MSEADbi 1.0.0
MSEADbi makes it easy to generate your own annotation package for MSEA. The package design is the same as MeSHDbi or LRBaseDbi, and the usage is also the same.
This document provides the way to use MSEADbi and MSEA.XXX.pb.db packages. MSEA.XXX.pb.db packages provide the metabolite set by pathway for XXX species in the PathBank database. The packages are generated by the MSEADbi package. MSEADbi has a role; the construction of MSEA.XXX.pb.db packages. The makeMSEAPackage function of MSEADbi generates the user’s original MSEA.XXX.pb.db packages.
Here we use makeMSEAPackage function to create a MSEA.XXX.pb.db package. Only user have to specify are 1. a csv file that is filtered to include only the lines of a species in PathBank Data File (Metabolite names linked to PathBank primary pathways CSV) and 2. a meta table describing the PathBank data. Here we use the demo data of Arabidopsis thaliana of PathBank primary pathways CSV.
library('MSEADbi')
tmp <- tempdir()
ath <- system.file("extdata","MSEA.Ath.pb.db_DATA.csv",package="MSEADbi")
meta <- system.file("extdata","MSEA.Ath.pb.db_METADATA.csv",package="MSEADbi")
athDf <- read.csv(ath, fileEncoding="utf8")
metaDf <- read.csv(meta)
# We need to avoid DOT from the column names (to query with the names)
names(athDf) <- gsub("\\.", "", names(athDf))
names(metaDf) <- gsub("\\.", "", names(metaDf))
makeMSEAPackage(pkgname = "MSEA.Ath.pb.db", data=athDf, metadata=metaDf,
organism = "Arabidopsis thaliana", version = "0.99.0",
maintainer = "Kozo Nishida <kozo.nishida@gmail.com>", author = "Kozo Nishida",
destDir = tmp, license = "Artistic-2.0")## Creating package in /tmp/RtmpbnKAsp/MSEA.Ath.pb.dbmseaPackageDir = paste(tmp, "MSEA.Ath.pb.db", sep="/")
install.packages(mseaPackageDir, repos=NULL, type="source")## Installing package into '/tmp/Rtmpw9PVnd/Rinst173e4c1c35d4'
## (as 'lib' is unspecified)All MSEA.XXX.pb.db package has same name object and it is instantiated by MSEADbclass. Many data access function for this object are implmented. For example, columns returns the rows which we can retrieve in MSEA.XXX.pb.db packages. keytypes returns the rows which can be used as the optional parameter in keys and select functions against MSEA.XXX.pb.db packages. select function returns the rows in particular columns, which are having user-specified keys. This function returns the result as a dataframe.
library(AnnotationDbi)## Loading required package: stats4## Loading required package: BiocGenerics## Loading required package: parallel##
