# junctions-methods

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##### Extract junctions from genomic alignments

Given an object x containing genomic alignments (e.g. a GAlignments, GAlignmentPairs, or GAlignmentsList object), junctions(x) extracts the junctions from it and summarizeJunctions(x) extracts and summarizes them.

readTopHatJunctions and readSTARJunctions are utilities for importing the junction file generated by the TopHat and STAR aligners, respectively.

Keywords
manip, methods
##### Usage
## junctions() and summarizeJunctions()
## ------------------------------------
junctions(x, use.mcols=FALSE, ...)
"junctions"(x, use.mcols=FALSE)
"junctions"(x, use.mcols=FALSE)
"junctions"(x, use.mcols=FALSE, ignore.strand=FALSE)
## summarizeJunctions() and NATURAL_INTRON_MOTIFS
## ----------------------------------------------
summarizeJunctions(x, with.revmap=FALSE, genome=NULL)
NATURAL_INTRON_MOTIFS
## Utilities for importing the junction file generated by some aligners
## --------------------------------------------------------------------
readSTARJunctions(file)
##### Arguments
x
A GAlignments, GAlignmentPairs, or GAlignmentsList object.
use.mcols
TRUE or FALSE (the default). Whether the metadata columns on x (accessible with mcols(x)) should be propagated to the returned object or not.
...
Additional arguments, for use in specific methods.
ignore.strand
TRUE or FALSE (the default). If set to TRUE, then the strand of x is set to "*" prior to any computation.
with.revmap
TRUE or FALSE (the default). If set to TRUE, then a revmap metadata column is added to the output of summarizeJunctions. This metadata column is an IntegerList object representing the mapping from each element in the ouput (i.e. each junction) to the corresponding elements in the input x.
genome
NULL (the default), or a BSgenome object containing the sequences of the reference genome that was used to align the reads, or the name of this reference genome specified in a way that is accepted by the getBSgenome function defined in the BSgenome software package. In that case the corresponding BSgenome data package needs to be already installed (see ?getBSgenome in the BSgenome package for the details).

If genome is supplied, then the intron_motif and intron_strand metadata columns are computed (based on the dinucleotides found at the intron boundaries) and added to the output of summarizeJunctions. See the Value section below for a description of these metadata columns.

file
The path (or a connection) to the junction file generated by the aligner. This file should be the junctions.bed or new_list.juncs file for readTopHatJunctions, and the SJ.out.tab file for readSTARJunctions.
file.is.raw.juncs
TRUE or FALSE (the default). If set to TRUE, then the input file is assumed to be a TopHat .juncs file instead of the junctions.bed file generated by TopHat. A TopHat .juncs file can be obtained by passing the junctions.bed file thru TopHat's bed_to_juncs script. See the TopHat manual at http://tophat.cbcb.umd.edu/manual.shtml for more information.
##### Details

An N operation in the CIGAR of a genomic alignment is interpreted as a junction. junctions(x) will return the genomic ranges of all junctions found in x.

More precisely, if x is a GAlignments object, junctions(x) is equivalent to:

  psetdiff(granges(x), grglist(x, order.as.in.query=TRUE))

On a x is a GAlignmentPairs object, it's equivalent to (but faster than):
  mendoapply(c, junctions(first(x)), junctions(last(x)))


NATURAL_INTRON_MOTIFS is a predefined character vector containing the 5 natural intron motifs described at http://www.ncbi.nlm.nih.gov/pmc/articles/PMC84117/.

