pileup {Rsamtools} | R Documentation

## Use filters and output formats to calculate pile-up statistics for a BAM file.

### Description

`pileup` uses `PileupParam` and `ScanBamParam` objects to calculate pileup statistics for a BAM file. The result is a `data.frame` with columns summarizing counts of reads overlapping each genomic position, optionally differentiated on nucleotide, strand, and position within read.

### Usage

    pileup(file, index=file, ..., scanBamParam=ScanBamParam(),
           pileupParam=PileupParam())

    ## PileupParam constructor
    PileupParam(max_depth=250, min_base_quality=13, min_mapq=0,
        min_nucleotide_depth=1, min_minor_allele_depth=0,
        distinguish_strands=TRUE, distinguish_nucleotides=TRUE,
        ignore_query_Ns=TRUE, include_deletions=TRUE, include_insertions=FALSE,
        left_bins=NULL, query_bins=NULL, cycle_bins=NULL)

    phred2ASCIIOffset(phred=integer(),
        scheme= c("Illumina 1.8+", "Sanger", "Solexa", "Illumina 1.3+",
                  "Illumina 1.5+"))

### Arguments

`file` | character(1) or `BamFile`; BAM file path.
`index` | When `file` is character(1), an optional character(1) of BAM index file path; see `scanBam`.
`...` | Additional arguments, perhaps used by methods.
`scanBamParam` | An instance of `ScanBamParam`.
`pileupParam` | An instance of `PileupParam`.

`max_depth` | integer(1); maximum number of overlapping alignments considered for each position in the pileup.
`min_base_quality` | integer(1); minimum ‘QUAL’ value for each nucleotide in an alignment. Use `phred2ASCIIOffset` to help translate numeric or character values to these offsets.
`min_mapq` | integer(1); minimum ‘MAPQ’ value for an alignment to be included in pileup.
`min_nucleotide_depth` | integer(1); minimum count of each nucleotide (independent of other nucleotides) at a given position required for said nucleotide to appear in the result.
`min_minor_allele_depth` | integer(1); minimum count of all nucleotides other than the major allele at a given position required for a particular nucleotide to appear in the result.
`distinguish_strands` | logical(1); `TRUE` if result should differentiate between strands.
`distinguish_nucleotides` | logical(1); `TRUE` if result should differentiate between nucleotides.
`ignore_query_Ns` | logical(1); `TRUE` if non-determinate nucleotides in alignments should be excluded from the pileup.
`include_deletions` | logical(1); `TRUE` to include deletions in pileup.
`include_insertions` | logical(1); `TRUE` to include insertions in pileup.
`left_bins` | numeric; all same sign; unique positions within a read to delimit bins. Position within read is based on counting from the 5' end regardless of strand. Minimum of two values are required so at least one range can be formed. `NULL` (default) indicates no binning. Use negative values to count from the opposite end. Sorted order not required. Floating-point values are coerced to `integer`. If you want to delimit bins based on sequencing cycles to, e.g., discard later cycles, `query_bins` probably gives the desired behavior.
`query_bins` | numeric; all same sign; unique positions within a read to delimit bins. Position within a read is based on counting from the 5' end when the read aligns to plus strand, counting from the 3' end when read aligns to minus strand. Minimum of two values are required so at least one range can be formed. `NULL` (default) indicates no binning. Use negative values to count from the opposite end. Sorted order not required. Floating-point values are coerced to `integer`.
`phred` | Either a numeric() (coerced to integer()) PHRED score (e.g., in the range (0, 41) for the ‘Illumina 1.8+’ scheme) or character() of printable ASCII characters. When `phred` is character(), it can be of length 1 with 1 or more characters, or of any length where all elements have exactly 1 character.
`scheme` | Encoding scheme, used to translate numeric() PHRED scores to their ASCII code. Ignored when `phred` is already character().
`cycle_bins` | DEPRECATED. See `left_bins` for identical behavior.

### Details

NB: Use of `pileup` assumes familiarity with `ScanBamParam`, and use of `left_bins` and `query_bins` assumes familiarity with `cut`.

