-
Notifications
You must be signed in to change notification settings - Fork 16
/
Copy pathfind-tails.R
641 lines (605 loc) · 34 KB
/
find-tails.R
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
#' Estimates poly(A)/(T) tail lengths in Oxford Nanopore RNA and DNA reads
#'
#' This function estimates poly(A) tail length in RNA reads, and both poly(A)
#' and poly(T) tail lengths in DNA reads. It can operate on reads base called
#' with any version of Albacore and Guppy using either the standard or the
#' recent 'flip-flop' model. The function outputs a CSV file containing poly(A)
#' tail information organised by the read ID; it also returns the same
#' information as a tibble for further processing by the end-user. Currently,
#' the algorithm works only on ONT 1D reads.
#'
#' @param fast5_dir character string. Full path of the directory to search the
#' basecalled fast5 files in. The Fast5 files can be single or multi-fast5 file.
#' The directory is searched recursively.
#'
#' @param save_dir character string. Full path of the directory where the CSV
#' file containing the tail-length information should be stored. If save_plots
#' is set to \code{TRUE}, then a \code{plots} directory is also created within
#' the \code{save_dir}.
#'
#' @param csv_filename character string ["tails.csv"]. Filename of the
#' CSV file in which to store the tail length data
#'
#' @param num_cores numeric [1]. Num of physical cores to use in processing
#' the data. Always use 1 less than the number of cores at your disposal.
#'
#' @param basecall_group a character string ["Basecall_1D_000"]. Name of the
#' level in the Fast5 file hierarchy from which tailfindr should read the data.
#'
#' @param save_plots logical [FALSE]. If set to \code{TRUE}, a plots
#' directory will be created within the save_dir, and plots showing poly(A) and
#' poly(T) tails in the raw squiggle will be saved in this \code{plots}
#' directory. Creating plots and saving them to the disk is a slow process. We
#' recommend that you keep this option set to \code{FALSE}. If you still want
#' to create plots, we recommend that you run tailfindr on a subset of reads.
#' Plots are automatically named by concatenating read ID with the name of the
#' Fast5 file containing this read; the read ID and fast5 file name are
#' separated by two underscores (__).
#'
#' @param plot_debug_traces logical [FALSE]. This option works only
#' if \code{save_plots} option is also set to \code{TRUE}.If set to \code{TRUE},
#' debugging information is plotted in the plots as well. This includes mean
#' signal, slope signal,thresholds, smoothened signal, etc. We use this option
#' internally to debug our algorithm.
#'
#' @param plotting_library character string ["rbokeh"]. \code{rbokeh}
#' is the default plotting library used if \code{save_plots} is set to
#' \code{TRUE}. The plots will be saved as HTML files in the
#' \code{/save_dir/plots} directory. You can open these HTLM files in any
#' web-browser and interactively view the plots showing the tail region in the
#' raw squiggle. If this option is set to \code{'ggplot2'}, then the polts will
#' be saved as static \code{.png} files.
#'
#' @param ... list. A list of optional parameters. This is currently, reserved
#' for internal use only.
#'
#' @return A data tibble containing tail information organzied by
#' the read ID is returned. Always save this returned tibble in a variable (see
#' examples below), otherwise the long tibble will be printed to the
#' console, which may hang up your R session.
#'
#' A CSV file containing the same information is also saved on disk in the
#' \code{save_dir}.
#'
#' @export
#'
#' @examples
#' \dontrun{
#'
#' library(tailfindr)
#'
#' # 1. Suppose you have 11 cores at your disposal, then you should run tailfindr
#' # on your data as following:
#' df <- find_tails(fast5_dir = system.file('extdata', 'rna', package = 'tailfindr'),
#' save_dir = '~/Downloads',
#' csv_filename = 'rna_tails.csv',
#' num_cores = 10)
#' # In the above example, we have used tailfindr on example RNA reads
#' # present in the tailfindr package. You should substitute the path of
#' # your data for the fast5_dir parameter.
#'
#' # 2. If you want to save interactive HTML plots using rbokeh,
#' # then you should run tailfindr as following:
#' df <- find_tails(fast5_dir = system.file('extdata', 'cdna', package = 'tailfindr'),
#' save_dir = '~/Downloads',
#' csv_filename = 'cdna_tails.csv',
#' num_cores = 10,
#' save_plots = TRUE,
#' plotting_library = 'rbokeh')
#'
#' # 3. If you also want to plot debug traces, then you should run tailfindr as
#' # below:
#' df <- find_tails(fast5_dir = system.file('extdata', 'cdna', package = 'tailfindr'),
#' save_dir = '~/Downloads',
#' csv_filename = 'cdna_tails.csv',
#' num_cores = 10,
#' save_plots = TRUE,
#' plot_debug_traces = TRUE,
#' plotting_library = 'rbokeh')
#'
#' # N.B.: Making and saving plots is a computationally slow process.
