Predicting uORFs in 5’ UTR regions

The function predORF can be used to identify open reading frames (ORFs) and coding sequences (CDSs) in DNA sequences provided as DNAString or DNAStringSet objects. The setting mode='ORF' returns continuous reading frames that begin with a start codon and end with a stop codon, while mode='CDS' returns continuous reading frames that do not need to begin or end with start or stop codons, respectively. Non-canonical start and stop condons are supported by allowing the user to provide any custom set of triplets under the startcodon and stopcodon arguments (i.e. non-ATG start codons). The argument n defines the maximum number of ORFs to return for each input sequence (e.g. n=1 returns only the longest ORF). It also supports the identification of overlapping and nested ORFs. Alternatively, one can return all non-overlapping ORFs including the longest ORF for each input sequence with n="all" and longest_disjoint=TRUE.

library(systemPipeRdata)
library(GenomicFeatures)
library(rtracklayer)
txdb <- makeTxDbFromGFF(file = "data/tair10.gff", format = "gff3", 
    organism = "Arabidopsis")
futr <- fiveUTRsByTranscript(txdb, use.names = TRUE)
dna <- extractTranscriptSeqs(FaFile("data/tair10.fasta"), futr)
uorf <- predORF(dna, n = "all", mode = "orf", longest_disjoint = TRUE, 
    strand = "sense")

To use the predicted ORF ranges for expression analysis given genome alignments as input, it is necessary to scale them to the corresponding genome coordinates. The function scaleRanges does this by transforming the mappings of spliced features (query ranges) to their corresponding genome coordinates (subject ranges). The method accounts for introns in the subject ranges that are absent in the query ranges. The above uORFs predicted in the provided 5’ UTRs sequences using predORF are a typical use case for this application. These query ranges are given relative to the 5’ UTR sequences and scaleRanges will convert them to the corresponding genome coordinates. The resulting GRangesList object (here grl_scaled) can be directly used for read counting.

grl_scaled <- scaleRanges(subject = futr, query = uorf, type = "uORF", 
    verbose = TRUE)
export.gff3(unlist(grl_scaled), "results/uorf.gff")

To confirm the correctness of the obtained uORF ranges, one can parse their corresponding DNA sequences from the reference genome with the getSeq function and then translate them with the translate function into proteins. Typically, the returned protein sequences should start with a M (corresponding to start codon) and end with * (corresponding to stop codon). The following example does this for a single uORF containing three exons.

translate(unlist(getSeq(FaFile("data/tair10.fasta"), grl_scaled[[7]])))

Adding custom features to other feature types

If required custom feature ranges can be added to the standard features generated with the genFeatures function above. The following does this for the uORF ranges predicted with predORF.

feat <- genFeatures(txdb, featuretype = "all", reduce_ranges = FALSE)
feat <- c(feat, GRangesList(uORF = unlist(grl_scaled)))

Predicting sORFs in intergenic regions

The following identifies continuous ORFs in intergenic regions. Note, predORF can only identify continuous ORFs in query sequences. The function does not identify and remove introns prior to the ORF prediction.

feat <- genFeatures(txdb, featuretype = "intergenic", reduce_ranges = TRUE)
intergenic <- feat$intergenic
strand(intergenic) <- "+"
dna <- getSeq(FaFile("data/tair10.fasta"), intergenic)
names(dna) <- mcols(intergenic)$feature_by
sorf <- predORF(dna, n = "all", mode = "orf", longest_disjoint = TRUE, 
    strand = "both")
sorf <- sorf[width(sorf) > 60]  # Remove sORFs below length cutoff, here 60bp
intergenic <- split(intergenic, mcols(intergenic)$feature_by)
grl_scaled_intergenic <- scaleRanges(subject = intergenic, query = sorf, 
    type = "sORF", verbose = TRUE)
export.gff3(unlist(grl_scaled_intergenic), "sorf.gff")
translate(getSeq(FaFile("data/tair10.fasta"), unlist(grl_scaled_intergenic)))



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