SplicingGraphs (version 1.10.0)

toy_data: Little helpers for quick access to the toy data included in the SplicingGraphs package

Description

TODO

Usage

toy_genes_gff()
toy_reads_sam()
toy_reads_bam()
toy_overlaps()

See Also

This man page is part of the SplicingGraphs package. Please see ?`SplicingGraphs-package` for an overview of the package and for an index of its man pages.

Other topics related to this man page and documented in other packages:

• The GRangesList class defined in the GenomicRanges package.

The GAlignments and GAlignmentPairs classes defined in the GenomicAlignments package.

The makeTxDbFromGFF function and the TxDb class defined in the GenomicFeatures package.

Examples

## ---------------------------------------------------------------------
## A. LOAD THE TOY GENE MODEL AS A TxDb OBJECT AND PLOT IT
## ---------------------------------------------------------------------
toy_genes_gff()

## Note that you can display the content of the file with:
cat(readLines(toy_genes_gff()), sep="\n")

library(GenomicFeatures)
suppressWarnings(
  txdb <- makeTxDbFromGFF(toy_genes_gff())
)

## Plot all the transcripts in the gene model:
plotTranscripts(txdb)

## ---------------------------------------------------------------------
## B. LOAD THE TOY READS AS A GAlignments OBJECT AND PLOT THEM
## ---------------------------------------------------------------------
## The reads are single-end reads. They are assumed to come from an
## RNA-seq experiment and to have been aligned to the exact same
## reference genome that the above toy gene model is based on.
toy_reads_sam()
toy_reads_bam()
gal <- readGAlignments(toy_reads_bam(), use.names=TRUE)

plotTranscripts(txdb, reads=gal)
plotTranscripts(txdb, reads=gal, from=1, to=320)

## ---------------------------------------------------------------------
## C. FIND THE OVERLAPS BETWEEN THE TOY READS AND THE TOY GENE MODEL
## ---------------------------------------------------------------------
grl <- grglist(gal, order.as.in.query=TRUE)
ex_by_tx <- exonsBy(txdb, by="tx", use.names=TRUE)

## Most of the times the RNA-seq protocol is unstranded so the strand
## reported in the BAM file for each alignment is meaningless. Thus we
## should call findOverlaps() with 'ignore.strand=TRUE':
ov0 <- findOverlaps(grl, ex_by_tx, ignore.strand=TRUE)

## Sort and put the overlaps in a data.frame to make them easier to
## read:
ov0 <- sort(ov0)
df0 <- data.frame(QNAME=names(grl)[queryHits(ov0)],
                  tx_id=names(ex_by_tx)[subjectHits(ov0)],
                  stringsAsFactors=FALSE)
head(df0)

## These overlaps have been manually checked and included in the
## SplicingGraphs package. They can be loaded with the toy_overlaps()
## helper:
toy_ov <- toy_overlaps()
head(toy_ov)
stopifnot(identical(df0, toy_ov[ , 1:2]))

## ---------------------------------------------------------------------
## D. DETECT THE OVERLAPS THAT ARE COMPATIBLE WITH THE GENE MODEL
## ---------------------------------------------------------------------
## First we encode the overlaps:
ovenc0 <- encodeOverlaps(grl, ex_by_tx, hits=ov0,
                         flip.query.if.wrong.strand=TRUE)
ovenc0

## Each encoding tells us whether the corresponding overlap is
## compatible or not with the gene model:
ov0_is_comp <- isCompatibleWithSplicing(ovenc0)
head(ov0_is_comp)

## Overlap compatibility has also been manually checked and included in
## the table returned by toy_overlaps():
stopifnot(identical(ov0_is_comp, toy_ov[ , 3]))