RNAmodR.Data 1.2.0
RNAmodR.Data contains example data for the RNAmodR and related packages.
The data is provided as gff3, fasta and bam files.
Four sets of data with multiple files are included
## snapshotDate(): 2020-04-27library(RNAmodR.Data)eh <- ExperimentHub()## snapshotDate(): 2020-04-27ExperimentHub::listResources(eh, "RNAmodR.Data")## [1] "RNAmodR.Data.example.fasta" "RNAmodR.Data.example.gff3"
## [3] "RNAmodR.Data.example.bam.1" "RNAmodR.Data.example.bam.2"
## [5] "RNAmodR.Data.example.bam.3" "RNAmodR.Data.example.RMS.fasta"
## [7] "RNAmodR.Data.example.RMS.gff3" "RNAmodR.Data.example.RMS.1"
## [9] "RNAmodR.Data.example.RMS.2" "RNAmodR.Data.example.AAS.fasta"
## [11] "RNAmodR.Data.example.AAS.gff3" "RNAmodR.Data.example.bud23.1"
## [13] "RNAmodR.Data.example.bud23.2" "RNAmodR.Data.example.trm8.1"
## [15] "RNAmodR.Data.example.trm8.2" "RNAmodR.Data.example.wt.1"
## [17] "RNAmodR.Data.example.wt.2" "RNAmodR.Data.example.wt.3"
## [19] "RNAmodR.Data.example.man.fasta" "RNAmodR.Data.example.man.gff3"
## [21] "RNAmodR.Data.snoRNAdb"These resources are grouped based on topic. Please have a look at the following man pages:
?RNAmodR.Data.example for general example data used for different purposes?RNAmodR.Data.RMS for example data for RiboMethSeq?RNAmodR.Data.AAS for example data for AlkAnilineSeq?RNAmodR.Data.man for small data set for man page examples?RNAmodR.Data.snoRNAdb for snoRNAdb as csv fileRNAmodR.Data.snoRNAdb consists of a table containing the published data from
the snoRNAdb [Lestrade and Weber (2006)]. The can be loaded as a GRanges
object.
library(GenomicRanges)table <- read.csv2(RNAmodR.Data.snoRNAdb(), stringsAsFactors = FALSE)## snapshotDate(): 2020-04-27## see ?RNAmodR.Data and browseVignettes('RNAmodR.Data') for documentation## loading from cachehead(table, n = 2)# keep only the current coordinates
table <- table[,1:7]
snoRNAdb <- GRanges(seqnames = table$hgnc_symbol,
ranges = IRanges(start = table$position, width = 1),strand = "+",
type = "RNAMOD",
mod = table$modification,
Parent = table$hgnc_symbol,
Activity = CharacterList(strsplit(table$guide,",")))
# convert to current gene name
snoRNAdb <- snoRNAdb[vapply(snoRNAdb$Activity != "unknown",all,logical(1)),]
snoRNAdb <- split(snoRNAdb,snoRNAdb$Parent)
head(snoRNAdb)## GRangesList object of length 6:
