#source("https://bioconductor.org/biocLite.R")
#biocLite("biomaRt")
setwd("/home/beissinger/Documents/MESH_Maize/Manuscript/Supplemental Data/")We first create a vector of background genes. We will use every gene with the required data (entrez id) as the background.
library(biomaRt)
## access to biomaRt
mart <- useMart(biomart = "plants_mart", host="plants.ensembl.org", dataset="zmays_eg_gene")
univ.geneID <- getBM(attributes=c("ensembl_gene_id", "entrezgene"), mart = mart) # 40481
## remove genes with no corresponding Entrez Gene ID
univ.geneID2 <- univ.geneID[!is.na(univ.geneID[,2]),] # 14142
## remove duplicated Entrez Gene ID
univ.geneID3 <- univ.geneID2[ !duplicated(univ.geneID2[,2]),] # 13630
##Get GO terms
univ.geneID4<-getBM(attributes=c("entrezgene","go_accession","go_name_1006","go_namespace_1003","go_linkage_type"),mart=mart,filters='entrezgene',values=univ.geneID3$entrezgene)
## Code evidence for genes without GO terms as NA
univ.geneID5<-univ.geneID4[-which(univ.geneID4[,2]==""),]
###Make dataframe for GOStats
goframeData <- data.frame(go_id = univ.geneID5$go_accession, Evidence = univ.geneID5$go_linkage_type, gene_id = univ.geneID5$entrezgene,stringsAsFactors=F)Secondly, we create a vector of significant genes by reading the input file: Domestication candidates from Hufford, 2012.
## read data
my.geneID <- read.csv("/home/beissinger/Documents/MESH_Maize/Hufford_Supp/Hufford_S2_Domestication.csv", header=T,stringsAsFactors = F)
my.geneID <- my.geneID[,1:5]
colnames(my.geneID)[1] <- "ensembl_gene_id"
## merge two files
my.geneID2 <- merge(my.geneID, univ.geneID3, by ="ensembl_gene_id") # 620
## remove duplicated Entrez Gene ID
my.geneID3 <- my.geneID2[ !duplicated(my.geneID2$entrezgene),] # 620We can perform a GO analysis using the GOstats package. Code to perform it is below.
#source("https://bioconductor.org/biocLite.R")
#biocLite("GOstats")
#biocLite("GOSemSim")
#biocLite("AnnotationForge")
#library("AnnotationForge")
#available.dbschemas() #maize is not available :-(
library("GOstats")
library("GOSemSim")
##Prepare GO to gene mappings
goFrame=GOFrame(goframeData,organism="Zea mays")
goAllFrame=GOAllFrame(goFrame)
library(GSEABase)
gsc <- GeneSetCollection(goAllFrame, setType = GOCollection())
params <- GSEAGOHyperGParams(name="Domestication Zea mays GO", geneSetCollection=gsc, geneIds = my.geneID3[,6],
universeGeneIds = univ.geneID5$entrezgene, ontology = "BP", pvalueCutoff = 0.05, conditional = TRUE,
testDirection = "over")GO enrichment analysis for BP.
BP <- hyperGTest(params)
summary(BP)[,c(1,2,7)] # 50## GOBPID Pvalue
## 1 GO:0044723 0.001305638
## 2 GO:0016024 0.001541595
## 3 GO:0018342 0.001541595
## 4 GO:0044262 0.005348111
## 5 GO:0046855 0.005654002
## 6 GO:0080022 0.005863320
## 7 GO:0030244 0.006899941
## 8 GO:0009744 0.008589903
## 9 GO:0000956 0.010291180
## 10 GO:0042753 0.010978486
## 11 GO:0048574 0.010978486
## 12 GO:0043647 0.014498072
## 13 GO:0040029 0.014512804
## 14 GO:0072529 0.017766260
## 15 GO:0010152 0.017766260
## 16 GO:0009901 0.017766260
## 17 GO:0048573 0.021703520
## 18 GO:0009743 0.022597705
## 19 GO:0090305 0.024636481
## 20 GO:0046031 0.025540953
## 21 GO:0009185 0.025540953
## 22 GO:0009135 0.025540953
## 23 GO:0006096 0.025540953
## 24 GO:0048443 0.029284054
## 25 