factR-run.RdThis wrapper function will perform the core factR workflow which includes:
1: Building CDS structures
2: Predicting NMD-sensitive transcripts
3: Translating open-reading frames
4: Quantify sequence conservation of alternative exons
runfactR(
object,
NMD_threshold = 50,
cons_db = "phastCons",
cons_type = "exon",
cons_padding = 0,
verbose = TRUE
)factR object
Minimum distance between PTC and downstream exon-exon junction to trigger NMD (Default: 50)
Database to extract sequence conservation. Can be "phastCons" (Default) or "phylop"
Feature to quantify conservation. Can be one of the following:
"exon" : Sequence conservation of the entire exon
"flanks" : Conservation of sequences flanking exons
"upstream" : Conservation of sequences upstream of exons
"downstream" : Conservation of sequences downstream of exons
Additional width to pad the sequence by. For cons_type "exons" and "flanks", paddings will be added on both sides.
Whether to show pipeline messages (Default: TRUE)
Update factRObject with additional data from runfactR. See links below for detailed description of each function in this wrapper.
buildCDS, predictNMD, testASNMDevents, getAScons, getAAsequence
## Load sample factRObject
data(factRsample)
## Run factR pipeline
factRsample <- runfactR(factRsample)
#> 🡆 Building CDS information
#> Searching for reference mRNAs in query
#> 22 reference mRNAs found and its CDS were assigned
#> Building database of annotated ATG codons
#> Selecting best ATG start codon for remaining transcripts and determining open-reading frame
#> 61 new CDSs constructed
#>
#> Summary: Out of 155 transcripts in `gtf`,
#> 83 transcript CDSs were built
#> ℹ Updating transcript feature data
#> 🡆 Running transcript-level NMD prediction
#> Predicting NMD sensitivities for 83 mRNAs
#>
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#> ℹ Updating transcript feature data
#> 🡆 Translating amino acid sequences
#> ❗ 1 CDSs do not begin with ATG
#> ❗ 2 CDSs contain internal stop codon. Truncating CDS sequence to retain ORF
#> 🡆 Running AS-NMD testing
#> ℹ Getting best reference for AS-NMD testing
#> ℹ Testing AS-NMD exons
#> ℹ Updating AS feature data
#> 🡆 Quantifiying conservation scores
#> ℹ Retrieving phastCons60way.UCSC.mm10 scores
#> ℹ Quantifying `exon` conservation scores with 0 padding