readGAlignments {GenomicAlignments} | R Documentation

## Reading genomic alignments from a file

### Description

Read genomic alignments from a file (typically a BAM file) into a GAlignments, GAlignmentPairs, GAlignmentsList, or GappedReads object.

### Usage

    readGAlignments(file, index=file, use.names=FALSE, param=NULL,
                          with.which_label=FALSE)

    readGAlignmentPairs(file, index=file, use.names=FALSE, param=NULL,
                              with.which_label=FALSE, strandMode=1)

    readGAlignmentsList(file, index=file, use.names=FALSE,
                              param=ScanBamParam(), with.which_label=FALSE)

    readGappedReads(file, index=file, use.names=FALSE, param=NULL,
                          with.which_label=FALSE)

### Arguments

`file` | The path to the file to read or a BamFile object. Can also be a BamViews object for `readGAlignments`.
`index` | The path to the index file of the BAM file to read. Must be given without the '.bai' extension. See `scanBam` in the Rsamtools packages for more information.
`use.names` | `TRUE` or `FALSE`. By default (i.e. `use.names=FALSE`), the resulting object has no names. If `use.names` is `TRUE`, then the names are constructed from the query template names (QNAME field in a SAM/BAM file). Note that the 2 records in a pair (when using `readGAlignmentPairs` or the records in a group (when using `readGAlignmentsList`) have the same QNAME.
`param` | `NULL` or a ScanBamParam object. Like for `scanBam`, this influences what fields and which records are imported. However, note that the fields specified thru this ScanBamParam object will be loaded in addition to any field required for generating the returned object (GAlignments, GAlignmentPairs, or GappedReads object), but only the fields requested by the user will actually be kept as metadata columns of the object. By default (i.e. `param=NULL` or `param=ScanBamParam()`), no additional field is loaded. The flag used is `scanBamFlag(isUnmappedQuery=FALSE)` for `readGAlignments`, `readGAlignmentsList`, and `readGappedReads`. (i.e. only records corresponding to mapped reads are loaded), and `scanBamFlag(isUnmappedQuery=FALSE, isPaired=TRUE, hasUnmappedMate=FALSE)` for `readGAlignmentPairs` (i.e. only records corresponding to paired-end reads with both ends mapped are loaded).
`with.which_label` | `TRUE` or `FALSE` (the default). If `TRUE` and if `param` has a `which` component, a `"which_label"` metadata column is added to the returned GAlignments or GappedReads object, or to the `first` and `last` components of the returned GAlignmentPairs object. In the case of `readGAlignmentsList`, it's added as an inner metadata column, that is, the metadata column is placed on the GAlignments object obtained by unlisting the returned GAlignmentsList object. The purpose of this metadata column is to unambiguously identify the range in `which` where each element in the returned object originates from. The labels used to identify the ranges are normally of the form `"seq1:12250-246500"`, that is, they're the same as the names found on the outer list that `scanBam` would return if called with the same `param` argument. If some ranges are duplicated, then the labels are made unique by appending a unique suffix to all of them. The `"which_label"` metadata column is represented as a factor-Rle.
`strandMode` | Strand mode to set on the returned GAlignmentPairs object. See `?strandMode` for more information.

### Details

- `readGAlignments` reads a file containing aligned reads as a GAlignments object. See `?GAlignments` for a description of GAlignments objects.

  When `file` is a BamViews object, `readGAlignments` visits each path in `bamPaths(file)`, returning the result of `readGAlignments` applied to the specified path. When `index` is missing, it is set equal to `bamIndicies(file)`. Only reads in `bamRanges(file)` are returned (if `param` is supplied, `bamRanges(file)` takes precedence over `bamWhich(param)`). The return value is a SimpleList object, with elements of the list corresponding to each path. `bamSamples(file)` is available as metadata columns (accessed with `mcols`) of the returned SimpleList object.

- `readGAlignmentPairs` reads a file containing aligned paired-end reads as a GAlignmentPairs object. See `?GAlignmentPairs` for a description of GAlignmentPairs objects.

- `readGAlignmentsList` reads a file containing aligned reads as a GAlignmentsList object. See `?GAlignmentsList` for a description of GAlignmentsList objects. `readGAlignmentsList` pairs records into mates according to the pairing criteria described below. The 1st mate will always be 1st in the GAlignmentsList list elements that have mate_status set to `"mated"`, and the 2nd mate will always be 2nd.