GO:0009749 0.029284054
## 26 GO:0046351 0.031096498
## 27 GO:0006165 0.033907713
## 28 GO:0051345 0.035186085
## 29 GO:0046174 0.035186085
## 30 GO:0005978 0.035186085
## 31 GO:0048511 0.037010276
## 32 GO:0009900 0.043888815
## 33 GO:0010325 0.044125465
## 34 GO:0010199 0.044125465
## 35 GO:0010165 0.044125465
## 36 GO:0042407 0.044125465
## 37 GO:0051776 0.044125465
## 38 GO:0019401 0.044125465
## 39 GO:0032467 0.044125465
## 40 GO:0000719 0.044125465
## 41 GO:0019593 0.044125465
## 42 GO:0018023 0.044125465
## 43 GO:0048859 0.044125465
## 44 GO:0046294 0.044125465
## 45 GO:0006059 0.044125465
## 46 GO:2000694 0.044125465
## 47 GO:0018106 0.044125465
## 48 GO:0009094 0.045568123
## 49 GO:1901616 0.045568123
## 50 GO:0006112 0.045568123
## 51 GO:0051273 0.046806857
## 52 GO:0034655 0.046806857
## Term
## 1 single-organism carbohydrate metabolic process
## 2 CDP-diacylglycerol biosynthetic process
## 3 protein prenylation
## 4 cellular carbohydrate metabolic process
## 5 inositol phosphate dephosphorylation
## 6 primary root development
## 7 cellulose biosynthetic process
## 8 response to sucrose
## 9 nuclear-transcribed mRNA catabolic process
## 10 positive regulation of circadian rhythm
## 11 long-day photoperiodism, flowering
## 12 inositol phosphate metabolic process
## 13 regulation of gene expression, epigenetic
## 14 pyrimidine-containing compound catabolic process
## 15 pollen maturation
## 16 anther dehiscence
## 17 photoperiodism, flowering
## 18 response to carbohydrate
## 19 nucleic acid phosphodiester bond hydrolysis
## 20 ADP metabolic process
## 21 ribonucleoside diphosphate metabolic process
## 22 purine nucleoside diphosphate metabolic process
## 23 glycolytic process
## 24 stamen development
## 25 response to glucose
## 26 disaccharide biosynthetic process
## 27 nucleoside diphosphate phosphorylation
## 28 positive regulation of hydrolase activity
## 29 polyol catabolic process
## 30 glycogen biosynthetic process
## 31 rhythmic process
## 32 dehiscence
## 33 raffinose family oligosaccharide biosynthetic process
## 34 organ boundary specification between lateral organs and the meristem
## 35 response to X-ray
## 36 cristae formation
## 37 detection of redox state
## 38 alditol biosynthetic process
## 39 positive regulation of cytokinesis
## 40 photoreactive repair
## 41 mannitol biosynthetic process
## 42 peptidyl-lysine trimethylation
## 43 formation of anatomical boundary
## 44 formaldehyde catabolic process
## 45 hexitol metabolic process
## 46 regulation of phragmoplast microtubule organization
## 47 peptidyl-histidine phosphorylation
## 48 L-phenylalanine biosynthetic process
## 49 organic hydroxy compound catabolic process
## 50 energy reserve metabolic process
## 51 beta-glucan metabolic process
## 52 nucleobase-containing compound catabolic process# GO similarity
#install.packages("corrplot")
library(corrplot)
#goListBP <- summary(BP)[,c(1)]
#goSimMatBP <- mgoSim(goListBP, goListBP, ont="BP", measure="Wang", combine=NULL)
#corrplot(goSimMatBP, is.corr = FALSE, type="lower", tl.col = "black", tl.cex = 0.8)GO enrichment analysis for MF
ontology(params) <- "MF"
MF <- hyperGTest(params)
summary(MF)[,c(1,2,7)] # 26## GOMFID Pvalue