##### Value

junctions(x) returns the genomic ranges of the junctions in a GRangesList object parallel to x (i.e. with 1 list element per element in x). If x has names on it, they're propagated to the returned object. If use.mcols is TRUE and x has metadata columns on it (accessible with mcols(x)), they're propagated to the returned object.summarizeJunctions returns the genomic ranges of the unique junctions in x in an unstranded GRanges object with the following metadata columns:
• score: The total number of alignments crossing each junction, i.e., that have the junction encoded in their CIGAR.
• plus_score and minus_score: The strand-specific number of alignments crossing each junction.
• revmap: [Only if with.revmap was set to TRUE.] An IntegerList object representing the mapping from each element in the ouput (i.e. each junction) to the corresponding elements in input x.
• intron_motif and intron_strand: [Only if genome was supplied.] The intron motif and strand for each junction, based on the dinucleotides found in the genome sequences at the intron boundaries. The intron_motif metadata column is a factor whose levels are the 5 natural intron motifs stored in predefined character vector NATURAL_INTRON_MOTIFS. If the dinucleotides found at the intron boundaries don't match any of these natural intron motifs, then intron_motif and intron_strand are set to NA and *, respectively.
readTopHatJunctions and readSTARJunctions return the junctions reported in the input file in a stranded GRanges object. With the following metadata columns for readTopHatJunctions (when reading in the junctions.bed file):
• name: An id assigned by TopHat to each junction. This id is of the form JUNC00000017 and is unique within the junctions.bed file.
• score: The total number of alignments crossing each junction.
With the following metadata columns for readSTARJunctions:
• intron_motif and intron_strand: The intron motif and strand for each junction, based on the code found in the input file (0: non-canonical, 1: GT/AG, 2: CT/AC, 3: GC/AG, 4: CT/GC, 5: AT/AC, 6: GT/AT). Note that of the 5 natural intron motifs stored in predefined character vector NATURAL_INTRON_MOTIFS, only the first 3 are assigned codes by the STAR software (2 codes per motif, one if the intron is on the plus strand and one if it's on the minus strand). Thus the intron_motif metadata column is a factor with only 3 levels. If code is 0, then intron_motif and intron_strand are set to NA and *, respectively.
• um_reads: The number of uniquely mapping reads crossing the junction (a pair where the 2 alignments cross the same junction is counted only once).
• mm_reads: The number of multi-mapping reads crossing the junction (a pair where the 2 alignments cross the same junction is counted only once).

##### References

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC84117/ for the 5 natural intron motifs stored in predefined character vector NATURAL_INTRON_MOTIFS.

TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions

STAR: ultrafast universal RNA-seq aligner

• GAlignments, GAlignmentPairs, and GAlignmentsList objects.

• The GRanges and GRangesList classes defined and documented in the GenomicRanges package.

• The IntegerList class defined and documented in the IRanges package.

• The getBSgenome function in the BSgenome package, for searching the installed BSgenome data packages for the specified genome and returning it as a BSgenome object.

• The readGAlignments and readGAlignmentPairs functions for reading genomic alignments from a file.

• The extractList function in the IRanges package, for extracting groups of elements from a vector-like object and returning them into a List object.

##### Aliases
• junctions-methods
• junctions
• junctions,GAlignments-method
• junctions,GAlignmentPairs-method
• junctions,GAlignmentsList-method
• NATURAL_INTRON_MOTIFS
• summarizeJunctions
• introns
##### Examples
library(RNAseqData.HNRNPC.bam.chr14)
bamfile <- RNAseqData.HNRNPC.bam.chr14_BAMFILES[1]

## ---------------------------------------------------------------------
## A. junctions()
## ---------------------------------------------------------------------

table(njunc(gal))  # some alignments have 3 junctions!
juncs <- junctions(gal)
juncs

stopifnot(identical(unname(elementLengths(juncs)), njunc(gal)))

juncs <- junctions(galp)
juncs

stopifnot(identical(unname(elementLengths(juncs)), njunc(galp)))

## ---------------------------------------------------------------------
## B. summarizeJunctions()
## ---------------------------------------------------------------------

## By default, only the "score", "plus_score", and "minus_score"
junc_summary <- summarizeJunctions(gal)
junc_summary

## The "score" metadata column reports the total number of alignments
## crossing each junction, i.e., that have the junction encoded in their
## CIGAR:
median(mcols(junc_summary)score) ## The "plus_score" and "minus_score" metadata columns report the ## strand-specific number of alignments crossing each junction: stopifnot(identical(mcols(junc_summary)score,
mcols(junc_summary)$plus_score + mcols(junc_summary)$minus_score))

## If 'with.revmap' is TRUE, the "revmap" metadata column is added to
## the output. This metadata column is an IntegerList object represen-
## ting the mapping from each element in the ouput (i.e. a junction) to
## the corresponding elements in the input 'x'. Here we're going to use
## this to compute a 'score2' for each junction. We obtain this score
## by summing the mapping qualities of the alignments crossing the
## junction:
junc_summary <- summarizeJunctions(gal, with.revmap=TRUE)
junc_score2 <- sum(extractList(mcols(gal)$mapq, mcols(junc_summary)$revmap))
mcols(junc_summary)score2 <- junc_score2 ## If the name of the reference genome is specified thru the 'genome' ## argument (in which case the corresponding BSgenome data package needs ## to be installed), then summarizeJunctions() returns the intron motif ## and strand for each junction. ## Since the reads in RNAseqData.HNRNPC.bam.chr14 were aligned to ## the hg19 genome, the following requires that you have ## BSgenome.Hsapiens.UCSC.hg19 installed: junc_summary <- summarizeJunctions(gal, with.revmap=TRUE, genome="hg19") mcols(junc_summary)score2 <- junc_score2  # putting 'score2' back

