sesameData package provides associated data for sesame package. This includes example data for testing and instructional purpose, as we ll as probe annotation for different Infinium platforms.
Sesame provides some utility functions to process transcript models, which can be represented as data.frame, GRanges and GRangesList objects. For example, sesameData_getTxnGRanges calls sesameDataGet("genomeInfo.mm10")$txns to retrieve a transcript-centric GRangesList object from GENCODE including its gene annotation, exon and cds (for protein-coding genes). It is then turned into a simple GRanges object of transcript:
## GRanges object with 142604 ranges and 9 metadata columns:
## seqnames ranges strand | transcript_type
## <Rle> <IRanges> <Rle> | <character>
## ENSMUST00000193812.1 chr1 3073253-3074322 + | TEC
## ENSMUST00000082908.1 chr1 3102016-3102125 + | snRNA
## ENSMUST00000162897.1 chr1 3205901-3216344 - | processed_transcript
## ENSMUST00000159265.1 chr1 3206523-3215632 - | processed_transcript
## ENSMUST00000070533.4 chr1 3214482-3671498 - | protein_coding
## ... ... ... ... . ...
## ENSMUST00000082419.1 chrM 13552-14070 - | protein_coding
## ENSMUST00000082420.1 chrM 14071-14139 - | Mt_tRNA
## ENSMUST00000082421.1 chrM 14145-15288 + | protein_coding
## ENSMUST00000082422.1 chrM 15289-15355 + | Mt_tRNA
## ENSMUST00000082423.1 chrM 15356-15422 - | Mt_tRNA
## transcript_name gene_name gene_id
## <character> <character> <character>
## ENSMUST00000193812.1 4933401J01Rik-201 4933401J01Rik ENSMUSG00000102693.1
## ENSMUST00000082908.1 Gm26206-201 Gm26206 ENSMUSG00000064842.1
## ENSMUST00000162897.1 Xkr4-203 Xkr4 ENSMUSG00000051951.5
## ENSMUST00000159265.1 Xkr4-202 Xkr4 ENSMUSG00000051951.5
## ENSMUST00000070533.4 Xkr4-201 Xkr4 ENSMUSG00000051951.5
## ... ... ... ...
## ENSMUST00000082419.1 mt-Nd6-201 mt-Nd6 ENSMUSG00000064368.1
## ENSMUST00000082420.1 mt-Te-201 mt-Te ENSMUSG00000064369.1
## ENSMUST00000082421.1 mt-Cytb-201 mt-Cytb ENSMUSG00000064370.1
## ENSMUST00000082422.1 mt-Tt-201 mt-Tt ENSMUSG00000064371.1
## ENSMUST00000082423.1 mt-Tp-201 mt-Tp ENSMUSG00000064372.1
## gene_type source level cdsStart
## <character> <character> <character> <numeric>
## ENSMUST00000193812.1 TEC HAVANA 2 NA
## ENSMUST00000082908.1 snRNA ENSEMBL 3 NA
## ENSMUST00000162897.1 protein_coding HAVANA 2 NA
## ENSMUST00000159265.1 protein_coding HAVANA 2 NA
## ENSMUST00000070533.4 protein_coding HAVANA 2 3216025
## ... ... ... ... ...
## ENSMUST00000082419.1 protein_coding ENSEMBL 3 13555
## ENSMUST00000082420.1 Mt_tRNA ENSEMBL 3 NA
## ENSMUST00000082421.1 protein_coding ENSEMBL 3 14145
## ENSMUST00000082422.1 Mt_tRNA ENSEMBL 3 NA
## ENSMUST00000082423.1 Mt_tRNA ENSEMBL 3 NA
## cdsEnd
## <numeric>
## ENSMUST00000193812.1 NA
## ENSMUST00000082908.1 NA
## ENSMUST00000162897.1 NA
## ENSMUST00000159265.1 NA
## ENSMUST00000070533.4 3671348
## ... ...
## ENSMUST00000082419.1 14070
## ENSMUST00000082420.1 NA
## ENSMUST00000082421.1 15288
## ENSMUST00000082422.1 NA
## ENSMUST00000082423.1 NA
## -------
## seqinfo: 22 sequences from an unspecified genome; no seqlengthsThe returned GRanges object does not contain the exon coordinates. We can further collapse different transcripts of the same gene (isoforms) to gene level. Gene start is the minimum of all isoform starts and end is the maximum of all isoform ends.
## GRanges object with 55401 ranges and 2 metadata columns:
## seqnames ranges strand | gene_name
## <Rle> <IRanges> <Rle> | <character>
## ENSMUSG00000102693.1 chr1 3073253-3074322 + | 4933401J01Rik
## ENSMUSG00000064842.1 chr1 3102016-3102125 + | Gm26206
## ENSMUSG00000051951.5 chr1 3205901-3671498 - | Xkr4
## ENSMUSG00000102851.1 chr1 3252757-3253236 + | Gm18956
## ENSMUSG00000103377.1 chr1 3365731-3368549 - | Gm37180
## ... ... ... ... . ...
## ENSMUSG00000064368.1 chrM 13552-14070 - | mt-Nd6
## ENSMUSG00000064369.1 chrM 14071-14139 - | mt-Te
## ENSMUSG00000064370.1 chrM 14145-15288 + | mt-Cytb
## ENSMUSG00000064371.1 chrM 15289-15355 + | mt-Tt
## ENSMUSG00000064372.1 chrM 15356-15422 - | mt-Tp
## gene_type
## <character>
## ENSMUSG00000102693.1 TEC
## ENSMUSG00000064842.1 snRNA
## ENSMUSG00000051951.5 protein_coding
## ENSMUSG00000102851.1 processed_pseudogene
## ENSMUSG00000103377.1 TEC
## ... ...
## ENSMUSG00000064368.1 protein_coding
## ENSMUSG00000064369.1 Mt_tRNA
## ENSMUSG00000064370.1 protein_coding
## ENSMUSG00000064371.1 Mt_tRNA
## ENSMUSG00000064372.1 Mt_tRNA
## -------
## seqinfo: 22 sequences from an unspecified genome; no seqlengthsThe following code get probes from different parts of the genome.
