Integration of multi-omics data for prediction of phenotypic traits using random forest

详细信息    查看全文

• 作者：Animesh Acharjee ; Bjorn Kloosterman ; Richard G. F. Visser…
• 关键词：Data integration ; Genetical genomics ; Networks ; Random forest
• 刊名：BMC Bioinformatics
• 出版年：2016
• 出版时间：December 2016
• 年：2016
• 卷：17
• 期：5-supp
• 全文大小：1,092 KB
• 参考文献：1.Fukushima A, Kusano M, Redestig H, Arita M, Saito K. Integrated omics approaches in plant systems biology. Curr Opin Chem Biol. 2009;13(5–6):532–8.CrossRef PubMed
  2.Kim TY, Kim HU, Lee SY. Data integration and analysis of biological networks. Curr Opin Biotech. 2010;21(1):78–84.CrossRef PubMed
  3.Fukushima A, Kanaya S, Nishida K. Integrated network analysis and effective tools in plant systems biology. Front Plant Sci. 2014;5:598.CrossRef PubMed PubMedCentral
  4.Brazma A, Vilo J. Gene expression data analysis. FEBS J. 2000;480(1):17–24.CrossRef
  5.Gaasterland T, Bekiranov S. Making the most of microarray data. Nat Genet. 2000;24(3):204–6.CrossRef PubMed
  6.Fiehn O. Metabolomics — the link between genotypes and phenotypes. Plant Mol Bio. 2002;48(1–2):155–71.CrossRef
  7.Dunn WB, Bailey NJC, Johnson HE. Measuring the metabolome: current analytical technologies. Analyst. 2005;130(5):606–25.CrossRef PubMed
  8.Aebersold R, Mann M. Mass spectrometry-based proteomics. Nature. 2003;422(6928):198–207.CrossRef PubMed
  9.Zhu H, Bilgin M, Snyder M. Proteomics. Ann Rev Biochem. 2003;72:783–812.CrossRef PubMed
  10.Bachem CW, van der Hoeven RS, de Bruijn SM, Vreugdenhil D, Zabeau M, Visser RG. Visualization of differential gene expression using a novel method of RNA fingerprinting based on AFLP: analysis of gene expression during potato tuber development. Plant J. 1996;9(5):745–53.CrossRef PubMed
  11.Steinfath M, Strehmel N, Peters R, Schauer N, Groth D, Hummel J, Steup M, Selbig J, Kopka J, Geigenberger P, Van Dongen JT. Discovering plant metabolic biomarkers for phenotype prediction using an untargeted approach. Plant Biotechnol J. 2010;8(8):900–11.CrossRef PubMed
  12.Jansen R, Nap J. Genetical genomics: the added value from segregation. Trends Genet. 2001;17(7):388–91.CrossRef PubMed
  13.Keurentjes JJB, Fu JY, de Vos CHR, Lommen A, Hall RD, Bino RJ, van der Plas LHW, Jansen RC, Vreugdenhil D, Koornneef M. The genetics of plant metabolism. Nat Genet. 2006;38(7):842–9.CrossRef PubMed
  14.Acharjee A, Kloosterman B, de Vos RCH, Werij JS, Bachem CWB, Visser RGF, Maliepaard C. Data integration and network reconstruction with ~ omics data using Random Forest regression in potato. Anal Chim Acta. 2011;705(1–2):56–63.
  15.Kloosterman B, Koeyer DD, Griffiths R, Flinn B, Steuernagel B, Scholz U, Sonnewald S, Sonnewald U, Bryan GJ, Prat S, Banfalvi Z, Hammond JP, Geigenberger P, Nielsen KL, Visser RGF, Bachem CWB. Genes driving potato tuber initiation and growth: identification based on transcriptional changes using the POCI array. Funct Integr Genomics. 2008;8(4):329–40.
  16.Carreno-Quintero N, Acharjee A, Maliepaard C, Bachem CW, Mumm R, Bouwmeester H, Visser RG, Keurentjes JJ. Untargeted metabolic quantitative trait loci analyses reveal a relationship between primary metabolism and potato tuber quality. Plant Physiol. 2012;158(3):1306–18.CrossRef PubMed PubMedCentral
