Many users will find that the GenomicAlignments
package provides a more useful representation of BAM files
in R; the GenomicFiles
package is also useful for iterating through BAM files.
The Rsamtools
package provides an interface to BAM files.
BAM files are produced by samtools and other
software, and represent a flexible format for storing ‘short’ reads
aligned to reference genomes. BAM files typically contain
sequence and base qualities, and alignment coordinates and quality
measures. BAM files are appealing for several reasons. The
format is flexible enough to represent reads generated and aligned using
diverse technologies. The files are binary so that file access is
relatively efficient. BAM files can be indexed, allowing
ready access to localized chromosomal regions. BAM files
can be accessed remotely, provided the remote hosting site supports such
access and a local index is available. This means that specific regions
of remote files can be accessed without retrieving the entire (large!)
file. A full description is available in the BAM format
specification (http://samtools.sourceforge.net/SAM1.pdf)
The main purpose of the Rsamtools
is to import BAM files into R.Rsamtools
also provides some facility for file access such as record counting,
index file creation, and filtering to create new files containing
subsets of the original. An important use case for Rsamtools
is as a starting point for creating objects suitable for a diversity of
work flows, e.g., AlignedRead objects in the ShortRead
package (for quality assessment and read manipulation), or
GAlignments objects in GenomicAlignments
package (for RNA-seq and other applications). Those desiring more
functionality are encouraged to explore samtools and related
software efforts.
ScanBamParamThe essential capability provided by Rsamtools
is BAM input. This is accomplished with the
scanBam function. scanBam takes as input the
name of the BAM file to be parsed. In addition, the
param argument determines which genomic coordinates of the
BAM file, and what components of each record, will be
input. Rparam is an instance of the ScanBamParam
class. To create a param object, call
ScanBamParam. Here we create a param object to
extract reads aligned to three distinct ranges (one on
seq1, two on seq2). From each of read in those
ranges, we specify that we would like to extract the reference name
(rname, e.g., seq1 ), strand, alignment
position, query (i.e., read) width, and query sequence:
which <- GRanges(c(
"seq1:1000-2000",
"seq2:100-1000",
"seq2:1000-2000"
))
## equivalent:
## GRanges(
## seqnames = c("seq1", "seq2", "seq2"),
## ranges = IRanges(
## start = c(1000, 100, 1000),
## end = c(2000, 1000, 2000)
## )
## )
what <- c("rname", "strand", "pos", "qwidth", "seq")
param <- ScanBamParam(which=which, what=what)Additional information can be found on the help page for
ScanBamParam. Reading the relevant records from the
BAM file is accomplished with
bamFile <- system.file("extdata", "ex1.bam", package="Rsamtools")
bam <- scanBam(bamFile, param=param)Like scan, scanBam returns a
list of values. Each element of the list corresponds to a
range specified by the which argument to
ScanBamParam.
## [1] "list"## [1] "seq1:1000-2000" "seq2:100-1000" "seq2:1000-2000"Each element is itself a list, containing the elements specified by
the what and tag arguments to
ScanBamParam.
## [1] "list"## [1] "rname" "strand" "pos" "qwidth" "seq"The elements are either basic R or IRanges data types
## rname strand pos qwidth seq
## "factor" "factor" "integer" "integer" "DNAStringSet"A paradigm for collapsing the list-of-lists into a single list is
.unlist <- function (x)
{
## do.call(c, ...) coerces factor to integer, which is undesired
x1 <- x[[1L]]
if (is.factor(x1)) {
structure(unlist(x), class = "factor", levels = levels(x1))
} else {
do.call(c, x)
}
}
bam <- unname(bam) # names not useful in unlisted result
elts <- setNames(bamWhat(param), bamWhat(param))
lst <- lapply(elts, function(elt) .unlist(lapply(bam, "[[", elt)))This might be further transformed, e.g., to a DataFrame, with
## DataFrame with 6 rows and 5 columns
## rname strand pos qwidth seq
## <factor> <factor> <integer> <integer> <DNAStringSet>
## 1 seq1 + 970 35 TATTAGGAAA...ACTATGAAGA
## 2 seq1 + 971 35 ATTAGGAAAT...CTATGAAGAG
## 3 seq1 + 972 35 TTAGGAAATG...TATGAAGAGA
## 4 seq1 + 973 35 TAGGAAATGC...ATGAAGAGAC
## 5 seq1 + 974 35 AGGAAATGCT...TGAAGAGACT
## 6 seq1 - 975 35 GGAAATGCTT...GAAGAGACTAOften, an alternative is to use a ScanBamParam object with
desired fields specified in what as an argument to
GenomicAlignments::readGAlignments; the specified fields
are added as columns to the returned GAlignments .