  A `GAlignmentsList` is returned with a ‘mate_status’ metadata column on the outer list elements. `mate_status` is a factor with 3 levels indicating mate status, ‘mated’, ‘ambiguous’ or ‘unmated’:

  - mated: primary or non-primary pairs

  - ambiguous: multiple segments matching to the same location (indistinguishable)

  - unmated: mate does not exist or is unmapped

  When the ‘file’ argument is a BamFile, ‘asMates=TRUE’ must be set, otherwise the data are treated as single-end reads. See the ‘asMates’ section of `?BamFile` in the Rsamtools package for details.

- `readGappedReads` reads a file containing aligned reads as a GappedReads object. See `?GappedReads` for a description of GappedReads objects.

For all these functions, flags, tags and ranges may be specified in the supplied ScanBamParam object for fine tuning of results.

### Value

A GAlignments object for `readGAlignments`.

A GAlignmentPairs object for `readGAlignmentPairs`. Note that a BAM (or SAM) file can in theory contain a mix of single-end and paired-end reads, but in practise it seems that single-end and paired-end are not mixed. In other words, the value of flag bit 0x1 (`isPaired`) is the same for all the records in a file. So if `readGAlignmentPairs` returns a GAlignmentPairs object of length zero, this almost always means that the BAM (or SAM) file contains alignments for single-end reads (although it could also mean that the user-supplied `ScanBamParam` is filtering out everything, or that the file is empty, or that all the records in the file correspond to unmapped reads).

A GAlignmentsList object for `readGAlignmentsList`. When the list contains paired-end reads a metadata data column of `mate_status` is added to the object. See details in the ‘Bam specific back-ends’ section on this man page.

A GappedReads object for `readGappedReads`.

### Pairing criteria

This section describes the pairing criteria used by `readGAlignmentsList` and `readGAlignmentPairs`.

- First, only records with flag bit 0x1 (multiple segments) set to 1, flag bit 0x4 (segment unmapped) set to 0, and flag bit 0x8 (next segment in the template unmapped) set to 0, are candidates for pairing (see the SAM Spec for a description of flag bits and fields). Records that correspond to single-end reads, or records that correspond to paired-end reads where one or both ends are unmapped, will remain unmated.

- Then the following fields and flag bits are considered:

  - (A) QNAME

  - (B) RNAME, RNEXT

  - (C) POS, PNEXT

  - (D) Flag bits Ox10 (segment aligned to minus strand) and 0x20 (next segment aligned to minus strand)

  - (E) Flag bits 0x40 (first segment in template) and 0x80 (last segment in template)

  - (F) Flag bit 0x2 (proper pair)

  - (G) Flag bit 0x100 (secondary alignment)

  2 records rec1 and rec2 are considered mates iff all the following conditions are satisfied:

  - (A) QNAME(rec1) == QNAME(rec2)

  - (B) RNEXT(rec1) == RNAME(rec2) and RNEXT(rec2) == RNAME(rec1)

  - (C) PNEXT(rec1) == POS(rec2) and PNEXT(rec2) == POS(rec1)

  - (D) Flag bit 0x20 of rec1 == Flag bit 0x10 of rec2 and Flag bit 0x20 of rec2 == Flag bit 0x10 of rec1

  - (E) rec1 corresponds to the first segment in the template and rec2 corresponds to the last segment in the template, OR, rec2 corresponds to the first segment in the template and rec1 corresponds to the last segment in the template

  - (F) rec1 and rec2 have same flag bit 0x2

  - (G) rec1 and rec2 have same flag bit 0x100

Note that this is actually the pairing criteria used by `scanBam` (when the BamFile passed to it has the `asMates` toggle set to `TRUE`), which `readGAlignmentsList` and `readGAlignmentPairs` call behind the scene. It is also the pairing criteria used by `findMateAlignment`.

### Note

BAM records corresponding to unmapped reads are always ignored.

Starting with Rsamtools 1.7.1 (BioC 2.10), PCR or optical duplicates are loaded by default (use `scanBamFlag(isDuplicate=FALSE)` to drop them).

### Author(s)

Hervé Pagès <hpages@fredhutch.org> and Valerie Obenchain <Valerie.Obenchain@RoswellPark.org>

### See Also

- `scanBam` and `ScanBamParam` in the Rsamtools package.

- GAlignments, GAlignmentPairs, GAlignmentsList, and GappedReads objects.

- RangesList objects (used in the examples below to specify the `which` regions) in the IRanges package.