## 1 GO:0047274 0.0003999399
## 2 GO:0052866 0.0003999399
## 3 GO:0008318 0.0018623972
## 4 GO:0004805 0.0124527513
## 5 GO:0016779 0.0171336532
## 6 GO:0019200 0.0180418532
## 7 GO:0004605 0.0201113370
## 8 GO:0047334 0.0201113370
## 9 GO:0008878 0.0201113370
## 10 GO:0004664 0.0292358070
## 11 GO:0051087 0.0308978035
## 12 GO:0060590 0.0396718416
## 13 GO:0008199 0.0470775529
## 14 GO:0015633 0.0470775529
## 15 GO:0004322 0.0470775529
## 16 GO:0018738 0.0470775529
## 17 GO:0004474 0.0470775529
## 18 GO:0004164 0.0470775529
## 19 GO:0015434 0.0470775529
## 20 GO:0080039 0.0470775529
## 21 GO:0003904 0.0470775529
## 22 GO:0008531 0.0470775529
## 23 GO:0018392 0.0470775529
## 24 GO:0042389 0.0470775529
## 25 GO:0031386 0.0470775529
## 26 GO:0003837 0.0470775529
## 27 GO:0030234 0.0486879681
## Term
## 1 galactinol-sucrose galactosyltransferase activity
## 2 phosphatidylinositol phosphate phosphatase activity
## 3 protein prenyltransferase activity
## 4 trehalose-phosphatase activity
## 5 nucleotidyltransferase activity
## 6 carbohydrate kinase activity
## 7 phosphatidate cytidylyltransferase activity
## 8 diphosphate-fructose-6-phosphate 1-phosphotransferase activity
## 9 glucose-1-phosphate adenylyltransferase activity
## 10 prephenate dehydratase activity
## 11 chaperone binding
## 12 ATPase regulator activity
## 13 ferric iron binding
## 14 zinc transporting ATPase activity
## 15 ferroxidase activity
## 16 S-formylglutathione hydrolase activity
## 17 malate synthase activity
## 18 diphthine synthase activity
## 19 cadmium-transporting ATPase activity
## 20 xyloglucan endotransglucosylase activity
## 21 deoxyribodipyrimidine photo-lyase activity
## 22 riboflavin kinase activity
## 23 glycoprotein 3-alpha-L-fucosyltransferase activity
## 24 omega-3 fatty acid desaturase activity
## 25 protein tag
## 26 beta-ureidopropionase activity
## 27 enzyme regulator activity# GO similarity
#goListMF <- summary(MF)[,c(1)]
#goSimMatMF <- mgoSim(goListMF, goListMF, ont="MF", measure="Wang", combine=NULL)
#corrplot(goSimMatMF, is.corr = FALSE, type="lower", tl.col = "black", tl.cex = 0.8,)GO enrichment analysis for CC
ontology(params) <- "CC"
CC <- hyperGTest(params)
summary(CC)[,c(1,2,7)] # 12## GOCCID Pvalue
## 1 GO:0005762 0.005040924
## 2 GO:0005759 0.005734820
## 3 GO:0005956 0.015894409
## 4 GO:0043233 0.024734227
## 5 GO:0061695 0.030212242
## 6 GO:0030915 0.031585643
## 7 GO:0000812 0.041646332
## 8 GO:0032578 0.041646332
## 9 GO:0005828 0.041646332
## 10 GO:0016363 0.041646332
## 11 GO:0016442 0.041646332
## 12 GO:0043234 0.046019274
## Term
## 1 mitochondrial large ribosomal subunit
## 2 mitochondrial matrix
## 3 protein kinase CK2 complex
## 4 organelle lumen
## 5 transferase complex, transferring phosphorus-containing groups
## 6 Smc5-Smc6 complex
## 7 Swr1 complex
## 8 aleurone grain membrane
## 9 kinetochore microtubule
## 10 nuclear matrix
## 11 RISC complex
## 12 protein complex# GO similarity
#goListCC <- summary(CC)[,c(1)]
#goSimMatCC <- mgoSim(goListCC, goListCC, ont="CC", measure="Wang", combine=NULL,organism="maize")
#corrplot(goSimMatCC, is.corr = FALSE, type="lower", tl.col = "black", tl.cex = 0.8)Then, we perform a MeSH ORA for the category Chemicals and Drugs by setting ‘category=“D”’. Different categories are set as different letters, as will become clear in the following sections.
