classify_sequence: Classify 16S rRNA gene sequence fragment

In phylotypr: Classifying DNA Sequences to Taxonomic Groupings

Classify 16S rRNA gene sequence fragment

Description

The classify_sequence() function implements the Wang et al. naive Bayesian classification algorithm for 16S rRNA gene sequences.

Usage

classify_sequence(
  unknown_sequence,
  database,
  kmer_size = 8,
  num_bootstraps = 100
)

Arguments

unknown_sequence	A character object representing a DNA sequence that needs to be classified
database	A kmer database generated using build_kmer_database
kmer_size	An integer value (default of 8) indicating the size of kmers to use for classifying sequences. Higher values use more RAM with potentially more specificity Lower values use less RAM with potentially less specificity. Benchmarking has found that the default of 8 provides the best specificity with the lowest possible memory requirement and fastest execution time.
num_bootstraps	An integer value (default of 100). The value of num_bootstraps is the number of randomizations to perform where 1/kmer_size of all kmers are sampled (without replacement) from unknown_sequence. Higher values will provide greater precision on the confidence score.

Value

A list object of two vectors. One vector (taxonomy) is the taxonomic assignment for each level. The second vector (confidence) is the percentage of num_bootstraps that the classifier gave the same classification at that level

References

Wang Q, Garrity GM, Tiedje JM, Cole JR. Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl Environ Microbiol. 2007 Aug;73(16):5261-7. doi:10.1128/AEM.00062-07 PMID: 17586664; PMCID: PMC1950982.

Examples

kmer_size <- 3
sequences <- c("ATGCGCTA", "ATGCGCTC", "ATGCGCTC")
genera <- c("A", "B", "B")

db <- build_kmer_database(sequences, genera, kmer_size)
unknown_sequence <- "ATGCGCTC"

classify_sequence(
  unknown_sequence = unknown_sequence,
  database = db,
  kmer_size = kmer_size
)

phylotypr documentation built on April 3, 2025, 5:51 p.m.