ARTICLE
https://doi.org/10.1038/s41467-022-30588-x

OPEN

A framework to efﬁciently describe and sharereproducible DNA materials and construction
protocols
1234567890():,;

Hideto Mori

1,2,3 & Nozomu Yachie

1,4 ✉DNA constructs and their annotated sequence maps have been rapidly accumulating with theadvancement of DNA cloning, synthesis, and assembly methods. Such resources have also
been utilized in designing and building new DNA materials. However, as commonly seen in
the life sciences, no framework exists to describe reproducible DNA construction processes.
Furthermore, the use of previously developed DNA materials and building protocols is usually
not appropriately credited. Here, we report a framework QUEEN (framework to generatequinable and efﬁciently editable nucleotide sequence resources) to resolve these issues andaccelerate the building of DNA. QUEEN enables the ﬂexible design of new DNA by using
existing DNA material resource ﬁles and recording its construction process in an output ﬁle(GenBank ﬁle format). A GenBank ﬁle generated by QUEEN can regenerate the process codesuch that it perfectly clones itself and bequeaths the same process code to its successive
GenBank ﬁles, recycling its partial DNA resources. QUEEN-generated GenBank ﬁles arecompatible with existing DNA repository services and software. We propose QUEEN as asolution to start signiﬁcantly advancing the material and protocol sharing of DNA resources.

1 Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo 153-8904, Japan. 2 Institute for Advanced Biosciences, Keio
University, Tsuruoka 997-0035, Japan. 3 Graduate School of Media and Governance, Keio University, Fujisawa, Kanagawa 252-0882, Japan. 4 School of
Biomedical Engineering, Faculty of Applied Science and Faculty of Medicine, The University of British Columbia, Vancouver, BC V6T 1Z3, Canada.
✉email: nozomu.yachie@ubc.ca

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esigning and building DNA are essential processes inmost life science research today. The introduction ofexogenous DNA into cells and animals allows for monitoring of molecular and cellular behaviors, and reverse engineering and functional enhancement of target systems. Chemical
DNA synthesis1 and assembly methods2 have largely advanced in
the last couple of decades, leading to the whole synthesis ofbacterial3,4 and yeast chromosomes5–7 and the establishment of
biofoundries towards the automated production of engineered
microorganisms8,9. A total of several hundred thousand DNA
plasmids have been deposited to public DNA repository services,such as AddGene10 and DNASU11. While the growing DNA
resources have been successfully accelerating life science research,previously established DNA resources have yet to be fully utilized
to produce new DNA constructs efﬁciently. Current DNA
resource sharing and building methods have major room for
improvement in optimal recycling and crediting of DNA
resources and protocols.
Standardization of materials and knowledge descriptions areimportant to best mobilize those established previously for new
material development. In synthetic biology, there have been
several efforts devoted for (1) the standardization of DNA
modules and (2) the standardization of functional DNA annotations. To standardize DNA modules, “DNA brick” systems havebeen proposed in which DNA fragments are sandwiched by alimited set of compatible restriction enzyme (RE) digestion sites
such that these modular DNA parts can be reused in different
DNA assemblies by ligation12–15. Such a system would accelerate
the DNA construction process when a sufﬁcient amount ofmodular parts are available. (They have yet to be widely used by
the community, probably because the number of compatible parts
have yet to meet the diverse demands in biology, or because PCRbased fragment preparation and highly speciﬁc overlap DNA
assembly methods16–21 have become the mainstream and freed
molecular cloning from RE-based techniques.) The synthetic
biology open language (SBOL)22 has been proposed to provide
functional annotation of gene circuits encoded in DNA sequences. SBOL enables users to deﬁne genetic parts and their functional wiring diagrams like electronic circuits in a computerreadable format, consequently allowing the design of sequencesthat confer speciﬁc functions through simulating different
circuits.
While the DNA brick systems and SBOL have contributed tothe efﬁcient collection of highly modular DNA resources and thesystematic annotation of functional DNA resources, respectively,
they focus on the standardization of input and output DNA
materials. The third key angle to advance new DNA construction
with rapidly accumulating DNA resources would be the development of a standardized “process ontology” or “protocol language” to describe DNA construction processes. We envision an
efﬁcient DNA design and construction system, whereby the most
optimal construction protocol of a target DNA is autonomously
formulated with a combination of maximal use of existing DNA
materials and knowledge on how previous DNA materials havebeen practically constructed. We are, however, one step awayfrom establishing such a system. Most of us have still been
designing DNA manually with GUI software tools, and the protocols have been described in natural language.
There are three major challenges to realize a process ontology
of DNA construction that is widely accepted by the life science
community. First, a standardized framework needs to be developed to universally frame any types of changes in DNA sequenceswith various types of annotations. While the methods to build
and alter DNA materials have been diversifying, such as we areseeing with genome editing technologies23, the current DNA
editing software tools cannot assist users in incorporating new
2