BAM index filesThe BAM file in the previous example includes an index,
represented by a separate file with extension .bai:
## [1] "ex1.bam" "ex1.bam.bai"Indexing provides two significant benefits. First, an index allows a
BAM file to be efficiently accessed by range. A corollary
is that providing a which argument to
scanBamPram requires an index. Second, coordinates for
extracting information from a BAM file can be derived from
the index, so a portion of a remote BAM file can be
retrieved with local access only to the index. For instance, provided an
index file exists on the local computer, it is possible to retrieve a
small portion of a BAM file residing on the 1000 genomes
HTTP server. The url ftp://ftp-trace.ncbi.nih.gov/1000genomes/ftp/pilot_data/data/NA19240/alignment/NA19240.chrom6.SLX.maq.SRP000032.2009_07.bam
points to the BAM file corresponding to individual NA19240
chromosome 6 Solexa (Illumina) sequences aligned using MAQ. The remote
file is very large (about 10 GB), but the corresponding index file is
small (about 500 KB). With na19240url set to the above
address, the following retrieves just those reads in the specified
range
which <- GRanges("6:100000-110000")
param <- ScanBamParam(which=which, what=scanBamWhat())
na19240bam <- scanBam(na19240url, param=param)Invoking scanBam without an index file, as above, first
retrieves the index file from the remote location, and then queries the
remote file using the index; for repeated queries, it is more efficient
to retrieve the index file first (e.g., with download.file)
and then use the local index as an argument to scanBam.
Many BAM files were created in a way that causes
scanBam to report that the “EOF marker is absent”; this
message can safely be ignored.
BAM filesBAM files may be read by functions in packages other
than Rsamtools,
in particular the readGAlignments family of functions in
GenomicAlignments.
Additional ways of interacting with BAM files include
scanBamHeader (to extract header information) and
countBam (to count records matching param).
filterBam filters reads from the source file according to
the criteria of the ScanBamParam parameter, writing reads
passing the filter to a new file. The function sorts a previously
unsorted BAM, while The function indexBam
creates an index file from a sorted BAM file.
readPileup reads a pileup file created by ,
importing SNP, indel, or all variants into a GRanges
object.
BAM files can be large, containing more information on
more genomic regions than are of immediate interest or than can fit in
memory. The first strategy for dealing with this is to select, using the
what and which arguments to
scanBamParam, just those portions of the BAM
file that are essential to the current analysis, e.g., specifying
what=c('rname', 'qname', 'pos') when wishing to calculate
coverage of ungapped reads.
When selective input of BAM files is still too
memory-intensive, the file can be processed in chunks, with each chunk
distilled to the derived information of interest. Chromosomes will often
be the natural chunk to process. For instance, here we write a summary
function that takes a single sequence name (chromosome) as input, reads
in specific information from the BAM file, and calculates
coverage over that sequence.
summaryFunction <-
function(seqname, bamFile, ...)
