encodeOverlaps-methods {GenomicAlignments} | R Documentation

## Encode the overlaps between RNA-seq reads and the transcripts of a gene model

### Description

In the context of an RNA-seq experiment, encoding the overlaps between the aligned reads and the transcripts of a given gene model can be used for detecting those overlaps that are compatible with the splicing of the transcript.

The central tool for this is the `encodeOverlaps` method for GRangesList objects, which computes the "overlap encodings" between a `query` and a `subject`, both list-like objects with list elements containing multiple ranges.

Other related utilities are also documented in this man page.

### Usage

    encodeOverlaps(query, subject, hits=NULL, ...)

    ## S4 method for signature 'GRangesList,GRangesList'
    encodeOverlaps(query, subject, hits=NULL,
                   flip.query.if.wrong.strand=FALSE)

    ## Related utilities:

    flipQuery(x, i)

    selectEncodingWithCompatibleStrand(ovencA, ovencB,
                                       query.strand, subject.strand, hits=NULL)

    isCompatibleWithSkippedExons(x, max.skipped.exons=NA)

    extractSteppedExonRanks(x, for.query.right.end=FALSE)
    extractSpannedExonRanks(x, for.query.right.end=FALSE)
    extractSkippedExonRanks(x, for.query.right.end=FALSE)

    extractQueryStartInTranscript(query, subject, hits=NULL, ovenc=NULL,
                                  flip.query.if.wrong.strand=FALSE,
                                  for.query.right.end=FALSE)

### Arguments

`query, subject` | Typically GRangesList objects representing the the aligned reads and the transcripts of a given gene model, respectively. If the 2 objects don't have the same length, and if the `hits` argument is not supplied, then the shortest is recycled to the length of the longest (the standard recycling rules apply). More generally speaking, `query` and `subject` must be list-like objects with list elements containing multiple ranges e.g. IntegerRangesList or GRangesList objects.
`hits` | An optional Hits object typically obtained from a previous call to `findOverlaps(query, subject)`. Strictly speaking, `hits` only needs to be compatible with `query` and `subject`, that is, `queryLength(hits)` and `subjectLength(hits)` must be equal to `length(query)` and `length(subject)`, respectively. Supplying `hits` is a convenient way to do `encodeOverlaps(query[queryHits(hits)], subject[subjectHits(hits)])`, that is, calling `encodeOverlaps(query, subject, hits)` is equivalent to the above, but is much more efficient, especially when `query` and/or `subject` are big. Of course, when `hits` is supplied, `query` and `subject` are not expected to have the same length anymore.
`...` | Additional arguments for methods.
`flip.query.if.wrong.strand` | See the "OverlapEncodings" vignette located in this package (GenomicAlignments).
`x` | For `flipQuery`: a GRangesList object. For `isCompatibleWithSkippedExons`, `extractSteppedExonRanks`, `extractSpannedExonRanks`, and `extractSkippedExonRanks`: an OverlapEncodings object, a factor, or a character vector.
`i` | Subscript specifying the elements in `x` to flip. If missing, all the elements are flipped.
`ovencA, ovencB, ovenc` | OverlapEncodings objects.
`query.strand, subject.strand` | Vector-like objects containing the strand of the query and subject, respectively.
`max.skipped.exons` | Not supported yet. If `NA` (the default), the number of skipped exons must be 1 or more (there is no max).
`for.query.right.end` | If `TRUE`, then the information reported in the output is for the right ends of the paired-end reads. Using `for.query.right.end=TRUE` with single-end reads is an error.

### Details

See `?OverlapEncodings` for a short introduction to "overlap encodings".

The topic of working with overlap encodings is covered in details in the "OverlapEncodings" vignette located this package (GenomicAlignments) and accessible with `vignette("OverlapEncodings")`.

### Value

For `encodeOverlaps`: An OverlapEncodings object. If `hits` is not supplied, this object is parallel to the longest of `query` and `subject`, that is, it has the length of the longest and the i-th encoding in it corresponds to the i-th element in the longest. If `hits` is supplied, then the returned object is parallel to it, that is, it has one encoding per hit.

For `flipQuery`: TODO

For `selectEncodingWithCompatibleStrand`: TODO

For `isCompatibleWithSkippedExons`: A logical vector parallel to `x`.

For `extractSteppedExonRanks`, `extractSpannedExonRanks`, and `extractSkippedExonRanks`: TODO

For `extractQueryStartInTranscript`: TODO

### Author(s)

Hervé Pagès

### See Also

- The OverlapEncodings class for a brief introduction to "overlap encodings".

- The Hits class defined and documented in the S4Vectors package.

- The "OverlapEncodings" vignette in this package.

- `findCompatibleOverlaps` for a specialized version of `findOverlaps` that uses `encodeOverlaps` internally to keep only the hits where the junctions in the aligned read are compatible with the splicing of the annotated transcript.

- The GRangesList class defined and documented in the GenomicRanges package.

- The `findOverlaps` generic function defined in the IRanges package.

### Examples

    ## ---------------------------------------------------------------------
    ## A. BETWEEN 2 IntegerRangesList OBJECTS
    ## ---------------------------------------------------------------------
    ## In the context of an RNA-seq experiment, encoding the overlaps
    ## between 2 GRangesList objects, one containing the reads (the query),
    ## and one containing the transcripts (the subject), can be used for
    ## detecting hits between reads and transcripts that are "compatible"
    ## with the splicing of the transcript. Here we illustrate this with 2
    ## IntegerRangesList objects, in order to keep things simple:

    ## 4 aligned reads in the query:
    read1 <- IRanges(c(7, 15, 22), c(9, 19, 23))  # 2 junctions
    read2 <- IRanges(c(5, 15), c(9, 17))  # 1 junction
    read3 <- IRanges(c(16, 22), c(19, 24))  # 1 junction
    read4 <- IRanges(c(16, 23), c(19, 24))  # 1 junction
    query <- IRangesList(read1, read2, read3, read4)

    ## 1 transcript in the subject:
    tx <- IRanges(c(1, 4, 15, 22, 38), c(2, 9, 19, 25, 47))  # 5 exons
    subject <- IRangesList(tx)

    ## Encode the overlaps:
    ovenc <- encodeOverlaps(query, subject)
    ovenc
    encoding(ovenc)

    ## ---------------------------------------------------------------------
    ## B. BETWEEN 2 GRangesList OBJECTS
    ## ---------------------------------------------------------------------
    ## With real RNA-seq data, the reads and transcripts will typically be
    ## stored in GRangesList objects. Please refer to the "OverlapEncodings"
    ## vignette in this package for realistic examples.

[Package GenomicAlignments version 1.26.0 Index]