methods or allow them to develop new plugins. Second, there
needs to be an innovative way to motivate the community
without substantial costs. Third, such a process ontology would
require another system to evaluate the reproducibility and completeness of protocols, which also needs to be accepted by thecommunity. Accompanying these three challenges, a system that
globally traces the inheritance of previously established DNA
material and protocol resources would be largely beneﬁcial tomeasure the impacts of previous resources and to credit theirdevelopers.
A quine in computer science is a program that replicates a copyof itself without the need for any inputs. The intriguing conceptof quine, which ﬁrst appeared in the mid-twentieth century24,
provides us fruitful thoughts on self-replicating machines andtheir potentials. In this study, inspired by this concept, wedeveloped a simple, versatile framework called QUEEN (a framework to generate “quinable” and efﬁciently editable nucleotide
sequence resources) that resolves all the above-mentioned challenges and enables efﬁcient description, sharing, and crediting of
DNA materials and building protocols25. QUEEN is a Python
package where step-by-step building processes of DNA and theirdynamic sequence changes can be freely described and simulated.
QUEEN-generated DNA products can be output in the GenBank(gbk) ﬁle format that is widely used to describe annotated DNA
maps. Due to its ﬂexible ﬁle format, QUEEN-generated gbk ﬁlesachieve several unique features. First, they can regenerate thequine codes that generated themselves using QUEEN (Fig. 1a).
Just like the research community has already been doing, DNA
materials can be deposited to a DNA repository service together
with their gbk ﬁles created by QUEEN (Fig. 1b). While it serves asa regular gbk ﬁle, the ﬁle’s construction process information canalso be recovered by QUEEN, where the reproducibility of the
protocol is certiﬁed by this clonability. Second, in designing a new
DNA construct using QUEEN, when DNA parts are taken fromexisting QUEEN-generated gbk ﬁles (Fig. 1c) or a protocol generated from a QUEEN-generated gbk ﬁle is modiﬁed (Fig. 1d), thenewly produced gbk ﬁle inherits the parental material information as well as their building process histories. This feature will bea backbone for establishing new ways of evaluating genetic
resources, protocols, and developers based on how they are
inherited in the community (Fig. 1e). QUEEN-generated gbk ﬁlescan easily be spread through the existing databases and DNA
repository services as they can be treated as regular gbk ﬁles byexisting tools. This feature will prime the promotion of the newmaterial and protocol sharing model.
Results
Queen. QUEEN is a Python module. It was designed so that othersoftware tools and databases can easily be developed to enhance
QUEEN’s basic capabilities. In QUEEN, double-stranded (ds)
DNA objects with their annotated feature objects are ﬁrst provided as inputs. A dsDNA object can be given by specifying a
DNA sequence or importing a sequence ﬁle in GenBank or
FASTA ﬁle format. The dsDNA objects can then be manipulated
by using (i) four basic operational functions, “cut,” “ﬂip,” “join,”and “modify ends” and (ii) two search functions for DNA
sequence and annotation features, which can collectively represent any of the standard molecular cloning processes. We also
prepared (iii) two other direct edit functions for DNA sequence
and annotation features (Fig. 2). A dsDNA object generated oredited by QUEEN can be output into a gbk ﬁle that encodes thequine code to replicate the entire operational process.
A linear or circular dsDNA object can be segmented into
multiple fragment objects or linearized by “cut.” Each cut site canbe deﬁned by a single DNA sequence position (blunt-end cut), two

