Last updated: 2022-04-22
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| Rmd | e372c81 | Dave Tang | 2022-04-22 | TSS |
Transcriptional starting sites (TSSs) demarcate the first position in the DNA sequence that gets transcribed into RNA. In this work, we will try to build a classifier to try to predict TSSs. We will train our classifier using the NCBI Reference Sequence Database also known as RefSeq.
Function for loading required Bioconductor packages (and install them first, if missing).
load_bioc_package <- function(x){
if(!require(x, character.only = TRUE, quietly = TRUE)){
BiocManager::install(x, character.only = TRUE)
library(x, character.only = TRUE)
}
}We will use the Bioconductor package biomaRt to download the entire collection of RefSeq sequences.
load_bioc_package('biomaRt')Find the human gene set.
ensembl <- useMart('ensembl')
biomaRt::listDatasets(ensembl) %>%
filter(grepl('human', description, TRUE)) dataset description version
1 hsapiens_gene_ensembl Human genes (GRCh38.p13) GRCh38.p13Find RefSeq attributes.
ensembl <- useMart('ensembl', dataset = 'hsapiens_gene_ensembl')
listAttributes(ensembl) %>%
filter(grepl('refseq', description, TRUE)) name description
1 transcript_mane_select RefSeq match transcript (MANE Select)
2 transcript_mane_plus_clinical RefSeq match transcript (MANE Plus Clinical)
3 refseq_mrna RefSeq mRNA ID
4 refseq_mrna_predicted RefSeq mRNA predicted ID
5 refseq_ncrna RefSeq ncRNA ID
6 refseq_ncrna_predicted RefSeq ncRNA predicted ID
7 refseq_peptide RefSeq peptide ID
8 refseq_peptide_predicted RefSeq peptide predicted ID
page
1 feature_page
2 feature_page
3 feature_page
4 feature_page
5 feature_page
6 feature_page
7 feature_page
8 feature_pageFetch all RefSeq mRNAs on assembled chromosomes.
my_chr <- c(1:22, 'X', 'Y')
my_refseq <- getBM(
attributes='refseq_mrna',
filters = 'chromosome_name',
values = my_chr,
mart = ensembl
)Number of RefSeq IDs.
dim(my_refseq)[1] 61953 1Build table. Note that when working with transcripts, use the attributes transcript_start and transcript_end, and not the attributes start_position and end_position; using those will give you the Ensembl gene coordinates and not the RefSeq coordinates, which is what we want! (I have retrieved the Ensembl coordinates for illustrative purposes.)
my_att <- c(
'refseq_mrna',
'chromosome_name',
'transcript_start',
'transcript_end',
'start_position',
'end_position',
'strand'
)
my_refseq_loci <- getBM(
attributes = my_att,
filters = c('refseq_mrna', 'chromosome_name'),
values = list(refseq_mrna = my_refseq$refseq_mrna, chromosome_name = my_chr),
mart = ensembl
)Check out the table.
head(my_refseq_loci) refseq_mrna chromosome_name transcript_start transcript_end start_position
1 NM_000016 1 75724709 75763679 75724431
2 NM_000028 1 99850489 99924020 99850361
3 NM_000036 1 114673098 114695546 114673090
4 NM_000069 1 201039512 201112426 201039512
5 NM_000081 1 235661041 235866906 235661041
6 NM_000098 1 53196824 53214197 53196792
end_position strand
1 75787575 1
2 99924023 1
3 114695618 -1
4 201112451 -1
5 235883640 -1
6 53214197 1Check for duplicated entries.
table(duplicated(my_refseq_loci$refseq_mrna))
FALSE TRUE
61953 25 Removed duplicated entries.
my_refseq_loci_uniq <- my_refseq_loci[!duplicated(my_refseq_loci$refseq_mrna),]
dim(my_refseq_loci_uniq)[1] 61953 7We will modify chromosome_name and strand to make it compatible with BSgenome.Hsapiens.UCSC.hg38.
my_refseq_loci_uniq %>%
mutate(strand = ifelse(strand == 1, yes = '+', no = '-')) %>%
mutate(chromosome_name = sub("^", "chr", chromosome_name)) -> my_refseq_loci_uniq
head(my_refseq_loci_uniq) refseq_mrna chromosome_name transcript_start transcript_end start_position
1 NM_000016 chr1 75724709 75763679 75724431
2 NM_000028 chr1 99850489 99924020 99850361
3 NM_000036 chr1 114673098 114695546 114673090
4 NM_000069 chr1 201039512 201112426 201039512
5 NM_000081 chr1 235661041 235866906 235661041
6 NM_000098 chr1 53196824 53214197 53196792
end_position strand
1 75787575 +
2 99924023 +
3 114695618 -
4 201112451 -
5 235883640 -
6 53214197 +The TSS is transcript_start when the transcript is on the + strand and is transcript_end on the - strand. We want to retrieve sequence upstream and downstream of the TSS, so we will subtract and add coordinates accordingly.