## probes in a region
sesameData_getProbesByRegion(GRanges('chr5',
IRanges(135313937, 135419936)), platform = 'Mammal40')
## get chrX probes
sesameData_getProbesByRegion(chrm = 'chrX', platform = "Mammal40")
## get autosomal probes
sesameData_getProbesByRegion(
chrm_to_exclude = c("chrX", "chrY"), platform = "Mammal40")
## get DNMT3A probes
sesameData_getProbesByGene('DNMT3A', "Mammal40", upstream=500)
## get DNMT3A promoter probes
sesameData_getProbesByGene('DNMT3A', "Mammal40", promoter = TRUE)
## get all promoter probes
sesameData_getProbesByGene(NULL, "Mammal40", promoter = TRUE)One can annotate given probe ID using any genomic features stored in GRanges objects. For example, the following demonstrate the annotation of 500 random Mammal40 probes for gene promoters.
input_probes <- names(sesameData_getManifestGRanges("Mammal40"))[1:500]
## annotate for promoter
regs <- promoters(sesameData_getTxnGRanges("hg38"))
sesameData_annoProbes(input_probes, regs, column = "gene_name")
## annotate for gene association
regs <- sesameData_getTxnGRanges("hg38", merge2gene = TRUE)
sesameData_annoProbes(input_probes, regs, column = "gene_name")
## get genes associated with probes
regs <- sesameData_getTxnGRanges("hg38", merge2gene = TRUE)
sesameData_annoProbes(input_probes, regs, return_ov_features=TRUE)
## get genes associated with probes extending 10kb
input_probes <- c("cg14620903","cg22464003")
sesameData_annoProbes(input_probes, regs+10000, column = "gene_name")Sesame provides access to array manfiest stored as GRanges object. These GRanges object are converted from the raw tsv files on our array annotation website using the conversion code
## [1] 485545Note that by default the GRanges object exclude decoy sequence probes (e.g., _alt, and _random contigs). To include them, we need to use the decoy = TRUE option in sesameData_getManifestDF.
Titles of all the available data can be shown with:
Each sesame datum from ExperimentHub is accessible through the sesameDataGet interface. It should be noted that all data must be pre-cached to local disk before they can be used. This design is to prevent conflict in annotation data caching and remove internet dependency. Caching needs only be done once per sesame/sesameData installation. One can cache data using
Once a data object is loaded, it is stored to a tempoary cache, so that the data doesn’t need to be retrieved again next time we call sesameDataGet. This design is meant to speeed up the run time.
For example, the annotation for HM27 can be retrieved with the title:
It’s worth noting that once a data is retrieved through the sesameDataGet inferface (below), it will stay in memory so next time the object will be returned immediately. This design avoids repeated disk/web retrieval. In some rare situation, one may want to redo the download/disk IO, or empty the cache to save memory. This can be done with:
## used (Mb) gc trigger (Mb) max used (Mb)
## Ncells 4674798 249.7 8124755 434.0 8124755 434.0
## Vcells 10591340 80.9 26872318 205.1 22326930 170.4## R version 4.4.1 (2024-06-14)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.1 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.20-bioc/R/lib/libRblas.so
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0
##
## 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] GenomicRanges_1.58.0 GenomeInfoDb_1.42.0 IRanges_2.40.0
## [4] S4Vectors_0.44.0 sesameData_1.24.0 ExperimentHub_2.14.0
## [7] AnnotationHub_3.14.0 BiocFileCache_2.14.0 dbplyr_2.5.0
## [10] BiocGenerics_0.52.0
##
## loaded via a namespace (and not attached):
## [1] KEGGREST_1.46.0 xfun_0.48 bslib_0.8.0
## [4] Biobase_2.66.0 tzdb_0.4.0 vctrs_0.6.5
## [7] tools_4.4.1 generics_0.1.3 curl_5.2.3
## [10] tibble_3.2.1 fansi_1.0.6 AnnotationDbi_1.68.0
## [13] RSQLite_2.3.7 blob_1.2.4 pkgconfig_2.0.3
## [16] lifecycle_1.0.4 GenomeInfoDbData_1.2.13 compiler_4.4.1
## [19] stringr_1.5.1 Biostrings_2.74.0 htmltools_0.5.8.1
## [22] sass_0.4.9 yaml_2.3.10 pillar_1.9.0
## [25] crayon_1.5.3 jquerylib_0.1.4 cachem_1.1.0
## [28] tidyselect_1.2.1 digest_0.6.37 stringi_1.8.4
## [31] purrr_1.0.2 dplyr_1.1.4 BiocVersion_3.20.0
## [34] fastmap_1.2.0 cli_3.6.3 magrittr_2.0.3
## [37] utf8_1.2.4 withr_3.0.2 readr_2.1.5
## [40] filelock_1.0.3 UCSC.utils_1.2.0 rappdirs_0.3.3
## [43] bit64_4.5.2 rmarkdown_2.28 XVector_0.46.0
## [46] httr_1.4.7 bit_4.5.0 hms_1.1.3
## [49] png_0.1-8 memoise_2.0.1 evaluate_1.0.1
## [52] knitr_1.48 rlang_1.1.4 glue_1.8.0
## [55] DBI_1.2.3 BiocManager_1.30.25 jsonlite_1.8.9
## [58] R6_2.5.1 zlibbioc_1.52.0