{
param <- ScanBamParam(
what=c('pos', 'qwidth'),
which=GRanges(seqname, IRanges(1, 1e7)),
flag=scanBamFlag(isUnmappedQuery=FALSE)
)
x <- scanBam(bamFile, ..., param=param)[[1]]
coverage(IRanges(x[["pos"]], width=x[["qwidth"]]))
}This might be used as follows; it is an ideal candidate for
evaluation in parallel, e.g., using the parallel package and
srapply function in ShortRead.
seqnames <- paste("seq", 1:2, sep="")
cvg <- lapply(seqnames, summaryFunction, bamFile)
names(cvg) <- seqnames
cvg## $seq1
## integer-Rle of length 1569 with 1054 runs
## Lengths: 2 2 1 3 4 2 3 4 2 4 1 ... 1 2 1 1 1 1 1 1 1 1
## Values : 1 2 3 4 5 7 8 9 11 12 13 ... 13 12 10 9 7 6 5 3 2 1
##
## $seq2
## integer-Rle of length 1567 with 1092 runs
## Lengths: 1 3 1 1 1 3 1 4 1 1 6 ... 1 1 1 1 1 2 1 4 4 1
## Values : 3 4 5 8 12 14 15 16 17 18 19 ... 15 14 13 10 8 7 6 3 2 1The result of the function (a coverage vector, in this case) will
often be much smaller than the input. The GenomicFiles
package implements strategies for iterating through BAM and
other files, including in parallel.
The functions described in the previous section import data in to
R. However, sequence data can be very large, and it does not
always make sense to read the data in immediately. Instead, it can be
useful to marshal references to the data into a container and
then act on components of the container. The BamViews class
provides a mechanism for creating ‘views’ into a set of BAM
files. The view itself is light-weight, containing references to the
relevant BAM files and metadata about the view (e.g., the
phenotypic samples corresponding to each BAM file).
One way of understanding a instance is as a rectangular data
structure. The columns represent BAM files (e.g., distinct
samples). The rows represent ranges (i.e., genomic coordinates). For
instance, a ChIP-seq experiment might identify a number of peaks of high
read counts.
To illustrate, suppose we have an interest in caffeine metabolism in humans. The ‘rows’ contain coordinates of genomic regions associated with genes in a KEGG caffeine metabolism pathway. The ‘columns’ represent individuals in the 1000 genomes project.
To create the ‘rows’, we identify possible genes that KEGG associates with caffeine metabolism. Using the KEGGREST package, the steps are
## uses KEGGREST, dplyr, and tibble packages
org <- "hsa"
caffeine_pathway <-
KEGGREST::keggList("pathway", org)
tibble::enframe(name = "pathway_id", value = "pathway")
dplyr::filter(startsWith(.data$pathway, "Caffeine metabolism"))
egid <-
KEGGREST::keggLink(org, "pathway") %>%
tibble::enframe(name = "pathway_id", value = "gene_id")
dplyr::left_join(x = caffeine_pathway, by = "pathway_id")
dplyr::mutate(gene_id = sub("hsa:", "", gene_id))
pull(gene_id)At the time of writing, genes in the caffeine metabolism pathway are
Then we use the appropriate TxDb package
to translate Entrez identifiers to obtain ranges of interest (one could
also use biomaRt
to retrieve coordinates for non-model organisms, perhaps making a
TxDb object as outlined in the GenomicFeatures
vignette). We’ll find that the names used for chromosomes in the
alignments differ from those used at Ensembl, so
seqlevels<- is used to map between naming schemes and to
drop unused levels.
library(TxDb.Hsapiens.UCSC.hg18.knownGene)
bamRanges <- transcripts(
TxDb.Hsapiens.UCSC.hg18.knownGene,
filter=list(gene_id=egid)
)
seqlevels(bamRanges) <- # translate seqlevels
sub("chr", "", seqlevels(bamRanges))
lvls <- seqlevels(bamRanges) # drop unused levels
seqlevels(bamRanges) <- lvls[lvls %in% as.character(seqnames(bamRanges))]
bamRanges## GRanges object with 18 ranges and 2 metadata columns:
