Article

ISSN 0006-2979, Biochemistry (Moscow), 2025, Vol. 90, No. 11, pp. 1602-1619 © Pleiades Publishing, Ltd., 2025.

Russian Text © The Author(s), 2025, published in Biokhimiya, 2025, Vol. 90, No. 11, pp. 1708-1726.

1602

REVIEW

20 Years of DNA Barcoding –

Achievements and Problems

Victoria S. Shneyer

1,a

* and Alexander V. Rodionov

Komarov Botanical Institute, Russian Academy of Sciences, 197022 Saint-Petersburg, Russia

e-mail: shneyer@binran.ru

Received September 13, 2025

Revised October 23, 2025

Accepted October 24, 2025

Abstract— Over 20 years of extensive studies on DNA barcoding of various types of multicellular organisms

have resulted in the selection of specific markers for multiple taxonomic groups, development of primers for

many selected markers, establishment of DNA barcodes for more than 400 thousand species, and creation

of the BOLD database. Next-generation sequencing methods allow DNA barcodes to be obtained immediately

for many samples, including those stored in museum collections. DNA barcode analysis has revealed many

previously unknown and undescribed species in various animal groups. DNA barcoding has been successfully

used in many practical applications. However, certain problems and controversial issues remain, primarily,

regarding description of new species based on DNA barcodes and the accuracy of sample identification using

reference libraries.

DOI: 10.1134/S0006297925602977

Keywords: DNA barcodes, metabarcoding, molecular markers, species identification, animals, plants, fungi

* To whom correspondence should be addressed.

INTRODUCTION

In 2007, the journal Biochemistry (Moscow) cele-

brated the 50th anniversary of the publication of the

seminal article that laid the foundations for the con-

cept of species-specificity of DNA [1]. That anniver-

sary issue also presented our article [2] in which we

described modern approaches to species identification

based on DNA comparison. In particular, we discussed

a new method based on the species specificity of DNA,

that had been proposed in 2003 [3, 4], four years be-

fore the publication of the anniversary issue, and its

first steps in science. The method was named DNA

barcoding. In the following twenty years, DNA bar-

coding has been extensively tested in various groups

of multicellular organisms. For some organisms, its

application was very successfully, while other cases

proved to be tough nuts to crack. Several thousand

experimental papers and numerous reviews, both on

this approach in general and its specific aspects, have

been published [5-10]. The purpose of this article is

to present examples of successful application of DNA

barcoding, as well as briefly outline the problems of

this method and emerging controversial issues.

DNA barcoding implies sequencing of a single

small fragment of genomic DNA (DNA barcode) in

all species of all taxonomic groups of animals and

plants within a reasonable timeframe and creation

of a database of these sequences as a tool for subse-

quent species identification. It was expected that this

approach would be helpful in taxonomy studies, as

well as facilitate the discovery of new species, partic-

ularly, cryptic ones (morphologically indistinguishable

but differing in other, e.g., molecular characteristics),

clarify the size of known species and relationships be-

tween them, and aide in the estimation of biodiversity

in various regions and environments, especially those

poorly studied. Beyond purely scientific issues, DNA

barcoding can be useful in many other areas, such

as legal practices related to the protection of endan-

gered species, identification of poisonous, dangerous,

and prohibited for cultivation and collection species,

assessment of declared species composition of herbal

materials in pharmacognosy, verification of declared

species composition of animal and plant products on

menus and shelves in markets and supermarkets,

20 YEARS OF DNA BARCODING 1603

BIOCHEMISTRY (Moscow) Vol. 90 No. 11 2025

fight against harmful species, forensics, custom prac-

tices, etc. Scientists had predicted that in 15 years,

a sequencer of a size of a mobile phone would be

created and databases of barcode sequences would

be complied, so it would be possible to collect a small

fragment of a plant or animal right in the field, place

it in the sequencer, obtain a sequence, connect to a

database (DNA barcode library) over the internet,

compare this sequence to those available in the data-

base, and find out what species it is.

First, we should acknowledge that recent techno-

logical advances have indeed been enormous: min-

iature sequencers have appeared, and next-genera-

tion (second- and third-generation) sequencing (NGS)

methods have been developed, allowing for simulta-

neous sequencing of a target region in multiple sam-

ples (the principle of metabarcoding) or identifica-

tion of all its variants (in the case of highly repeated

genome regions). Introduction of NGS has facilitated

analysis of DNA from old specimens from museum

collections and even damaged samples. Consequently,

new bioinformatics methods have been developed to

process massive amounts of sequencing data. Howev-

er, creating a reference database, which is necessary

for comparing the sequences of interest, has present-

ed significant challenges, particularly for plants, but

not only.

In addition to the long-existing NCBI database

of DNA sequences (GenBank) (https://www.ncbi.nlm.

nih.gov/), which contains many sequences of nucle-

ar, mitochondrial, and chloroplast genomes, but often

without information on the origin of biological mate-

rial or the site where it was collected, in 2005, the Ca-

nadian Centre for Biodiversity Genomics (the world’s

largest scientific institution engaged in DNA barcod-

ing) created a special DNA barcode database within

the framework of the BOLD (Barcode of Life Data

System) web platform (https://boldsystems.org/). The

website states that the database accepts sequences of

more than 150 markers, however, the main ones are

COI (a fragment of the cytochrome  c oxidase subunitI

gene), rbcL (ribulose diphosphate carboxylase gene),

matK (maturase gene), and ITSs (transcribed intergen-

ic spacers of ribosomal genes). It was reported that by

2015, the International Barcode of Life Project would

obtain reference sequences for 5  million samples

representing approximately 60,000 plant species and

450,000 animal species, including those that had not

yet been described [11]. As of 2019, the main databas-

es contained sequences of ~300,000 described species

(including 15% of all described animal species) [12,

13]. It was expected that by 2025, accumulated DNA

barcodes would sufficiently represent the biotas of

Europe and North America. It was predicted that the

work on compiling a complete DNA barcode library

(~100 million individual organisms) of the world flora

and fauna could be finished within a few decades,

provided sufficient funding [11]. So far, DNA barcodes

have been obtained for 19.7 million specimens (as of

June 27, 2025) (https://v4.boldsystems.org/index.php/

TaxBrowser_Home).

To enter a barcode sequence into BOLD, it is nec-

essary to provide a minimally necessary information

about the specimen, such as the country/ocean where

the specimen was collected and the date of collection.

Other desirable information includes a photograph of

the specimen, GPS coordinates of the collection site,

taxonomic identification (up to the order or fami-

ly level), and genetic information (gene region, PCR

primer sequences and PCR conditions, chromatogram

files used in contig assembly). The length of the final

sequence should be more than 75% of the accepted

barcode marker length (e.g., 500 bp for COI). The ac-

ceptable sequence quality is specified as less than 1%

erroneous bases in the final trimmed contig. The se-

quence should correspond to the putative high-level

taxonomic position (DNA barcode should cluster with

related taxa).

Each DNA barcode is a sequence of a particular

DNA fragment from a single individual. BOLD uses a

special program (algorithm) that assigns barcode se-

quences to the clusters (operational taxonomic units,

OTUs) with the extent of similarity selected by a re-

searcher, here referred to as BINs (Barcode Index

Numbers) [14, 15]. OTUs were proposed in numerical

taxonomy for groups of organisms possessing mor-

phological features that, to a certain extent, are char-

acterized by similar numerical parameters and differ

in these parameters from other groups [16]. If these

features are DNA sequences of specific regions, such

groups are sometimes referred to as mOTUs. OTUs/

BINs may not correspond to species or genera, as they

are “working” provisional (temporary) groupings.

The Table  1 shows the data retrieved from the

BOLD website in July 2025, which might give an idea

on the number of DNA sequences already obtained

for the major groups of plants and animals. Of course,

the overall estimate of the number of known species

is very approximate and was taken by us from var-

ious sources. The studied species are those reported

by the authors as included in the analysis, and not

for all of them DNA sequences have been obtained.

The depth of analysis in the groups varies widely –

from 18% species (arachnids) to almost 100% species

(fishes). The reported 100% study rate for vertebrates

(which ranges from 33 to 68% in some major groups)

can apparently be explained by different taxonomic

interpretations used by different researchers. Clear-

ly, the number of specimens examined per species

varies widely, and not all studied species have been

DNA barcoded, likely for various reasons, such as

amplification failure or poor sequencing quality.

SHNEYER, RODIONOV1604

BIOCHEMISTRY (Moscow) Vol. 90 No. 11 2025

Table 1. Species, specimens, and DNA barcodes for some groups of multicellular organisms available in the BOLD database (July 2025)

Taxon

Approx.

number

of known

species

Number

of examined

specimens

Number

of examined

species

% examined

species

of known

species

Number

of examined

specimens per

examined species

Number

of species

with DNA

barcodes

% species with

DNA barcodes

of examined

species

% species

with DNA

barcodes from

known species

Number

of BINs

Chordata

(chordates)

>50,000 1,155,037 50,101 100 23 45,594 92 92 47,936

Mammalia

(mammals)

>6500 226,969 4393 68 9 3993 77 61 5089

Aves

(birds)

~11,000 103,007 7119 65 14 6248 87 57 6107

Reptilia

(reptiles)

~12,000 36,934 3982 33 9 3562 89 30 5168

Actinopterygii

(ray-finned fish)

~27,000 487,312 26,930 100 18 25,511 95 94 23,469

Primates

(primates)

518 101,475 346 67 29 329 95 63 390

Arthropoda

(arthropods)

>1,000,000 23,808,577 381,919 38 62 304,555 79 30 793,702

Insecta

(insects)

>1,000,000 21,791,896 337,000 34 64 265,391 78 27 715,102

Lepidoptera

(butterflies)

~160,000 2,968,511 127,519 80 23 107,521 84 67 157,100

Arachnida

(arachnids)

~115,000 784,910 21,632 18 35 18,706 86 16 42,058

Echinodermata

(echinoderms)

~7000 75,473 3451 50 22 2925 84 41 2873

Mollusca

(mollusks)

~76,000 345,116 23,990 31 14 20,889 87 27 25,807

Nematoda

(round worms)

>24,000 120,243 3718 15 40 1781 48 7 1919

Magnoliopsida

(dicotyledons)

>200,000 369,713 97,749 51 4 49,879 51 26 0

Liliopsida

(monocotyledons)

>60,000 108,318 28,216 48 4 17,903 63 30 0

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BIOCHEMISTRY (Moscow) Vol. 90 No. 11 2025

This is especially typical for nematodes and plants

(DNA barcodes were obtained for approximately half

of the studied species). In most groups, the number

of BINs exceeds the number of species with DNA

barcodes, either slightly (vertebrates, primates, nema-

todes) or significantly (mammals, arthropods, insects,

arachnids). In the latter case, it can be assumed that

these groups contain many undescribed, possibly

cryptic, species. In some groups (birds, fishes, echi-

noderms), the number of studied species with DNA

barcodes roughly corresponds to or even exceeds the

number of BINs. The number of BINs for plants has

not been determined.

MARKERS USED IN DNA BARCODING

DNA barcoding was proposed by zoologists, who

already had a good candidate for the barcoding re-

gion– a fragment of the mitochondrial COI gene that

had been tested in numerous animal species. It exhib-

its sufficient and uniform variability and, therefore,

can be used for species identification in important

groups, such as birds, fish, insects, crustaceans, cili-

ates, and others. This DNA fragment contains a rel-

atively conserved region, allowing the use of small

primer sets for large groups. There are over 400 cur-

rently known primers for this marker listed in the

BOLD database. However, for some groups, the COI

fragment fails to yield good results, so other mark-

ers had to be used, either in combination with COI

or alone. For many reptiles, amphibians, and gastro-

pods, the mitochondrial 16S rDNA gene (typically, its

fragment) is added to COI (amplified with degenerate

primer sets), although it was reported that the use of

complete gene sequences produced better results [17].

In coral polyps, for which a standard COI sequence

proved to be insufficiently variable, the situation is

complicated by the difficulty of species identifica-

tion and a confusing taxonomy with many synony-

mous names. Other mitochondrial genome sequences

selected for DNA barcoding of these organisms are

ND6-ATP6, ND4-12S, COX3-COX2, ND5-ATP8 [18], mt-

MutS (msh1), iGR1, and ND2 genes [19]. In species

of sponges, which differ in a few morphological fea-

tures that, at the same time, are highly variable with-

in a species, the situation is even more complicated.

