Treat each bam file as a bulk sample and identify variants against the reference
Usage
find_variants(
bam_path,
reference,
annotation,
min_nucleotide_depth = 100,
homopolymer_window = 3,
annotated_region_only = FALSE,
names_from = "gene_name",
threads = 1
)Arguments
- bam_path
character(1) or character(n): path to the bam file(s) aligned to the reference genome (NOT the transcriptome!).
- reference
DNAStringSet: the reference genome
- annotation
GRanges: the annotation of the reference genome. You can load a GTF/GFF annotation file with
anno <- rtracklayer::import(file).- min_nucleotide_depth
integer(1): minimum read depth for a position to be considered a variant.
- homopolymer_window
integer(1): the window size to calculate the homopolymer percentage. The homopolymer percentage is calculated as the percentage of the most frequent nucleotide in a window of
-homopolymer_windowtohomopolymer_windownucleotides around the variant position, excluding the variant position itself. Calculation of the homopolymer percentage is skipped whenhomopolymer_window = 0. This is useful for filtering out Nanopore sequencing errors in homopolymer regions.- annotated_region_only
logical(1): whether to only consider variants outside annotated regions. If
TRUE, only variants outside annotated regions will be returned. IfFALSE, all variants will be returned, which could take significantly longer time.- names_from
character(1): the column name in the metadata column of the annotation (
mcols(annotation)[, names_from]) to use for theregioncolumn in the output.- threads
integer(1): number of threads to use. Threading is done over each annotated region and (if
annotated_region_only = FALSE) unannotated gaps for each bam file.
Value
A tibble with columns: seqnames, pos, nucleotide, count, sum, freq, ref, region,
homopolymer_pct, bam_path The homopolymer percentage is calculated as the percentage of the
most frequent nucleotide in a window of homopolymer_window nucleotides around
the variant position, excluding the variant position itself.
Details
Each bam file is treated as a bulk sample to perform pileup and identify variants.
You can run sc_mutations with the variants identified with this function
to get single-cell allele counts. Note that reference genome FASTA files may have
the chromosome names field as `>chr1 1` instead of `>chr1`. You may need to remove
the trailing number to match the chromosome names in the bam file, for example with
names(ref) <- sapply(names(ref), function(x) strsplit(x, " ")[[1]][1]).
Examples
ppl <- example_pipeline("SingleCellPipeline")
#> Writing configuration parameters to: /tmp/RtmpgpEV0i/file25bf2842cb6e/config_file_9663.json
#> Configured steps:
#> barcode_demultiplex: TRUE
#> genome_alignment: TRUE
#> gene_quantification: TRUE
#> isoform_identification: TRUE
#> read_realignment: TRUE
#> transcript_quantification: TRUE
#> samtools not found, will use Rsamtools package instead
ppl <- run_step(ppl, "genome_alignment")
#> Running step: genome_alignment
#> Creating junction bed file from GFF3 annotation.
#> Aligning sample /__w/_temp/Library/FLAMES/extdata/fastq/musc_rps24.fastq.gz -> /tmp/RtmpgpEV0i/file25bf2842cb6e/align2genome.bam
#> Warning: samtools not found, using Rsamtools instead, this could be slower and might fail for large BAM files.
#> Sorting BAM files by genome coordinates with 8 threads...
#> Indexing bam files
variants <- find_variants(
bam_path = ppl@genome_bam,
reference = ppl@genome_fa,
annotation = ppl@annotation,
min_nucleotide_depth = 4
)
#> 03:22:37 Reading reference ...
#> 03:22:37 Reading annotation ...
#> Merging overlapping genes ...
#> 0 overlapping regions, their gene_name(s) are merged with `, ` as separator
#> 03:22:37 Adding unannotated gaps ...
#> 03:22:37 Got 1 bam file, parallelizing over each region ...
#>
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#>
#> 03:22:42 Merging results ...
#> 03:22:42 Calculating homopolymer percentages ...
#>
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#>
head(variants)
#> # A tibble: 6 × 10
#> seqnames pos nucleotide count sum freq ref bam_path region
#> <fct> <dbl> <fct> <int> <dbl> <dbl> <fct> <chr> <chr>
#> 1 chr14 8 G 4 30 0.133 A /tmp/RtmpgpEV0i/fil… Rps24
#> 2 chr14 11 A 4 36 0.111 G /tmp/RtmpgpEV0i/fil… Rps24
#> 3 chr14 15 A 4 37 0.108 G /tmp/RtmpgpEV0i/fil… Rps24
#> 4 chr14 22 - 12 227 0.0529 T /tmp/RtmpgpEV0i/fil… Rps24
#> 5 chr14 26 + 6 302 0.0199 C /tmp/RtmpgpEV0i/fil… Rps24
#> 6 chr14 26 - 8 302 0.0265 C /tmp/RtmpgpEV0i/fil… Rps24
#> # ℹ 1 more variable: homopolymer_pct <dbl>