## seqnames ranges strand | tx_id tx_name
## <Rle> <IRanges> <Rle> | <integer> <character>
## [1] 2 31410692-31491115 - | 9095 uc002rnv.1
## [2] 8 18111895-18125100 + | 26333 uc003wyq.1
## [3] 8 18111895-18125100 + | 26334 uc003wyr.1
## [4] 8 18111895-18125100 + | 26335 uc003wys.1
## [5] 8 18113074-18125100 + | 26336 uc003wyt.1
## ... ... ... ... . ... ...
## [14] 19 46042667-46048192 - | 57448 uc010ehe.1
## [15] 19 46043701-46048191 - | 57449 uc010ehf.1
## [16] 19 46073184-46080497 - | 57450 uc002opm.1
## [17] 19 46073184-46080497 - | 57451 uc002opn.1
## [18] 19 46073184-46226008 - | 57452 uc002opo.1
## -------
## seqinfo: 4 sequences from hg18 genomeThe bamRanges ‘knows’ the genome for which the ranges
are defined
## [1] "hg18"Here we retrieve a vector of BAM file URLs
(slxMaq09) from the package.
slxMaq09 <- local({
fl <- system.file("extdata", "slxMaq09_urls.txt", package="Rsamtools")
readLines(fl)
})We now assemble the BamViews instance from these objects; we
also provide information to annotated the BAM files (with
the bamSamples function argument, which is a
DataFrame instance with each row corresponding to a
BAM file) and the instance as a whole (with
bamExperiment, a simple named list containing
information structured as the user sees fit).
bamExperiment <-
list(description="Caffeine metabolism views on 1000 genomes samples",
created=date())
bv <- BamViews(
slxMaq09, bamRanges=bamRanges, bamExperiment=bamExperiment
)
metadata(bamSamples(bv)) <-
list(description="Solexa/MAQ samples, August 2009",
created="Thu Mar 25 14:08:42 2010")
bv## BamViews dim: 18 ranges x 24 samples
## names: NA06986.SLX.maq.SRP000031.2009_08.bam NA06994.SLX.maq.SRP000031.2009_08.bam ... NA12828.SLX.maq.SRP000031.2009_08.bam NA12878.SLX.maq.SRP000031.2009_08.bam
## detail: use bamPaths(), bamSamples(), bamRanges(), ...The BamViews object can be queried for its component parts, e.g.,
## $description
## [1] "Caffeine metabolism views on 1000 genomes samples"
##
## $created
## [1] "Fri Dec 27 05:47:46 2024"More usefully, methods in Rsamtools are designed to work with BamViews objects, retrieving data from all files in the view. These operations can take substantial time and require reliable network access.
To illustrate, the following code (not evaluated when this vignette
was created) downloads the index files associated with the
bv object
bamIndexDir <- tempfile()
indexFiles <- paste(bamPaths(bv), "bai", sep=".")
dir.create(bamIndexDir)
bv <- BamViews(
slxMaq09,
file.path(bamIndexDir, basename(indexFiles)), # index file location
bamRanges=bamRanges,
bamExperiment=bamExperiment
)
idxFiles <- mapply(
download.file, indexFiles,
bamIndicies(bv),
MoreArgs=list(method="curl")
)and then queries the 1000 genomes project for reads overlapping our transcripts.
The resulting object is about 11 MB in size. To avoid having to download this data each time the vignette is run, we instead load it from disk
## List of length 24
## names(24): NA06986.SLX.maq.SRP000031.2009_08.bam ...## GAlignments object with 6 alignments and 0 metadata columns:
## seqnames strand cigar qwidth start end width
## <Rle> <Rle> <character> <integer> <integer> <integer> <integer>
## [1] 2 + 51M 51 31410650 31410700 51
## [2] 2 + 51M 51 31410658 31410708 51
## [3] 2 - 51M 51 31410663 31410713 51
## [4] 2 + 51M 51 31410666 31410716 51
## [5] 2 - 51M 51 31410676 31410726 51
## [6] 2 + 51M 51 31410676 31410726 51
## njunc
## <integer>
## [1] 0
## [2] 0
## [3] 0
## [4] 0
## [5] 0
## [6] 0
## -------
## seqinfo: 114 sequences from an unspecified genomeThere are 33964 reads in NA06986.SLX.maq.SRP000031.2009_08.bam overlapping at least one of our transcripts. It is easy to explore this object, for instance discovering the range of read widths in each individual.
