Informatic sequence classification trees

insect is an R package for taxonomic identification of amplicon sequence variants generated by DNA meta-barcoding analysis. The learning and classification algorithms implemented in the package are based on full probabilistic models (profile hidden Markov models) and offer highly accurate taxon IDs, albeit at a relatively high computational cost.

The package also contains functions for searching and downloading reference sequences and taxonomic information from NCBI, a “virtual PCR” tool for sequence trimming, a function for purging erroneously labeled reference sequences, and several other tools.

insect is designed to be used in conjunction with the dada2 pipeline or other de-noising tools that produce a list of amplicon sequence variants (ASVs). While unfiltered sequences can also be processed with high accuracy, the insect classification algorithm is relatively slow, since it uses a computationally intensive dynamic programming algorithm to find the likelihood values of each sequence given the models at each node of the classification tree. Hence filtered input datasets are generally be much faster to process.


To download insect from CRAN and load the package, run


To download the latest development version from GitHub, run:

devtools::install_github("shaunpwilkinson/insect", build_vignettes = TRUE) 

Classifying sequences

Classifiers for some of the more commonly used metabarcoding primer sets are available here:

Marker Target Primers Source Version Date Download
12S Fish MiFishUF/MiFishUR (Miya et al 2015) GenBank 1 20181111 RDS (9MB)
16S Marine crustaceans Crust16S_F/Crust16S_R (Berry et al 2017) GenBank 4 20180626 RDS (7.1 MB)
16S Marine fish Fish16sF/16s2R (Berry et al 2017; Deagle et al 2007) GenBank 4 20180627 RDS (6.8MB)
18S Marine eukaryotes 18S_1F/18S_400R (Pochon et al 2017) SILVA_132_LSUParc, GenBank 5 20180709 RDS (11.8 MB)
18S Marine eukaryotes 18S_V4F/18S_V4R (Stat et al 2017) GenBank 4 20180525 RDS (11.5 MB)
23S Algae p23SrV_f1/p23SrV_r1 (Sherwood & Presting 2007) SILVA_132_LSUParc 1 20180715 RDS (26.9MB)
COI Metazoans mlCOIintF/jgHCO2198 (Leray et al 2013) Midori, GenBank 5 20181124 RDS (140 MB)
ITS2 Cnidarians and sponges scl58SF/scl28SR (Wilkinson et al in prep) GenBank 5 20180920 RDS (6.6 MB)

To classify a sequence or set of sequences, first read them into R as a “DNAbin” list object. FASTA files can be parsed as follows:

x <- readFASTA("<path-to-file>.fasta")

Alternatively users may wish to assign taxon IDs to the output from the DADA2 pipeline, in which case the column names of the ouput table can be parsed as in the following example:

x <- char2dna(colnames(samoa))
## name the sequences sequentially
names(x) <- paste0("ASV", seq_along(x))

The next step is to download and read in the classifier. It is important to ensure that the classifier was trained using the same primer set as that used to generate the query data. In this example the data were generated from autonomous reef monitoring structures in American Samoa (ARMS) using the COI metabarcoding primers mlCOIintF and jgHCO2198 (Leray et al 2013), and de-noised, filtered and merged following the DADA2 tutorial.

The COI classifier was created using the MIDORI UNIQUE 20180221 trainingset, supplemented with around 14,000 non-metazoan COI sequences downloaded from GenBank.

The 140 MB classifier can be downloaded to the current working directory and read into R as follows:

              destfile = "classifier.rds", mode = "wb")
classifier <- readRDS("classifier.rds")

There is an option to perform a nearest-neighbor search prior to the computationally-expensive recursive model test procedure, which can save time and improve resolution (‘recall’) at lower taxonomic ranks. Note that this can be a double-edged sword; if multiple species share an identical or near-identical sequence, and the true taxon of the query sequence is missing from the trainingset, the algorithm may over-classify the sequence and return a congeneric taxon. To perform a nearest-neighbor search with a similarity threshold of 0.99 (meaning any sequence in the trainingset with a similarity greater than or equal to 99% is considered a match), set ping = 0.99. To stay on the safe side, we will set ping = 1 (i.e. only sequences with 100% identity are considered matches).

out <- classify(x, classifier, threshold = 0.8)
representative taxID taxon rank score kingdom phylum class order family genus species
ASV1 2806 Florideophyceae class 0.9981 Florideophyceae
ASV2 6379 Chaetopterus genus 1.0000 Metazoa Annelida Polychaeta Spionida Chaetopteridae Chaetopterus
ASV3 2806 Florideophyceae class 0.9989 Florideophyceae
ASV4 2172821 Multicrustacea superclass 1.0000 Metazoa Arthropoda
ASV5 131567 cellular organisms no rank 0.9952
ASV6 2806 Florideophyceae class 0.9981 Florideophyceae
ASV7 39820 Nereididae family 1.0000 Metazoa Annelida Polychaeta Phyllodocida Nereididae
ASV8 116571 Podoplea superorder 0.9995 Metazoa Arthropoda Hexanauplia
ASV9 2806 Florideophyceae class 0.9482 Florideophyceae
ASV10 1 root no rank NA
ASV11 115834 Hesionidae family 1.0000 Metazoa Annelida Polychaeta Phyllodocida Hesionidae
ASV12 1443949 Corallinophycidae subclass 0.9910 Florideophyceae
ASV13 33213 Bilateria no rank 1.0000 Metazoa
ASV14 131567 cellular organisms no rank 0.9952
ASV15 2806 Florideophyceae class 0.9993 Florideophyceae
ASV16 39820 Nereididae family 1.0000 Metazoa Annelida Polychaeta Phyllodocida Nereididae

Further reading

A more detailed overview of the package and its functions can be found here or by running



If you experience a problem using this software please feel free to raise it as an issue on GitHub.


This software was developed at Victoria University of Wellington with funding from a Rutherford Foundation Postdoctoral Research Fellowship award from the Royal Society of New Zealand. Unpublished COI data care of Molly Timmers (NOAA).