`pileup` visits each position in the BAM file, subject to constraints implied by `which` and `flag` of `scanBamParam`. For a given position, all reads overlapping the position that are consistent with constraints dictated by `flag` of `scanBamParam` and `pileupParam` are used for counting. `pileup` also automatically excludes reads with flags that indicate any of:

- unmapped alignment (`isUnmappedQuery`)

- secondary alignment (`isSecondaryAlignment`)

- not passing quality controls (`isNotPassingQualityControls`)

- PCR or optical duplicate (`isDuplicate`)

If no `which` argument is supplied to the `ScanBamParam`, behavior reflects that of `scanBam`: the entire file is visited and counted.

Use a `yieldSize` value when creating a `BamFile` instance to manage memory consumption when using pileup with large BAM files. There are some differences in how `pileup` behavior is affected when the `yieldSize` value is set on the BAM file. See more details below.

Many of the parameters of the `pileupParam` interact. A simple illustration is `ignore_query_Ns` and `distinguish_nucleotides`, as mentioned in the `ignore_query_Ns` section.

Parameters for `pileupParam` belong to two categories: parameters that affect only the filtering criteria (so-called ‘behavior-only’ policies), and parameters that affect filtering behavior and the schema of the results (‘behavior+structure’ policies).

`distinguish_nucleotides` and `distinguish_strands` when set to `TRUE` each add a column (`nucleotide` and `strand`, respectively) to the resulting `data.frame`. If both are TRUE, each combination of `nucleotide` and `strand` at a given position is counted separately. Setting only one to `TRUE` behaves as expected; for example, if only `nucleotide` is set to `TRUE`, each nucleotide at a given position is counted separately, but the distinction of on which strand the nucleotide appears is ignored.

`ignore_query_Ns` determines how ambiguous nucloetides are treated. By default, ambiguous nucleotides (typically ‘N’ in BAM files) in alignments are ignored. If `ignore_query_Ns` is `FALSE`, ambiguous nucleotides are included in counts; further, if `ignore_query_Ns` is `FALSE` and `distinguish_nucleotides` is `TRUE` the ‘N’ nucleotide value appears in the nucleotide column when a base at a given position is ambiguous.

By default, deletions with respect to the reference genome to which the reads were aligned are included in the counts in a pileup. If `include_deletions` is `TRUE` and `distinguish_nucleotides` is `TRUE`, the nucleotide column uses a ‘-’ character to indicate a deletion at a position.

The ‘=’ nucleotide code in the `SEQ` field (to mean ‘identical to reference genome’) is supported by pileup, such that a match with the reference genome is counted separately in the results if `distinguish_nucleotides` is `TRUE`.

CIGAR support: `pileup` handles the extended CIGAR format by only heeding operations that contribute to counts (‘M’, ‘D’, ‘I’). If you are confused about how the different CIGAR operations interact, see the SAM versions of the BAM files used for `pileup` unit testing for a fairly comprehensive human-readable treatment.

- Deletions and clipping:

  The extended CIGAR allows a number of operations conceptually similar to a ‘deletion’ with respect to the reference genome, but offer more specialized meanings than a simple deletion. CIGAR ‘N’ operations (not to be confused with ‘N’ used for non-determinate bases in the `SEQ` field) indicate a large skipped region, ‘S’ a soft clip, and ‘H’ a hard clip. ‘N’, ‘S’, and ‘H’ CIGAR operations are never counted: only counts of true deletions (‘D’ in the CIGAR) can be included by setting `include_deletions` to `TRUE`.

  Soft clip operations contribute to the relative position of nucleotides within a read, whereas hard clip and refskip operations do not. For example, consider a sequence in a bam file that starts at 11, with a CIGAR `2S1M` and `SEQ` field `ttA`. The cycle position of the `A` nucleotide will be `3`, but the reported position will be `11`. If using `left_bins` or `query_bins` it might make sense to first preprocess your files to remove soft clipping.

- Insertions and padding:

  `pileup` can include counts of insertion operations by setting `include_insertions` to `TRUE`. Details about insertions are effectively truncated such that each insertion is reduced to a single ‘+’ nucleotide. Information about e.g. the nucleotide code and base quality within the insertion is not included.