## [,1] [,2]
## NA06986.SLX.maq.SRP000031.2009_08.bam 51 51
## NA06994.SLX.maq.SRP000031.2009_08.bam 36 51
## NA07051.SLX.maq.SRP000031.2009_08.bam 51 51
## NA07346.SLX.maq.SRP000031.2009_08.bam 48 76
## NA07347.SLX.maq.SRP000031.2009_08.bam 51 51
## NA10847.SLX.maq.SRP000031.2009_08.bam 36 51Suppose we were particularly interested in the first transcript,
which has a transcript id uc002rnv.1. Here we extract reads overlapping
this transcript from each of our samples. As a consequence of the
protocol used, reads aligning to either strand could be derived from
this transcript. For this reason, we set the strand of our range of
interest to *. We use the endoapply function,
which is like lapply but returns an object of the same
class (in this case, SimpleList) as its first argument.
rng <- bamRanges(bv)[1]
strand(rng) <- "*"
olap1 <- endoapply(olaps, subsetByOverlaps, rng)
olap1 <- lapply(olap1, "seqlevels<-", value=as.character(seqnames(rng)))
head(olap1[[24]])## GAlignments object with 6 alignments and 0 metadata columns:
## seqnames strand cigar qwidth start end width
## <Rle> <Rle> <character> <integer> <integer> <integer> <integer>
## [1] 2 + 36M 36 31410660 31410695 36
## [2] 2 - 36M 36 31410670 31410705 36
## [3] 2 + 36M 36 31410683 31410718 36
## [4] 2 - 36M 36 31410687 31410722 36
## [5] 2 - 36M 36 31410694 31410729 36
## [6] 2 - 36M 36 31410701 31410736 36
## njunc
## <integer>
## [1] 0
## [2] 0
## [3] 0
## [4] 0
## [5] 0
## [6] 0
## -------
## seqinfo: 1 sequence from an unspecified genomeTo carry the example a little further, we calculate coverage of each sample:
minw <- min(sapply(olap1, function(elt) min(start(elt))))
maxw <- max(sapply(olap1, function(elt) max(end(elt))))
cvg <- endoapply(
olap1, coverage,
shift=-start(ranges(bamRanges[1])),
width=width(ranges(bamRanges[1]))
)
cvg[[1]]## RleList of length 1
## $`2`
## integer-Rle of length 80424 with 13290 runs
## Lengths: 8 8 5 2 1 3 7 7 10 2 3 ... 4 3 1 11 8 7 17 4 4 9
## Values : 6 5 4 3 4 3 5 3 4 5 6 ... 4 5 4 5 4 5 4 3 2 1Since the example includes a single region of uniform width across all samples, we can easily create a coverage matrix with rows representing nucleotide and columns sample and, e.g., document variability between samples and nucleotides
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 10.00 74.00 82.00 81.63 91.00 133.00## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 133924 173925 248333 273528 350823 567727This vignette has summarized facilities in the Rsamtools
package. Important additional packages include GenomicRanges
(for representing and manipulating gapped alignments), ShortRead
for I/O and quality assessment of ungapped short read alignments, Biostrings
and BSgenome
for DNA sequence and whole-genome manipulation, IRanges
for range-based manipulation, and rtracklayer
for I/O related to the UCSC genome browser. Users might also find the
interface to the integrative genome browser (IGV) in SRAdb
useful for visualizing BAM files.