For common sponges (class Demospongiae), the stan-

dard COI fragment is often suitable, especially with

the addition of the 28S rDNA gene C region [20, 21].

However, in many calcareous sponges (class Calcarea),

the mitochondrial genome has an increased level of

mutability, a modified genetic code, and a number of

other features [22, 23]. Therefore, it was proposed to

use fragments of the 28S large (LSU) and 18S small

(SSU) ribosomal subunit genes, internal transcribed

spacers (ITSs), histone H3 gene, and some others as

DNA barcodes [20, 24]. Hence, for many animal groups,

DNA barcoding requires selection of specific DNA

barcodes, as well as development of special primers.

For higher plants, the Consortium for the Barcode

of Life (CBOL) initially approved the fragments of two

regions – the chloroplast genes rbcL and matK – as

DNA barcodes [25], although the percentage of resolu-

tion achieved with these barcodes was relatively low.

In addition, both rbcL gene and, especially, evolution-

arily labile matK gene usually require several primers

per taxon, which complicates the study and signifi-

cantly increases its cost. Soon, it was proposed [26] to

use the ITS1 and ITS2 sequences, which are popular

in phylogenetic studies and had been accumulated in

large quantities by that time in the NCBI database.

As a result, in 2011, the ITS regions, as well as the vari-

able chloroplast spacer trnH–psbA, were recognized

as plant DNA barcodes [27]. In practice, researchers

are often dissatisfied with the results obtained with

these markers, so other, usually chloroplast, sequenc-

es (trnL, trnL-trnF, ycf1, nadF, rpoB, accD, clpP1, etc.)

are added. For some algae, the V domain of the plas-

tid 23S rDNA gene (universal plasmid amplicon, UPA)

was found to be a successful region [28]. So, different

markers or their combinations proved to be best for

different taxonomic groups. Examples of the use of

these markers as plant DNA barcodes are presented

in our earlier publication [29]. It was found to be

especially difficult to select satisfactory regions for

rapidly evolving groups, and attempts to do this are

often unsuccessful.

The main accepted DNA barcodes for such large

and complex group as fungi, are ITS1 and ITS2 (to-

gether or separately), sometimes with the addition

of the translation elongation factor  1α (TEF1) gene.

However, for a number of species, better results

were obtained with the fungal intergenic spacer

(IGS) and fragments of genes encoding β-tubulin  II

(TUB2), RNA polymerase II subunits, DNA topoisom-

erase  I (TOPI), phosphoglycerate kinase (PGK), cyto-

chrome c oxidase subunits (COI and COII), 28S and

18S nuclear genes of ribosomal RNA subunits, and

others [30].

The standards adopted by the CBOL establish the

optimal length of each DNA barcode (e.g., 648 bp for

COI). When studying museum and damaged samples,

only short sequences can be obtained sometimes [31,

32]; however, for many specimens, even such shorter

fragments have allowed correct species identification

[13, 33]. In some cases, DNA barcodes of 100 bp or

less, called mini-barcodes, have yielded satisfactory

results [21, 34]. Mini-barcodes are frequently used

for solving applied problems, for example, when it

is necessary to establish that the tested sample be-

longs (or, conversely, does not belong) to a particular

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species rather than to exactly identify the species it

belongs to. Thus, DNA mini-barcodes have been used

to analyze food products for the presence of declared

and undeclared impurities [35] and to facilitate mea-

sures to prevent illegal poaching and export of ani-

mals [36] or plants [34]. On the other hand, the use

of super-barcodes, such as complete chloroplast ge-

nome sequences, has also been proposed, for exam-

ple, for the precise identification of medicinal plants

[37] or the fight against illegal logging of rosewood

trees [38]. It was suggested that in the coming years

and decades, DNA barcodes will undoubtedly include

multiple markers, if not entire genomes [39].

SAMPLE SIZE

It has been repeatedly emphasized that for

DNA barcoding libraries to reliably serve for species

identification, each species must be represented by

multiple samples covering its geographic distribu-

tion. Only then can the intraspecific variability be

assessed and the interspecific boundaries established

[12, 40]. Initially, the standards prescribed that in

order to determine the intraspecific distances, DNA

barcoding should be performed for 5-10 samples per

species, and in the case of phylogeographic structure,

this should be done for different locations. Later, it

was proposed to increase this number to 20 [41]

and even to 50 or more [42]. However, this is diffi-

cult to achieve, especially in large-scale studies and/

or with multiple markers. Multiple specimens have

been obtained for insects. Thus, in the study of Ca-

nadian insects (~30,000 species), DNA barcoding was

performed for approximately one million samples,

i.e., 30 samples per species [43]. For butterflies of

the North America (814 species, representing 96% of

all butterfly species), the sequences were obtained for

18 (on average) specimens per species, although 59

species were represented by singletons (single speci-

mens) [44]. At the same time, the number of samples

tested in large screening studies of regional floras

rarely exceeds 2-4. In DNA barcoding of angiosperms

and conifers of Wales (1143 species) [45] and vascular

plants of Canada (more than 5000 species) [46], the

number of samples per species was 3 (on average)

and 4, respectively. In  silico analysis of five marker

sequences (rbcL, matK, trnL-trnF, psbA-trnH, ITS) of

flowering plants (40,000-70,000 species from 547 fami-

lies) available in the NCBI database showed that even

an increase in the number of DNA barcodes for each

species from one to 2-4 increased the reliability of

species identification for each individual marker, so

it was recommended to use at least three samples

per species in the analysis [47]. It was shown that for

insects, the presence of species with a small number

of samples (less than 5) in the DNA barcode array

can lead to a reduction in the detectable gap between

the intraspecific and interspecific distances in a given

group of species, since these distances overlap [48].

When analyzing a large dataset, it is preferable to

exclude such taxa for better species resolution.

DNA BARCODING OF MUSEUM SAMPLES

From the very beginning, analysis of museum

specimens – the foundation of taxonomy – has been

considered an important goal of DNA barcoding.

The term hDNA (historical DNA) was suggested for

DNA isolated from museum specimens, as opposed

to aDNA (ancient DNA) isolated from naturally pre-

served specimens over 1000 years old. Although

museum specimens are typically younger (rarely

over 200 years old), they have often been subjected

to various treatments, including storage in alcohol

or formalin, or treated for pest control, which can

lead to DNA degradation. Advances in NGS technolo-

gies have enabled the use of an ever-wider range of

museum specimens for DNA barcoding, thereby im-

proving species identification. The ability to use very

small amounts of tissue, even heavily damaged one,

for DNA analysis allows to study the type specimens

with virtually no disruption to their integrity, which is

crucial. Many natural history museums and herbaria

are establishing DNA banks. Herbarium DNA banks

contain samples of isolated and purified genomic DNA

from freshly collected or herbarium material and/or

plant tissues dried in silica gel and intended for DNA

extraction [49-52]. Plant DNA preparations are stored

in freezers at −20°C (which usually guarantees the

quality of the preparation for 3 years of storage) or

at −80°C (guarantee for 10 years or longer), usually in

small aliquots to avoid repeated thawing. Animal DNA

banks contain DNA preparations, as well as tissue

fragments, which are usually stored in liquid nitrogen

(−190°C). DNA and tissue preparations are provided

upon request to colleagues from other institutions (by

exchange or for a fee). Such DNA samples should be

of high molecular weight, with a concentration suffi-

cient for multiple analysis procedures, without RNA

and inhibitors of DNA polymerases.

The largest collections of plant DNA are at the

Royal Kew Gardens (London, UK), with over 60,000

specimens (48,000 DNA samples and 12,000 tissue

samples) and ~35,000 plant species (https://www.

kew.org/science/collections-and-resources/collections/

dna-and-tissue-bank), and the Botanic Garden and Bo-

tanical Museum (Berlin, Germany), with over 50,000

DNA samples of plants, algae, fungi, and protozoa

(https://www.bgbm.org/en/dna-bank). The Museum für

Naturkunde in Berlin has over 30,000 DNA and tissue

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BIOCHEMISTRY (Moscow) Vol. 90 No. 11 2025

samples of vertebrates, mollusks, and arthropods

(https://www.museumfuernaturkunde.berlin/en/

research/dna-and-tissue-collection). If the DNA of a

given species (or a specific sample in a collection of

a particular institution) is not yet available in a DNA

bank, some other institutions can offer the option of

isolating it for a fee. The experience of the first years

of operation of several DNA banks was used to create

the protocols (workflows) for the optimal procedures

[52], and the need for cooperation between DNA

banks and maintenance of uniform standards has led

to the creation of the Global Genome Biodiversity Net-

work (GGBN) (https://www.ggbn.org/ggbn_portal/ [53],

which includes 117 institutions (as of July 2025).

The study of herbarium specimens of 98% vascu-

lar plants (more than 5000 species) of the Canadian

flora showed that the ability to successfully amplify

DNA and to sequence the barcodes depends not only

on the specimen’s age and storage conditions, but also

on its taxonomic (family) affiliation [54]. For speci-

mens from large families, such as Apiaceae, Asterace-

ae, Brassicaceae, and Poaceae, the acceptable age was

over 60 years. For plants of the Ericaceae, Rosaceae,

and Pinaceae families, it was 10 years, after which

the possibility of a successful procedure rapidly de-

creased. For Onagraceae, Polygonaceae, Saxifragaceae,

and Dryopteridaceae, it was less than 10 years, while

for representatives of Boraginaceae and Orchidaceae,

DNA from even recent collections was poorly ampli-

fied, requiring development of specific protocols. This

difference was explained by the insufficiently suitable

primers, inhibitory effect of secondary metabolites

present, and intragenomic polymorphism of ITS se-

quences [54].

The differences in the acceptable storage time of

specimens in different taxonomic groups were also

found for animal samples, although not within the

framework of a single study. Thus, study of ~12,700

butterfly specimens (Lepidoptera) from the Australian

National Insect Collection revealed a number of fac-

tors that affected the efficiency of detection of the

standard-length COI amplicon by Sanger sequencing

[55]. One of them was the specimen size, as the suc-

cess of detection decreased rapidly with the increase

in storage time in small specimens. In most groups,

the probability of detection decreased during the first

30 years of specimen storage, followed by a plateau

over the next 30 years, after which there was a further

decrease, and only shorter amplicons were detected.

Interestingly, the success of amplification sometimes

varied in specimens collected by different collectors,

even if they were collected at the same time. It was

suggested that this may be related to the methods

used to kill the animals and process the specimens

[55]. In the study of specimens of saproxylic beetles

from museum collections stored for 1 to 17 years,

the success of obtaining DNA barcodes by NGS did

not depend on the sample age [56].

Development of new technologies has made it

possible to analyze new and unexpected DNA sourc-

es, such as bird nests constructed from plants, tens of

thousands of which are stored in museum collections.

Identification of plant species used in the construction

of these nests can provide valuable information about

changes in the landscape, bird ecology and biogeog-

raphy, etc. [57]. Thus, a method has been developed

for a more precise identification of bird species by

analyzing DNA from museum specimens of bird egg-

shells by sampling very small fragments of eggshells

without damaging the eggs [58].

METABARCODING

One of the first applications of metabarcoding

(a technique that combines DNA barcoding with

high-throughput sequencing to identify multiple spe-

cies within a community of living organisms) was

studying the intestinal content of vertebrates, includ-

ing extinct ones. The DNA obtained from the gut is

referred to as iDNA (ingested-derived DNA). [Note

that the same abbreviation is used to denote inver-

tebrate-derived DNA, i.e., DNA isolated from inver-

tebrates, for example, bloodsucking and scavenging

insects (fleas, mosquitoes, and flies), whose entire

bodies are typically ground for analysis.] Analysis

of iDNA often allows for non-invasive monitoring

of animal biodiversity in a given area and is used

for assessing the presence and relative abundance of

various species, as well as for clarifying the biology

of both insects and their food sources [59-61]. Insect

species have often been evaluated and compared for

solving specific scientific problems. For example, it

was found that mammalian blood can remain in the

intestines of fleas for up to several months, during

which the flea can travel far from the location of the

blood source [60].

Metabarcoding often uses the so-called eDNA

(environmental, or ecological DNA) isolated from the

environmental samples, such as soil, air, water, and

atmospheric precipitation, that contain small remains

and waste products of various animals (feathers,

hairs, feces, mucus, etc.). In recent decades, particular

attention has been focused on the studies of aquatic

environments because of the constant decline in the

number of marine and, especially, freshwater animal

species. In these experiments, collected water samples

are analyzed for all present DNA sequences. Insects for

metabarcoding studies are often collected using Mal-

aise traps, special devices that efficiently catch flying

insects. Metabarcoding is also used in plant studies for

the analysis of the herbivores’ diet, plant sediments

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BIOCHEMISTRY (Moscow) Vol. 90 No. 11 2025

in water bodies, interactions of pollinating insects

with plants, pollen composition in the air, composition

of food products and herbal materials, etc. [62-65].