source("https://bioconductor.org/biocLite.R")
#biocLite("meshr")
#biocLite("MeSH.db")
#biocLite("MeSH.Zma.eg.db")
#biocLite("MeSHSim")
library(meshr)
library(MeSH.db)
library("MeSH.Zma.eg.db")
meshParams <- new("MeSHHyperGParams", geneIds = my.geneID3[,6], universeGeneIds = univ.geneID3[,2],
annotation = "MeSH.Zma.eg.db", category = "D", database = "gene2pubmed",
pvalueCutoff = 0.05, pAdjust = "none")
meshR <- meshHyperGTest(meshParams)
summary(meshR)[!duplicated(summary(meshR)[,7]),c(1,2,7)] ## MESHID Pvalue MESHTERM
## 6 D004251 0.0004705092 DNA Transposable Elements
## 90060 D018076 0.0020607780 DNA, Complementary
## 1 D000226 0.0020659628 Mitochondrial ADP, ATP Translocases
## 154011 D035683 0.0069713393 MicroRNAs
## 119745 D018626 0.0070456659 Retroelements
## 142568 D018749 0.0071540082 RNA, Plant
## 131038 D018744 0.0089103658 DNA, Plant
## 165658 D051599 0.0116590073 Glucose-1-Phosphate Adenylyltransferase
## 44697 D011061 0.0177853289 Poly A
## 27130 D010743 0.0188487517 Phospholipids
## 60487 D014157 0.0235344655 Transcription Factors
## 76518 D017931 0.0384314684 DNA Primers
## 27140 D010940 0.0429618625 Plant Proteins
## 27128 D006836 0.0454878944 Hydro-Lyases
## 27129 D008081 0.0454878944 Liposomes
## 60484 D012967 0.0454878944 Sodium Dodecyl Sulfate
## 154007 D026342 0.0454878944 AGAMOUS Protein, Arabidopsis
## 165657 D051103 0.0454878944 1-Acylglycerol-3-Phosphate O-Acyltransferase# Store list of terms
headingListD <- summary(meshR)[!duplicated(summary(meshR)[,7]),c(7)]Switching to a different category is easily done by the ‘category<-’ function. Here, we use Diseases (category = “C”).
category(meshParams) <- "C"
meshR <- meshHyperGTest(meshParams)
summary(meshR)[!duplicated(summary(meshR)[,7]),c(1,2,7)] ## MESHID Pvalue MESHTERM
## 1 D011123 0.04548789 Polyploidy# Store list of terms
headingListC <- summary(meshR)[!duplicated(summary(meshR)[,7]),c(7)]MeSH ORA for Anatomy (category = “A”).
category(meshParams) <- "A"
meshR <- meshHyperGTest(meshParams)
summary(meshR)[!duplicated(summary(meshR)[,7]),c(1,2,7)] ## MESHID Pvalue MESHTERM
## 11559 D032461 0.0004616885 Chromosomes, Plant
## 38705 D035264 0.0052472546 Flowers
## 1 D002503 0.0068977091 Centromere
## 11286 D012639 0.0257363443 Seeds
## 11558 D022162 0.0454878944 Cytoplasmic Vesicles# Store list of terms
headingListA <- summary(meshR)[!duplicated(summary(meshR)[,7]),c(7)]MeSH ORA for Phenomena and Processes (category = “G”).
category(meshParams) <- "G"
meshR <- meshHyperGTest(meshParams)
summary(meshR)[!duplicated(summary(meshR)[,7]),c(1,2,7)] ## MESHID Pvalue MESHTERM
## 321696 D032461 0.0004616885 Chromosomes, Plant
## 54987 D004251 0.0004705092 DNA Transposable Elements
## 275248 D020224 0.0013770421 Expressed Sequence Tags
## 1 D001483 0.0030370439 Base Sequence
## 213740 D017434 0.0045438091 Protein Structure, Tertiary
## 43702 D002503 0.0068977091 Centromere
## 93457 D011003 0.0068977091 Ploidies
## 348842 D056915 0.0068977091 DNA Copy Number Variations
## 82110 D007178 0.0069713393 Inbreeding
## 240881 D018626 0.0070456659 Retroelements
## 213813 D018556 0.0071206945 Crops, Agricultural
## 263938 D019175 0.0071206945 DNA Methylation
## 118601 D011995 0.0074279396 Recombination, Genetic
## 146855 D014644 0.0075859199 Genetic Variation
## 158398 D017343 0.0076155471 