## Attaching package: 'BiocGenerics'## The following objects are masked from 'package:parallel':
##
## clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
## clusterExport, clusterMap, parApply, parCapply, parLapply,
## parLapplyLB, parRapply, parSapply, parSapplyLB## The following objects are masked from 'package:stats':
##
## IQR, mad, sd, var, xtabs## The following objects are masked from 'package:base':
##
## Filter, Find, Map, Position, Reduce, anyDuplicated, append,
## as.data.frame, basename, cbind, colnames, dirname, do.call,
## duplicated, eval, evalq, get, grep, grepl, intersect, is.unsorted,
## lapply, mapply, match, mget, order, paste, pmax, pmax.int, pmin,
## pmin.int, rank, rbind, rownames, sapply, setdiff, sort, table,
## tapply, union, unique, unsplit, which.max, which.min## Loading required package: Biobase## Welcome to Bioconductor
##
## Vignettes contain introductory material; view with
## 'browseVignettes()'. To cite Bioconductor, see
## 'citation("Biobase")', and for packages 'citation("pkgname")'.## Loading required package: IRanges## Loading required package: S4Vectors##
## Attaching package: 'S4Vectors'## The following object is masked from 'package:base':
##
## expand.gridlibrary(MSEA.Ath.pb.db)
columns(MSEA.Ath.pb.db)## [1] "CAS" "ChEBIID" "DrugBankID" "Formula"
## [5] "HMDBID" "IUPAC" "InChI" "InChIKey"
## [9] "KEGGID" "MetaboliteID" "MetaboliteName" "PathBankID"
## [13] "PathwayName" "PathwaySubject" "SMILES"keytypes(MSEA.Ath.pb.db)## [1] "CAS" "ChEBIID" "DrugBankID" "Formula"
## [5] "HMDBID" "IUPAC" "InChI" "InChIKey"
## [9] "KEGGID" "MetaboliteID" "MetaboliteName" "PathBankID"
## [13] "PathwayName" "PathwaySubject" "SMILES"ids <- c('SMP0012018', 'SMP0012019')
select(MSEA.Ath.pb.db, ids, c("MetaboliteID", "CAS", "HMDBID", "ChEBIID",
"KEGGID"), "PathBankID")## MetaboliteID CAS HMDBID ChEBIID KEGGID
## 1 PW_C000437 7235-40-7 HMDB0000561 17579 C02094
## 2 PW_C057881 13312-52-2 HMDB0112264 67188 C20484
## 3 PW_C009794 7439-89-6 HMDB0015531 18248 C00023
## 4 PW_C001065 7782-44-7 HMDB0001377 15379 C00007
## 5 PW_C057882 67192 C20692
## 6 PW_C017456 79-77-6 HMDB0036565 32325 C12287
## 7 PW_C000544 15438-31-0 HMDB0000692 29033 C14818
## 8 PW_C057883 67190 C20693
## 9 PW_C057884 67191 C20694
## 10 PW_C057885 NA
## 11 PW_C040034 HMDB0059597 15378 C00080
## 12 PW_C001799 14875-96-8 HMDB0003178 17627 C00032
## 13 PW_C057879 81466 C18037
## 14 PW_C001778 126-29-4 HMDB0003101 NA C08614
## 15 PW_C001757 30743-41-0 HMDB0003020 25501 C08606
## 16 PW_C057886 14660-91-4 35306 C13431
## 18 PW_C057887 32304 C13453
## 19 PW_C057888 34596 C14044
## 21 PW_C000721 53-84-9 HMDB0000902 15846 C00003
## 22 PW_C001144 58-68-4 HMDB0001487 16908 C00004
## 24 PW_C057889 31157 C13455
## 25 PW_C057878 71302
## 26 PW_C001420 7732-18-5 HMDB0002111 15377 C00001
## 27 PW_C016115 21293-29-8 HMDB0035140 18743 C06082
## 28 PW_C001783 7722-84-1 HMDB0003125 16240 C00027cls <- columns(MSEA.Ath.pb.db)
kts <- keytypes(MSEA.Ath.pb.db)
kt <- kts[2]
ks <- head(keys(MSEA.Ath.pb.db, keytype = kts[2]))
res <- select(MSEA.Ath.pb.db, keys = ks, columns = cls, keytype = kt)
head(res)## CAS ChEBIID DrugBankID Formula HMDBID
