In fmicompbio/eisaR: Exon-Intron Split Analysis (EISA) in R

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Introduction

In this vignette, we show how to prepare reference files for estimating abundances of spliced and unspliced abundances with alignment-free methods (e.g., Salmon, alevin or kallisto). Such abundances are used as input, e.g., for estimation of RNA velocity in single-cell data.

library(eisaR)

Generate feature ranges

The first step is to generate a GRangesList object containing the genomic ranges for the features of interest (spliced transcripts, and either unspliced transcripts or intron sequences). This is done using the getFeatureRanges() function, based on a reference GTF file. Here, we exemplify this with a small subset of a GTF file from Gencode release 28. We extract genomic ranges for spliced transcript and introns, where the latter are defined for each transcript separately (using the same terminology as in the r Biocpkg("BUSpaRse") package). For each intron, a flanking sequence of 50 nt is added on each side to accommodate reads mapping across an exon/intron boundary.

gtf <- system.file("extdata/gencode.v28.annotation.sub.gtf", 
                   package = "eisaR")
grl <- getFeatureRanges(
  gtf = gtf,
  featureType = c("spliced", "intron"), 
  intronType = "separate", 
  flankLength = 50L, 
  joinOverlappingIntrons = FALSE, 
  verbose = TRUE
)

The output from getFeatureRanges() is a GRangesList object, with all the features of interest (both spliced transcripts and introns):

grl

The metadata slot of the GRangesList object contains a list of the feature IDs for each type of feature:

lapply(S4Vectors::metadata(grl)$featurelist, head)

As we can see, the intron IDs are identified by a -I suffix. Each feature is further annotated to a gene ID. For the intronic features, the corresponding gene ID also bears the -I suffix appended to the original gene ID. Having separate gene IDs for spliced transcripts and introns allows, for example, joint quantification of spliced and unspliced versions of a gene with alevin. Adding a suffix rather than defining a completely new gene ID allows us to easily match corresponding spliced and unspliced feature IDs. To further simplify the latter, the metadata of the GRangesList object returned by getFeatureRanges() contains data.frames matching the corresponding gene IDs (as well as transcript IDs, if unspliced transcripts are extracted):

head(S4Vectors::metadata(grl)$corrgene)

Extract feature sequences

Once the genomic ranges of the features of interest are extracted, we can obtain the nucleotide sequences using the extractTranscriptSeqs() function from the r Biocpkg("GenomicFeatures") package. In addition to the ranges, this requires the genome sequence. This can be obtained, for example, from the corresponding BSgenome package, or from an external FASTA file.

suppressPackageStartupMessages({
  library(BSgenome)
  library(BSgenome.Hsapiens.UCSC.hg38)
})
seqs <- GenomicFeatures::extractTranscriptSeqs(
  x = BSgenome.Hsapiens.UCSC.hg38, 
  transcripts = grl
)
seqs

The resulting DNAStringSet can be written to a FASTA file and used to generate an index for alignment-free methods such as Salmon and kallisto.

Generate an expanded GTF file

In addition to extracting feature sequences, we can also export a GTF file with the full set of features. This is useful, for example, in order to generate a linked transcriptome for later import of estimated abundances with r Biocpkg("tximeta").

exportToGtf(
  grl, 
  filepath = file.path(tempdir(), "exported.gtf")
)

In the exported GTF file, each entry of grl will correspond to a "transcript" feature, and each individual range corresponds to an "exon" feature. In addition, each gene is represented as a "gene" feature.

Generate a transcript-to-gene mapping

Finally, we can export a data.frame (or a tab-separated test file) providing a conversion table between "transcript" and "gene" identifiers. This is needed to aggregate the transcript-level abundance estimates from alignment-free methods such as Salmon and kallisto to the gene level.