#' # Only generate plots by running tailfindr on a small subset of your reads.
#'
#' # 4. By default, tailfindr uses Events/Move table in the Basecall_1D_000
#' # section of the FAST5 file. If you want tailfindr to pick Events/Move table
#' # from some other section of the FAST5 file -- lets say Basecall_1D_001--
#' # then you should use tailfindr like below:
#' df <- find_tails(fast5_dir = system.file('extdata', 'rna_basecall_1D_001', package = 'tailfindr'),
#' save_dir = '~/Downloads',
#' csv_filename = 'rna_tails.csv',
#' num_cores = 2,
#' basecall_group = 'Basecall_1D_001',
#' save_plots = TRUE,
#' plot_debug_traces = TRUE,
#' plotting_library = 'rbokeh')
#' # N.B.: tailfindr cannot work if it can't find Events or Move table in
#' # your FAST5 files. MinKNOW Live Basecalling currently does not save the
#' # Events/Move table in the FAST5 file. If your reads have been live
#' # basecalled, then you should rebasecall them using Albacore or Guppy, and
#' # subsequently use tailfindr and specify the basecall_group parameter. Most
#' # probably, in the second round of your basecalling, the Events/Move table
#' # is stored in the 'Basecall_1D_001' section, so set this as the value of the
#' # basecall_group parameter. You can also confirm this by viewing your
#' # re-basecalled reads in HDFView.
#' }
#'
find_tails <- function(fast5_dir,
save_dir,
csv_filename = 'tails.csv',
num_cores = 1,
basecall_group = 'Basecall_1D_000',
save_plots = FALSE,
plot_debug_traces = FALSE,
plotting_library = 'rbokeh',
...) {
plot_debug <- plot_debug_traces
if (save_plots == FALSE) {
plot_debug <- FALSE
}
# Taking out these parameter from the function parameters list
# as they may be dangerous for normal users
show_plots <- FALSE
dna_datatype <- get_dna_datatype(...) # R CMD NOTE
if (dna_datatype == 'custom-cdna') {
fp_ep_list <- get_custom_fp_ep(...) # R CMD NOTE
}
# start a log file
if (dir.exists(file.path(save_dir))) {
logfile_name <- paste(format(Sys.time(), "%Y-%m-%d_%H-%M-%S"), "_tailfinder.log", sep = "")
logfile_path <- file.path(save_dir, logfile_name, fsep = .Platform$file.sep)
con <- file(logfile_path, open = "a")
sink(con, append=TRUE, split = TRUE, type='output')
on.exit(sink(file=NULL, type = 'output'))
}
# display console messages
version <- utils::packageDescription("tailfindr")$Version
cat(cli::rule(left=''), '\n', sep = "")
cat(cli::rule(left=paste("Started tailfindr ", '(version ', version, ')', sep='')), '\n', sep = "")
cat(cli::rule(left=''), '\n', sep = "")
# display the user-specified parameters
cat(paste(cli::symbol$menu, ' You have configured tailfindr as following:', '\n', sep=''))
cat(paste(cli::symbol$pointer, ' fast5_dir: ', fast5_dir, '\n', sep=''))
cat(paste(cli::symbol$pointer, ' save_dir: ', save_dir, '\n', sep=''))
cat(paste(cli::symbol$pointer, ' csv_filename: ', csv_filename, '\n', sep=''))
cat(paste(cli::symbol$pointer, ' num_cores: ', num_cores, '\n', sep=''))
cat(paste(cli::symbol$pointer, ' basecall_group: ', basecall_group, '\n', sep=''))
cat(paste(cli::symbol$pointer, ' save_plots: ', save_plots, '\n', sep=''))
cat(paste(cli::symbol$pointer, ' plot_debug_traces: ', plot_debug_traces, '\n', sep=''))
cat(paste(cli::symbol$pointer, ' plotting_library: ', plotting_library, '\n', sep=''))
if (dna_datatype == 'pcr-dna' | dna_datatype == 'custom-cdna') {
cat(paste(cli::symbol$pointer, ' dna_datatype: ', dna_datatype, '\n', sep=''))
}
cat(cli::rule(left=paste('Processing started at ', Sys.time(), sep = '')), '\n', sep = "")
# Try to create the save directory
if (!dir.exists(file.path(save_dir))) {
cat(paste(cli::symbol$bullet, ' Save dir does not exist. Trying to create it...\n', sep=''))