  17.Wolters AMA, Uitdewilligen JGAML, Kloosterman BA, Hutten RCB, Visser RGF, Van Eck HJ. Identification of alleles of carotenoid pathway genes important for zeaxanthin accumulation in potato tubers. Plant Mol Biol. 2010;73(6):659–71.CrossRef PubMed PubMedCentral
  18.Werij JS, Kloosterman B, Celis-Gamboa C, de Vos CH, America T, Visser RG, Bachem CW. Unravelling enzymatic discoloration in potato through a combined approach of candidate genes, QTL, and expression analysis. Theor Appl Genet. 2007;115(2):245–52.CrossRef PubMed PubMedCentral
  19.Kloosterman B, Anithakumari AM, Chibon PY, Oortwijn M, van der Linden GC,Visser RG, Bachem CW. Organ specificity and transcriptional control of metabolic routes revealed by expression QTL profiling of source sink tissues in a segregating potato population. BMC Plant Biol. 2012;7:12-17.
  20.Celis-Gamboa C, Struik P, Jacobsen E, Visser RGF. Temporal dynamics of tuber formation and related processes in a crossing population of potato (Solanum tuberosum). Ann Appl Biol. 2003;143(2):175–87.CrossRef
  21.Celis-Gamboa BC. The life cycle of the potato (Solanum tuberosum L.): from crop physiology to genetics, Ph.D. Thesis, Wageningen University: The Netherlands, ISBN 2002, 90-5808-688-7.
  22.Kloosterman B, Oortwijn M, uitdeWilligen J, America T, de Vos R, Visser RG, Bachem CW. From QTL to candidate gene: genetical genomics of simple and complex traits in potato using a pooling strategy. BMC Genomics. 2010;11:158.CrossRef PubMed PubMedCentral
  23.Kohyama K, Sasaki T. Differential scanning calorimetry and a model calculation of starches annealed at 20 and 50 °C. Carbohydr Polym. 2006;63(1):82–8.CrossRef
  24.Bachem CW, Horvath B, Trindade L, Claassens M, Davelaar E, Jordi W, Visser RG. A potato tuber-expressed mRNA with homology to steroid dehydrogenases affects gibberellin levels and plant development. Plant J. 2001;25(6):595-604.
  25.Tikunov YM, Laptenok S, Hall RD, Bovy A, de Vos RC. MSClust: a tool for unsupervised mass spectra extraction of chromatography-mass spectrometry ion-wise aligned data. Metabolomics. 2012;8(4):714–8.CrossRef PubMed
  26.Breiman L. Random forests. Mach Learn. 2001;45(1):5–32.CrossRef
  27.Hastie T, Tibshirani R, Friedman J. The elements of statistical learning: data mining, inference, and prediction. New York: Springer; 2001.CrossRef
  28.Xu X, Pan S, Cheng S, Zhang B, Mu D, Ni P, Zhang G, Yang S, Li R, Wang J, Orjeda G, Guzman F, Torres M, Lozano R, Ponce O, Martinez D, De la Cruz G, Chakrabarti SK, Patil VU, Skryabin KG, Kuznetsov BB, Ravin NV, Kolganova TV, Beletsky AV, Mardanov AV, Di Genova A, Bolser DM, Martin DM, Li G, Yang Y, Kuang H, Hu Q, Xiong X, Bishop GJ, Sagredo B, Mejia N, Zagorski W, Gromadka R, Gawor J, Szczesny P, Huang S, Zhang Z, Liang C, He J, Li Y, He Y, Xu J, Zhang Y, Xie B, Du Y, Qu D, Bonierbale M, Ghislain M, Herrera Mdel R, Giuliano G, Pietrella M, Perrotta G, Facella P, O'Brien K, Feingold SE, Barreiro LE, Massa GA, Diambra L, Whitty BR, Vaillancourt B, Lin H, Massa AN, Geoffroy M, Lundback S, DellaPenna D, Buell CR, Sharma SK, Marshall DF, Waugh R, Bryan GJ, Destefanis M, Nagy I, Milbourne D, Thomson SJ, Fiers M, Jacobs JM, Nielsen KL, Sonderkar M, Iovene M, Torres GA, Jiang J, Veilleux RE, Bachem CW, de Boer J, Borm T, Kloosterman B, van Eck H, Datema E,Hekkert BL, Goverse A, van Ham RC, Visser RG. Genome sequence and analysis of the tuber crop potato. Nature. 2011;475(7355):189–95.
