Reclassifies ambiguous non-A residues to mitigate potential errors inherited from nanopolish segmentation.

High-level wrapper that combines correct_residue_data and correct_class_data into a single call, producing cleaned class and residue data frames ready for downstream analysis and visualisation.

Usage

reclassify_ninetails_data(
  residue_data,
  class_data,
  grouping_factor = NULL,
  transcript_column,
  ref = NULL
)

Arguments

residue_data

Data frame or tibble containing non-A residue predictions from the ninetails pipeline.

class_data

Data frame or tibble containing read_classes predictions from the ninetails pipeline.

grouping_factor

Character string or NULL (default). A grouping variable (e.g. "sample_name").

transcript_column

Character string. Name of the column containing transcript identifiers (e.g. "contig", "ensembl_transcript_id_short").

ref

Character string, character vector, or NULL (default). Whitelist of transcripts with hybrid tails. Built-in options:

"athaliana"

Arabidopsis thaliana

"hsapiens"

Homo sapiens

"mmusculus"

Mus musculus

"scerevisiae"

Saccharomyces cerevisiae

"celegans"

Caenorhabditis elegans

"tbrucei"

Trypanosoma brucei

A custom character vector may also be provided. Must be consistent with the content of transcript_column. Using a whitelist is optional but allows retrieval of more true positive data.

Value

A named list with two data frames:

class_data

Data frame. Corrected read classifications with class and comments columns updated. Compatible with plotting functions.

residue_data

Data frame. Filtered non-A residue predictions with ambiguous positions removed. Intermediate QC columns are dropped.

Details

Nanopolish segmentation can misidentify nucleotides from A-rich 3' UTR regions as part of the poly(A) tail. When tail boundaries are recognised incorrectly, non-A positions accumulate near the 3' end of the transcript, significantly affecting analysis results. This function flags and removes those ambiguous positions and reclassifies affected reads accordingly.

The procedure:

1. correct_residue_data annotates each non-A position with a quality flag (qc_pos).

2. correct_class_data reclassifies reads whose all non-A positions are flagged as ambiguous.

3. Ambiguous positions (qc_pos == "N") are dropped from the residue table.

4. Corrected columns (corr_class, corr_comments) are renamed to class and comments.

Caution

Reads containing only ambiguous non-A positions are reclassified as "blank" in the class column, and their comments are changed from "YAY" to "MPU".

See also

correct_residue_data and correct_class_data for the underlying steps, check_tails_guppy and create_outputs for the pipeline that produces the input data.

Examples

if (FALSE) { # \dontrun{

rec_results <- ninetails::reclassify_ninetails_data(
  residue_data = results[[2]],
  class_data = results[[1]],
  transcript_column = "contig")

} # }