Much attention in biodiversity studies is paid to

comparing the accuracy of estimates and performance

of metabarcoding methods vs. traditional methods.

Many authors have reported that metabarcoding pro-

vided more accurate and rapid (with fewer analyses

than traditional methods) estimate of species inhabit-

ing a given habitat [66, 67]. Thus, eDNA metabarcod-

ing was able to evaluate the local fish fauna within

a season with the same degree of completeness as

the long-term irregular collection [66]. However, it has

been repeatedly emphasized that methods based on

the use of eDNA require verification, calibration at

each stage, and careful interpretation to the same ex-

tent that is needed when working with aDNA [5, 68].

DNA BARCODING DATABASES –

SEQUENCE QUALITY AND ACCURACY

OF SAMPLE IDENTIFICATION

It is commonly agreed that DNA libraries ob-

tained by large-scale DNA barcoding of any major

taxonomic group associated with a specific region

or habitat with the use of specified DNA barcodes,

rarely allow for the species-level identification. The

high incidence of misidentifications in DNA sequence

databases, including NCBI, has long been known. In

the early days of molecular systematics, the studies

had been conducted not by taxonomists or even bota-

nists or zoologists, but by biochemists and geneticists,

who had only a superficial understanding of the im-

portance of accurate species identification and infor-

mation about the sample provenance. For example,

plants for analysis were ordered from catalogues pro-

vided by botanical gardens and not always properly

verified. As DNA barcoding has become increasingly

popular and DNA analysis started to be conducted by

commercial firms, the situation became even worse.

DNA barcoding is now performed not only by biolo-

gists, but also by specialists in agriculture, medicine,

pharmacology, archaeology, bioinformatics, and other

fields mostly unrelated to taxonomy. As a result, mate-

rial for analysis can be purchased commercially with

only the country or origin indicated. Even when the

material is taken from a seed bank, sometimes only

the bank’s location (and not the catalogue’s number)

is provided. The name of the species may be given

without the author, a voucher is missing, and it is

unclear who, how, or even approximately by what

characteristics identified the material. Nevertheless, a

DNA barcode can be assigned to such sample, which

can be entered into the reference database. The NBCI

does not provide a mechanism for mandatory remov-

al of sequences of incorrectly identified specimens,

even when the error is detected.

From the very beginning of DNA barcoding, it

has been stated that the created databases should be

curated so that the quality of sequences meets the

standards and the accuracy of species identification

is verified by specialists, which was not (and is not)

the case of NCBI database. It was assumed that these

requirements would be implemented in the BOLD

data base, but, apparently, due to the declining num-

ber of taxonomists and biodiversity specialists world-

wide, this could not be achieved [69, 70]. Although

BOLD administrators check whether the submitted

sequence meets all the requirements, including infor-

mation about the specimen, they are likely unable to

verify its correct taxonomic identification. Recently,

an attempt was made to quantify the completeness of

information (standard DNA barcodes) for metazoans

of the central and eastern Pacific Ocean deposited in

the NCBI and BOLD databases, and to compare these

databases [70]. It was stated that NCBI leads in the

data quantity, and BOLD leads in the sequence quality.

However, both databases reported sequences that did

not meet the standards – were too short, too long, or

had a large number of ambiguous nucleotides –and

were proposed for removal. Some datasets lacked tax-

onomic information. Uneven representation of differ-

ent groups was noted, with a clear paucity of data for

Porifera (sponges), Bryozoa (bryozoans), and Platyhel-

minthes (flatworms). However, it was also proposed to

remove “sequences of overrepresented species” and

“sequences with conflicting taxonomy” (i.e., which

showed similarity to a group other than the one they

were claimed to belong to, according to BLAST) and to

“standardize taxonomic metadata to ensure taxonomic

completeness” (when indicating species affiliation of a

specimen). It is difficult to imagine how such actions

could be implemented or who would undertake them

(considering the data already entered).

Standard barcode sequences of plant, fungal, and

insect specimens (a total of about a hundred species,

including poisonous plants and forensic fungi and in-

sects) from systematic collections of reputable scien-

tific institutions were analyzed in [13]. Most of insect

species were obtained from the Smithsonian Nation-

al Museum of Natural History, USA. The comparison

of DNA barcodes obtained in this study with the se-

quences available in the NCBI and BOLD databases

showed a very high accuracy of plant (~81%) and fun-

gal (~57%) species identifications. However, for insect

species (including well-known and widespread ones),

the accuracy of identification was less – 53% species

in NCBI and 35% species in BOLD. The authors con-

cluded that both databases contain many errors and

that the curated BOLD database is not superior to the

non-curated NCBI, although a significant proportion

20 YEARS OF DNA BARCODING 1609

BIOCHEMISTRY (Moscow) Vol. 90 No. 11 2025

of animal sequences in both databases represented

insects. Researchers from the Canadian Centre for

Biodiversity Genomics, which maintains and curates

the BOLD database, decided to investigate [71] wheth-

er the source of the errors was in the databases or

in the sequences obtained by Meiklejohn et al. [13].

They re-examined all samples, procedures, and re-

sults obtained for the insect species from the work

of Meiklejohn et al. [13] and revealed several rea-

sons why many identifications were incorrect. Along

with a number of technical errors, it was found that

during analysis, museum insect specimens could be

contaminated with foreign DNA. Some discrepancies

were related to the complexity and uncertainty of tax-

onomy of certain species [71].

It should be emphasized that ordering tissue and

DNA samples from other institutions exacerbates the

problems of species identification. These problems

were examined in detail in the article by zoologists

specializing in studying reptiles and DNA barcoding of

these animals from the Smithsonian Institution (USA),

which houses the largest collection of reptiles [72].

The authors compiled a DNA barcoding library for

more than 500 reptile species. The barcodes were se-

quenced by the authors and employees of other insti-

tutions from 52 countries that had received materials

from the museum collections. When possible, the cor-

rectness of identification of the provided specimens

was verified. The three most significant problems

were the following. First, the species names assigned

to DNA barcodes were the names provided by the

lending institution, without further verification and

without considering recent taxonomic changes (which

may not have been incorporated into the lending mu-

seum’s database). Second, researchers did not conduct

BLAST searches in the GenBank to verify the identi-

fication. Third, researchers submitted the data on a

new species under the old name in the case of taxo-

nomic splitting and failed to update the records after

publication of the paper describing the new species.

As a result, both NCBI and BOLD databases typically

list correctly the genera of reptile specimens, but the

accuracy of species identification is much lower. The

authors noted that many institutions are experiencing

funding and staff cuts or have to deal with additional

tasks related to the database support, which increases

the workload for the staff and makes it impossible to

properly maintain the collections, or even to change

the labels.

BIODIVERSITY DESCRIPTION

AND NEW SPECIES

Traditional taxonomic analysis, which is neces-

sary for identifying diagnostic traits and describing

new species, often requires painstaking studies and

a high level of professionalism. Analysis of various

groups using molecular methods, in particular, DNA

barcoding, has shown that animal species diversity

is often higher than expected based on morpholog-

ical analysis, and cryptic species are frequently dis-

covered [73-75]. When the number of such species is

high relative to the number of known species, it was

been proposed to call them “dark” taxa [76, 77]. There

are great concerns that many species, especially, in

poorly studied regions, risk extinction without ever

being discovered or described. Moreover, such species

are still discovered in regions where the biodiversity

has been studied for a long time and to a great extent,

for example, in Germany and Sweden [78-81]. A DNA

barcoding study of arthropods in Germany showed

that the number of species of small arachnids Pseu-

doscorpionida (pseudoscorpions) should be increased

by more than 40% [78] and even more for Diptera in-

sects [79]. In Sweden, where the diversity of butterfly

species has been studied better than anywhere else,

with 2990 species described, DNA barcoding revealed

more than 300 undescribed species [81]. The largest

number of undescribed species has been found for

arthropods, but also for many other invertebrates and

vertebrates, as well as for fungi. The smallest num-

ber of undescribed species has been found in higher

plants, which are characterized by a widespread hy-

bridization and have a large number of hybrids (the

so-called cryptic diversity) [82-85].

The proponents of DNA barcoding have per-

sistently emphasized that one of its primary goals is

to help in slowing down the loss of biodiversity, as

monitoring of biodiversity is complicated by a large

number of dark species, as well as species described

so superficially that their specimens can only be iden-

tified by comparison with the type specimens, which

is not always easy. Therefore, these researchers have

adopted a new tactic. Two papers were recently pub-

lished that described dark species in a large group

of parasitoid wasps (ichneumon flies, order Hyme-

noptera) that lay eggs on or inside their hosts (other

insects). After hatching, the larva consumes the host,

eventually killing it. Ichneumon flies is an economi-

cally significant group, as many wasp species para-

sitize on agricultural pests, reducing their numbers.

The first paper [86] was entitled “A revolutionary

protocol to describe understudied hyperdiverse taxa

and overcome the taxonomic impediment.” It exam-

ined parasitoid wasps of the Ichneumonoidea super-

family, which has ~44,000 described species (although

it was suggested that this superfamily can include up

to a million species). DNA barcodes were obtained

for 336 wasps collected in Costa Rica. Based on BINs

calculated by the BOLD system, 18 new species were

identified that were assigned to two existing genera.

SHNEYER, RODIONOV1610

BIOCHEMISTRY (Moscow) Vol. 90 No. 11 2025

The species were given conventional binomial names

and described with a minimum diagnosis consisting

of the consensus DNA barcode (sequence) and a photo

of the specimen (side view). For the second article

[39], the same authors collaborated with a large team

of scientists from different countries and institutions.

The article’s title began with the words: “Minimalist

revision and description of 403 new species”. The ar-

ticle contained descriptions of 403 new species of par-

asitoid wasps from other genera belonging to 11 sub-

families, also from Costa Rica. The descriptions were

based on the same principles. And although both ar-

ticles stated that the authors considered DNA-based

descriptions as the first step in solving the taxonom-

ic problems associated with megadiversity and lack

of taxonomic resources (which standard approaches

failed to resolve), both articles received critical re-

sponses [87-89].

The studies [39, 86] were criticized for the lack of

adequate morphological descriptions and comparisons

with previously described species, as well as for the

fact that the identified species were unstable when

the original data were analyzed using different spe-

cies delimitation algorithms. Several other technical

objections were raised against the use of the “min-

imalist approach” based on COI barcodes. According

to the critics, such approach would only complicate

subsequent taxonomic analysis of these groups rath-

er than facilitate it. The authors of studies on para-

sitoid wasps immediately followed the criticism and

presented their arguments against the critics’ disap-

pointing conclusions [90]. However, the discrepancies

in the numbers of new species identified using dif-

ferent data processing methods have also been found

in other studies [91, 92]. The debate still continues

[93-95]. Since the description of new species with the

possibility of their subsequent identification requires

a great deal of work and studying the intra- and

intergroup variability (at least at the morphological

and molecular levels), such groups often remain, at

best, in the OTU (BIN) status. Some of the most im-

pressive arguments in favor of DNA barcoding were

provided by the studies of the South American but-

terfly Astraptes fulgerator. The sequences of the COI

fragment from hundreds of individuals of this species

were divided into ten clusters, with some correlation

between the cluster affiliation, type of caterpillar col-

oration, and caterpillar feeding on different plants.

Hebert et al. [96] hypothesized that Astraptes fulger-

ator represents a complex including about ten unde-

scribed species. This article has been cited more than

4,700 times, including in some reputable works, as an

example of well-documented case of cryptic species in

butterflies [97]. However, over the past 20 years, not

a single new species based on the mentioned clusters

has been described.

In their article, Meier et al. [95] noted that until

recently, the calls for the integrative taxonomy, i.e.,

the use of both molecular and morphological char-

acteristics in analysis, have remained largely decla-

rations of intent. The authors estimated that the da-

tabases already contain 15 million DNA barcodes for

insects, but only 10% taxonomic publications in 2018

included molecular data [95]. Note that 15 years ago,

there were approximately 15,000 taxonomists world-

wide that worked using traditional methods, and it is

unlikely that their number has increased, if anything,

by now. DNA barcoding has become cheaper, while

qualified morphological expertise has not. We believe

that DNA barcoding can at least help in identifying

groups containing many unknown, undescribed spe-

cies. The alternative for dark species is not a complete

taxonomic analysis, but complete obscurity. Mean-

while, the knowledge of a DNA barcode sequence that

distinguishes such species from a known related spe-

cies, will lift them out of obscurity and make subjects

of further investigations.