tryCatch({
dir.create(file.path(save_dir, fsep = .Platform$file.sep))
cat(' Done!\n')
},
error=function(e){
cat(paste(cli::symbol$bullet, ' Failed to create the save dir. Results will be stored in the "~/" directory instead.\n', sep=''))
save_dir <- '~/'
})
logfile_name <- paste(format(Sys.time(), "%Y-%m-%d_%H-%M-%S"), "_tailfinder.log", sep = "")
logfile_path <- file.path(save_dir, logfile_name, fsep = .Platform$file.sep)
con <- file(logfile_path, open = "a")
sink(con, append=TRUE, split = TRUE, type='output')
on.exit(sink(file=NULL, type = 'output'))
}
# Create a sub-direcotry to save all the plots
if (save_plots){
plots_dir <- file.path(save_dir, 'plots', fsep = .Platform$file.sep)
if (!dir.exists(file.path(plots_dir))) {
cat(paste(cli::symbol$bullet, ' Creating a sub-directory to save the plots in.\n', sep=''))
dir.create(plots_dir)
cat(' Done! All plots will be saved in the following direcotry:\n')
cat(paste(' ', file.path(save_dir, 'plots', fsep = .Platform$file.sep), '\n', sep = ''))
}
}
# search for all the fast5 files in the user-specified directory
cat(paste(cli::symbol$bullet,' Searching for all Fast5 files...\n', sep=''))
# must handle fast5 dir, or a pre-made character vector of file paths
if (is.na(fast5_dir[2])) {
fast5_files_list <- list.files(path = fast5_dir,
pattern = "\\.fast5$",
recursive = TRUE,
full.names = TRUE)
} else {
fast5_files_list <- fast5_dir
}
num_files <- length(fast5_files_list)
cat(paste0(' Done! Found ', num_files, ' Fast5 files.\n'))
# read the first read in the list of reads,
# and determine all the properties of the data
cat(paste(cli::symbol$bullet,' Analyzing a single Fast5 file to assess if your data \n', sep=''))
cat(' is in an acceptable format...\n')
type_info <- explore_basecaller_and_fast5type(fast5_files_list[1],
basecall_group = basecall_group)
# currently MinKNOW does not store Events/Move table without which we
# cannot compute the read-specific normalizer
if (type_info$basecalled_with == 'minknow' & type_info$model == 'unknown') {
cat(paste0(' ', cli::symbol$cross,
' Fatal error! Your data has been basecalled with MinKNOW\n'))
cat(' live basecalling which currently does not save the\n')
cat(' Events/Move table in the Analyses/Basecall_1D_000 section of\n')
cat(' the FAST5 file. You should rebasecall your FAST5 files using \n')
cat(' standalone Guppy or Albacore, and then use tailfindr on the \n')
cat(' rebasecalled files. Please adjust the value of basecall_group\n')
cat(' parameter in such a case, so that tailfindr can find the \n')
cat(' Events/Move table in the specified basecall_group. You can\n')
cat(' check which basecall_group the Event/Move is residing by\n')
cat(' opening your FAST5 file in HDFView.\n\n')
cat(' If the Events/Move is present in the data and you have\n')
cat(' specified the correct basecall_group, but you still\n')
cat(' get this error then please open an issue on GitHub:\n')
cat(' https://github.com/adnaniazi/tailfindr/issues\n')
cat(' Remember to attach a few (around 5) of your FAST5 files\n')
cat(' to help us understand the issue.\n')
cat(cli::rule(left=paste('Processing ended at ',
Sys.time(), sep = '')), '\n', sep = "")
cat(paste(crayon::green(cli::symbol$cross),
' tailfindr finished unsuccessfully!\n', sep=''))
return(0)
}
basecalled_with <- type_info$basecalled_with
multifast5 <- ifelse(type_info$fast5type == 'multi', TRUE, FALSE)
experiment_type <- type_info$experiment_type
read_is_1d <- type_info$read_is_1d
model <- type_info$model
if (basecalled_with == 'albacore'){
cat(paste(' ', crayon::green(cli::symbol$tick),
' The data has been basecalled using Albacore.\n', sep=''))
} else {
