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Annotate FST/DMR slide windows with genomic features

Source: R/anno_fst_dmr.R
anno_fst_dmr.Rd

Annotate FST/DMR slide windows with genomic features.

Usage

anno_fst_dmr(
  gff_file,
  format = "auto",
  genomic_ranges,
  chrom_col = "CHROM",
  start_col = "BIN_START",
  end_col = "BIN_END",
  upstream = 2000,
  downstream = 200,
  ignore_strand = TRUE,
  features = c("promoter", "UTR5", "gene", "exon", "intron", "CDS", "UTR3", "intergenic")
)

Arguments

gff_file

Genomic structural annotation GFF3/GTF file path.

format

Format of GFF3/GTF file. ("auto", "gff3", "gtf").

genomic_ranges

Genomic ranges file (e.g., FST, DMR). (chromosome (prefix: chr), start, end).

chrom_col

Chromosome column name. (FST: "CHROM", DMR: "chr").

start_col

Start column name. (FST: "BIN_START", DMR: "start").

end_col

End column name. (FST: "BIN_END", DMR: "end").

upstream

Promoter upstream (bp). (2000).

downstream

Promoter downstream (bp). (200).

ignore_strand

Whether to ignore strand when computing overlaps. (TRUE).

features

Which feature types to annotate. c("promoter", "UTR5", "gene", "exon", "intron", "CDS", "UTR3", "intergenic").

Value

Data.frame with input intervals and annotation results.

Author

benben-miao

Examples

# Example GFF3 file in GAnnoViz
gff_file <- system.file(
  "extdata",
  "example.gff3.gz",
  package = "GAnnoViz")

# Annotate FST
fst_file <- system.file(
    "extdata",
    "example.fst",
    package = "GAnnoViz")

fst <- read.table(
  file = fst_file,
  header = TRUE,
  sep = "\t",
  na.strings = NA,
  stringsAsFactors = FALSE
)
head(fst)
#>   CHROM BIN_START BIN_END N_VARIANTS WEIGHTED_FST     MEAN_FST
#> 1  chr1         1   10000        119  0.034869746  0.027472144
#> 2  chr1     20001   30000        150 -0.009735326 -0.019103882
#> 3  chr1     40001   50000        587 -0.013269573 -0.004886184
#> 4  chr1     60001   70000        256  0.036700847  0.027643440
#> 5  chr1     80001   90000        842  0.020847846  0.011547497
#> 6  chr1    100001  110000        892  0.001750007  0.002121159

res <- anno_fst_dmr(
  gff_file = gff_file,
  format = "auto",
  genomic_ranges = fst_file,
  chrom_col = "CHROM",
  start_col = "BIN_START",
  end_col = "BIN_END",
  upstream = 2000,
  downstream = 200,
  ignore_strand = TRUE,
  features = c("promoter", "UTR5", "gene", "exon", "intron", "CDS", "UTR3", "intergenic")
)
#> Import genomic features from the file as a GRanges object ... 
#> OK
#> Prepare the 'metadata' data frame ... 
#> OK
#> Make the TxDb object ... 
#> OK
head(res)
#>   CHROM BIN_START BIN_END N_VARIANTS WEIGHTED_FST     MEAN_FST  anno_type
#> 1  chr1         1   10000        119  0.034869746  0.027472144 intergenic
#> 2  chr1     20001   30000        150 -0.009735326 -0.019103882 intergenic
#> 3  chr1     40001   50000        587 -0.013269573 -0.004886184 intergenic
#> 4  chr1     60001   70000        256  0.036700847  0.027643440 intergenic
#> 5  chr1     80001   90000        842  0.020847846  0.011547497 intergenic
#> 6  chr1    100001  110000        892  0.001750007  0.002121159 intergenic
#>   gene_id
#> 1        
#> 2        
#> 3        
#> 4        
#> 5        
#> 6        

# Annotate DMR
dmr_file <- system.file(
    "extdata",
    "example.dmr",
    package = "GAnnoViz")

dmr <- read.table(
  file = dmr_file,
  header = TRUE,
  sep = "\t",
  na.strings = NA,
  stringsAsFactors = FALSE
)
head(dmr)
#>    chr   start     end strand pvalue   qvalue meth.diff
#> 1 chr1 4930001 4932000      *  1e-07 0.009117    89.850
#> 2 chr1 4936001 4938000      *  1e-08 0.005656   -82.314
#> 3 chr1 5670001 5672000      *  1e-09 0.011313   -69.908
#> 4 chr1 5914001 5916000      *  1e-09 0.001782   -93.310
#> 5 chr1 8576001 8578000      *  1e-08 0.009556   -68.589
#> 6 chr1 9098001 9100000      *  1e-07 0.009733   -68.756

res <- anno_fst_dmr(
  gff_file = gff_file,
  format = "auto",
  genomic_ranges = dmr_file,
  chrom_col = "chr",
  start_col = "start",
  end_col = "end",
  upstream = 2000,
  downstream = 200,
  ignore_strand = TRUE,
  features = c("promoter", "UTR5", "gene", "exon", "intron", "CDS", "UTR3", "intergenic")
)
#> Import genomic features from the file as a GRanges object ... 
#> OK
#> Prepare the 'metadata' data frame ... 
#> OK
#> Make the TxDb object ... 
#> OK
head(res)
#>    chr   start     end strand pvalue   qvalue meth.diff        anno_type
#> 1 chr1 4930001 4932000      *  1e-07 0.009117    89.850      gene,intron
#> 2 chr1 4936001 4938000      *  1e-08 0.005656   -82.314 gene,exon,intron
#> 3 chr1 5670001 5672000      *  1e-09 0.011313   -69.908      gene,intron
#> 4 chr1 5914001 5916000      *  1e-09 0.001782   -93.310       intergenic
#> 5 chr1 8576001 8578000      *  1e-08 0.009556   -68.589      gene,intron
#> 6 chr1 9098001 9100000      *  1e-07 0.009733   -68.756      gene,intron
#>                                       gene_id
#> 1 ENSMUSG00000033813(g),ENSMUSG00000104217(g)
#> 2 ENSMUSG00000033813(g),ENSMUSG00000104217(g)
#> 3                       ENSMUSG00000025905(g)
#> 4                                            
#> 5                       ENSMUSG00000025909(g)
#> 6                       ENSMUSG00000025909(g)