## 1 7235-40-7 17579 C40H56 HMDB0000561
## 2 13312-52-2 67188 C40H56 HMDB0112264
## 3 7439-89-6 18248 DB01592 Fe HMDB0015531
## 4 7782-44-7 15379 O2 HMDB0001377
## 5 67192 C27H36O
## 6 79-77-6 32325 C13H20O HMDB0036565
## IUPAC
## 1 1,3,3-trimethyl-2-[(1E,3E,5E,7E,9Z,11Z,13E,15E,17E)-3,7,12,16-tetramethyl-18-(2,6,6-trimethylcyclohex-1-en-1-yl)octadeca-1,3,5,7,9,11,13,15,17-nonaen-1-yl]cyclohex-1-ene
## 2 1,3,3-trimethyl-2-[(1E,3E,5E,7E,9E,11E,13E,15Z,17E)-3,7,12,16-tetramethyl-18-(2,6,6-trimethylcyclohex-1-en-1-yl)octadeca-1,3,5,7,9,11,13,15,17-nonaen-1-yl]cyclohex-1-ene
## 3 lambda2-iron(2+) ion
## 4 oxidanone
## 5 (2E,4E,6E,8E,10E,12Z,14E)-4,9,13-trimethyl-15-(2,6,6-trimethylcyclohex-1-en-1-yl)pentadeca-2,4,6,8,10,12,14-heptaenal
## 6 (3E)-4-(2,6,6-trimethylcyclohex-1-en-1-yl)but-3-en-2-one
## InChI
## 1 InChI=1S/C40H56/c1-31(19-13-21-33(3)25-27-37-35(5)23-15-29-39(37,7)8)17-11-12-18-32(2)20-14-22-34(4)26-28-38-36(6)24-16-30-40(38,9)10/h11-14,17-22,25-28H,15-16,23-24,29-30H2,1-10H3/b12-11+,19-13+,20-14+,27-25+,28-26+,31-17+,32-18+,33-21+,34-22+
## 2 InChI=1S/C40H56/c1-31(19-13-21-33(3)25-27-37-35(5)23-15-29-39(37,7)8)17-11-12-18-32(2)20-14-22-34(4)26-28-38-36(6)24-16-30-40(38,9)10/h11-14,17-22,25-28H,15-16,23-24,29-30H2,1-10H3/b12-11+,19-13+,20-14+,27-25+,28-26+,31-17+,32-18+,33-21-,34-22+
## 3 InChI=1S/Fe/q+2
## 4 InChI=1S/O2/c1-2
## 5 InChI=1S/C27H36O/c1-22(12-7-8-13-23(2)16-11-21-28)14-9-15-24(3)18-19-26-25(4)17-10-20-27(26,5)6/h7-9,11-16,18-19,21H,10,17,20H2,1-6H3/b8-7+,14-9+,16-11+,19-18+,22-12+,23-13+,24-15-
## 6 InChI=1S/C13H20O/c1-10-6-5-9-13(3,4)12(10)8-7-11(2)14/h7-8H,5-6,9H2,1-4H3/b8-7+
## InChIKey KEGGID MetaboliteID MetaboliteName
## 1 OENHQHLEOONYIE-JLTXGRSLSA-N C02094 PW_C000437 B-Carotene
## 2 OENHQHLEOONYIE-BVZAMQQESA-N C20484 PW_C057881 9-cis-β-Carotene
## 3 CWYNVVGOOAEACU-UHFFFAOYSA-N C00023 PW_C009794 Iron
## 4 MYMOFIZGZYHOMD-UHFFFAOYSA-N C00007 PW_C001065 Oxygen
## 5 PJEHRCCPERVGEC-OLTPUEGQSA-N C20692 PW_C057882 9-cis-10'-apo-β-carotenal
## 6 PSQYTAPXSHCGMF-BQYQJAHWSA-N C12287 PW_C017456 β-Ionone
## PathBankID PathwayName PathwaySubject
## 1 SMP0012018 5-Deoxystrigol Biosynthesis Metabolic
## 2 SMP0012018 5-Deoxystrigol Biosynthesis Metabolic
## 3 SMP0012018 5-Deoxystrigol Biosynthesis Metabolic
## 4 SMP0012018 5-Deoxystrigol Biosynthesis Metabolic
## 5 SMP0012018 5-Deoxystrigol Biosynthesis Metabolic
## 6 SMP0012018 5-Deoxystrigol Biosynthesis Metabolic
## SMILES
## 1 C\\C(\\C=C\\C=C(/C)\\C=C\\C1=C(C)CCCC1(C)C)=C/C=C/C=C(\\C)/C=C/C=C(\\C)/C=C/C1=C(C)CCCC1(C)C
## 2 C/C(/C=C/C=C(/C)\\C=C\\C1=C(C)CCCC1(C)C)=C\\C=C\\C=C(/C)\\C=C\\C=C(/C)\\C=C\\C1=C(C)CCCC1(C)C
## 3 [Fe++]
## 4 O=O
## 5 [H]C(=O)\\C=C\\C(\\C)=C\\C=C\\C=C(/C)\\C=C\\C=C(\\C)/C=C/C1=C(C)CCCC1(C)C
## 6 CC(=O)\\C=C\\C1=C(C)CCCC1(C)COther additional functions like species, dbInfo, and dbconn are available. In each MSEA.XXX.pb.db package, species function returns the common name. dbInfo returns the information of the package. dbfile returns the directory where sqlite file is stored. dbschema returns the schema of database. dbconn returns the connection to the sqlite database.
