Train the Coding Potential Model — build_coding

Builds the hexamer log-odds table and the logistic regression used by coding_potential() from a set of coding ORF sequences and non-coding transcript sequences, and writes both as Parquet files. The shipped model was trained on GENCODE human protein-coding CDS versus lncRNA transcripts by data-raw/build_coding_model.R; rerun that script to refresh it for a new GENCODE release or another organism.

Usage

build_coding_model(
  coding_cds,
  coding,
  noncoding,
  output_dir,
  min_orf_length = 90L,
  pseudocount = 1
)

Arguments

coding_cds: Character vector of CDS sequences (start to stop codon), used to build the hexamer table.
coding: Character vector of full-length coding transcript sequences (mRNAs), the positive class of the logistic model.
noncoding: Character vector of non-coding transcript sequences, the negative class (and the hexamer background).
output_dir: Directory receiving hexamer_logodds.parquet and coding_model_coefficients.parquet.
min_orf_length: Minimum ORF length used when scoring the training sequences.
pseudocount: Pseudocount added to hexamer counts.

Value

A list with the hexamer table, the fitted glm and the training accuracy, invisibly.

Examples

if (FALSE) { # \dontrun{
build_coding_model(cds_sequences, mrna_sequences, lncrna_sequences, output_dir = "coding_model")
} # }