cat(paste(' ', crayon::green(cli::symbol$tick),
' The data has been basecalled using Guppy.\n', sep=''))
}
if (model == 'flipflop'){
cat(paste(' ', crayon::green(cli::symbol$tick),
' Flipflop model was used during basecalling.\n', sep=''))
} else {
cat(paste(' ', crayon::green(cli::symbol$tick),
' Standard model was used during basecalling.\n', sep=''))
}
if (multifast5){
cat(paste(' ', crayon::green(cli::symbol$tick),
' The reads are packed in multi-fast5 file(s).\n', sep=''))
} else {
cat(paste(' ', crayon::green(cli::symbol$tick),
' Every read is in a single fast5 file of its own.\n', sep=''))
}
if (experiment_type == 'rna'){
cat(paste(' ', crayon::green(cli::symbol$tick),
' The experiment type is RNA, so we will search\n', sep=''))
cat(' for poly(A) tails.\n')
} else {
cat(paste(' ', crayon::green(cli::symbol$tick),
' The experiment type is DNA, so we will search\n', sep=''))
cat(' for both poly(A) and poly(T) tails.\n')
}
if (read_is_1d == TRUE){
cat(paste(' ', crayon::green(cli::symbol$tick),
' The reads are 1D reads.\n', sep=''))
} else {
cat(paste(' ', crayon::red(cli::symbol$cross),
' The reads are not 1D. Currently, we only support\n', sep=''))
cat(' 1D reads. If you believe your reads are 1D, and you are\n')
cat(' getting this cat erroneously, please feel free\n')
cat(' to contact us at [email protected]. Do not forget to\n')
cat(' send us one of the problematic reads so that we can\n')
cat(' debug our software, and send you a patch.\n')
cat(paste(' ', crayon::red(cli::symbol$cross),
'Finished because of the error!\n', sep=''))
cat(cli::rule(left=paste('tailfindr finished with a fatal error at ',
Sys.time(), sep = '')), '\n', sep = "")
return(0)
}
# Make a compute cluster
cat(paste(cli::symbol$bullet,' Starting a parallel compute cluster...\n', sep=''))
#cl <- parallel::makeCluster(num_cores, outfile='')
cl <- parallel::makeCluster(num_cores)
on.exit(parallel::stopCluster(cl))
doSNOW::registerDoSNOW(cl)
`%dopar%` <- foreach::`%dopar%`
`%do%` <- foreach::`%do%`
cat(' Done!\n')
mcoptions <- list(preschedule = TRUE, set.seed = FALSE, cleanup = FALSE)
# if the data is DNA then make a substitution matrix
if (experiment_type == 'dna') {
match <- 1
mismatch <- -1
type <- 'local'
gapOpening <- 0
gapExtension <- 1
submat <- Biostrings::nucleotideSubstitutionMatrix(match = match,
mismatch = mismatch,
baseOnly = TRUE)
dna_opts <- list(match = match,
mismatch = mismatch,
type = type,
gapOpening = gapOpening,
gapExtension = gapExtension,
submat = submat)
# append custom front and end primers to DNA options if it is custom
# cDNA
if (dna_datatype == 'custom-cdna') {
dna_opts <- c(dna_opts, fp_ep_list)
}
}
# If the fast5 are multifast5, then build an index of all the reads within these files
remove_last_duplicate_read <- FALSE
if (multifast5) {
cat(paste(cli::symbol$bullet, ' Discovering reads in the ',
num_files, ' multifast5 files...\n', sep=''))
read_id_fast5_file <- dplyr::tibble(read_id = character(),
fast5_file = character())
for (fast5_file in fast5_files_list) {
f5_obj <- hdf5r::H5File$new(fast5_file, mode = 'r')
f5_tree <- f5_obj$ls(recursive = FALSE)
f5_tree <- f5_tree$name
f5_tree <- dplyr::mutate(dplyr::tibble(f5_tree), fast5_file = fast5_file)
value <- NULL # R CMD CHECK
f5_tree <- dplyr::rename(f5_tree, read_id = f5_tree)
read_id_fast5_file <- rbind(read_id_fast5_file, f5_tree)
f5_obj$close_all()
}
cat(paste0(' Done! Found ', nrow(read_id_fast5_file), ' reads\n'))
# convert the data frame to list with rows as elements of the list
read_id_fast5_file <- split(read_id_fast5_file, seq(nrow(read_id_fast5_file)))
# Split the data into chunks
files_per_chunk <- 4000
total_files <- length(read_id_fast5_file)