## Package: Rsamtools
## Type: Package
## Title: Binary alignment (BAM), FASTA, variant call (BCF), and tabix
## file import
## Description: This package provides an interface to the 'samtools',
## 'bcftools', and 'tabix' utilities for manipulating SAM
## (Sequence Alignment / Map), FASTA, binary variant call (BCF)
## and compressed indexed tab-delimited (tabix) files.
## biocViews: DataImport, Sequencing, Coverage, Alignment, QualityControl
## URL: https://bioconductor.org/packages/Rsamtools
## Video:
## https://www.youtube.com/watch?v=Rfon-DQYbWA&list=UUqaMSQd_h-2EDGsU6WDiX0Q
## BugReports: https://github.com/Bioconductor/Rsamtools/issues
## Version: 2.23.1
## License: Artistic-2.0 | file LICENSE
## Encoding: UTF-8
## Authors@R: c( person("Martin", "Morgan", role = "aut"), person("Hervé",
## "Pagès", role = "aut"), person("Valerie", "Obenchain", role =
## "aut"), person("Nathaniel", "Hayden", role = "aut"),
## person("Busayo", "Samuel", role = "ctb", comment = "Converted
## Rsamtools vignette from Sweave to RMarkdown / HTML."),
## person("Bioconductor Package Maintainer", email =
## "[email protected]", role = "cre"))
## Depends: methods, GenomeInfoDb (>= 1.1.3), GenomicRanges (>= 1.31.8),
## Biostrings (>= 2.47.6), R (>= 3.5.0)
## Imports: utils, BiocGenerics (>= 0.25.1), S4Vectors (>= 0.17.25),
## IRanges (>= 2.13.12), XVector (>= 0.19.7), bitops,
## BiocParallel, stats
## Suggests: GenomicAlignments, ShortRead (>= 1.19.10), GenomicFeatures,
## TxDb.Dmelanogaster.UCSC.dm3.ensGene,
## TxDb.Hsapiens.UCSC.hg18.knownGene, RNAseqData.HNRNPC.bam.chr14,
## BSgenome.Hsapiens.UCSC.hg19, RUnit, BiocStyle, knitr
## LinkingTo: Rhtslib (>= 3.3.1), S4Vectors, IRanges, XVector, Biostrings
## LazyLoad: yes
## SystemRequirements: GNU make
## VignetteBuilder: knitr
## Config/pak/sysreqs: make libssl-dev
## Repository: https://bioc.r-universe.dev
## RemoteUrl: https://github.com/bioc/Rsamtools
## RemoteRef: HEAD
## RemoteSha: 21a5945271e78bd38ebbe42a1368a8e8287c32f9
## Author: Martin Morgan [aut], Hervé Pagès [aut], Valerie Obenchain
## [aut], Nathaniel Hayden [aut], Busayo Samuel [ctb] (Converted
## Rsamtools vignette from Sweave to RMarkdown / HTML.),
## Bioconductor Package Maintainer [cre]
## Maintainer: Bioconductor Package Maintainer
## <[email protected]>
## Built: R 4.4.2; x86_64-pc-linux-gnu; 2024-12-27 05:47:09 UTC; unix
##
## -- File: /tmp/RtmpRWWb7t/Rinst17065473f270/Rsamtools/Meta/package.rds## R version 4.4.2 (2024-10-31)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.1 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so; LAPACK version 3.12.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=C
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## time zone: Etc/UTC
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] GenomicAlignments_1.43.0