### Examples

    ## ---------------------------------------------------------------------
    ## A. readGAlignments()
    ## ---------------------------------------------------------------------

    ## Simple use:
    bamfile <- system.file("extdata", "ex1.bam", package="Rsamtools",
                           mustWork=TRUE)
    gal1 <- readGAlignments(bamfile)
    gal1
    names(gal1)

    ## Using the 'use.names' arg:
    gal2 <- readGAlignments(bamfile, use.names=TRUE)
    gal2
    head(names(gal2))

    ## Using the 'param' arg to drop PCR or optical duplicates as well as
    ## secondary alignments, and to load additional BAM fields:
    param <- ScanBamParam(flag=scanBamFlag(isDuplicate=FALSE,
                                           isSecondaryAlignment=FALSE),
                          what=c("qual", "flag"))
    gal3 <- readGAlignments(bamfile, param=param)
    gal3
    mcols(gal3)

    ## Using the 'param' arg to load alignments from particular regions.
    which <- RangesList(seq1=IRanges(1000, 1100),
                        seq2=IRanges(c(1546, 1555, 1567), width=10))
    param <- ScanBamParam(which=which)
    gal4 <- readGAlignments(bamfile, use.names=TRUE, param=param)
    gal4

    ## IMPORTANT NOTE: A given record is loaded one time for each region
    ## it overlaps with. We call this "duplicated record selection" (this
    ## is a scanBam() feature, readGAlignments() is based on scanBam()):
    which <- IRangesList(seq2=IRanges(c(1555, 1567), width=10))
    param <- ScanBamParam(which=which)
    gal5 <- readGAlignments(bamfile, use.names=TRUE, param=param)
    gal5  # record EAS114_26:7:37:79:581 was loaded twice

    ## This becomes clearer if we use 'with.which_label=TRUE' to identify
    ## the region in 'which' where each element in 'gal5' originates from.
    gal5 <- readGAlignments(bamfile, use.names=TRUE, param=param,
                                     with.which_label=TRUE)
    gal5

    ## Not surprisingly, we also get "duplicated record selection" when
    ## 'which' contains repeated or overlapping regions. Using the same
    ## regions as we did for 'gal4' above, except that now we're
    ## repeating the region on seq1:
    which <- RangesList(seq1=rep(IRanges(1000, 1100), 2),
                        seq2=IRanges(c(1546, 1555, 1567), width=10))
    param <- ScanBamParam(which=which)
    gal4b <- readGAlignments(bamfile, use.names=TRUE, param=param)
    length(gal4b)  # > length(gal4), because all the records overlapping
                   # with bases 1000 to 1100 on seq1 are now duplicated

    ## The "duplicated record selection" will artificially increase the
    ## coverage or affect other downstream results. It can be mitigated
    ## (but not completely eliminated) by first "reducing" the set of
    ## regions:
    which <- reduce(which)
    which
    param <- ScanBamParam(which=which)
    gal4c <- readGAlignments(bamfile, use.names=TRUE, param=param)
    length(gal4c)  # < length(gal4), because the 2 first original regions
                   # on seq2 were merged into a single one

    ## Note that reducing the set of regions didn't completely eliminate
    ## "duplicated record selection". Records that overlap the 2 reduced
    ## regions on seq2 (which$seq2) are loaded twice (like for 'gal5'
    ## above). See example D. below for how to completely eliminate
    ## "duplicated record selection".

    ## Using the 'param' arg to load tags. Except for MF and Aq, the tags
    ## specified below are predefined tags (see the SAM Spec for the list
    ## of predefined tags and their meaning).
    param <- ScanBamParam(tag=c("MF", "Aq", "NM", "UQ", "H0", "H1"),
                          what="isize")
    gal6 <- readGAlignments(bamfile, param=param)
    mcols(gal6)  # "tag" cols always after "what" cols

    ## With a BamViews object:
    fls <- system.file("extdata", "ex1.bam", package="Rsamtools",
                       mustWork=TRUE)
    bv <- BamViews(fls,
                   bamSamples=DataFrame(info="test", row.names="ex1"),
                   auto.range=TRUE)
    ## Note that the "readGAlignments" method for BamViews objects
    ## requires the ShortRead package to be installed.
    aln <- readGAlignments(bv)
    aln
    aln[[1]]
    aln[colnames(bv)]
    mcols(aln)

    ## ---------------------------------------------------------------------
    ## B. readGAlignmentPairs()
    ## ---------------------------------------------------------------------
    galp1 <- readGAlignmentPairs(bamfile)
    head(galp1)
    names(galp1)