total_chunks <- ceiling(total_files/files_per_chunk)
#loop
if (experiment_type == 'dna') {
cat(paste(cli::symbol$bullet,
' Searching for Poly(A) and Poly(T) tails...\n', sep=''))
} else {
cat(paste(cli::symbol$bullet,
' Searching for Poly(A) tails...\n', sep=''))
}
counter <- 0
result <- list()
for(chunk in seq_len(total_chunks)){
# divide data in chunks
if(chunk == total_chunks) {
read_id_fast5_file_subset <-
read_id_fast5_file[((counter*files_per_chunk)+1):total_files]
# if the last chunk has only one read then just duplicate this
# one read so that the progressbar works, and does not throw an error
if (length(read_id_fast5_file_subset) == 1) {
read_id_fast5_file_subset[[2]] <- read_id_fast5_file_subset
remove_last_duplicate_read <- TRUE
}
} else {
read_id_fast5_file_subset <-
read_id_fast5_file[((counter*files_per_chunk)+1):((counter+1)*files_per_chunk)]
}
counter <- counter + 1
cat(paste(' Processing chunk ', chunk, ' of ', total_chunks, '\n', sep = ''))
# progress bar
pb <- utils::txtProgressBar(min = 1,
max = length(read_id_fast5_file_subset),
style = 3)
progress <- function(n) utils::setTxtProgressBar(pb, n)
opts <- list(progress = progress)
# foreach loop
sink(file=NULL, type = 'output')
close(con)
if (experiment_type == 'dna') {
riff <- NULL # R CMD CHECK
data_list <- foreach::foreach(riff = read_id_fast5_file_subset,
.combine = 'rbind',
.inorder = FALSE,
.errorhandling = 'pass',
.options.snow = opts,
.options.multicore = mcoptions) %dopar% {
tryCatch({
find_dna_tail_per_read(read_id_fast5_file = riff,
file_path = NA,
basecall_group = basecall_group,
dna_datatype = dna_datatype,
save_plots = save_plots,
show_plots = show_plots,
plot_debug = plot_debug,
save_dir = save_dir,
plotting_library = plotting_library,
multifast5 = multifast5,
basecalled_with = basecalled_with,
model = model,
dna_opts = dna_opts)
},
error=function(e){
ls <- list(read_id = riff$read_id,
read_type = NA,
tail_is_valid = NA,
tail_start = NA,
tail_end = NA,
samples_per_nt = NA,
tail_length = NA,
file_path = riff$fast5_file,
has_precise_boundary = NA)
})
}
} else {
riff <- NULL # R CMD CHECK
data_list <- foreach::foreach(riff = read_id_fast5_file_subset,
.combine = 'rbind',
.inorder = FALSE,
.errorhandling = 'pass',
.options.snow = opts,
.options.multicore = mcoptions) %dopar% {
tryCatch({
find_rna_polya_tail_per_read(file_path = NA,
read_id_fast5_file = riff,
basecall_group = basecall_group,
multifast5 = multifast5,
basecalled_with = basecalled_with,
model = model,
save_plots = save_plots,
show_plots = show_plots,
save_dir = save_dir,
plotting_library = plotting_library,
plot_debug = plot_debug)
},
error=function(e){
ls <- list(read_id = NA,
tail_start = NA,
tail_end = NA,
samples_per_nt = NA,
tail_length = NA,
polya_fastq = NA,
file_path = riff$fast5_file)
})
}
}
cat('\n')
if (remove_last_duplicate_read) data_list <- data_list[[1]]
result[[chunk]] <- data_list
con <- file(logfile_path, open = "a")
sink(con, append=TRUE, split = TRUE, type='output')
}
} else if (!multifast5) {
# Split the data into chunks
files_per_chunk <- 4000
total_files <- length(fast5_files_list)
total_chunks <- ceiling(total_files/files_per_chunk)
counter <- 0
result <- list()
if (experiment_type == 'dna') {
cat(paste(cli::symbol$bullet,
' Searching for Poly(A) and Poly(T) tails...\n', sep=''))
} else {
cat(paste(cli::symbol$bullet,
' Searching for Poly(A) tails...\n', sep=''))
}
for (chunk in seq_len(total_chunks)) {
if(chunk == total_chunks) {
fast5_files_subset <-
fast5_files_list[((counter*files_per_chunk)+1):total_files]
# if the last chunk has only one read then just duplicate this
# one read so that the progressbar works and does not throw an error
if (length(fast5_files_subset) == 1) {