PRACTICAL APPLICATIONS OF DNA BARCODING

The use of DNA barcodes in some applied fields

have become even more successful and advanced

than in the biodiversity studies, as the range of sub-

jects in the former is often limited and specific. The

number of publications on this topic is so large that

we will limit ourselves to referencing only recent re-

views that discuss or mention such works.

DNA barcodes can be used to analyze and de-

termine the composition of food products, both raw

(fish and other seafood, meat, vegetables, spices, etc.)

and cooked, to check for the presence of undeclared

impurities, and to identify plants visited by bees in

the production of honey [35, 98-100]. Other import-

ant areas include testing medicinal plants used as

raw materials and in medicines [101-103] and species

identification (especially insects) in forensic science

and forensic examinations [33, 104, 105]. Increasingly

relevant applications of DNA barcoding are control of

agricultural pests and invasive species [106-108], en-

vironmental protection, and assistance in preventing

illegal poaching and export of animals [36, 109] and

plants [110, 111], in particular, with the participation

of customs services.

FUTURE PROSPECTS

It has been suggested that compiling a complete

DNA barcode library might require analysis of ~100

million samples and that this work could be com-

pleted by approximately 2040 [11]. Apparently, this

20 YEARS OF DNA BARCODING 1611

BIOCHEMISTRY (Moscow) Vol. 90 No. 11 2025

implied that one or few standard markers would be

used. However, it has become clear that the initially

stated goal of using one marker for all organisms is

unlikely to be achieved. Many groups required appli-

cation of specific markers or set of markers. Howev-

er, the progress in sequencing technologies has been

so significant that it is currently implied (or wished)

that the sequencing of a complete genome of any or-

ganism will soon become a reality and even a rou-

tine procedure, and that such genomes will be used

as DNA barcodes [39, 112]. Moreover, according to

evolutionary genomics studies, all modern species of

flowering plants have undergone one or more whole-

genome duplications (polyploidizations), which usual-

ly accompanied interspecific, sometimes very distant,

hybridization events [113]. According to very conser-

vative estimates, at least 15% of angiosperm species

are recent polyploids [114]. This complicates DNA

barcoding of plants. The polyploid origin of a species

might be indicated not by a single specific DNA mark-

er sequence (DNA barcode), but by a combination of

DNA barcodes obtained by the plant from its relative-

ly recent ancestors. This combination of genomes of

different origin can be identified by the locus-specific

NGS of DNA barcodes, such as internal transcribed

spacers ITS1 and ITS2 [115-117]. When combined with

the establishment of close relationships, DNA barcod-

ing provides new opportunities not only for the spe-

cies identification but also for selection.

In conclusion, we would like to note that in

the Russian Federation, DNA barcoding of different

groups of organisms is one of the actively develop-

ing research fields. In addition to the reports cited

above, we would like to mention the studies, mainly

from recent years, on fishes [118-121], bats [122], ro-

dents [123], reptiles [124], crustaceans [125], mollusks

[126], insects [127-132], mites [133-134], annelids [74,

135-138], tardigrades [139], and other animal groups,

which have been published in major journals. Plants

[140-144], algae [145, 146], and fungi [147-150] have

also been studied. Beside purely scientific investiga-

tions, there is also a number of applied studies [151-

153]. DNA barcoding is entering our lives.

Abbreviations

COI

cytochrome c oxidase subunit I frag-

ment

ITS

ribosomal gene intergenic transcribed

spacer

matK maturase gene

NGS next-generation sequencing

rbcL ribulose diphosphate carboxylase gene

Contributions

V.S.Sh. collected and analyzed published data and

wrote the text of the article; A.V.R. discussed the topic

and the structure of the review and edited the manu-

script.

Funding

This study was supported by State Assignment

no.124020100136-0.

Ethics approval and consent to participate

This work does not contain any studies involving hu-

man and animal subjects.

Conflict of interest

The authors of this work declare that they have no

conflicts of interest.

REFERENCES

1. Spirin, A. S., Belozersky, A. N., Shugaeva, N. V., and

Vanyushin, B.F. (1957) Study of species specificity of

nucleic acids in bacteria [in Russian], Biokhimiya, 22,

744-754.

2. Shneyer, V.S. (2007) On the species specificity of DNA:

fifty years after, Biochemistry (Moscow), 72, 1377-

1384, https://doi.org/10.1134/S0006297907120127.

3. Hebert, P. D. N., Cywinska, A., Ball, S. L., and de-

Waard, J. (2003) Biological identifications through

DNA barcodes, Proc. Biol. Sci., 270, 313-321, https://

doi.org/10.1098/rspb.2002.2218.

4. Hebert, P.D.N., Ratnasingham,S., and deWaard, J.R.

(2003) Barcoding animal life: cytochrome c oxidase

subunit 1 divergences among closely related species,

Proc. Biol. Sci., 270, S96-S99, https://doi.org/10.1098/

rsbl.2003.0025.

5. Cristescu, M. E., and Hebert, P.D.N. (2018) Uses and

misuses of environmental DNA in biodiversity sci-

ence and conservation, Annu. Rev. Ecol. Evol. Syst.,

49, 209-230, https://doi.org/10.1146/annurev-ecolsys-

110617-062306.

6. De Salle, R., and Goldstein, P. (2019) Review and in-

terpretation of trends in DNA barcoding, Front. Ecol.

Evol., 7, 302, https://doi.org/10.3389/fevo.2019.00.

7. Shekhovtsov, S. W., Shekhovtsova, I. N., and Peltek,

S. E. (2019) DNA barcoding: methods and approach-

es, Usp. Sovr. Biol., 139, 211-220, https://doi.org/

10.1134/S0042132419030074.

8. Yang, C., Lv, Q., and Zhang, A. (2020) Sixteen years

of DNA barcoding in China: what has been done?

What can be done? Front. Ecol. Evol., 8, 57, https://

doi.org/10.3389/fevo.2020.00057.

9. Liberty, J.T., Lin,H., Kucha,C., Sun,S., and Alsalman,

F. B. (2025) Innovative approaches to food trace-

ability with DNA barcoding: beyond traditional la-

bels and certifications, Ecol. Genet. Genom., 34,

100317, https://doi.org/10.1146/annurev-ento-040124-

014001.

SHNEYER, RODIONOV1612

BIOCHEMISTRY (Moscow) Vol. 90 No. 11 2025

10. Meier,R., Lawniczak, M.K., and Srivathsan,A. (2025)

Illuminating entomological dark matter with DNA

barcodes in an era of insect decline, deep learning,

and genomics, Annu. Rev. Entomol., 70, 185-204,

https://doi.org/10.1016/j.egg.2024.100317.

11. Hebert, P. D. N., Hollingsworth, P. M., and Hajib-

abaei, M. (2016) From writing to reading the ency-

clopedia of life, Phil. Trans. R. Soc. B, 371, 20150321,

https://doi.org/10.1098/rstb.2015.0321.

12. deWaard, J., Ratnasingham, S., Zakharov, E.,

Borisenko, A., Steinke, D., Telfer, A., Perez, K. J.,

Sones, J., Young, M., Levesque-Beaudin, V., Sobel, C.,

Abrahamyan,A., Bessonov,K., Blagoev,G., deWaard,S.,

Ho,C., Ivanova,N., Layton, K.S., Lu,L., Manjunath,R.,

McKeown, J.A., Milton,M., Miskie,R., Monkhouse,N.,

Naik, S., Nikolova, N., Pentinsaari, M., Prosser, S. J.,

Radulovici, A., Steinke, C., Warne, C., and Hebert,

P. N. H. (2019) A reference library for Canadian in-

vertebrates with 1.5million barcodes, voucher spec-

imens, and DNA samples, Sci. Data, 6, 308, https://

doi.org/10.1038/s41597-019-0320-2.

13. Meiklejohn, K. A., Damaso, N., and Robertson, J. M.

(2019) Assessment of BOLD and GenBank – their

accuracy and reliability for the identification of bi-

ological materials, PLoS One, 14, e0217084, https://

doi.org/10.1371/journal.pone.0217084.

14. Ratnasingham,S., and Hebert, P.D.N. (2013) ADNA-

based registry for all animal species: the Barcode

Index Number (BIN), system, PLoS One, 8, e66213,

https://doi.org/10.1371/journal.pone.0066213.

15. Kartavtsev, Y. P. (2018) Barcode Index Number, taxo-

nomic rank and modes of speciation: examples from

fish, Mitochondrial DNA A DNA Mapp. Seq. Anal.,

29, 535-542, https://doi.org/10.1080/24701394.2017.

1315570.

16. Sokal, R. R., and Sneath, P. H. A. (1963) Principles of

Numerical Taxonomy, W.H. Freeman & Co., San Fran-

cisco.

17. Chan, K. O., Hertwig, S. T., Neokleous, D. N., Flury,

J. M., and Brown, R. M. (2022) Widely used, short

16S rRNA mitochondrial gene fragments yield

poor and erratic results in phylogenetic estima-

tion and species delimitation of amphibians, BMC

Ecol. Evol., 22, 37, https://doi.org/10.1186/s12862-022-

01994-y.

18. Combosch, D. J., Burdick, D., Primov, K., Rios, D.,

Rios, K., and Fernandez, J. (2024) Barcoding and

mitochondrial phylogenetics of Porites corals, PLoS

One, 19, e0290505, https://doi.org/10.1371/journal.

pone.02905.

19. McFadden, C. S., Reynolds, A. M., and Janes, M. P.

(2014) DNA barcoding of xeniid soft corals (Octocor-

allia: Alcyonacea: Xeniidae) from Indonesia: species

richness and phylogenetic relationships, Syst. Bio-

divers., 12, 247-257, https://doi.org/10.1080/14772000.

2014.9028.

20. Vargas,S., Kelly,M., Schnabel,K., Mills,S., Bowden,D.,

and Wörheide, G. (2015) Diversity in a cold hot-

spot: DNA-barcoding reveals patterns of evolution

among antarctic Demosponges (class Demospongiae,

phylum Porifera), PLoS One, 10, e0127573, https://

doi.org/10.1371/journal.pone.0127573.

21. Erpenbeck,D., Steiner,M., Schuster,A., Genner, M.J.,

Manconi, R., Pronzato, R., Ruthensteiner, B., van den

Spiegel, D., van Soest, R. W. M., and Wörheide, G.

(2019) Minimalist barcodes for sponges: a case study

classifying African freshwater Spongillid, Genome, 62,

1-10, https://doi.org/10.1139/gen-2018-0098.

22. Lavrov, D. V., Pett, W., Voigt, O., Wörheide, G.,

Forget,L., Lang, B.F., and Kayal,E. (2013) Mitochon-

drial DNA of Clathrina clathrus (Calcarea, Calcinea):

six linear chromosomes, fragmented rRNAs, tRNA ed-

iting, and a novel genetic code, Mol. Biol. Evol., 30,

865-880, https://doi.org/10.1093/molbev/mss274.

23. Lavrov, D. V., Adamski, M., Chevaldonné, P., and

Adamska, M. (2016) Extensive mitochondrial mRNA

editing and unusual mitochondrial genome organi-

zation in calcaronean sponges, Curr. Biol., 26, 86-92,

https://doi.org/10.1016/j.cub.2015.11.043.

24. Voigt,O., and Wörheide, G. (2016) Ashort LSU rRNA

fragment as a standard marker for integrative taxon-

omy in calcareous sponges (Porifera: Calcarea), Org.

Divers. Evol., 16, 53-64, https://doi.org/10.1007/s13127-

015-0247-1.

25. CBOL Plant Working Group, Hollingsworth, P. M.,

Forrest, L. L., and Little, D. P. (2009) A DNA barcode

for land plants, Proc. Natl. Acad. Sci. USA, 106, 12794-

12797, https://doi.org/10.1073/pnas.0905845106.

26. China Plant BOL Group, Li, D.Z., Gao, L.M., and Guan,

D. W. (2011) Comparative analysis of a large data-

set indicates that internal transcribed spacer (ITS)

should be incorporated into the core barcode for seed

plants, Proc. Natl. Acad. Sci. USA, 108, 19641-19646,

https://doi.org/10.1073/pnas.1104551108.