  Because ‘+’ is used as a nucleotide code to represent insertions in `pileup`, counts of the ‘+’ nucleotide participate in voting for `min_nucleotide_depth` and `min_minor_allele_depth` functionality.

  The position of an insertion is the position that precedes it on the reference sequence. Note: this means if `include_insertions` is `TRUE` a single read will contribute two nucleotide codes (`+`, plus that of the non-insertion base) at a given position if the non-insertion base passes filter criteria.

  ‘P’ CIGAR (padding) operations never affect counts.

The “bin” arguments `query_bins` and `left_bins` allow users to differentiate pileup counts based on arbitrary non-overlapping regions within a read. `pileup` relies on `cut` to derive bins, but limits input to numeric values of the same sign (coerced to `integer`s), including `+/-Inf`. If a “bin” argument is set `pileup` automatically excludes bases outside the implicit outer range. Here are some important points regarding bin arguments:

- `query_bins` vs. `left_bins`:

  BAM files store sequence data from the 5' end to the 3' end (regardless of to which strand the read aligns). That means for reads aligned to the minus strand, cycle position within a read is effectively reversed. Take care to use the appropriate bin argument for your use case.

  The most common use of a bin argument is to bin later cycles separately from earlier cycles; this is because accuracy typically degrades in later cycles. For this application, `query_bins` yields the correct behavior because bin positions are relative to cycle order (i.e., sensitive to strand).

  `left_bins` (in contrast with `query_bins`) determines bin positions from the 5' end, regardless of strand.

- Positive or negative bin values can be used to delmit bins based on the effective “start” or “end” of a read. For `left_bin` the effective start is always the 5' end (left-to-right as appear in the BAM file).

  For `query_bin` the effective start is the first cycle of the read as it came off the sequencer; that means the 5' end for reads aligned to the plus strand and 3' for reads aligned to the minus strand.

  - From effective start of reads: use positive values, `0`, and `(+)Inf`. Because `cut` produces ranges in the form (first,last], ‘0’ should be used to create a bin that includes the first position. To account for variable-length reads in BAM files, use `(+)Inf` as the upper bound on a bin that extends to the end of all reads.

  - From effective end of reads: use negative values and `-Inf`. `-1` denotes the last position in a read. Because `cut` produces ranges in the form (first,last], specify the lower bound of a bin by using one less than the desired value, e.g., a bin that captures only the second nucleotide from the last position: `query_bins=c(-3, -2)`. To account for variable-length reads in BAM files, use `-Inf` as the lower bound on a bin that extends to the beginning of all reads.

`pileup` obeys `yieldSize` on `BamFile` objects with some differences from how `scanBam` responds to `yieldSize`. Here are some points to keep in mind when using `pileup` in conjunction with `yieldSize`:

- `BamFile`s with a `yieldSize` value set, once opened and used with `pileup`, should not be used with other functions that accept a `BamFile` instance as input. Create a new `BamFile` instance instead of trying to reuse.

- `pileup` only returns genomic positions for which all input has been processed. `pileup` will hold in reserve positions for which only partial data has been processed. Positions held in reserve will be returned upon subsequent calls to `pileup` when all the input for a given position has been processed.

- The correspondence between yieldSize and the number of rows in the `data.frame` returned from `pileup` is not 1-to-1. `yieldSize` only limits the number of alignments processed from the BAM file each time the file is read. Only a handful of alignments can lead to many distinct records in the result.

- Like `scanBam`, `pileup` uses an empty return object (a zero-row `data.frame`) to indicate end-of-input.

- Sometimes reading `yieldSize` records from the BAM file does not result in any completed positions. In order to avoid returning a zero-row `data.frame` `pileup` will repeatedly process `yieldSize` additional records until at least one position can be returned to the user.

### Value

For `pileup` a `data.frame` with 1 row per unique combination of differentiating column values that satisfied filter criteria, with frequency (`count`) of unique combination. Columns `seqnames`, `pos`, and `count` always appear; optional `strand`, `nulceotide`, and `left_bin` / `query_bin` columns appear as dictated by arguments to `PileupParam`.