Genes, Plant
## 252174 D018745 0.0109163418 Genome, Plant
## 305159 D020641 0.0112497046 Polymorphism, Single Nucleotide
## 321514 D026801 0.0114802052 Synteny
## 225109 D018598 0.0168319011 Minisatellite Repeats
## 321493 D023061 0.0188487517 Gene Order
## 129907 D012639 0.0257363443 Seeds
## 130179 D012689 0.0306628356 Sequence Homology, Nucleic Acid
## 104739 D011401 0.0361928775 Promoter Regions, Genetic
## 200259 D017398 0.0377590492 Alternative Splicing
## 82109 D005720 0.0454878944 Gamma Rays
## 104737 D011123 0.0454878944 Polyploidy
## 213812 D018257 0.0454878944 Helix-Loop-Helix Motifs
## 360122 D057895 0.0454878944 Haploinsufficiency
## 93404 D010766 0.0454951398 Phosphorylation
## 275233 D019521 0.0482030685 Body Patterning# Store list of terms
headingListG <- summary(meshR)[!duplicated(summary(meshR)[,7]),c(7)]DomesticationMeshList <- list(headingListA,headingListC,headingListD,headingListG)
save(DomesticationMeshList,file="DomesticationMeshList.Robj")sessionInfo()## R version 3.3.0 (2016-05-03)
## Platform: x86_64-redhat-linux-gnu (64-bit)
## Running under: Fedora 23 (Workstation Edition)
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] grid stats4 parallel stats graphics grDevices utils
## [8] datasets methods base
##
## other attached packages:
## [1] MeSH.Zma.eg.db_1.6.0 meshr_1.8.0
## [3] MeSH.Syn.eg.db_1.6.0 MeSH.Bsu.168.eg.db_1.6.0
## [5] MeSH.Aca.eg.db_1.6.0 MeSH.Hsa.eg.db_1.6.0
## [7] MeSH.PCR.db_1.6.0 MeSH.AOR.db_1.6.0
## [9] MeSH.db_1.6.0 MeSHDbi_1.8.0
## [11] org.Hs.eg.db_3.3.0 cummeRbund_2.14.0
## [13] Gviz_1.16.1 rtracklayer_1.32.1
## [15] GenomicRanges_1.24.2 GenomeInfoDb_1.8.1
## [17] fastcluster_1.1.20 reshape2_1.4.1
## [19] ggplot2_2.1.0 fdrtool_1.2.15
## [21] BiocInstaller_1.22.3 corrplot_0.77
## [23] GSEABase_1.34.0 annotate_1.50.0
## [25] XML_3.98-1.4 GO.db_3.3.0
## [27] RSQLite_1.0.0 DBI_0.4-1
## [29] GOSemSim_1.30.2 GOstats_2.38.1
## [31] graph_1.50.0 Category_2.38.0
## [33] Matrix_1.2-6 AnnotationDbi_1.34.4
## [35] IRanges_2.6.1 S4Vectors_0.10.2
## [37] Biobase_2.32.0 BiocGenerics_0.18.0
## [39] biomaRt_2.28.0
##
## loaded via a namespace (and not attached):
## [1] bitops_1.0-6 matrixStats_0.50.2
## [3] RColorBrewer_1.1-2 httr_1.2.1
## [5] tools_3.3.0 R6_2.1.2
## [7] rpart_4.1-10 Hmisc_3.17-4
## [9] colorspace_1.2-6 nnet_7.3-12
## [11] gridExtra_2.2.1 chron_2.3-47
## [13] formatR_1.4 scales_0.4.0
## [15] genefilter_1.54.2 RBGL_1.48.1
## [17] stringr_1.0.0 digest_0.6.9
## [19] Rsamtools_1.24.0 foreign_0.8-66
## [21] rmarkdown_1.0 AnnotationForge_1.14.2
## [23] XVector_0.12.0 dichromat_2.0-0
## [25] htmltools_0.3.5 ensembldb_1.4.7
## [27] BSgenome_1.40.1 shiny_0.13.2
## [29] BiocParallel_1.6.2 acepack_1.3-3.3
## [31] VariantAnnotation_1.18.3 RCurl_1.95-4.8
## [33] magrittr_1.5 Formula_1.2-1
## [35] Rcpp_0.12.5 munsell_0.4.3
## [37] stringi_1.1.1 yaml_2.1.13
## [39] SummarizedExperiment_1.2.3 zlibbioc_1.18.0
## [41] plyr_1.8.4 AnnotationHub_2.4.2
## [43] lattice_0.20-33 Biostrings_2.40.2
## [45] splines_3.3.0 GenomicFeatures_1.24.4
## [47] knitr_1.13 evaluate_0.9
## [49] biovizBase_1.20.0 latticeExtra_0.6-28
## [51] data.table_1.9.6 httpuv_1.3.3
## [53] gtable_0.2.0 mime_0.5
## [55] xtable_1.8-2 survival_2.39-5
## [57] GenomicAlignments_1.8.4 cluster_2.0.4
## [59] interactiveDisplayBase_1.10.3