## The "intron_motif" metadata column is a factor whose levels are the
## 5 natural intron motifs stored in predefined character vector
## 'NATURAL_INTRON_MOTIFS':
table(mcols(junc_summary)intron_motif) ## --------------------------------------------------------------------- ## C. STRANDED RNA-seq PROTOCOL ## --------------------------------------------------------------------- ## Here is a simple test for checking whether the RNA-seq protocol was ## stranded or not: strandedTest <- function(plus_score, minus_score) (sum(plus_score ^ 2) + sum(minus_score ^ 2)) / sum((plus_score + minus_score) ^ 2) ## The result of this test is guaranteed to be >= 0.5 and <= 1. ## If, for each junction, the strand of the crossing alignments looks ## random (i.e. "plus_score" and "minus_score" are close), then ## strandedTest() will return a value close to 0.5. If it doesn't look ## random (i.e. for each junction, one of "plus_score" and "minus_score" ## is much bigger than the other), then strandedTest() will return a ## value close to 1. ## If the reads are single-end, the test is meaningful when applied ## directly on 'junc_summary'. However, for the test to be meaningful ## on paired-end reads, it needs to be applied on the first and last ## alignments separately: junc_summary1 <- summarizeJunctions(first(galp)) junc_summary2 <- summarizeJunctions(last(galp)) strandedTest(mcols(junc_summary1)plus_score,
mcols(junc_summary1)$minus_score) strandedTest(mcols(junc_summary2)$plus_score,
mcols(junc_summary2)minus_score) ## Both values are close to 0.5 which suggests that the RNA-seq protocol ## used for this experiment was not stranded. ## --------------------------------------------------------------------- ## UTILITIES FOR IMPORTING THE JUNCTION FILE GENERATED BY SOME ALIGNERS ## --------------------------------------------------------------------- ## The TopHat aligner generates a junctions.bed file where it reports ## all the junctions satisfying some "quality" criteria (see the TopHat ## manual at http://tophat.cbcb.umd.edu/manual.shtml for more ## information). This file can be loaded with readTopHatJunctions(): runname <- names(RNAseqData.HNRNPC.bam.chr14_BAMFILES)[1] junctions_file <- system.file("extdata", "tophat2_out", runname, "junctions.bed", package="RNAseqData.HNRNPC.bam.chr14") th_junctions <- readTopHatJunctions(junctions_file) ## Comparing the "TopHat junctions" with the result of ## summarizeJunctions(): th_junctions14 <- th_junctions seqlevels(th_junctions14, force=TRUE) <- "chr14" mcols(th_junctions14)intron_strand <- strand(th_junctions14)
strand(th_junctions14) <- "*"

## All the "TopHat junctions" are in 'junc_summary':
stopifnot(all(th_junctions14 %in% junc_summary))

## But not all the junctions in 'junc_summary' are reported by TopHat
## (that's because TopHat reports only junctions that satisfy some
## "quality" criteria):
is_in_th_junctions14 <- junc_summary %in% th_junctions14
table(is_in_th_junctions14)  # 32 junctions are not in TopHat's
# junctions.bed file
junc_summary2 <- junc_summary[is_in_th_junctions14]

## 'junc_summary2' and 'th_junctions14' contain the same junctions in
## the same order:
stopifnot(all(junc_summary2 == th_junctions14))

## Let's merge their metadata columns. We use our own version of
## merge() for this, which is stricter (it checks that the common
## columns are the same in the 2 data frames to merge) and also
## simpler:
merge2 <- function(df1, df2)
{
common_colnames <- intersect(colnames(df1), colnames(df2))
lapply(common_colnames,
function(colname)
stopifnot(all(df1[ , colname] == df2[ , colname])))
extra_mcolnames <- setdiff(colnames(df2), colnames(df1))
cbind(df1, df2[ , extra_mcolnames, drop=FALSE])
}

mcols(th_junctions14) <- merge2(mcols(th_junctions14),
mcols(junc_summary2))

## Here is a peculiar junction reported by TopHat:
idx0 <- which(mcols(th_junctions14)$score2 == 0L) th_junctions14[idx0] gal[mcols(th_junctions14)$revmap[[idx0]]]
## The junction is crossed by 5 alignments (score is 5), all of which
## have a mapping quality of 0!

Documentation reproduced from package GenomicAlignments, version 1.2.2, License: Artistic-2.0

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