27. Hollingsworth, P.M. (2011) Refining the DNA barcode

for land plants, Proc. Natl. Acad. Sci. USA, 108, 19451-

19452, https://doi.org/10.1073/pnas.1116812108.

28. Gu, Z., Yang, L.-E., Chen, Z., and Chen, W. (2020)

Comparative analysis of different DNA barcodes for

applications in the identification and production of

Pyropia, Algal Res., 47, 101874, https://doi.org/10.1016/

j.algal.2020.101874.

29. Shneyer, V. S., and Rodionov, A. V. (2019) Plant

DNA-barcodes, Biol. Bull. Rev., 9, 295-300, https://

doi.org/10.1134/s207908641904008x.

30. Lücking, R., Aime, M. C., Robbertse, B., Miller, A. N.,

Hiran, A., Ariyawansa, H. A., Aoki, T., Cardinali, G.,

Crous, P. W., Druzhinina, I. S., Geiser, D. M., Hawk-

sworth, D. L., Hyde, K. D., Irinyi, L., Jeewon, R.,

Johnston, P. R., Kirk, P. M., Malosso, E., May, T. W.,

Meyer,W., Öpik,M., Robert,V., Stadler,M., Thines,M.,

Vu, D., Yurkov, A. M., Zhang, N., and Schoch, C. L.

20 YEARS OF DNA BARCODING 1613

BIOCHEMISTRY (Moscow) Vol. 90 No. 11 2025

(2020) Unambiguous identification of fungi: where

do we stand and how accurate and precise is

fungal DNA barcoding? IMA Fungus, 11, 14, https://

doi.org/10.1186/s43008-020-00033-z.

31. Hajibabaei, M. D., Smith, M. A., Janzen, D. H.,

Rodriguez, J. J., Whitfield, J. B., and Hebert, P. D. N.

(2006) Aminimalist barcode can identify a specimen

whose DNA is degraded, Mol. Ecol. Notes, 6, 959-964,

https://doi.org/10.1111/j.1471-8286.2006.01470.x.

32. Gao, Z., Liu, Y., Wang, X., Wei, X., and Han, J. (2019)

DNA Mini-barcoding: a derived barcoding method for

herbal molecular identification, Front. Plant Sci., 10,

987, https://doi.org/10.3389/fpls.2019.00987.

33. Grzywacz,A., Wyborska,D., and Piwczyński,M. (2017)

DNA barcoding allows identification of European Fan-

niidae (Diptera) of forensic interest, Forensic Sci. Int.,

278, 106-114, https://doi.org/10.1016/j.forsciint.

34. Dong,W., Liu,H., Xu,C., Zuo,Y., Chen,Z., and Zhou,S.

(2014) A chloroplast genomic strategy for design-

ing taxon specific DNA mini-barcodes: a case study

on ginsengs, BMC Genet., 15, 138, https://doi.org/

10.1186/s12863-014-0138-z.

35. Gorini, T., Mezzasalma, V., Deligia, M., De Mattia, F.,

Campone,L., Labra,M., and Frigerio,J. (2023) Check

your shopping cart: DNA barcoding and mini-barcod-

ing for food authentication, Foods, 12, 2392, https://

doi.org/10.3390/foods12122392.

36. Xie, X., Ye, H., Cai, X., Li, C., Li, F., Tian, E., and

Chao,Z. (2021) DNA mini-barcodes, a potential weap-

on for conservation and combating illegal trade

of pangolin, Trop. Conserv. Sci., 14, 1-10, https://

doi.org/10.1177/19400829211017361.

37. Somaratne, Y., Guan, D.-L., Wang, W.-Q., Zhao, L.,

and Xu, S.-Q. (2019) Complete chloroplast genome

sequence of Xanthium sibiricum provides useful

DNA barcodes for future species identification and

phylogeny, Plant. Syst. Evol., 305, 949-960, https://

doi.org/10.1007/s00606-019-01614-1.

38. Rather, S. A., Wang, K., Wang, T., Liu, H., and

Schneider,H. (2025) Comparative chloroplast genome

analysis reveals powerful barcodes for combatting

illegal logging of CITES-listed threatened Asian rose-

woods (Dalbergia, Leguminosae, Papilionoideae),

Bot.J. Linn. Soc., 208, 347-368, https://doi.org/10.1093/

botlinnean/boae086.

39. Sharkey, M.J., Janzen, D.H., Hallwachs,W., Chapman,

E. G., Smith, M. A., Dapkey, T., Brown, A., Ratnasing-

ham,S., Naik,S., Manjunath,R., Perez,K., Milton,M.,

Hebert, P., Shaw, S. R., Kittel, R. N., Alma Solis, M.,

Metz, M. A., Goldstein, P. Z., Brown, J. W., Quicke,

D.L. J., van Achterberg, C., Brown, B. V., and Burns,

J. M. (2021) Minimalist revision and description

of 403 new species in 11 subfamilies of Costa Ri-

can braconid parasitoid wasps, including host re-

cords for 219 species, ZooKeys, 1013, 1-665, https://

doi.org/10.3897/zookeys.1013.55600.

40. Coddington, J.A., Agnarsson,I., Cheng, R.-C., Čandek,K.,

Driskell, A., Frick, H., Gregorič, M., Kostanjšek, R.,

Kropf, C., Kweskin, M., Lokovšek, T., Pipan, M., Vid-

ergar, N., and Kuntner, M. (2016) DNA barcode data

accurately assign higher spider taxa, PeerJ, 4, e2201,

https://doi.org/10.7717/peerj.2201.

41. Luo, A., Lan, H., Ling, C., Zhang, A., Shi, L., Ho,

S.Y.W., and Zhu,C. (2015) Asimulation study of sam-

ple size for DNA barcoding, Ecol. Evol., 5, 5869-5879,

https://doi.org/10.1002/ece3.1846.

42. Zhang, A. B., He, L. J., Crozier, R. H., Muster, C., and

Zhu, C.-D. (2010) Estimating sample sizes for DNA bar-

coding, Mol. Phylogenet. Evol., 54, 1035-1039, https://

doi.org/10.1016/j.ympev.2009.09.014.

43. Hebert, P. D. N., Ratnasingham, S., Zakharov, E. V.,

Telfer, A. C., Levesque-Beaudin, V., Milton, M. A.,

Pedersen, S., Jannetta, P., and deWaard, J. R. (2016)

Counting animal species with DNA barcodes: Canadi-

an insects, Philos. Transact. R. Soc. B., 371, 20150333,

https://doi.org/10.1098/rstb.2015.0333.

44. D’Ercole, J., Dincă, V., Opler, P. A., Kondla, N.,

Schmidt, C., Phillips, J. D., Robbins, R., Burns, J. M.,

Miller, S.E., Grishin,N., Zakharov, E.V., DeWaard, J.R.,

Ratnasingham,R., and Hebert, P. D.N. (2021) ADNA

barcode library for the butterflies of North America,

PeerJ, 9, e11157, https://doi.org/10.7717/peerj.11157.

45. De Vere, N., Rich, T. C. G., Ford, C. R., Trinder, S. A.,

Long, C., Moore, C. W., Satterthwaite, D., Davies, H.,

Allainguillaume, J., Ronca, S., Tatarinova, T., Gar-

bett, H., Walker, K., and Wilkinson, M. J. (2012) DNA

barcoding the native flowering plants and conifers

of Wales, PLoS One, 7, e37945, https://doi.org/10.1371/

journal.pone.0037945.

46. Braukmann, T. W. A., Kuzmina, M. L., Sills, J.,

Zakharov, E. V., and Hebert, P. D. N. (2017) Testing

the efficacy of DNA barcodes for identifying the

vascular plants of Canada, PLoS One, 12, e0169515,

https://doi.org/10.1371/journal.pone.0169515.

47. Kolter, A., and Gemeinholzer, B. (2021) Plant DNA

barcoding necessitates marker-specific efforts to

establish more comprehensive reference databas-

es, Genome, 64, 265-298, https://doi.org/10.1139/gen-

2019-0198.

48. Cheng, Z., Li, Q., Deng, J., Liu, Q., and Huang, X.

(2023) The devil is in the details: Problems in DNA

barcoding practices indicated by systematic evalua-

tion of insect barcodes, Front. Ecol. Evol., 11, 1149839,

https://doi.org/10.3389/fevo.2023.1149839.

49. Särkinen, T., Staats, M., Richardson, J. E., Cowan,

R. S., and Bakker, F. T. (2012) How to open the trea-

sure chest? Optimising DNA extraction from her-

barium specimens, PLoS One, 7, e43808, https://

doi.org/10.1371/journal.pone.0043808.

50. Spooner, D., and Ruess, H. (2014) Curating DNA

specimens, in Proceedings of the U.S. Workshop on

DNA Banking: DNA Banking for the 21st Century,

SHNEYER, RODIONOV1614

BIOCHEMISTRY (Moscow) Vol. 90 No. 11 2025

eds. W. L. Applequist, L. M. Campbell. St. Louis,

TheWilliam L. Brown Center at the Missouri Botani-

cal Garden, pp.71-80.

51. Wilkie,P., Poulsen, A.D., Harris,D., and Forrest, L.L.

(2013) Thecollection and storage of plant material for

DNA extraction: TheTeabag Method, Gardens’ Bulle-

tin Singapore, 65, 231-234.

52. Gostel, M. R., Kelloff, C., Wallick, K., and Funk, V. A.

(2016) A workflow to preserve genome-quality tis-

sue samples from plants in botanical gardens and

arboreta, Appl. Plant Sci., 4, 1600039, https://doi.org/

10.3732/apps.1600039.

53. Seberg, O., Droege, G., Barker, K., Coddington, J. A.,

Funk, V., Gostel, M., Petersen, G., and Smith, P. P.

(2016) Global Genome Biodiversity Network: saving

a blueprint of the Tree of Life– a botanical perspec-

tive, Ann. Bot., 118, 393-399, https://doi.org/10.1093/

aob/mcw121.

54. Kuzmina, M. L., Braukmann, T. W. A., Fazekas, A. J.,

Graham, S. W., Dewaard, S. L., Rodrigues, A., Ben-

nett, B. A., Dickinson, T. A., Saarela, J. M., Catling,

P. M., Newmaster, S. G., Percy, D. M., Fenneman, E.,

Lauron-Moreau, A., Ford, B., Gillespie, L., Subra-

manyam, R., Whitton, J., Jennings, L., Metsger, D.,

Warne, C. P., Brown, A., Sears, E., DeWaard, J. R.,

Zakharov, E.V., and Hebert, P.D.N. (2017) Using her-

barium-derived DNA s to assemble a large-scale DNA

barcode library for the vascular plants of Canada,

Appl. Plant Sci., 5, 1700079, https://doi.org/10.3732/

apps.1700079.

55. Hebert, P.D.N., deWaard, J.R., Zakharov, E.V., Prosser,

S. W. J., Sones, J. E., McKeown, J. T. A., Mantle, B.,

and La Salle, J. (2013) A DNA ‘Barcode Blitz’: rapid

digitization and sequencing of a natural history col-

lection, PLoS One, 8, e68535, https://doi.org/10.1371/

journal.pone.0068535.

56. Sire, L., Gey, D., Debruyne, R., Noblecourt, T.,

Soldati, F., Barnouin, T., Parmain, G., Bouget, C.,

Lopez- Vaamonde,C., and Rougerie,R. (2019) Thechal-

lenge of DNA barcoding saproxylic beetles in natu-

ral history collections – exploring the potential of

parallel multiplex sequencing with Illumina MiSeq,

Front. Ecol. Evol., 7, 495, https://doi.org/10.3389/fevo.

2019.00495.

57. Rinkert,A., Misiewicz,T., Carter,B., Salmaan,A., and

Whittall,J. (2021) Bird nests as botanical time capsules:

DNA barcoding identifies the contents of contempo-

rary and historical nests, PLoS One, 16, e0257624,

https://doi.org/10.1371/journal.pone.0257624.

58. Grealy, A., Langmore, N. E., Joseph, L., and Holleley,

C.E. (2021) Genetic barcoding of museum eggshell im-

proves data integrity of avian biological collections,

Sci. Rep., 11, 1605, https://doi.org/10.1038/s41598-

020-79852-4.

59. Calvignac-Spencer,S., Merkel,K., Kutzner,N., Kühl,H.,

Boesch, C., Kappeler, P. M., Metzger, S., Schubert, G.,

and Leendertz, F. H. (2013) Carrion fly-derived DNA

as a tool for comprehensive and cost-effective assess-

ment of mammalian biodiversity, Mol. Ecol., 22, 915-

924, https://doi.org/10.1111/mec.12183.