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Fig. 1 QUEEN. a QUEEN enables the generation of a quine code from a gbk ﬁle. b A QUEEN-generated gbk ﬁle can be deposited to an existing DNA
repository as a regular gbk ﬁle. c When DNA sequences are partially or fully inherited from previous QUEEN-generated gbk ﬁles to build a new DNA, theproducing gbk ﬁle can contain all the production histories of the ancestral gbk ﬁles. d When a QUEEN script obtained from a previously established gbk ﬁleis modiﬁed to design the building process of a new DNA, such a history is also inherited by the produced gbk ﬁle. e A QUEEN-generated gbk ﬁle traces its
previously utilized DNA materials and protocols, enabling the assessment of community impact of each DNA resource or developer.

Fig. 2 Overview of QUEEN operational functions.

sequence positions for both strands (sticky-end cut), or a feature
object that represents an RE rule. Any feature objects, such as genenames and primer binding sites, are inherited from the input
dsDNA objects to the corresponding sequence regions of the
newly generating objects. Annotation feature objects on the cut
boundaries that are split are also passed to the new fragments,
each with a qualiﬁer specifying it as a broken feature. For
convenience, we also implemented “crop” as a branch function of“cut” to extract a segment bound by two cut sites. Any number of
dsDNA objects can be assembled at once by “join,” which requires
the connecting DNA end structures to be compatible (i.e., only
blunt ends and compatible sticky ends can be joined). If theassembly reconstitutes any sequences that are associated to any
feature objects annotated in the parental dsDNA, the same feature
objects are also restored in the generating dsDNA. Any singlestranded (ss)DNA sequence can be added to or removed fromwhichever strand of dsDNA ends by the “modify ends” operation.
This operation enables the description of any overlap-based DNA
assembly methods requiring long overlapping DNA end sequencesby changing such ends to sticky ends. dsDNA fragments can also
be “ﬂipped.” This operation can be used to, for example, model

DNA sequence inversion via site-speciﬁc DNA recombination(e.g., Cre-loxP and FLP-FRT) with “cut” and “join.”
When a user constructs a new DNA using QUEEN, they do notneed to remember the complete information of all the dsDNA
objects being treated and their feature objects. The searchfunctions allow them to obtain information on target sequenceand feature objects storing a target data by text search, using
regular expression or fuzzy matching. Similarly, the direct edit
functions enable searching of DNA sequence and feature objects,
but they also enable direct editing of the target objects obtainedby the search. The direct edit function of dsDNA objects can be
employed to model genome editing, and the direct edit functionof feature objects can enable creation of new feature objects andremoval and editing of existing feature objects. To export anddisplay information of dsDNA objects, QUEEN also provides ﬁve
output functions: “output gbk,” “print sequence,” “print feature,”“visualize map,” and “visualize ﬂow.” Especially, “visualize map”
and “visualize ﬂow” generate annotated sequence maps andoperational history ﬂow charts of dsDNA objects, respectively.
These output functions support users in programming QUEEN
codes with an interactive programming environment, such as
Jupyter Notebook, where users can promptly check progress of
their DNA construction. (The links to some examples areprovided in Supplementary Table 1.)
QUEEN progressively records all operations into the “history”
attribute of a feature object deﬁned for the entire dsDNA object.
This history attribute stores not only the operational history forthe present DNA construction but also the past operational
histories of the parental dsDNA objects whose partial fragments
are inherited to the present construction. Therefore, while a
QUEEN-generated gbk ﬁle can be operated by other non-QUEEN
supported tools, it also has abilities to produce a quine code that
self-replicates and provides the information of how previous
DNA materials have been manipulated and inherited to thepresent DNA construct. Furthermore, while programming
QUEEN codes, users can group subsets of operational ﬂows

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Fig. 3 Simple molecular cloning. a Conceptual diagram representing the cloning of eGFP into a lentiviral plasmid backbone. b QUEEN operational ﬂow
representing the same DNA cloning process. The visualization was directly generated from the output gbk ﬁle using QUEEN. The ﬁle shape, round, anduncolored rectangular objects represent the input gbk ﬁles, dsDNA objects, and feature objects, respectively. Colored boxes represent QUEEN operational
functions with the colors corresponding to Fig. 2. Open and closed circle-headed lines represent information ﬂows as QUEEN objects and input parameters,
respectively.