my_span <- 2
my_refseq_loci_uniq %>%
dplyr::select(-c(start_position, end_position)) %>%
mutate(tss_start = if_else(strand == "+", transcript_start - my_span, transcript_end - my_span)) %>%
mutate(tss_end = if_else(strand == "+", transcript_start + my_span, transcript_end + my_span)) -> my_tss
head(my_tss) refseq_mrna chromosome_name transcript_start transcript_end strand tss_start
1 NM_000016 chr1 75724709 75763679 + 75724707
2 NM_000028 chr1 99850489 99924020 + 99850487
3 NM_000036 chr1 114673098 114695546 - 114695544
4 NM_000069 chr1 201039512 201112426 - 201112424
5 NM_000081 chr1 235661041 235866906 - 235866904
6 NM_000098 chr1 53196824 53214197 + 53196822
tss_end
1 75724711
2 99850491
3 114695548
4 201112428
5 235866908
6 53196826
sessionInfo()R version 4.1.2 (2021-11-01)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 20.04.3 LTS
Matrix products: default
BLAS/LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.8.so
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] stats graphics grDevices utils datasets methods base
other attached packages:
[1] biomaRt_2.50.3 forcats_0.5.1 stringr_1.4.0 dplyr_1.0.8
[5] purrr_0.3.4 readr_2.1.2 tidyr_1.2.0 tibble_3.1.6
[9] ggplot2_3.3.5 tidyverse_1.3.1 workflowr_1.7.0
loaded via a namespace (and not attached):
[1] bitops_1.0-7 fs_1.5.2 lubridate_1.8.0
[4] bit64_4.0.5 filelock_1.0.2 progress_1.2.2
[7] httr_1.4.2 GenomeInfoDb_1.30.1 rprojroot_2.0.3
[10] tools_4.1.2 backports_1.4.1 bslib_0.3.1
[13] utf8_1.2.2 R6_2.5.1 DBI_1.1.2
[16] BiocGenerics_0.40.0 colorspace_2.0-3 withr_2.5.0
[19] prettyunits_1.1.1 tidyselect_1.1.2 processx_3.5.3
[22] curl_4.3.2 bit_4.0.4 compiler_4.1.2
[25] git2r_0.30.1 cli_3.2.0 rvest_1.0.2
[28] Biobase_2.54.0 xml2_1.3.3 sass_0.4.1
[31] scales_1.2.0 callr_3.7.0 rappdirs_0.3.3
[34] digest_0.6.29 rmarkdown_2.13 XVector_0.34.0
[37] pkgconfig_2.0.3 htmltools_0.5.2 dbplyr_2.1.1
[40] fastmap_1.1.0 rlang_1.0.2 readxl_1.4.0
[43] rstudioapi_0.13 RSQLite_2.2.12 jquerylib_0.1.4
[46] generics_0.1.2 jsonlite_1.8.0 RCurl_1.98-1.6
[49] magrittr_2.0.3 GenomeInfoDbData_1.2.7 Rcpp_1.0.8.3
[52] munsell_0.5.0 S4Vectors_0.32.4 fansi_1.0.3
[55] lifecycle_1.0.1 stringi_1.7.6 whisker_0.4
[58] yaml_2.3.5 zlibbioc_1.40.0 BiocFileCache_2.2.1
[61] grid_4.1.2 blob_1.2.3 promises_1.2.0.1
[64] crayon_1.5.1 Biostrings_2.62.0 haven_2.5.0
[67] hms_1.1.1 KEGGREST_1.34.0 knitr_1.38
[70] ps_1.6.0 pillar_1.7.0 stats4_4.1.2
[73] reprex_2.0.1 XML_3.99-0.9 glue_1.6.2
[76] evaluate_0.15 getPass_0.2-2 modelr_0.1.8
[79] vctrs_0.4.1 png_0.1-7 tzdb_0.3.0
[82] httpuv_1.6.5 cellranger_1.1.0 gtable_0.3.0
[85] assertthat_0.2.1 cachem_1.0.6 xfun_0.30
[88] broom_0.8.0 later_1.3.0 AnnotationDbi_1.56.2
[91] memoise_2.0.1 IRanges_2.28.0 ellipsis_0.3.2