Column details:

- `seqnames`: factor. SAM ‘RNAME’ field of alignment.

- `pos`: integer(1). Genomic position of base. Derived by offset from SAM ‘POS’ field of alignment.

- `strand`: factor. ‘strand’ to which read aligns.

- `nucleotide`: factor. ‘nucleotide’ of base, extracted from SAM ‘SEQ’ field of alignment.

- `left_bin` / `query_bin`: factor. Bin in which base appears.

- `count`: integer(1). Frequency of combination of differentiating fields, as indicated by values of other columns.

See `scanBam` for more detailed explanation of SAM fields.

If a `which` argument is specified for the `scanBamParam`, a `which_label` column (`factor` in the form ‘rname:start-end’) is automatically included. The `which_label` column is used to maintain grouping of results, such that two queries of the same genomic region can be differentiated.

Order of rows in `data.frame` is first by order of `seqnames` column based on the BAM index file, then non-decreasing order on columns `pos`, then `nucleotide`, then `strand`, then `left_bin` / `query_bin`.

`PileupParam` returns an instance of PileupParam class.

`phred2ASCIIOffset` returns a named integer vector of the same length or number of characters in `phred`. The names are the ASCII encoding, and the values are the offsets to be used with `min_base_quality`.

### Author(s)

Nathaniel Hayden nhayden@fredhutch.org

### See Also

- Rsamtools

- BamFile

- ScanBamParam

- scanBam

- cut

For the relationship between PHRED scores and ASCII encoding, see https://en.wikipedia.org/wiki/FASTQ_format#Encoding.

### Examples

    ## The examples below apply equally to pileup queries for specific
    ## genomic ranges (specified by the 'which' parameter of 'ScanBamParam')
    ## and queries across entire files; the entire-file examples are
    ## included first to make them easy to find. The more detailed examples
    ## of pileup use queries with a 'which' argument.

    library("RNAseqData.HNRNPC.bam.chr14")

    fl <- RNAseqData.HNRNPC.bam.chr14_BAMFILES[1]
    ## Minimum base quality to be tallied
    p_param <- PileupParam(min_base_quality = 10L)

    ## no 'which' argument to ScanBamParam: process entire file at once
    res <- pileup(fl, pileupParam = p_param)
    head(res)
    table(res$strand, res$nucleotide)

    ## no 'which' argument to ScanBamParam with 'yieldSize' set on BamFile
    bf <- open(BamFile(fl, yieldSize=5e4))
    repeat {
        res <- pileup(bf, pileupParam = p_param)
        message(nrow(res), " rows in result data.frame")
        if(nrow(res) == 0L)
            break
    }
    close(bf)

    ## pileup for the first half of chr14 with default Pileup parameters
    ## 'which' argument: process only specified genomic range(s)
    sbp <- ScanBamParam(which=GRanges("chr14", IRanges(1, 53674770)))
    res <- pileup(fl, scanBamParam=sbp, pileupParam = p_param)
    head(res)
    table(res$strand, res$nucleotide)

    ## specify pileup parameters: include ambiguious nucleotides
    ## (the 'N' level in the nucleotide column of the data.frame)
    p_param <- PileupParam(ignore_query_Ns=FALSE, min_base_quality = 10L)
    res <- pileup(fl, scanBamParam=sbp, pileupParam=p_param)
    head(res)
    table(res$strand, res$nucleotide)

    ## Don't distinguish strand, require a minimum frequency of 7 for a
    ## nucleotide at a genomic position to be included in results.

    p_param <- PileupParam(distinguish_strands=TRUE,
                           min_base_quality = 10L,
                           min_nucleotide_depth=7)
    res <- pileup(fl, scanBamParam=sbp, pileupParam=p_param)
    head(res)
    table(res$nucleotide, res$strand)

    ## Any combination of the filtering criteria is possible: let's say we
    ## want a "coverage pileup" that only counts reads with mapping
    ## quality >= 13, and bases with quality >= 10 that appear at least 4
    ## times at each genomic position
    p_param <- PileupParam(distinguish_nucleotides=FALSE,
                           distinguish_strands=FALSE,
                           min_mapq=13,
                           min_base_quality=10,
                           min_nucleotide_depth=4)
    res <- pileup(fl, scanBamParam=sbp, pileupParam=p_param)
    head(res)
    res <- res[, c("pos", "count")] ## drop seqnames and which_label cols
    plot(count ~ pos, res, pch=".")