and provide them with narrative descriptions of methodologicalprocedures, which enables DNA builders to provide experimental
procedures in natural language along with their process semantic
descriptions. This also enables the direct generation of “Materials
and Methods” descriptions from QUEEN-generated gbk ﬁles. We
envision that the further development of GUI-based softwaretools will allow users to program the DNA construction process
without making them recognize any of the above-mentioned
QUEEN programming semantics.
Simple molecular cloning. We demonstrated that QUEEN
enables the description of various DNA construction processes,
simulation of dynamic DNA programs, and production of “quinable” QUEEN-generated gbk ﬁles. We ﬁrst tested a description of asimple gene cloning procedure to derive a lentiviral plasmid(pRS112) that we constructed previously26 (Fig. 3a). In this plasmid construction, an enhanced green ﬂuorescent protein (eGFP)encoding cassette was ampliﬁed from the pLV-eGFP plasmid by
PCR with primers that have overhang sequences encoding EcoRI
and BamHI RE sites. The ampliﬁed PCR product and the destination plasmid pLV-SIN-CMV-Puro were both digested by EcoRI
and BamHI and ligated to obtain the ﬁnal product.
This entire process was described using QUEEN with 11
operational steps (Fig. 3b). To obtain the PCR product, the 18-bp
3′ regions of the two primers were searched in the template. Theinternal DNA sequence ﬂanked by the primer sites was thenobtained by “crop,” followed by the concatenation of the entire
primer sequence to both ends by “modify ends” to produce the
PCR product. Next, EcoRI and BamHI cut sites and digestion
patterns were searched and deﬁned as feature objects for both the
PCR product and destination lentiviral plasmid backbone. The
feature objects deﬁning the cut sites were used for the double
digestion by “cut.” Finally, the digested fragments with compatible sticky ends were connected by “join.” We conﬁrmed that thegenerated sequence was identical to that of the previously
generated gbk ﬁle, and that the QUEEN-generated gbk ﬁle could
produce a quine code that clones the same plasmid.
Resource inheritance. To demonstrate QUEEN can describe
overlap DNA assembly and produce gbk ﬁles that can track their4building histories and those of inherited DNA parts, we replicatedthe construction processes of six CRISPR base-editor plasmids:
pCMV-Target-AID, pCMV-Target-ACE, pCMV-AIDmax, pCMVTarget-ACEmax, pCMV-BE4max(C), and pCMV-ACBEmax, that
we constructed previously26 (Fig. 4a). The entire construction processes of these plasmids were based on PCR ampliﬁcation of DNA
fragments and Gibson Assembly, where, upon some plasmid constructions, their DNA parts were recycled for other plasmid
constructions.
In brief, PCR fragment preparations were modeled bysearching for primer annealing sites followed by “crop” and“modify ends,” as in the preceding example above. Gibson
Assembly reactions were modeled by generating long compatible
sticky ends using “modify ends” and assembling them by “join.”
We started from four existing plasmids and one plasmid that we
prepared by cloning a synthetic DNA fragment. pCMV-TargetAID, pCMV-Target-ACE, pCMV-AIDmax, and pCMV-TargetACEmax were constructed by Gibson Assembly of fragments
ampliﬁed from the initial set of plasmids, but pCMV-BE4max(C)and pCMV-ACBEmax were constructed using fragments partiallyobtained from pCMV-AIDmax and pCMV-Target-ACEmax,
respectively. Through these assemblies, we sometimes ampliﬁedadjacent small DNA blocks for annotated DNA units separately
and assembled them back in the same order in a destination
plasmid for better PCR ampliﬁcation of shorter fragments, rather
than facing difﬁculties in amplifying longer PCR products. We
demonstrated that even if DNA sequence regions with feature
objects were once broken by this operation, they could be restored
in the ﬁnal plasmids (Fig. 4b and Supplementary Table 2).
We conﬁrmed that the QUEEN scripts could generate target
DNA sequences identical to those constructed previously. Theirquine codes and complete operational histories could also be
produced from the generated gbk ﬁles (Supplementary Figs. 1 and 2).
Notably, we conﬁrmed that the process histories of pCMVBE4max(C) and pCMV-ACBEmax successfully inherited those ofpCMV-Target-AIDmax and pCMV-Target-ACEmax, respectively(Supplementary Fig. 2). Furthermore, the construction processes ofpCMV-Target-ACEmax and pCMV-ACBEmax were also able to be
designed similarly to those of pCMV-Target-AIDmax and pCMVBE4max(C), where two and three fragments were shared, respectively. Therefore, we also generated two other gbk ﬁles for pCMV-