60. Schnell, I. B., Sollmann, R., Calvignac-Spencer, S.,

Siddall, M. E., Yu, D. W., Wilting, A., and Gilbert,

M. T. P. (2015) iDNA from terrestrial haematopha-

gous leeches as a wildlife surveying and monitoring

tool– prospects, pitfalls and avenues to be developed,

Front Zool., 12, 24, https://doi.org/10.1186/s12983-

015-0115-z.

61. Saranholi, B. H., Rodriguez-Castro, K. G., Carvalho,

C.S., Chahad-Ehlers,S., Gestich, C.C., Andrade, S.C.S.,

Freitas, P.D., and Galetti, P.D. (2023) Comparing iDNA

from mosquitoes and flies to survey mammals in a

semi-controlled Neotropical area, Mol. Ecol. Resour.,

23, 1790-1799, https://doi.org/10.1111/1755-0998.13851.

62. Alsos, I. G., Lammers, Y., Yoccoz, N. G., Jørgensen,T.,

Sjögren, P., Gielly, L., and Edwards, M. E. (2018)

Plant DNA metabarcoding of lake sediments: how

does it represent the contemporary vegetation, PLoS

One, 13, e0195403, https://doi.org/10.1371/journal.

pone.0195403.

63. Bruno,A., Sandionigi,A., Agostinetto,G., Bernabovi,L.,

Frigerio, J., Casiraghi, M., and Labra, M. (2019) Food

tracking perspective: DNA metabarcoding to iden-

tify plant composition in complex and processed

food products, Genes, 10, 248, https://doi.org/10.3390/

genes10030248.

64. Da Silva, L. P., Mata, V. A., Lopes, P. B., Pereira, P.,

Jarman, S.N., Lopes, R.J., and Beja,P. (2019) Advanc-

ing the integration of multi-marker metabarcoding

data in dietary analysis of trophic generalists, Mol.

Ecol. Resour., 19, 1420-1432, https://doi.org/10.1111/

1755-0998.13060.

65. Baensch,S., Tscharntke,T., Wuenschiers,R., Netter,L.,

Brenig, B., Gabriel, D., and Westphal, C. (2020) Us-

ing ITS2 metabarcoding and microscopy to analyse

shifts in pollen diets of honey bees and bumble bees

along a mass-flowering crop gradient, Mol. Ecol., 29,

5003-5018, https://doi.org/10.1111/mec.15675.

66. Valentini, A., Taberlet, P., Miaud, C., Civade, R.,

Herder, J., Thomsen, P. F., Bellemain, E., Besnard, A.,

Coissac, E., Boyer, F., Gaboriaud, C., Jean, P.,

Poulet, N., Roset, N., Copp, G. H., Geniez, P., Pont, D.,

Argillier, C., Baudoin, J.-M., Peroux, T., Crivelli, A. J.,

Olivier,A., Acqueberge,M., Le Brun,M., Møller, P.R.,

Willerslev, E., and Dejean, T. (2016) Next-generation

monitoring of aquatic biodiversity using environ-

mental DNA metabarcoding, Mol. Ecol., 25, 929-942,

https://doi.org/10.1111/mec.13428.

67. Carvalho, C. S., de Oliveira, M. E., Rodriguez-Castro,

K. G., Saranholi, B. H., and Galetti, P. M., Jr. (2022)

Efficiency of eDNA and iDNA in assessing vertebrate

diversity and its abundance, Mol. Ecol. Resour., 22,

1262-1273, https://doi.org/10.1111/1755-0998.13543.

20 YEARS OF DNA BARCODING 1615

BIOCHEMISTRY (Moscow) Vol. 90 No. 11 2025

68. Rimet,F., Aylagas,E., Borja,Á., Bouchez,A., Canino,A.,

Chauvin, C., Chonova, T., Ciampor, F., Costa, F. O.,

Ferrari, B.J.D., Gastineau,R., Goulon,C., Gugger,M.,

Holzmann, M., Jahn, R., Kahlert, M., Kusber, W.-H.,

Laplace-Treyture,C., Leese,F., Leliaert,F., Mann, D.G.,

Marchand, F., Méléder, V., Pawlowski, J., Rasconi, S.,

Rivera, S., Rougerie, R., Schweizer, M., Trobajo, R.,

Vasselon, V., Vivien, R., Weigand, A., Witkowski, A.,

Zimmermann,J., and Ekrem,T. (2021) Metadata stan-

dards and practical guidelines for specimen and DNA

curation when building barcode reference libraries

for aquatic life, Metabarcoding Metagenom., 5, 17-33,

https://doi.org/10.3897/mbmg.5.58056.

69. Collins, R.A., and Cruickshank, R.H. (2013) The seven

deadly sins of DNA barcoding, Mol. Ecol. Resour., 13,

969-975, https://doi.org/10.1111/1755-0998.12046.

70. Zhou, Y., Trujillo-González, A., Nicol, S., Huerli-

mann, R., Sarre, S. D., and Gleeson, D. (2025) Evalu-

ation of DNA barcoding reference databases for ma-

rine species in the western and central Pacific Ocean,

PeerJ, 13, e19674, https://doi.org/10.7717/peerj.19674.

71. Pentinsaari, M., Ratnasingham, S., Miller, S. E., and

Hebert, P. D. N. (2020) BOLD and GenBank revisit-

ed– do identification errors arise in the lab or in the

sequence libraries? PLoS One, 15, e0231814, https://

doi.org/10.1371/journal.pone.0231814.

72. Mulcahy, D.G., Ibáñez,R., Jaramillo, C.A., Crawford,

A.J., Ray, J.M., Gotte, S.W., Jacobs, J.F., Wynn, A.H.,

Gonzalez-Porter, G. P., McDiarmid, R. W., Crombie,

R.I., Zug, G.R., and deQueiroz,K. (2022) DNA barcod-

ing of the National Museum of Natural History reptile

tissue holdings raises concerns about the use of nat-

ural history collections and the responsibilities of sci-

entists in the molecular age, PLoS One, 17, e0264930,

https://doi.org/10.1371/journal.pone.0264930.

73. Bickford,D., Lohman, D.J., Navjot,S., Sodhi, N.S., Ng,

P.K.L., Meier,R., Winker,K., Ingram, K.K., and Das,I.

(2007) Cryptic species as a window on diversity and

conservation, Trends Ecol. Evol., 22, 148-155, https://

doi.org/10.1016/j.tree.2006.11.004.

74. Shekhovtsov, S. V., Bazarova, N. E., Lyubechanskii,

I. I., Peltek, S. E., Berman, D. I., Bulakhova, N. A.,

Golovanova, E.V., Konyaev, S.V., and Krugova, T. M.

(2017) DNA barcoding: how many earthworm spe-

cies are there in the south of West Siberia? Russ. J.

Genet. Appl. Res., 7, 57-62, https://doi.org/10.1134/

S2079059717010130.

75. Wang,T., Zhang, Y.-P., Yang, Z.-Y., Liu,Z., and Du, Y.-Y.

(2020) DNA barcoding reveals cryptic diversity in the

underestimated genus Triplophysa (Cypriniformes:

Cobitidae, Nemacheilinae) from the northeastern

Qinghai-Tibet Plateau, BMC Evol. Biol., 20, 151, https://

doi.org/10.1186/s12862-020-01718-0.

76. Page, R. D. M. (2016) DNA barcoding and taxonomy:

dark taxa and dark texts, Phil. Trans. R. Soc. B., 371,

20150334, https://doi.org/10.1098/rstb.2015.0334.

77. Hartop,E., Srivathsan, A., Ronquist, F., and Meier, R.

(2022) Towards large-scale integrative taxonomy

(LIT): resolving the data conundrum for dark taxa,

Syst. Biol., 71, 1404-1422, https://doi.org/10.1093/

sysbio/syac033.

78. Muster, C., Spelda, J., Rulik, B., Thormann, J., von

der Mark, L., and Astrin, J. J. (2021) The dark side

of pseudoscorpion diversity: The German Barcode of

Life campaign reveals high levels of undocumented

diversity in European false scorpions, Ecol. Evol., 11,

13815-13829, https://doi.org/10.1002/ece3.8088.

79. Morinière, J., Balke, M., Doczkal, D., Geiger, M. F.,

Hardulak, L. A., Haszprunar, G., Hausmann, A.,

Hendrich, L., Regalado, L., Rulik, B., Schmidt, S.,

Wägele, J.-W., and Hebert, P.D.N. (2019) ADNA bar-

code library for 5,200 German flies and midges (In-

secta: Diptera) and its implications for metabarcod-

ing-based biomonitoring, Mol. Ecol. Resour., 19, 900-

928, https://doi.org/10.1111/1755-0998.13022.

80. Caruso,V., Hartop,E., Chimeno,C., Noori,S., Srivath-

san,A., Haas,M., Lee,L., Meier,R., and Whitmore,D.

(2024) An integrative framework for dark taxa bio-

diversity assessment at scale: A case study using

Megaselia (Diptera, Phoridae), Insect. Conserv. Div-

ers., 17, 968-987, https://doi.org/10.1111/icad.12762.

81. Iwaszkiewicz-Eggebrecht, E., Goodsell, R. M., Bengs-

son, B.-Å., Mutanen, M., Klinth, M., van Dijk, L. J.A.,

Łukasik, P., Miraldo, A., Andersson, A., Tack, A. J. T.,

Roslin, T., and Ronquist, F. (2025) High-throughput

biodiversity surveying sheds new light on the bright-

est of insect taxa, Proc. R. Soc. B, 292, 20242974,

https://doi.org/10.1098/rspb.2024.2974.

82. Preston, C. D., and Pearman, D. A. (2015) Plant hy-

brids in the wild: evidence from biological recording,

Biol.J. Linn. Soc., 115, 555-572, https://doi.org/10.1111/

bij.12577.

83. Prančl,J., Koutecký,P., Lučanová,M., Nagy Nejedlá,M.,

Brožová,V., Dolejšek,V., Košnar,J., Trávníček,P., and

Kaplan,Z. (2025) Identifying the richness and evolu-

tionary relationships of Ranunculus sect. Batrachi-

um in its diversity centre in south-western Europe,

Sci. Rep., 15, 14008, https://doi.org/10.1038/s41598-

025-98292-6.

84. Bobrov, A. A., Volkova, P. A., Kopylov-Guskov, Yu. O.,

Mochalova, O. A., Kravchuk, A. E., and Nekrasova,

D. M. (2022) Unknown sides of Utricularia (Lentib-

ulariaceae) diversity in East Europe and North Asia

or how hybridization explained old taxonomical

puzzles, Perspect. Plant Ecol. Evol. Syst., 54, 125649,

https://doi.org/10.1016/j.ppees.2021.125649.

85. Bobrov, A. A., Volkova, P. A., Mochalova, O. A., and

Chemeris, E.V. (2023) High diversity of aquatic Spar-

ganium (Xanthosparganium, Typhaceae) in North

Eurasia is mostly explained by recurrent hybrid-

ization, Perspect. Plant Ecol. Evol. Syst., 60, 125746,

https://doi.org/10.1016/j.ppees.2023.125746.

SHNEYER, RODIONOV1616

BIOCHEMISTRY (Moscow) Vol. 90 No. 11 2025

86. Meierotto, S., Sharkey, M. J., Janzen, D. H., Hall-

wachs,W., Chapman, E.G., Smith, M.A., and Hebert,

P. D. N. (2019) A revolutionary protocol to describe

understudied hyperdiverse taxa and overcome the

taxonomic impediment, Dtsch. Entomol.Z., 66, 119-

145, https://doi.org/10.3897/dez.66.34683.

87. Ahrens,D., Ahyong, S.T., Ballerio,A., Barclay, M.V.L.,

Eberle, J., Espeland, M., Huber, B. A., Mengual, X.,

Pacheco, T.L., Peters, R.S., Rulik,B., Vaz-De-Mello,F.,

Wesener, T., and Krell, F.-T. (2021) Is it time to de-

scribe new species without diagnoses? Acomment on

Sharkey etal. (2021), Zootaxa, 5027, 151-159, https://

doi.org/10.11646/zootaxa.5027.2.1.

88. Zamani, A., Vahtera, V., Sääksjärvi, I. E., and

Scherz,M. (2021) The omission of critical data in the

pursuit of ‘revolutionary’ methods to accelerate the

description of species, Syst. Entomol., 46, 1-4, https://

doi.org/10.1111/syen.12444.

