slicing {GenomicDataCommons} | R Documentation

## Query GDC for data slices

### Description

This function returns a BAM file representing reads overlapping regions specified either as chromosomal regions or as gencode gene symbols.

### Usage

    slicing(uuid, regions, symbols, destination = file.path(tempdir(),
      paste0(uuid, ".bam")), overwrite = FALSE, progress = interactive(),
      token = gdc_token(), legacy = FALSE)

### Arguments

`uuid` | character(1) identifying the BAM file resource
`regions` | character() vector describing chromosomal regions, e.g., `c("chr1", "chr2:10000", "chr3:10000-20000")` (all of chromosome 1, chromosome 2 from position 10000 to the end, chromosome 3 from 10000 to 20000).
`symbols` | character() vector of gencode gene symbols, e.g., `c("BRCA1", "PTEN")`
`destination` | character(1) default `tempfile()` file path for BAM file slice
`overwrite` | logical(1) default FALSE can destination be overwritten?
`progress` | logical(1) default `interactive()` should a progress bar be used?
`token` | character(1) security token allowing access to restricted data. Almost all BAM data is restricted, so a token is usually required. See https://gdc-docs.nci.nih.gov/API/Users_Guide/Authentication_and_Authorization/.
`legacy` | logical(1) whether or not to use the "legacy" archive, containing older, non-harmonized data.

### Details

This function uses the Genomic Data Commons "slicing" API to get portions of a BAM file specified either using "regions" or using HGNC gene symbols.

### Value

character(1) destination to the downloaded BAM file

### Examples

     slicing("df80679e-c4d3-487b-934c-fcc782e5d46e",
            regions="chr17:75000000-76000000",
            token=gdc_token())

    # Get 10 BAM files.
    bamfiles = files() %>%
               filter(data_format=='BAM') %>%
               results(size=10) %>% ids()

    # Current alignments at the GDC are to GRCh38
    library('TxDb.Hsapiens.UCSC.hg38.knownGene')
    all_genes = genes(TxDb.Hsapiens.UCSC.hg38.knownGene)

    first3genes = all_genes[1:3]
    # remove strand info
    strand(first3genes) = '*'

    # We can get our regions easily now
    as.character(first3genes)

    # Use parallel downloads to speed processing
    library(BiocParallel)
    register(MulticoreParam())

    fnames = bplapply(bamfiles, slicing, overwrite = TRUE,
                    regions=as.character(first3genes))

    # 10 BAM files
    fnames

    library(GenomicAlignments)
    lapply(unlist(fnames), readGAlignments)

[Package GenomicDataCommons version 1.12.0 Index]