    ## ASCII offsets to help specify min_base_quality, e.g., quality of at
    ## least 10 on the Illumina 1.3+ scale
    phred2ASCIIOffset(10, "Illumina 1.3+")

    ## Well-supported polymorphic positions (minor allele present in at
    ## least 5 alignments) with high map quality
    p_param <- PileupParam(min_minor_allele_depth=5,
                           min_mapq=40,
                           min_base_quality = 10,
                           distinguish_strand=FALSE)
    res <- pileup(fl, scanBamParam=sbp, pileupParam=p_param)
    dim(res) ## reduced to our biologically interesting positions
    head(xtabs(count ~ pos + nucleotide, res))

    ## query_bins

    ## basic use of positive bins: single pivot; count bases that appear in
    ## first 10 cycles of a read separately from the rest
    p_param <- PileupParam(query_bins=c(0, 10, Inf), min_base_quality = 10)
    res <- pileup(fl, scanBamParam=sbp, pileupParam=p_param)

    ## basic use of positive bins: simple range; only include bases in
    ## cycle positions 6-10 within read
    p_param <- PileupParam(query_bins=c(5, 10), min_base_quality = 10)
    res <- pileup(fl, scanBamParam=sbp, pileupParam=p_param)

    ## basic use of negative bins: single pivot; count bases that appear in
    ## last 3 cycle positions of a read separately from the rest.
    p_param <- PileupParam(query_bins=c(-Inf, -4, -1), min_base_quality = 10)
    res <- pileup(fl, scanBamParam=sbp, pileupParam=p_param)

    ## basic use of negative bins: drop nucleotides in last two cycle
    ## positions of each read
    p_param <- PileupParam(query_bins=c(-Inf, -3), min_base_quality = 10)
    res <- pileup(fl, scanBamParam=sbp, pileupParam=p_param)

    ## typical use: beginning, middle, and end segments; because of the
    ## nature of sequencing technology, it is common for bases in the
    ## beginning and end segments of each read to be less reliable. pileup
    ## makes it easy to count each segment separately.

    ## Assume determined ahead of time that for the data 1-7 makes sense for
    ## beginning, 8-12 middle, >=13 end (actual cycle positions should be
    ## tailored to data in actual BAM files).
    p_param <- PileupParam(query_bins=c(0, 7, 12, Inf), ## note non-symmetric
                           min_base_quality = 10)
    res <- pileup(fl, scanBamParam=sbp, pileupParam=p_param)
    xt <- xtabs(count ~ nucleotide + query_bin, res)
    print(xt)
    t(t(xt) / colSums(xt)) ## cheap normalization for illustrative purposes

    ## 'implicit outer range': in contrast to c(0, 7, 12, Inf), suppose we
    ##  still want to have beginning, middle, and end segements, but know
    ##  that the first three cycles and any bases beyond the 16th cycle are
    ##  irrelevant. Hence, the implicit outer range is (3,16]; all bases
    ##  outside of that are dropped.
    p_param <- PileupParam(query_bins=c(3, 7, 12, 16), min_base_quality = 10)
    res <- pileup(fl, scanBamParam=sbp, pileupParam=p_param)
    xt <- xtabs(count ~ nucleotide + query_bin, res)
    print(xt)
    t(t(xt) / colSums(xt))

    ## single-width bins: count each cycle within a read separately.
    p_param <- PileupParam(query_bins=seq(1,20), min_base_quality = 10)
    res <- pileup(fl, scanBamParam=sbp, pileupParam=p_param)
    xt <- xtabs(count ~ nucleotide + query_bin, res)
    print(xt[ , c(1:3, 18:19)]) ## fit on one screen
    print(t(t(xt) / colSums(xt))[ , c(1:3, 18:19)])

[Package Rsamtools version 2.10.0 Index]