fast5_files_subset[[2]] <- fast5_files_subset
remove_last_duplicate_read <- TRUE
}
} else {
fast5_files_subset <-
fast5_files_list[((counter*files_per_chunk)+1):((counter+1)*files_per_chunk)]
}
counter <- counter + 1
# progress bar
cat(paste(' Processing chunk ', chunk, ' of ', total_chunks, '\n', sep=''))
pb <- utils::txtProgressBar(min = 1,
max = length(fast5_files_subset),
style = 3)
progress <- function(n) utils::setTxtProgressBar(pb, n)
opts <- list(progress = progress)
# foreach loop
sink(file=NULL, type = 'output')
close(con)
if (experiment_type == 'dna') {
file_path <- NULL # R CMD CHECK
data_list <- foreach::foreach(file_path = fast5_files_subset,
.combine = 'rbind',
.inorder = FALSE,
.options.snow = opts,
.options.multicore = mcoptions) %dopar% {
tryCatch({
find_dna_tail_per_read(file_path = file_path,
basecall_group = basecall_group,
dna_datatype = dna_datatype,
save_plots = save_plots,
show_plots = show_plots,
plot_debug = plot_debug,
save_dir = save_dir,
plotting_library = plotting_library,
multifast5 = multifast5,
basecalled_with = basecalled_with,
model = model,
dna_opts = dna_opts)
},
error=function(e){
ls <- list(read_id = NA,
read_type = NA,
tail_is_valid = NA,
tail_start = NA,
tail_end = NA,
samples_per_nt = NA,
tail_length = NA,
file_path = file_path,
has_precise_boundary = NA)
})
}
} else {
file_path <- NULL # R CMD CHECK
data_list <-foreach::foreach(file_path = fast5_files_subset,
.combine = 'rbind',
.inorder = FALSE,
.options.snow = opts,
.options.multicore = mcoptions) %dopar% {
tryCatch({
find_rna_polya_tail_per_read(file_path = file_path,
read_id_fast5_file = NA,
basecall_group = basecall_group,
multifast5 = multifast5,
basecalled_with = basecalled_with,
model = model,
save_plots = save_plots,
show_plots = show_plots,
save_dir = save_dir,
plotting_library = plotting_library,
plot_debug = plot_debug)
},
error=function(e){
ls <- list(read_id = NA,
tail_start = NA,
tail_end = NA,
samples_per_nt = NA,
tail_length = NA,
polya_fastq = NA,
file_path = file_path)
})
}
}
cat('\n')
if (remove_last_duplicate_read) data_list <- data_list[[1]]
result[[chunk]] <- data_list
con <- file(logfile_path, open = "a")
sink(con, append=TRUE, split = TRUE, type='output')
}
}
# format the results list into a tibble
cat(paste0(cli::symbol$bullet,' Formatting the tail data...\n'))
result <- purrr::map(result, function(.x) tibble::as_tibble(.x))
result <- dplyr::bind_rows(result, .id = "chunk")
result <- dplyr::select(result, -chunk)
# cleanup the tibble
result <- tidyr::unnest(result)
if (experiment_type == 'dna') {
has_precise_boundary <- NULL # R CMD CHECK
result <- within(result, rm(has_precise_boundary))
} else {
polya_fastq <- NULL # R CMD CHECK
result <- within(result, rm(polya_fastq))
}
result$tail_length <- round(result$tail_length, digits = 2)
result$samples_per_nt <- round(result$samples_per_nt, digits = 2)
cat(' Done!\n')
# write the result to a csv file
cat(paste(cli::symbol$bullet,
' Saving the data in the CSV file...\n', sep=''))
data.table::fwrite(result, file.path(save_dir, csv_filename, fsep = .Platform$file.sep))
cat(' Done! Below is the path of the CSV file:\n')
cat(paste0(' ', file.path(save_dir, csv_filename, fsep = .Platform$file.sep), '\n'))
cat(paste0(cli::symbol$bullet,
' A logfile containing all this information has been saved in this path: \n'))
cat(paste0(' ', logfile_path, '\n'))
cat(cli::rule(left=paste('Processing ended at ',
Sys.time(), sep = '')), '\n', sep = "")
cat(paste(crayon::green(cli::symbol$tick),
' tailfindr finished successfully!\n', sep=''))
return(result)
# close logfile connection
close(con)
}