In FelixErnst/EpiTxDb: Storing and accessing epitranscriptomic information using the AnnotationDbi interface

BiocStyle::markdown(css.files = c('custom.css'))

Installation

if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install(c("EpiTxDb","EpiTxDb.Hs.hg38"))

Introduction

The epitranscriptome includes all post-transcriptional modifications of the RNA and describes and additional layer of information encoded on RNA. Like the term epigenome it is not about a change in nucleotide sequences, but the addition of functional elements through modifications.

With the development of high throughput detection strategies for specific RNA modifications, such as miCLIP and Pseudo-Seq amongst other, a large number of modified positions have been identified and were summarized via the RMBase project [@Xuan.2017;@Sun.2015] project.

To make these information avaialble within the Bioconductor universe EpiTxDb was developed, which facilitates the storage of epitranscriptomic information. More specifically, it can keep track of modification identity, position, the enzyme for introducing it on the RNA, a specifier which determines the position on the RNA to be modified and the literature references each modification is associated with.

Getting started

library(EpiTxDb)
library(EpiTxDb.Hs.hg38)

The class EpiTxDb is the class for storing the epitranscriptomic data. It inherits the inner workings of AnnotationDb class from the AnnotationDbi package.

As an example for the vignette the snoRNAdb data [@Lestrade.2006] from the EpiTxDb.Hs.hg38 package will be used. The data is stored in the AnnotationHub and is downloaded and cached upon the first request.

etdb <- EpiTxDb.Hs.hg38.snoRNAdb()
etdb

As expected for an AnnotationDb class the general accessors are available.

keytypes(etdb)
columns(etdb)
head(keys(etdb, "MODID"))
select(etdb, keys = "1",
       columns = c("MODNAME","MODTYPE","MODSTART","MODSTRAND","SNNAME",
                   "RXGENENAME","SPECTYPE","SPECGENENAME"),
       keytype = "MODID")

The columns with the prefix RX or SPEC reference the reaction enzyme and the location specifier. This can be the same information, but for ribosomal modifications from the snoRNAdb it is of course fibrillarin and a snoRNA.

In addition the following accessor for metadata are available as well.

species(etdb)
organism(etdb)
seqlevels(etdb)

Accessing RNA modifications

The specialized accessors are modifications() and modificationsBy(). modifications() allows for filtering results, whereas modificationsBy() returns all the modifications in batches separated by certain information.

modifications(etdb, columns = c("mod_id","mod_type","mod_name",
                                "rx_genename","spec_genename",
                                "ref_type","ref"),
              filter = list(mod_id = 1:3))

# split by sequence name, usually a transcipt identifier
modificationsBy(etdb, by = "seqnames")
# split modification type
modificationsBy(etdb, by = "modtype")

Shifting coordinates from genomic to transcriptomic

Since epitranscriptomic modifications by their nature can have different meaning for each of the individual transcript variants. This also introduces conflicts for saving epitranscriptomics coordinates. In the example above the coordinates are given per transcript, because of the source data.

However, not all sources report transcript coordinates. It might be of interest to shift the coordinates to transcript coordinates and at the same time taking care that with transcript variants multiple options exist for each of the transcript maturation process: From one genomic coordinate, multiple transcriptomic coordinates can be spawned.

Whether this is biologically relevant or whether biological evidence does exist for each modification on each transcript cannot be guaranteed or differentiated technically depending on the methods used. This might change with the arrival of new techniques allowing for detection of modified nucleotides per individual transcript variant.

suppressPackageStartupMessages({
  library(TxDb.Hsapiens.UCSC.hg38.knownGene)
  library(BSgenome.Hsapiens.UCSC.hg38)
})

library(TxDb.Hsapiens.UCSC.hg38.knownGene)
library(BSgenome.Hsapiens.UCSC.hg38)

txdb <- TxDb.Hsapiens.UCSC.hg38.knownGene
seqlevels(txdb) <- "chr1"
bs <- BSgenome.Hsapiens.UCSC.hg38

etdb <- EpiTxDb.Hs.hg38.RMBase()

tx <- exonsBy(txdb)
mod <- modifications(etdb, filter = list(sn_name = "chr1"))
length(mod)

In the following example we will focus on shifting the coordinates to individual mature transcripts. However, keep in mind, that premature transcript might be of interest as well and this can be controlled via the tx arguments of shiftGenomicToTranscript()

mod_tx <- shiftGenomicToTranscript(mod, tx)
length(mod_tx)

Due to multiple matches for each transcript variant the number of modifications has increased.

With the we can plot the relative positions of modifications by type on chr1 transcripts.

mod_tx <- split(mod_tx,seqnames(mod_tx))
names <- Reduce(intersect,list(names(mod_tx),names(tx)))

# Getting the corresponding 5'-UTR and 3'-UTR annotations
fp <- fiveUTRsByTranscript(txdb)
tp <- threeUTRsByTranscript(txdb)
tx <- tx[names]
mod_tx <- mod_tx[names]
fp_m <- match(names,names(fp))
fp_m <- fp_m[!is.na(fp_m)]
tp_m <- match(names,names(tp))
tp_m <- tp_m[!is.na(tp_m)]
fp <- fp[fp_m]
tp <- tp[tp_m]

# Getting lengths of transcripts, 5'-UTR and 3'-UTR
tx_lengths <- sum(width(tx))
fp_lengths <- rep(0L,length(tx))
names(fp_lengths) <- names
fp_lengths[names(fp)] <- sum(width(fp))
tp_lengths <- rep(0L,length(tx))
names(tp_lengths) <- names
tp_lengths[names(tp)] <- sum(width(tp))

# Rescale modifications
# CDS start is at position 1L and cds end at position 1000L
from <- IRanges(fp_lengths+1L, tx_lengths - tp_lengths)
to <- IRanges(1L,1000L)
mod_rescale <- rescale(mod_tx, to, from)

# Construct result data.frame
rel_pos <- data.frame(mod_type = unlist(mcols(mod_rescale,level="within")[,"mod_type"]),
                      rel_pos = unlist(start(mod_rescale)))
rel_pos <- rel_pos[rel_pos$rel_pos < 1500 & rel_pos$rel_pos > -500,]

library(ggplot2)
ggplot(rel_pos[rel_pos$mod_type %in% c("m6A","m1A","Y"),],
       aes(x = rel_pos, colour = mod_type)) + 
  geom_density()

Session info

sessionInfo()

References

FelixErnst/EpiTxDb documentation built on March 31, 2024, 1:21 a.m.