## [2] SummarizedExperiment_1.37.0
## [3] MatrixGenerics_1.19.0
## [4] matrixStats_1.4.1
## [5] TxDb.Hsapiens.UCSC.hg18.knownGene_3.2.2
## [6] GenomicFeatures_1.59.1
## [7] AnnotationDbi_1.69.0
## [8] Biobase_2.67.0
## [9] Rsamtools_2.23.1
## [10] Biostrings_2.75.3
## [11] XVector_0.47.1
## [12] GenomicRanges_1.59.1
## [13] GenomeInfoDb_1.43.2
## [14] IRanges_2.41.2
## [15] S4Vectors_0.45.2
## [16] BiocGenerics_0.53.3
## [17] generics_0.1.3
## [18] BiocStyle_2.35.0
##
## loaded via a namespace (and not attached):
## [1] KEGGREST_1.47.0 rjson_0.2.23 xfun_0.49
## [4] bslib_0.8.0 lattice_0.22-6 vctrs_0.6.5
## [7] tools_4.4.2 bitops_1.0-9 curl_6.0.1
## [10] parallel_4.4.2 RSQLite_2.3.9 blob_1.2.4
## [13] pkgconfig_2.0.3 Matrix_1.7-1 lifecycle_1.0.4
## [16] GenomeInfoDbData_1.2.13 compiler_4.4.2 codetools_0.2-20
## [19] htmltools_0.5.8.1 sys_3.4.3 buildtools_1.0.0
## [22] sass_0.4.9 RCurl_1.98-1.16 yaml_2.3.10
## [25] crayon_1.5.3 jquerylib_0.1.4 BiocParallel_1.41.0
## [28] DelayedArray_0.33.3 cachem_1.1.0 abind_1.4-8
## [31] digest_0.6.37 restfulr_0.0.15 maketools_1.3.1
## [34] fastmap_1.2.0 grid_4.4.2 SparseArray_1.7.2
## [37] cli_3.6.3 S4Arrays_1.7.1 XML_3.99-0.17
## [40] UCSC.utils_1.3.0 bit64_4.5.2 rmarkdown_2.29
## [43] httr_1.4.7 bit_4.5.0.1 png_0.1-8
## [46] memoise_2.0.1 evaluate_1.0.1 knitr_1.49
## [49] BiocIO_1.17.1 rtracklayer_1.67.0 rlang_1.1.4
## [52] DBI_1.2.3 BiocManager_1.30.25 jsonlite_1.8.9
## [55] R6_2.5.1 zlibbioc_1.52.0Note: The following operations were performed at the time
the vignette was written; location of on-line resources, in particular
the organization of the 1000 genomes BAM files, may have
changed.
We are interested in collecting the URLs of a number of
BAM files from the 1000 genomes project. Our first goal is
to identify files that might make for an interesting comparison. First,
let’s visit the 1000 genomes FTP site and discover available files.
We’ll use the RCurl package
to retrieve the names of all files in an appropriate directory
library(RCurl)
ftpBase <-
"ftp://ftp-trace.ncbi.nih.gov/1000genomes/ftp/pilot_data/data/"
indivDirs <-
strsplit(getURL(ftpBase, ftplistonly=TRUE), "\n")[[1]]
alnDirs <-
paste(ftpBase, indivDirs, "/alignment/", sep="")
urls0 <-
strsplit(getURL(alnDirs, dirlistonly=TRUE), "\n")From these, we exclude directories without any files in them, select
only the BAM index (extension .bai) files, and
choose those files that exactly six '.' characters in their
name.
urls <- urls[sapply(urls0, length) != 0]
fls0 <- unlist(unname(urls0))
fls1 <- fls0[grepl("bai$", fls0)]
fls <- fls1[sapply(strsplit(fls1, "\\."), length)==7]After a little exploration, we focus on those files obtained form
Solexa sequencing, aligned using MAQ, and archived in
August, 2009; we remove the .bai extension, so that the URL
refers to the underlying file rather than index. There are 24 files.
urls1 <- Filter(
function(x) length(x) != 0,
lapply(urls, function(x) x[grepl("SLX.maq.*2009_08.*bai$", x)])
)
slxMaq09.bai <-
mapply(paste, names(urls1), urls1, sep="", USE.NAMES=FALSE)
slxMaq09 <- sub(".bai$", "", slxMaq09.bai)As a final step to prepare for using a file, we create local copies of the index files. The index files are relatively small (about 190 Mb total).