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Fig. 4 Base editor plasmid constructions. a The construction lineages of base editor plasmids with recycling of their DNA sequences. The plasmid nameswith gray, yellow, and white boxes represent those obtained from Addgene, newly synthesized, or described by QUEEN in this study, respectively.
b Recovery of the “Cas9(D10A)” feature after joining two fragments each from a different DNA object having the same feature.

Target-ACEmax and pCMV-ACBEmax by retrieving the quine
codes from the pCMV-Target-AIDmax and pCMV-BE4max(C) gbk
ﬁles and directly editing them. The gbk ﬁles generated through thisediting strategy successfully recorded the partial inheritance of theprevious protocols (Supplementary Fig. 3), showing that QUEEN
enables tracking of the partial and full inheritance of both DNA
materials and their construction processes.
Simulation of dynamic DNA changes. As seen in yeast matingtype cassette switching, site-speciﬁc DNA recombination, meiotic
chromosomal recombination, and genome editing, DNA is not a
static object but can act dynamically in a programmed manner.
Although highly efﬁcient genome editing methods can be used toconstruct DNA plasmids27,28 and synthetic genetic circuits29–32(both involving DNA sequence changes), no standard framework
is proposed to simulate changes in annotated dsDNA sequences.
To demonstrate QUEEN can also program, simulate, and sharesuch dynamic DNA circuits, we demonstrated the construction ofa genetic six-input, one-output Boolean Logic Look Up Table(LUT) demonstrated in BLADE33 using QUEEN (Fig. 5a). In thiscircuit, upon input of site-speciﬁc DNA recombinases, the circuit
DNA sequence alters by multistep deletions and/or inversions ofsegments sandwiched by corresponding recombinase target
sequences. The input patterns of four recombinases (Vica, B3,
PhiC31, and Bxb1) conﬁgures one of the 16 Boolean logic gates,
and the remaining two recombinases (Cre and FLP) serve as two
input signals to the logic gate (Fig. 5b). The output is given as
GFP expression. The intertwined segment recombinations yield
one or none of the four GFP genes to be expressed, depending on
the signal input pattern.
We implemented DNA segment deletion by “cut” and “join,”
and inversion by “cut,” “ﬂip,” and “join.” After obtaining a gbk ﬁledescribing the initial state of the DNA circuit, we loaded it to a
QUEEN script and simulated its behaviors for all of the 64 possible
signal input patterns (Fig. 5c and Supplementary Table 3). All the
input patterns conferred the expected DNA sequence outcomes.
We also output the resulting dsDNA objects to gbk ﬁles and

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Vica

vox

B3

B3RT

PhiC31att (PhiC31)

Bxb1

att (Bxb1)

Cre

lox2272

attB (PhiC31)

vox

T

loxP

1

lox2272

FRT

FRT

A(Cre)
B(Flp)

Flp

6-input-1-output genetic circuitlox2272

S1 ( C31)
S2 (Vika)
S3 (B3)S4 (Bxb1)

Boolean logic
selection

Expressed

GFP

Non-expressed

GFP

loxP
vox

GFP-1

NeoR-1

1000

2000

B3RT

T

GFP-2
3000

4000

attP (PhiC31)

IN
Logic gate
A B S1 S2 S3 S4
0 0
OR gate
1 0
A
0 1 1 1
0 1
B
1 1
0 0
NOR gate
1 0
1 0 0 0 A
0 1
B
1 1
0 0
AND gate
1 0
0 0 0 1 A
0 1
B
1 1
0 0
NAND gate
1 0
1 1 1 0 A
0 1
B
1 1
0 0
A
1 0
0 1 0 1 A
0 1