  29.Fu J, Swertz MA, Keurentjes JJ, Jansen RC. MetaNetwork: a computational protocol for the genetic study of metabolic networks. Nat Protoc. 2007;2(3):685–94.CrossRef PubMed
  30.Yuan JS, Galbraith DW, Dai SY, Griffin P, Neal Stewart CJ. Plant systems biology comes of age. Trends Plant Sci. 2008;13:165–71.CrossRef PubMed
  31.Zuo Y, Yu G, Tadesse MG, Ressom HW. Biological network inference using low order partial correlation. Methods. 2014;69(3):266–73.CrossRef PubMed PubMedCentral
  32.Lasserre J, Chung HR, Vingron M. Finding associations among histone modifications using sparse partial correlation networks. PLoS Comput Biol. 2013;9(9):e1003168.CrossRef PubMed PubMedCentral
  33.Krämer N, Schäfer J, Boulesteix AL. Regularized estimation of large-scale gene association networks using graphical Gaussian models. BMC Bioinformatics. 2009;10:384.CrossRef PubMed PubMedCentral
  34.Lamers PP, Janssen M, De Vos RC, Bino RJ, Wijffels RH. Exploring and exploiting carotenoid accumulation in Dunaliella salina for cell-factory applications. Trends Biotechnol. 2008;26:631–8.CrossRef PubMed
  35.Brown CR, Kim TS, Ganga Z, Haynes K, De Jong D, Jahn M, Paran I, De Jong W. Segregation of total carotenoid in high level potato germplasm and its relationship to beta-carotene hydroxylase polymorphism. Am J Potato Res. 2006;83(5):365–72.CrossRef
  36.Ruiz D, Egea J. Phenotypic diversity and relationships of fruit quality traits in apricot (Prunus armeniaca L.) germplasm. Euphytica. 2008;163(1):143–58.CrossRef
  37.Van Eck HJ, Jacobs JM, Stam P, Ton J, Jacobsen E. Multiple alleles for tuber shape in diploid potato detected by qualitative and quantitative genetic analysis using RFLPs. Genetics. 1994;137(1):303–9.PubMed
  38.Jacobs JM, Van Eck HJ, Arens P, Verkerk-Bakker B, Te Lintel Hekkert B, Bastiaanssen HJ, El-Kharbotly A, Pereira A, Jacobsen E, Stiekema WJ. A genetic map of potato (Solanum tuberosum) integrating molecular markers, including transposons, and classical markers. Theor Appl Genet. 1995;91(2):289–300.CrossRef PubMed
  39.de la Fuente A, Bing N, Hoeschele I, Mendes P. Discovery of meaningful associations in genomic data using partial correlation coefficients. Bioinformatics. 2004;20(18):3565–74.CrossRef PubMed
  
• 作者单位：Animesh Acharjee (1) (3)
  Bjorn Kloosterman (1) (2)
  Richard G. F. Visser (1)
  Chris Maliepaard (1)
  
  1. Wageningen UR Plant Breeding, Wageningen University & Research Centre, PO Box 6700 AJ, Wageningen, The Netherlands
  3. MRC Human Nutrition Research, 120 Fulbourn Road, Cambridge, CB1 9NL, UK
  2. Keygene NV, PO Box 216, 6700 AE, Wageningen, The Netherlands
  
• 刊物主题：Bioinformatics; Microarrays; Computational Biology/Bioinformatics; Computer Appl. in Life Sciences; Combinatorial Libraries; Algorithms;
• 出版者：BioMed Central
• ISSN：1471-2105
• 卷排序：17

文摘

Background In order to find genetic and metabolic pathways related to phenotypic traits of interest, we analyzed gene expression data, metabolite data obtained with GC-MS and LC-MS, proteomics data and a selected set of tuber quality phenotypic data from a diploid segregating mapping population of potato. In this study we present an approach to integrate these ~ omics data sets for the purpose of predicting phenotypic traits. This gives us networks of relatively small sets of interrelated ~ omics variables that can predict, with higher accuracy, a quality trait of interest.