Extracts features of poly(A) tails of ONT RNA reads required for finding non-A nucleotides within the given tails.

This is the training-set variant of the feature extraction wrapper. It processes all reads in parallel, calling extract_tail_data_trainingset for each read, then filters out reads with zero-moved tails or pseudomove chains too short to represent potential modifications.

Usage

create_tail_feature_list_trainingset(
  nanopolish,
  sequencing_summary,
  workspace,
  num_cores,
  basecall_group,
  pass_only = TRUE
)

Arguments

nanopolish

Character string. Full path of the .tsv file produced by nanopolish polya.

sequencing_summary

Character string. Full path of the .txt file with the sequencing summary.

workspace

Character string. Full path of the directory containing basecalled multi-Fast5 files.

num_cores

Numeric [1]. Number of physical cores to use. Do not exceed 1 less than the number of cores at your disposal.

basecall_group

Character string ["Basecall_1D_000"]. Name of the level in the Fast5 file hierarchy from which the data should be extracted.

pass_only

Logical [TRUE]. If TRUE, only reads tagged by nanopolish as "PASS" are retained. Otherwise, reads tagged as "PASS" or "SUFFCLIP" are included.

Value

A named list with three elements:

tail_feature_list

Named list of per-read feature lists (as returned by extract_tail_data_trainingset).

zeromoved_readnames

Character vector. Read IDs discarded because their tail moves summed to zero.

nonpseudomoved_readnames

Character vector. Read IDs discarded because their pseudomove chains were too short (< 4).

Always assign this returned list to a variable; printing the full list to the console may crash the R session.

Details

The function differs from its production counterpart in that it retains reads whose pseudomove chains satisfy a length >= 4 criterion, which is required for subsequent modification-centered chunk splitting. Two categories of discarded reads are tracked (zero-moved and non-pseudomoved) and returned alongside the valid feature list.

See also

extract_tail_data_trainingset for the per-read extraction step, create_tail_chunk_list_trainingset for the next pipeline step, extract_polya_data for input data preparation.

Examples

if (FALSE) { # \dontrun{

create_tail_feature_list_trainingset(
  nanopolish = '/path/to/file',
  sequencing_summary = '/path/to/file',
  workspace = '/path/to/guppy/workspace',
  num_cores = 10,
  basecall_group = 'Basecall_1D_000')

} # }