89. Meier, R., Blaimer, B., Buenaventura, E., Hartop, E.,

von Rintelen, T., Srivathsan, A., and Yeo, D. (2021)

A re-analysis of the data in Sharkey et al.’s (2021)

minimalist revision reveals that BINs do not deserve

names, but BOLD Systems needs a stronger commit-

ment to open science, Cladistics, 38, 264-275, https://

doi.org/10.1111/cla.12489.

90. Sharkey, M. J., Tucker, E. M., Baker, A., Smith, M. A.,

Ratnasingham, S., Manjunath, R., Hebert, P., Hall-

wachs, W., and Janzen, D. (2022) More discussion of

minimalist species descriptions and clarifying some

misconceptions contained in Meier et al. 2021, Zoo-

Keys, 1110, 135-149, https://doi.org/10.3897/zookeys.

1110.85491.

91. Hlebec,D., Sivec,I., Podnar,M., and Kučinić,M. (2022)

DNA barcoding for biodiversity assessment: Croatian

stoneflies (Insecta: Plecoptera), PeerJ, 10, e13213,

https://doi.org/10.7717/peerj.13213.

92. Ranasinghe, U. G. S. L., Eberle, J., Thormann, J.,

Bohacz, C., Benjamin, S. P., and Ahrens, D. (2022)

Multiple species delimitation approaches with COI

barcodes poorly fit each other and morphospecies –

an integrative taxonomy case of Sri Lankan Sericini

chafers (Coleoptera: Scarabaeidae), Ecol. Evol., 12,

e8942, https://doi.org/10.1002/ece3.8942.

93. Miralles, A., Puillandre, N., and Vences, M. (2024)

DNA barcoding in species delimitation: from genet-

ic distances to integrative taxonomy, Methods Mol.

Biol., 2744, 77-104, https://doi.org/10.1007/978-1-0716-

3581-0_4.

94. Emerson, B. C. (2025) Delimiting species – prospects

and challenges for DNA barcoding, Mol. Ecol., 34,

e17677, https://doi.org/10.1111/mec.17677.

95. Meier,R., Srivathsan,A., Oliveira, S.S., Balbi, M.I.P.A.,

Ang, Y., Yeo, D., Kjærandsen, J., and Amorim, D. S.

(2025) “Dark taxonomy”: A new protocol for over-

coming the taxonomic impediments for dark taxa and

broadening the taxon base for biodiversity assess-

ment, Cladistics, 41, 223-238, https://doi.org/10.1111/

cla.12609.

96. Hebert, P.D., Penton, E.H., Burns, J.M., Janzen, D.H.,

and Hallwachs,W. (2004) Ten species in one: DNA bar-

coding reveals cryptic species in the neotropical skip-

per butterfly Astraptes fulgerator, Proc. Natl. Acad.

Sci. USA, 101, 14812-14817, https://doi.org/10.1073/

pnas.0406166101.

97. Stork,N. (2018) How many species of insects and oth-

er terrestrial arthropods are there on Earth? Annu.

Rev. Entomol., 63, 31-45, https://doi.org/10.1146/

annurev-ento-020117-04.

98. Fernandes, T. J. R., Amaral, J.S., and Mafra,I. (2021)

DNA barcode markers applied to seafood authen-

tication: an updated review, Crit. Rev. Food Sci.

Nutr., 61, 3904-3935, https://doi.org/10.1080/10408398.

2020.1811200.

99. Dawan,J., and Ahn,J. (2022) Application of DNA bar-

coding for ensuring food safety and quality, Food

Sci. Biotechnol., 31, 1355-1364, https://doi.org/10.1007/

s10068-022-01143-7.

100. Soares,S., Rodrigues,F., and Delerue-Matos,C. (2023)

Towards DNA-based methods analysis for honey: an

update, Molecules, 28, 2106, https://doi.org/10.3390/

molecules28052106.

101. Raclariu, A. C., Heinrich, M., Ichim, M. C., and

de Boer, H. (2018) Benefits and limitations of DNA

barcoding and metabarcoding in herbal product au-

thentication, Phytochem. Anal., 29, 123-128, https://

doi.org/10.1002/pca.2732.

102. Yu, J., Wu, X., Liu, C., Newmaster, S., Ragupathy, S.,

and Kress, W. J. (2021) Progress in the use of DNA

barcodes in the identification and classification of

medicinal plants, Ecotox. Environ. Saf., 208, 111691,

https://doi.org/10.1016/j.ecoenv.2020.111691.

103. Chen,S., Yin,X., Han,J., Sun,W., Yao,H., Song,J., and

Li,X. (2023) DNA barcoding in herbal medicine: Ret-

rospective and prospective, J. Pharm. Anal., 13, 431-

441, https://doi.org/10.1016/j.jpha.2023.03.008.

104. Meiklejohn, K. A., Jackson, M. L., Stern, L. A., and

Robertson, J.M. (2018) Aprotocol for obtaining DNA

barcodes from plant and insect fragments isolated

from forensic-type soils, Int.J. Legal Med., 132, 1515-

1526, https://doi.org/10.1007/s00414-018-1772-1.

105. Chimeno,C., Moriniére,J., Podhorna,J., Hardulak,L.,

Hausmann,A., Reckel,F., Grunwald, J.E., Penning,R.,

and Haszprunar,G. (2019) DNA barcoding in forensic

entomology – establishing a DNA reference library

of potentially forensic relevant arthropod species,

J. Forensic Sci., 64, 593-601, https://doi.org/10.1111/

1556-4029.13869.

106. Wang, A., Gopurenko, D., Wu, H., and Lepschi, B.

(2017) Evaluation of six candidate DNA barcode loci

for identification of five important invasive grasses

in eastern Australia, PLoS One, 12, e0175338, https://

doi.org/10.1371/journal.pone.0175338.

20 YEARS OF DNA BARCODING 1617

BIOCHEMISTRY (Moscow) Vol. 90 No. 11 2025

107. Xu, S.Z., Li, Z.Y., and Jin, X.H. (2018) DNA barcoding

of invasive plants in China: A resource for identify-

ing invasive plants, Mol. Ecol. Resour., 18, 128-136,

https://doi.org/10.1111/1755-0998.12715.

108. Madden, M. J. L., Young, R. G., Brown, J. W., Miller,

S.E., Frewin, A.J., and Hanner, R.H. (2019) Using DNA

barcoding to improve invasive pest identification at

U.S. ports-of-entry, PLoS One, 14, e0222291, https://

doi.org/10.1371/journal.pone.0222291.

109. Mendoza, Á.M., Torres, M.F., Paz,A., Trujillo-Arias,N.,

López-Alvarez,D., Sierra,S., Forero,F., and Gonzalez,

M. A. (2016) Cryptic diversity revealed by DNA bar-

coding in Colombian illegally traded bird species,

Mol. Ecol. Resour., 16, 862-873, https://doi.org/10.1111/

1755-0998.12515.

110. De Boer, H. J., Ghorbani,A., Manzanilla, V., Raclariu,

A.-C., Kreziou, A., Ounjai, S., Osathanunkul, M., and

Gravendeel, B. (2017) DNA metabarcoding of or-

chid-derived products reveals widespread illegal

orchid trade, Proc. R. Soc. B, 284, 20171182, https://

doi.org/10.1098/rspb.2017.1182.

111. Onyia, C. O., Ilo, O. P., Obih, C. E., Ugbogu, O.,

Ojiego, B. O., Rufai, S. S., Onyemaechi, P. S., and

Chukwuma, E. C. (2022) DNA barcoding of Nigeria’s

forest species listed in CITES and other endangered

plant species of national interest, Amer. J. Plant

Sci., 13, 1335-1346, https://doi.org/10.4236/ajps.2022.

1311090.

112. Zhou, L.-W. (2024) The strategy for naming fun-

gal “dark taxa” may involve a transition period

and genomics, Fungal Biol. Rev., 48, 100358, https://

doi.org/10.1016/j.fbr.2024.100358.

113. Rodionov, A. V. (2023) Eupolyploidy as a mode in

plant speciation, Russ. J. Genet., 59, 419-431, https://

doi.org/10.1134/s1022795423050113.

114. Wood, T.E., Takebayashi,N., Barker, M.S., Mayrose,I.,

Greenspoond, P. B., Loren, H., and Riesebergb, L. H.

(2009) The frequency of polyploid speciation in vas-

cular plants, Proc. Natl. Acad. Sci. USA, 106, 13875-

13879, https://doi.org/10.1073/pnas.0811575106.

115. Rodionov, A. V., Gnutikov, A. A., Nosov, N. N.,

Mikhaylova, Y. V., Shneyer, V. S., and Punina, E. O.

(2020) Intragenomic polymorphism of the ITS 1 re-

gion of 35S rRNA gene in the group of grasses with

two-chromosome species: different genome compo-

sition in closely related Zingeria species, Plants, 9,

1647, https://doi.org/10.3390/plants9121647.

116. Suissa, J.S., Kinosian, S.P., Schafran, P.W., Bolin, J.F.,

Taylor, W. C., and Zimmer, E. A. (2022) Homoploid

hybrids, allopolyploids, and high ploidy levels char-

acterize the evolutionary history of a western North

American quillwort (Isoёtes) complex, Mol. Phyl.

Evol., 166, 107332, https://doi.org/10.1016/j.ympev.

2021.107332.

117. Punina, E. O., Mikhaylova, Y. V., Shneyer, V. S.,

Krapivskaya, E. E., Machs, E. M., and Rodionov, A. V.

(2024) Intragenomic polymorphism of the 35S rDNA

internal transcribed spacers (ITS) in three species

and three interspecific hybrids of Pulsatilla (Ranun-

culaceae), Turczaninowia, 27, 67-85, https://doi.org/

10.14258/turczaninowia.27.4.8.

118. Kartavtsev, Y. P., Batischeva, N. M., Katugina, A. O.,

Bogutskaya, N. G., and Hanzawa, N. (2017) Molecu-

lar systematics and DNA barcoding of Altai osmans,

Oreoleuciscus (Pisces, Cyprinidae, and Leuciscinae),

and their nearest relatives, inferred from sequences

of cytochromeB (Cyt-B), cytochrome oxidaseC (Co-1),

and complete mitochondrial genome, Mitochondrial

DNA A DNA Mapp. Seq. Anal., 28, 502-517, https://

doi.org/10.3109/24701394.2016.1149822.

119. Levin, B. A., Simonov, E., Matveyev, M. P., Artaev,

O.N., Mustafayev, N. J., Pashkov, A. N., and Rouben-

yan, H. R. (2018) DNA barcoding of the fishes of the

genus Alburnoides (Actinopterygii, Cyprinidae) from

Caucasus, Mitochondrial DNA A DNA Mapp. Seq.

Anal., 29, 49-55, https://doi.org/10.1080/24701394.

2016.1238900.

120. Turanov, S.V., and Kartavtsev, Y.Ph. (2021) Acomple-

ment to DNA barcoding reference library for identifi-

cation of fish from the Northeast Pacific, Genome, 64,

927-936, https://doi.org/10.1139/gen-2020-0192.

121. Orlova, S. Yu., Emelianova, O. R., Nebesikhina, N.A.,

Rabazanov, N.I., and Orlov, A.M. (2024) The problems

of DNA-barcoding the shads of genus Alosa (Alosidae)

of the ponto-Caspian basin, J. Ichthyol., 64, 510-520,

https://doi.org/10.1134/S0032945224700188.

122. Gorobeyko, U. V., Kartavtseva, I. V., Sheremetyeva,

I. N., Kazakov, D. V., and Guskov, V. Yu. (2020) DNA-

barcoding and a new data about the karyotype of

Myotis petax (Chiroptera, Vespertilionidae) in the

Russian Far East, Comp. Cytogen., 14, 483-500, https://

doi.org/10.3897/CompCytogen.v14.i4.54955.

123. Abramson, N., Petrova, T., and Dokuchaev, N. (2022)

Analysis of “historical” DNA of museum samples re-

solve taxonomic, nomenclature and biogeography is-

sues: case study of true lemmings, Biol. Comm., 67,

340-348, https://doi.org/10.21638/spbu03.2022.408.

124. Doronina, M. A., Doronin, I. V., Lukonina, S. A.,

Mazanaeva, L. F., Lotiev, K. Yu., and Ananjeva,

N. B. (2023) Application of DNA barcoding to the

study of green lizards (Sauria: Lacertidae: Lacerta),

Russ. J. Genet., 59, 297-306, https://doi.org/10.1134/

s1022795423030031.