species(MSEA.Ath.pb.db)## [1] "Thale cress"dbInfo(MSEA.Ath.pb.db)## NAME
## 1 SOURCENAME1
## 2 SOURCEURL1
## 3 DBSCHEMA
## 4 DBSCHEMAVERSION
## 5 ORGANISM
## 6 SPECIES
## 7 package
## 8 Db type
## 9 MSEAVERSION
## VALUE
## 1 PathBank
## 2 http://pathbank.org/downloads/pathbank_primary_metabolites.csv.zip
## 3 MSEA.Ath.pb.db
## 4 1.0.0
## 5 Arabidopsis thaliana
## 6 Thale cress
## 7 AnnotationDbi
## 8 MSEADb
## 9 2020dbfile(MSEA.Ath.pb.db)## [1] "/tmp/Rtmpw9PVnd/Rinst173e4c1c35d4/MSEA.Ath.pb.db/extdata/MSEA.Ath.pb.db.sqlite"dbschema(MSEA.Ath.pb.db)## [1] "CREATE TABLE `METADATA` (\n `NAME` TEXT,\n `VALUE` TEXT\n)"
## [2] "CREATE TABLE `DATA` (\n `PathBankID` TEXT,\n `PathwayName` TEXT,\n `PathwaySubject` TEXT,\n `MetaboliteID` TEXT,\n `MetaboliteName` TEXT,\n `HMDBID` TEXT,\n `KEGGID` TEXT,\n `ChEBIID` REAL,\n `DrugBankID` TEXT,\n `CAS` TEXT,\n `Formula` TEXT,\n `IUPAC` TEXT,\n `SMILES` TEXT,\n `InChI` TEXT,\n `InChIKey` TEXT\n)"dbconn(MSEA.Ath.pb.db)## <SQLiteConnection>
## Path: /tmp/Rtmpw9PVnd/Rinst173e4c1c35d4/MSEA.Ath.pb.db/extdata/MSEA.Ath.pb.db.sqlite
## Extensions: TRUEFILL bibtex for PathBank
Here is the output of sessionInfo() on the system on which this
document was compiled:
## R version 4.0.3 (2020-10-10)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 18.04.5 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.12-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.12-bioc/R/lib/libRlapack.so
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=C
## [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] parallel stats4 stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] MSEA.Ath.pb.db_0.99.0 AnnotationDbi_1.52.0 IRanges_2.24.0
## [4] S4Vectors_0.28.0 Biobase_2.50.0 BiocGenerics_0.36.0
## [7] MSEADbi_1.0.0 BiocStyle_2.18.0
##
## loaded via a namespace (and not attached):
## [1] Rcpp_1.0.5 knitr_1.30 magrittr_1.5
## [4] bit_4.0.4 rlang_0.4.8 stringr_1.4.0
## [7] blob_1.2.1 tools_4.0.3 xfun_0.18
## [10] DBI_1.1.0 htmltools_0.5.0 yaml_2.2.1
## [13] bit64_4.0.5 digest_0.6.27 bookdown_0.21
## [16] BiocManager_1.30.10 vctrs_0.3.4 memoise_1.1.0
## [19] evaluate_0.14 RSQLite_2.2.1 rmarkdown_2.5
## [22] stringi_1.5.3 compiler_4.0.3 pkgconfig_2.0.3