## $RNA18SN5
## GRanges object with 69 ranges and 4 metadata columns:
## seqnames ranges strand | type mod Parent
## <Rle> <IRanges> <Rle> | <character> <character> <character>
## [1] RNA18SN5 27 + | RNAMOD Am RNA18SN5
## [2] RNA18SN5 34 + | RNAMOD Y RNA18SN5
## [3] RNA18SN5 36 + | RNAMOD Y RNA18SN5
## [4] RNA18SN5 93 + | RNAMOD Y RNA18SN5
## [5] RNA18SN5 99 + | RNAMOD Am RNA18SN5
## ... ... ... ... . ... ... ...
## [65] RNA18SN5 1643 + | RNAMOD Y RNA18SN5
## [66] RNA18SN5 1678 + | RNAMOD Am RNA18SN5
## [67] RNA18SN5 1692 + | RNAMOD Y RNA18SN5
## [68] RNA18SN5 1703 + | RNAMOD Cm RNA18SN5
## [69] RNA18SN5 1804 + | RNAMOD Um RNA18SN5
## Activity
## <CharacterList>
## [1] SNORD27
## [2] SNORA50A,SNORA76
## [3] SNORA69,SNORA55
## [4] SNORA75
## [5] SNORD57
## ... ...
## [65] SNORA41
## [66] SNORD82
## [67] SNORD70A,SNORD70B,SNORD70C,...
## [68] SNORD43
## [69] SNORD20
## -------
## seqinfo: 9 sequences from an unspecified genome; no seqlengths
##
## ...
## <5 more elements>sessionInfo()## R version 4.0.0 (2020-04-24)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 18.04.4 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.11-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.11-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] stats4 parallel stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] RNAmodR.Data_1.2.0 ExperimentHubData_1.14.0 AnnotationHubData_1.18.0
## [4] futile.logger_1.4.3 GenomicRanges_1.40.0 GenomeInfoDb_1.24.0
## [7] IRanges_2.22.1 S4Vectors_0.26.0 ExperimentHub_1.14.0
## [10] AnnotationHub_2.20.0 BiocFileCache_1.12.0 dbplyr_1.4.3
## [13] BiocGenerics_0.34.0 BiocStyle_2.16.0
##
## loaded via a namespace (and not attached):
## [1] bitops_1.0-6 matrixStats_0.56.0
## [3] bit64_0.9-7 progress_1.2.2
## [5] httr_1.4.1 tools_4.0.0
## [7] R6_2.4.1 DBI_1.1.0
## [9] tidyselect_1.0.0 prettyunits_1.1.1
## [11] bit_1.1-15.2 curl_4.3
## [13] compiler_4.0.0 graph_1.66.0
## [15] Biobase_2.48.0 BiocCheck_1.24.0
## [17] formatR_1.7 DelayedArray_0.14.0
## [19] rtracklayer_1.48.0 bookdown_0.18
## [21] RBGL_1.64.0 askpass_1.1
## [23] rappdirs_0.3.1 stringr_1.4.0
## [25] digest_0.6.25 Rsamtools_2.4.0
## [27] rmarkdown_2.1 stringdist_0.9.5.5
## [29] AnnotationForge_1.30.1 XVector_0.28.0
## [31] rBiopaxParser_2.28.0 pkgconfig_2.0.3
## [33] htmltools_0.4.0 fastmap_1.0.1
## [35] rlang_0.4.6 RSQLite_2.2.0
## [37] shiny_1.4.0.2 jsonlite_1.6.1
## [39] BiocParallel_1.22.0 dplyr_0.8.5
## [41] RCurl_1.98-1.2 magrittr_1.5
## [43] GenomeInfoDbData_1.2.3 Matrix_1.2-18
## [45] Rcpp_1.0.4.6 lifecycle_0.2.0
## [47] stringi_1.4.6 yaml_2.2.1
## [49] SummarizedExperiment_1.18.1 zlibbioc_1.34.0
## [51] biocViews_1.56.0 grid_4.0.0
## [53] blob_1.2.1 promises_1.1.0
## [55] crayon_1.3.4 lattice_0.20-41
## [57] Biostrings_2.56.0 GenomicFeatures_1.40.0
## [59] hms_0.5.3 knitr_1.28
## [61] pillar_1.4.4 optparse_1.6.6
## [63] RUnit_0.4.32 codetools_0.2-16
## [65] biomaRt_2.44.0 futile.options_1.0.1
## [67] XML_3.99-0.3 glue_1.4.0
## [69] BiocVersion_3.11.1 evaluate_0.14
## [71] lambda.r_1.2.4 data.table_1.12.8
## [73] BiocManager_1.30.10 vctrs_0.2.4
## [75] httpuv_1.5.2 getopt_1.20.3
## [77] openssl_1.4.1 purrr_0.3.4
## [79] assertthat_0.2.1 xfun_0.13
## [81] mime_0.9 xtable_1.8-4
## [83] later_1.0.0 tibble_3.0.1
## [85] OrganismDbi_1.30.0 GenomicAlignments_1.24.0
## [87] AnnotationDbi_1.50.0 memoise_1.1.0
## [89] ellipsis_0.3.0 interactiveDisplayBase_1.26.0Lestrade, Laurent, and Michel J. Weber. 2006. “snoRNA-LBME-db, a comprehensive database of human H/ACA and C/D box snoRNAs.” Nucleic Acids Research 34 (January):D158–D162. https://doi.org/10.1093/nar/gkj002.