    ## Here we use the 'param' arg to filter by proper pair, drop PCR /
    ## optical duplicates, and drop secondary alignments. Filtering by
    ## proper pair and dropping secondary alignments can help make the
    ## pairing algorithm run significantly faster:
    param <- ScanBamParam(flag=scanBamFlag(isProperPair=TRUE,
                                           isDuplicate=FALSE,
                                           isSecondaryAlignment=FALSE))
    galp2 <- readGAlignmentPairs(bamfile, use.names=TRUE, param=param)
    galp2
    head(galp2)
    head(names(galp2))

    ## ---------------------------------------------------------------------
    ## C. readGAlignmentsList()
    ## ---------------------------------------------------------------------
    library(pasillaBamSubset)

    ## 'file' as character.
    bam <- untreated3_chr4()
    galist1 <- readGAlignmentsList(bam)
    galist1[1:3]
    length(galist1)
    table(elementNROWS(galist1))

    ## When 'file' is a BamFile, 'asMates' must be TRUE. If FALSE,
    ## the data are treated as single-end and each list element of the
    ## GAlignmentsList will be of length 1. For single-end data
    ## use readGAlignments().
    bamfile <- BamFile(bam, yieldSize=3, asMates=TRUE)
    readGAlignmentsList(bamfile)

    ## Use a 'param' to fine tune the results.
    param <- ScanBamParam(flag=scanBamFlag(isProperPair=TRUE))
    galist2 <- readGAlignmentsList(bam, param=param)
    length(galist2)

    ## ---------------------------------------------------------------------
    ## D. COMPARING 4 STRATEGIES FOR LOADING THE ALIGNMENTS THAT OVERLAP
    ##    WITH THE EXONIC REGIONS ON FLY CHROMMOSOME 4
    ## ---------------------------------------------------------------------
    library(pasillaBamSubset)
    bam <- untreated1_chr4()

    library(TxDb.Dmelanogaster.UCSC.dm3.ensGene)
    txdb <- TxDb.Dmelanogaster.UCSC.dm3.ensGene
    ex <- exons(txdb)
    seqlevels(ex, pruning.mode="coarse") <- "chr4"
    length(ex)

    ## Some of the exons overlap with each other:
    isDisjoint(ex)  # FALSE
    exonic_regions <- reduce(ex)
    isDisjoint(exonic_regions)  # no more overlaps
    length(exonic_regions)

    ## Strategy #1: slow and loads a lot of records more than once (see
    ## "duplicated record selection" in example A. above).
    param1 <- ScanBamParam(which=ex)
    gal1 <- readGAlignments(bam, param=param1)
    length(gal1)  # many "duplicated records"

    ## Strategy #2: faster and generates less duplicated records but
    ## doesn't eliminate them.
    param2 <- ScanBamParam(which=exonic_regions)
    gal2 <- readGAlignments(bam, param=param2)
    length(gal2)  # less "duplicated records"

    ## Strategy #3: fast and completely eliminates duplicated records.
    gal0 <- readGAlignments(bam)
    gal3 <- subsetByOverlaps(gal0, exonic_regions, ignore.strand=TRUE)
    length(gal3)  # no "duplicated records"

    ## Note that, in this case using 'exonic_regions' or 'ex' makes no
    ## difference:
    gal3b <- subsetByOverlaps(gal0, ex, ignore.strand=TRUE)
    stopifnot(identical(gal3, gal3b))

    ## Strategy #4: strategy #3 however can require a lot of memory if the
    ## file is big because we load all the alignments into memory before we
    ## select those that overlap with the exonic regions. Strategy #4
    ## addresses this by loading the file by chunks.
    bamfile <- BamFile(bam, yieldSize=50000)
    open(bamfile)
    while (length(chunk0 <- readGAlignments(bamfile))) {
        chunk <- subsetByOverlaps(chunk0, ex, ignore.strand=TRUE)
        cat("chunk0:", length(chunk0), "- chunk:", length(chunk), "\n")
        ## ... do something with 'chunk' ...
    }
    close(bamfile)

    ## ---------------------------------------------------------------------
    ## E. readGappedReads()
    ## ---------------------------------------------------------------------
    greads1 <- readGappedReads(bamfile)
    greads1
    names(greads1)
    qseq(greads1)
    greads2 <- readGappedReads(bamfile, use.names=TRUE)
    head(greads2)
    head(names(greads2))

[Package GenomicAlignments version 1.14.2 Index]