. V., Yemets, A. I., Хотылева, Л. В., Blume, Y. B., & Kilchevsky, A. V. (2017) · Cytology and Genetics · doi:10.3103/s0095452717010121 · Cited by 1 · see citing works
A combinatorial approach to construct core and generic gene co-expression networks of colon cancer
Cingiz, M. Ö., Biricik, G., & Di̇ri̇, B. (2017) · International Conference on Innovations in Intelligent Systems and Applications (INISTA) · doi:10.1109/inista.2017.8001140 · Cited by 1 · see citing works
Gene network reverse engineering: The Next Generation
Giorgi, F. M. (2020) · Biochimica et Biophysica Acta (BBA) - Gene Regulatory Mechanisms · doi:10.1016/j.bbagrm.2020.194523 · Cited by 1 · see citing works
Self-citationGomes, L. H. F., Alves-Ferreira, M., & Carels, N. (2015) · Springer eBooks · doi:10.1007/978-81-322-2283-5_10 · Cited by 1 · see citing works
Inferring Gene Regulatory Networks from RNA-seq Data Using Kernel Classification
Al-Aamri, A., Kudlicki, A. S., Maalouf, M., Taha, K., & Homouz, D. (2023) · Biology · doi:10.3390/biology12040518 · Cited by 1 · see citing works
Big data analytics in bioinformatics
Bjørnholt), G. A. (. (2020) · doi:10.26481/dis.20200828ag · Cited by 1 · see citing works
Copper chelation inhibits TGF- β pathways and suppresses epithelial-mesenchymal transition in cancerPreprint
Poursani, E. M., Mercatelli, D., Raninga, P. V., Bell, J. L., Saletta, F., Kohane, F. V., Zheng, Y., Rouaen, J. R. C., Jue, T. R., Michniewicz, F., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.10.03.510707 · Cited by 1 · see citing works
Self-citationGenomics approaches to study music perception and performance
Kanduri, C. (2015) · Työväentutkimus Vuosikirja
Causal inference and prior integration in bioinformatics using information theory
Olsen, C., & Bontempi, G. (2013) · Dépôt institutionnel de l'Université libre de Bruxelles (Université Libre de Bruxelles)
Gene and protein networks in understanding cellular function
Lehtinen, S. (2015) · UCL Discovery (University College London)
Comparative Co-expression Analysis between Soybean and Arabidopsis
Zhu, Z., Peng, J., Fu, Y., Gan, Y., & Luo, D. (2018) · International Conference on Bioscience, Biochemistry and Bioinformatics (ICBBB) · doi:10.1145/3180382.3180404
Bonaccorso, O. (2017) · Minerva Access (University of Melbourne)
Azaman, S. N. A. (2016) · Universiti Putra Malaysia Institutional Repository (Universiti Putra Malaysia)
Molecular Network Analysis in Rabies Pathogenesis Using Cooperative Game Theory.
Bostani, R., & Mirzaie, M. (2020) · PubMed Central · doi:10.30498/ijb.2020.2551
A broad introduction to RNA-Seq
Richter, F. (2021) · WikiJournal of Science · doi:10.15347/wjs/2021.004 · Cited by 2 · see citing works
A Qualitative Expert Interview Study: Mobility Health in IndonesiaPreprint
Yosia, M., Basrowi, R. W., Sundjaya, T., & Masita, B. M. (2021) · medRxiv · doi:10.1101/2021.12.13.21267758
Chong, G. L., & Tu, S. (2022) · Methods in enzymology on CD-ROM/Methods in enzymology · doi:10.1016/bs.mie.2022.08.022
Sikkink, K. L. (2014) · Scholars' Bank (University of Oregon)
- Authors
- Giorgi, F. M., Del Fabbro, C., & Licausi, F.
- Affiliations
- University of Udine (IT)
- 1 Institute of Applied Genomics, Udine 33100, 2Scuola Superiore Sant’Anna, Pisa 56124 and 3Department of Agriculture and Environmental Sciences, University of Udine, Udine 33100, Italy
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2013-02-01
- Volume
- 29
- Issue
- 6
- Pages
- 717-724
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- OpenAlex
- W2111591809
- Cited by
- 103
- References
- 80
- Citations by year
- 2026: 1 · 2024: 1 · 2023: 3 · 2022: 7 · 2021: 10 · 2020: 8 · 2019: 7 · 2018: 11 · 2017: 16 · 2016: 14 · 2015: 7 · 2014: 11 · 2013: 7
- FWCI
- 5.82
- Type
- journal article
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- cc-by-nc-nd
- Locations
- Bioinformatics (publishedVersion)
- PubMed (publishedVersion)
- Institutional Research Information System (University of Udine) (submittedVersion, cc-by-nc-nd)
- CINECA IRIS Institutial research information system (University of Pisa) (submittedVersion)
- Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna) (submittedVersion)
- CINECA IRIS Institutional Research Information System (Sant'Anna School of Advanced Studies) (submittedVersion)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Keywords
- co-expression network, Arabidopsis thaliana, RNA-seq, DNA microarrays, platform comparison, network topology, betweenness centrality, essential genes, protein-protein interaction network, transcriptome, gene network inference
- MeSH terms
- Arabidopsis, Sequence Analysis, RNA, Gene Expression Regulation, Plant, Oligonucleotide Array Sequence Analysis, Gene Expression Profiling, Gene Regulatory Networks
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