1 Available resources

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

## Warning: replacing previous import 'utils::findMatches' by
## 'S4Vectors::findMatches' when loading 'ExperimentHubData'
library(RNAmodR.Data)
eh <- ExperimentHub()
ExperimentHub::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 file

2 snoRNAdb

RNAmodR.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)
## see ?RNAmodR.Data and browseVignettes('RNAmodR.Data') for documentation
## loading from cache
head(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>

3 Sessioninfo

sessionInfo()
## R version 4.3.1 (2023-06-16)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 22.04.3 LTS
## 
## Matrix products: default
## BLAS:   /home/biocbuild/bbs-3.18-bioc/R/lib/libRblas.so 
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_GB              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       
## 
## time zone: America/New_York
## tzcode source: system (glibc)
## 
## attached base packages:
## [1] stats4    stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] RNAmodR.Data_1.16.0      ExperimentHubData_1.28.0 AnnotationHubData_1.32.0
##  [4] futile.logger_1.4.3      GenomicRanges_1.54.0     GenomeInfoDb_1.38.0     
##  [7] IRanges_2.36.0           S4Vectors_0.40.0         ExperimentHub_2.10.0    
## [10] AnnotationHub_3.10.0     BiocFileCache_2.10.0     dbplyr_2.3.4            
## [13] BiocGenerics_0.48.0      BiocStyle_2.30.0        
## 
## loaded via a namespace (and not attached):
##   [1] DBI_1.1.3                     bitops_1.0-7                 
##   [3] RBGL_1.78.0                   formatR_1.14                 
##   [5] biomaRt_2.58.0                rlang_1.1.1                  
##   [7] magrittr_2.0.3                matrixStats_1.0.0            
##   [9] compiler_4.3.1                RSQLite_2.3.1                
##  [11] GenomicFeatures_1.54.0        png_0.1-8                    
##  [13] vctrs_0.6.4                   stringr_1.5.0                
##  [15] pkgconfig_2.0.3               crayon_1.5.2                 
##  [17] fastmap_1.1.1                 XVector_0.42.0               
##  [19] ellipsis_0.3.2                utf8_1.2.4                   
##  [21] biocViews_1.70.0              Rsamtools_2.18.0             
##  [23] promises_1.2.1                rmarkdown_2.25               
##  [25] graph_1.80.0                  purrr_1.0.2                  
##  [27] bit_4.0.5                     xfun_0.40                    
##  [29] zlibbioc_1.48.0               cachem_1.0.8                 
##  [31] jsonlite_1.8.7                progress_1.2.2               
##  [33] blob_1.2.4                    later_1.3.1                  
##  [35] DelayedArray_0.28.0           BiocParallel_1.36.0          
##  [37] interactiveDisplayBase_1.40.0 parallel_4.3.1               
##  [39] prettyunits_1.2.0             R6_2.5.1                     
##  [41] bslib_0.5.1                   stringi_1.7.12               
##  [43] rtracklayer_1.62.0            jquerylib_0.1.4              
##  [45] Rcpp_1.0.11                   bookdown_0.36                
##  [47] SummarizedExperiment_1.32.0   knitr_1.44                   
##  [49] BiocBaseUtils_1.4.0           httpuv_1.6.12                
##  [51] Matrix_1.6-1.1                tidyselect_1.2.0             
##  [53] stringdist_0.9.10             abind_1.4-5                  
##  [55] yaml_2.3.7                    codetools_0.2-19             
##  [57] RUnit_0.4.32                  curl_5.1.0                   
##  [59] lattice_0.22-5                tibble_3.2.1                 
##  [61] withr_2.5.1                   Biobase_2.62.0               
##  [63] shiny_1.7.5.1                 KEGGREST_1.42.0              
##  [65] evaluate_0.22                 lambda.r_1.2.4               
##  [67] AnnotationForge_1.44.0        xml2_1.3.5                   
##  [69] Biostrings_2.70.1             pillar_1.9.0                 
##  [71] BiocManager_1.30.22           filelock_1.0.2               
##  [73] MatrixGenerics_1.14.0         OrganismDbi_1.44.0           
##  [75] generics_0.1.3                RCurl_1.98-1.12              
##  [77] BiocVersion_3.18.0            hms_1.1.3                    
##  [79] BiocCheck_1.38.0              xtable_1.8-4                 
##  [81] glue_1.6.2                    tools_4.3.1                  
##  [83] BiocIO_1.12.0                 GenomicAlignments_1.38.0     
##  [85] XML_3.99-0.14                 grid_4.3.1                   
##  [87] AnnotationDbi_1.64.0          GenomeInfoDbData_1.2.11      
##  [89] restfulr_0.0.15               cli_3.6.1                    
##  [91] rappdirs_0.3.3                futile.options_1.0.1         
##  [93] fansi_1.0.5                   S4Arrays_1.2.0               
##  [95] dplyr_1.1.3                   sass_0.4.7                   
##  [97] digest_0.6.33                 SparseArray_1.2.0            
##  [99] rjson_0.2.21                  memoise_2.0.1                
## [101] htmltools_0.5.6.1             lifecycle_1.0.3              
## [103] httr_1.4.7                    mime_0.12                    
## [105] bit64_4.0.5

References

Lestrade, 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.