125. Fefilova, E., Novikov, A., Popova, E., Golubev, M.,

Bakashkina, A., and Velegzhaninov, I. (2024) Evalu-

ating the effectiveness of DNA barcoding for genet-

ic identification of Canthocamptidae (Harpacticoida,

Copepoda) of Lake Baikal, Zool. Syst., 49, 285-298,

https://doi.org/10.11865/zs.2024402CSTR:32106.14.

126. Bespalaya, Y. V., Bolotov, I. N., Aksenova, O. V., Go-

farov, M. Y., Kondakov, A. V., Vikhrev, I. V., and Vi-

narski, M. V. (2018) DNA barcoding reveals invasion

SHNEYER, RODIONOV1618

BIOCHEMISTRY (Moscow) Vol. 90 No. 11 2025

of two cryptic Sinanodonta mussel species (Bival-

via: Unionidae) into the largest siberian river, Lim-

nologica, 69, 94-102, https://doi.org/10.1016/j.limno.

2017.11.009.

127. Lukhtanov, V.A., Dantchenko, A.V., Balayan, K.V., and

Gagarina, A. V. (2020) Karyotype and DNA barcode

of Polyommatus (Agrodiaetus) cyaneus (Staudinger,

1899) from its type locality: implication for taxonomic

and evolutionary research in Polyommatus blue but-

terflies (Lepidoptera, Lycaenidae), Comp. Cytogen.,

14, 567-575, https://doi.org/10.3897/CompCytogen.

v14.i4.59574.

128. Kirichenko, N. I., Zakharov, E. V., and Lopez-

Vaamonde, C. (2022) Tracing the invasion of a

leaf-mining moth in the palearctic through DNA

barcoding of historical herbaria, Sci. Rep., 12, 5065,

https://doi.org/10.1038/s41598-022-08894-7.

129. Kulakova, O. I., Tatarinov, A. G., and Shadrin, D. M.

(2022) Species composition of wood whites of the

genus Leptidea Billberg (Lepidoptera, Pieridae)

in northeastern Europe based on DNA barcoding,

Entomol. Rev., 102, 996-1003, https://doi.org/10.1134/

s0013873822070090.

130. Krivosheeva, V., Solodovnikov, A., Shulepov, A., Se-

merikova, D., Ivanova, A., and Salnitska, M. (2023)

Assessment of the DNA barcode libraries for the study

of the poorly-known rove beetle (Staphylinidae) fau-

na of West Siberia, Biodivers. Data J., 11, e115477,

https://doi.org/10.3897/BDJ.11.e115477.

131. Makarchenko, E. A., Semenchenko, A.A., Krashenin-

nikov, A. B., Yanygina, L. V., and Yavorskaya, N. M.

(2024) Review of archaic nymphomyiids (Diptera,

Nymphomyiidae) of the Russian Far East and border-

ing territories, with describing of new taxa and DNA

barcoding of known species, Zootaxa, 5448, 183-211,

https://doi.org/10.11646/zootaxa.5448.2.2.

132. Semenchenko, A., and Tiunova, T. M. (2025) DNA

barcoding of mayflies (Ephemeroptera) in Kamchat-

ka peninsula, Russia, Far East. Entomol., 533, 7-19,

https://doi.org/10.25221/fee.533.2.

133. Klimov, P.B., Stolbov, V.A., Kazakov, D.V., Filimonova,

M.O., and Sheykin, S.D. (2022) A DNA Barcoding and

photo-documentation resource of water mites (Acar-

iformes, Hydrachnidia) of Siberia: accurate species

identification for global climate change monitor-

ing programs, Syst. Appl. Acarol., 27, 2493, https://

doi.org/10.11158/saa.27.12.8.

134. Chetverikov, P.E., Burlakovskiy, M.S., Paponova, S.S.,

Sukhareva, S. I., Klimov, P., Bolton, S. J., Craemer, C.,

Efimov, P. G., Neser, S., Romanovich, A., and Am-

rine, J. (2019) Supplementary descriptions and DNA

barcodes of two rarely encountered Trisetacus spe-

cies (Eriophyoidea, Phytoptidae) associated with ter-

tiary relict conifers from the mediterranean region,

Syst. Appl. Acarol., 24, 1631-1652, https://doi.org/

10.11158/saa.24.9.5.

135. Shekhovtsov, S. V., Peltek, S. E., Sundukov, Y. N.,

Blakemore, R. J., and Gongalsky, K. B. (2017) Iden-

tifying earthworms (Oligochaeta, Megadrili) of the

southern Kuril Islands using DNA barcodes, Animal

Biodivers. Conserv., 41, 9-17, https://doi.org/10.32800/

abc.2018.41.0009.

136. Latif, R., Aminjan, A. R., Malek, M., Shekhovtsov,

S. V., Poluboyarova, T. V., and Briones, M. J. I. (2025)

Barcode gap delimitation and phylogenetic analyses

in integrative earthworm taxonomy: Dendrobae-

na byblica complex, Zootaxa, 5589, 68-79, https://

doi.org/10.11646/zootaxa.5589.1.7.

137. Kaygorodova,I., Bolbat,N., and Bolbat,A. (2020) Spe-

cies delimitation through DNA barcoding of fresh-

water leeches of the Glossiphonia genus (Hirudinea:

Glossiphoniidae) from Eastern Siberia, Russia, J.Syst.

Evol. Res., 58, 1437-1446, https://doi.org/10.1111/

jzs.12385.

138. Baturina, M. A., Golubev, M. A., Bakashkina, A. S.,

and Marchenko, J. V. (2025) The first results of DNA

barcoding of freshwater Oligochaeta (Annelida) of

the European North-East of Russia, Limnol. Freshw.

Biol., 4, 414-427, https://doi.org/10.31951/2658-3518-

2025-A-4-414.

139. Tumanov, D. (2021) Presence of Notahypsibius pal-

lidoides (Tardigrada: Hypsibiidae) in the fauna of

Russia confirmed with the methods of DNA barcod-

ing, Biol. Comm., 66, 274-280, https://doi.org/10.21638/

spbu03.2021.309.

140. Nuzhdina, N. S., Erofeeva, A. S., Bondar, A. A., and

Kovtonyuk, N. K. (2024) DNA barcoding of some

endangered plant species of the Altai Mountains

based on five genetic markers, Botanica Pacifica, 13,

193-196, https://doi.org/10.17581/bp.2024.13113.

141. Erst, A.S., Nikulin, A.Yu., Nikulin, V.Yu., Ebel, A.L.,

Zibzeev, E.V., Sharples, M.T., Baasanmunkh,S., Choi,

H.J. A.E., Olonova, M.V., Pyak, A.I., Gureyeva, I. I.,

Erst, T.V., Kechaykin,A., Luferov,A., Maltseva, S.Yu.,

Nobis, M., Lian, L., and Wang, W. (2022) Distribu-

tion analysis, updated checklist, and DNA barcodes

of the endemic vascular flora of the Altai moun-

tains, a Siberian biodiversity hotspot, Syst. Biodiv-

ers., 20, 1-30, https://doi.org/10.1080/14772000.2022.

2049391.

142. Shadrin, D.M., Dalke, I.V., Zakhozhiy, I.G., Shilnikov,

D. S., Kozhin, M. N., and Chadin, I. F. (2024) DNA

barcode marker analysis of Heracleum sosnowskyi

Manden. and Heracleum mantegazzianum Sommier

& Levier (Apiaceae) from European Russia, Russ. J.

Biol. Invasions, 15, 416-431, https://doi.org/10.1134/

S2075111724700309.

143. Vaganov, A.V., Sinitsyna, T.A., Kutsev, M.G., Skaptsov,

M. V., Zholnerova, E. A., Kosachev, P. A., Kechaykin,

A. A., Smirnov, S. V., and Shmakov, A. I. (2022) DNA

barcodes of the vascular flora of the Altai Moun-

tain Country: type material of the Herbarium ALTB,

20 YEARS OF DNA BARCODING 1619

BIOCHEMISTRY (Moscow) Vol. 90 No. 11 2025

Turczaninowia, 25, 5-11, https://doi.org/10.14258/

turczaninowia.25.4.1.

144. Samarina, L. S., Koninskaya, N. G., Shkhalakhova,

R. M., Simonyan, T. A., and Kuzmina, D. O. (2025)

DNA-barcoding for cultivar identification and in-

traspecific diversity analysis of agricultural crops,

Int. J. Mol. Sci., 26, 6808, https://doi.org/10.3390/

ijms26146808.

145. Bukin, Yu. S., Mikhailov, I. S., Petrova, D. P.,

Galachyants, Yu.P., Zakharova, Yu.R., and Likhoshway,

Ye. V. (2023) The effect of metabarcoding 18S RRNA

region choice on diversity of microeukaryotes includ-

ing phytoplankton, World J. Microbiol. Biotechnol.,

39, 229, https://doi.org/10.1007/s11274-023-03678-1.

146. Lukyanov,V., Gaysina,L., Bukin,Yu., Renganathan,P.,

and Tupikin,A. (2024) DNA-metabarcoding of cyano-

bacteria and microalgae in chernozem soils of tem-

perate continental climate of the forest-steppe zone

of Eurasia under different degrees of agrotechnology

intensification, World J. Microbiol. Biotechnol., 40,

351, https://doi.org/10.1007/s11274-024-04133-5.

147. Filippova,N., Zvyagina,E., Rudykina, E.A., Ishmanov,

T.F., Filippov, I.V., Bulyonkova, T.M., and Dobrynina,

A. S. (2024) DNA-based occurrence dataset on peat-

land fungal communities studied by metabarcod-

ing in north-western Siberia, Biodivers. DataJ., 12,

e119851, https://doi.org/10.3897/BDJ.12.e119851.

148. Shchepin, O. N., López Villalba, Á., Inoue, M.,

Prikhodko, I. S., Erastova, D. A., Okun, M. V.,

Woyzichovski, Ja., Yajima, Yu., Gmoshinskiy, V. I.,

Moreno, G., Novozhilov, Yu. K., and Schnittler, M.

(2024) DNA barcodes reliably differentiate between

nivicolous species of Diderma (Myxomycetes, Amoe-

bozoa) and reveal regional differences within Eurasia,

Protist, 175, 126023, https://doi.org/10.1016/j.protis.

2024.126023.

149. Kolesnikova, A. I., Pavlov, I. N., Litovka, Y. A.,

Oreshkova, N. V., Timofeev, A. A., Litvinova, E. A.,

Petrenko, S. M., and Krutovsky, K. V. (2024) Molec-

ular identification of wood-decaying fungi of Ar-

millaria genus widespread in Eastern Siberia and

the Far East of Russia using ITS, IGS-1-1 and Tef-1α

genetic markers, Mycol. Phytopathol., 58, 231-245,

https://doi.org/10.31857/S0026364824030052.

150. Psurtseva, N. V., Kiyashko, A. A., Senik, S. V., and

Pham, T.H.G. (2025) Exsitu conservation, DNA bar-

coding and enzymatic potential evaluation of mac-

rofungi (Basidiomycota, Ascomycota) from Vietnam,

J.Fungi, 11, 34, https://doi.org/10.3390/jof11010034.

151. Syromyatnikov, M. Y., Golub, V. B., Kokina, A. V.,

Soboleva, V. A., and Popov, V.N. (2017) DNA barcod-

ing and morphological analysis for rapid identifica-

tion of most economically important crop-infesting

sunn pests belonging to Eurygaster Laporte, 1833 (He-

miptera, Scutelleridae), ZooKeys, 706, 51-71, https://

doi.org/10.3897/zookeys.706.13888.

152. Nedunoori, A., Turanov, S. V., and Kartavtsev, Y. P.

(2017) Fish product mislabeling identified in the Rus-

sian Far East using DNA barcoding, Gene Rep., 8, 144-

149, https://doi.org/10.1016/j.genrep.2017.07.006.

153. Syromyatnikov, M. Yu., Kokina, A. V., Savinkova,

O.V., Panevina, A.V., Solodskikh, S.A., Orlova, M.V.,

Grabovich, M. Yu., Starkov, A. A., and Popov, V. N.

(2018) Study of the microbiological composition of

dairy products and mayonnaise using DNA barcod-

ing and metabarcoding, Foods Raw Mater., 6, 144-153,

https://doi.org/10.21603/2308-4057-2018-1-144-153.

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