# line_plots <- function()
filter_alignments <- function(alignments, regions, regions_filter="both", minimum=10, maximum=30, cutoff=.001){
alignments <- filter_by_regions(alignments = alignments, regions = regions, type = regions_filter)
alignments <- filter_alignments_by_size_range(alignments = alignments, minimum = minimum, maximum = maximum)
alignments <- remove_overrepresented_sequences(alignments = alignments, cutoff = cutoff)
return(sort.GenomicRanges(alignments))
}
set_dataset_names <- function(input_dir, output_dir, dataset_info){
dataset_names <- list(fastq_file=NA, # The full name of the file (basename.ext)
basename=NA, # The basename of the file (basename)
name=NA, # The descriptive name of the file (basename or other)
output_dir=NA # The output directory (.../outputdir/basename/)
)
dataset_names["fastq_file"] <- dataset_info[[1]]
dataset_names["basename"] <- strsplit(dataset_info, "\\.")[[1]][1]
if (is.na(names(dataset_info))){
dataset_names["name"] <- dataset_names[["basename"]]
} else{
dataset_names["name"] <- names(dataset_info[1])}
dataset_names["output_dir"] <- create_output_dirs(out_dir = output_dir, name = dataset_names[["name"]])
dataset_names["figure_dir"] <- create_output_dirs(out_dir = dataset_names[["output_dir"]], name =c("figures"))
return(dataset_names)
}
#
# main_workflow <- function()
# {
# incProgress(amount = .2, detail = "Loading intervals")
# values$genome_data <- load_genome_data(path = values$genomes_dir,
# genome = values$selected_genome[["Version"]])
# print(values$genome_data)
# print(values$selected_genome[["Gene.Sets"]])
# incProgress(amount = .2, detail = "Loading gene sets")
# values$gene_sets <- load_gene_sets(gene_sets = values$selected_genome[["Gene.Sets"]])
#
# ### Load alignment
# incProgress(amount = .2, detail = "Loading alignments")
# values$alignments <- load_alignments(path = values$bam_path)
#
# incProgress(amount = .2, detail = "Filtering regions")
# values$alignments <- filter_by_regions(alignments = values$alignments,
# regions = values$genome_data[["gene_intervals"]],
# type = "both")
# incProgress(amount = .1, detail = "Filtering alignment sizes")
# values$alignments <- filter_alignments_by_size(alignments = values$alignments,
# minimum = input$get_range[[1]],
# maximum = input$get_range[[2]])
# incProgress(amount = .1, detail = "Filtering overrpreseented reads")
# values$alignments <- remove_overrepresented_sequences(alignments = values$alignments,
# cutoff = input$read_cutoff)
#
# values$alignments <- get_genome_sequence(gr = values$alignments,
# genome_sequence = load_fasta_genome(
# path = values$selected_genome[['Genome.FASTA']]
# ))
#
# print('print(values$alignments)')
# print(values$alignments)
#
# incProgress(amount = .1, detail = "Filtering mismatches")
# mismatch_indexes <- filter_BAM_tags(values$alignments)
# values$two_mm <- values$alignments[mismatch_indexes$two_mm]
# values$no_mm <- values$alignments[mismatch_indexes$no_mm]
# values$no_mm_in_seed <- values$alignments[mismatch_indexes$no_mm_seed]
# values$shuffled <- shuffle_alignments(alignments = values$two_mm,
# intervals = values$genome_data[["gene_intervals"]],
# antisense = TRUE)
# #values$two_mm <- filter_alignments(alignments = values$two_mm, regions = )
#
# print('The length of values$two_mm is: ')
# print(length(x = print(values$two_mm)))
# #print(values$two_mm)
# #print(values$no_mm)
# #print(values$no_mm_in_seed)
# print('making the plots')
# print('making the plots two_mm')
# print(width(values$two_mm))
# print(strand(values$two_mm))
# print(mcols(values$two_mm))
#
# create_output_dirs(out_dir = values$output_dir,
# name = 'five_prime')
# p <- make_length_plots(gr = values$two_mm,
# path = paste(values$output_dir,
# '/five_prime',
# sep = ''),
# label = "two_mm__all_genes")
# output$fivepp_all <- renderPlot({p})
# print('making the plots no_mm')
# make_length_plots(gr = values$no_mm,
# path = paste(values$output_dir,
# '/five_prime',
# sep = ''),
# label = "no_mm__all_genes")
# print('making the plots no_mm_in_seed')
# make_length_plots(gr = values$no_mm_in_seed,
# path = paste(values$output_dir,
# '/five_prime',
# sep = ''),
# label = "no_mm_in_seed__all_genes")
# }
# process_fastq <- function(input_dir, output_dir, dataset_info, adapter_file, genome, alignment_settings){
#
# # Trim the adapter sequences
# run_cutadapt()
#
# #Align the reads using bowtie
# for(j in 1:length(alignment_settings)){
# dataset_names[names(alignment_settings[j])] <- run_bowtie(
# alignment_settings[j], # The options for this alignment file
# names(alignment_settings[j]), # The name of the sam file configuration
# genome # ID of genome
# )
# }
# system(command = paste("gzip -f",
# paste(dataset_names["output_dir"],"/", dataset_names["basename"], ".trimmed.fastq", sep = ""),
# wait = TRUE)
# )
# return(dataset_names)
# }
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