0
1
1
1
1
0
0
0
0
0
0
1
1
1
1
0
0
1
0
1 1
1
0 0
B
0
1 0
0
0 0 1 1 B
0 1
1
1 1
1
0 0
NOT A
1
1 0
0
1 0 1 0 A
0 1
1
1 1
0
0 0
NOT B
1
1 0
1
1 1 0 0 B
0 1
0
1 1
0
0 0
A IMPLY B 1
1 00
1 0 1 1 A
0 1
1
B
1 1
1
0 0
B IMPLY A 1
1 0
1A
1 1 0 1
0 1
0
B
1 1
1
0 0
A NIMPLY B 0
1 0
1A
0 1 0 0
0 1
0
B
1 1
0
0 0
B NIMPLY A 0
1 00A0 0 1 0
0 1
1
B
1 1
0
0 0
XOR gate 01 0
1
A0 1 1 0
0 1
1
B
1 1
0
0 0
XNOR gate 1
1 0
0
A
1 0 0 1
0 1
0
B
1 1
1
0 0
TRUE
1
1
1 0
1 1 1 1 1
0 1
1
1 1
1
0 0
FALSE
0
1 0
0
0 0 0 0 1
0 1
0
1 1
0

attB (Bxb1)

B3RT
NeoR-2

T

T

GFP-3

5000

T

attP (Bxb1)

T

6000

GFP-4

7000

FRT

8000

loxP

OUT
Sequence
T

GFP-1

GFP-2

T

GFP-2

T

GFP-4

T

GFP-3

T

GFP-4

T

GFP-4

T

GFP-1

T

NeoR-1

GFP-3

T

GFP-2

T

T

NeoR-1

T

GFP-2

T

T

GFP-4

NeoR-2

T

GFP-3

T

T

GFP-4

T

GFP-4

T

GFP-1

NeoR-1

T

GFP-2

T

NeoR-1

T

GFP-2

T

GFP-4

T

NeoR-2

T

GFP-3

T

GFP-4

T

GFP-4

T
T

T

GFP-4

GFP-3

T

T

GFP-4

GFP-2

T

T

GFP-4

T

GFP-1

GFP-2

T

GFP-2

T

GFP-4

T

NeoR-2

T

GFP-4

T

T

GFP-1

NeoR-1

T
T
T
T

NeoR-1

T

GFP-3

T

GFP-3

GFP-3

GFP-2

GFP-4
T

T

T
T

T

GFP-4
T

GFP-2

T

GFP-2

T

T

GFP-4

NeoR-2

T

T

GFP-4

GFP-1

T

NeoR-1

T
T

GFP-3

GFP-2

T

GFP-4
GFP-2

T

GFP-2

T

GFP-4

T

NeoR-2

T

GFP-3

T

GFP-1

GFP-2

T

GFP-2

T

T

GFP-4

T

T

GFP-4

T

GFP-1

NeoR-1

T
T

GFP-3

GFP-4

NeoR-2

T

GFP-3

T

GFP-4

T

GFP-4
GFP-1

GFP-2

T

GFP-2

T

T

GFP-4

T

GFP-4

T

T

GFP-4

GFP-1

T

NeoR-1

T

GFP-2

T

T

GFP-3

T

NeoR-2

NeoR-1

GFP-4

T
T

GFP-2

T

GFP-2

T

GFP-4

T

T

GFP-4

T

T

T

GFP-1

GFP-4

T

GFP-3

T

GFP-4

T

GFP-3

T

GFP-4

T

GFP-3

T

T

GFP-4

GFP-3

T

T

GFP-4

GFP-3

T

GFP-4

T

GFP-3

T

GFP-4

T

GFP-3

T

T

GFP-4

GFP-3

T

T

GFP-4

GFP-4

GFP-2

T

GFP-4

T

T
T

GFP1 GFP2
GFP3
GFP2
GFP4
GFP3
GFP4
GFP4
GFP1
NeoR1
GFP2
NeoR1
GFP2
GFP4
4
NeoR2
GFP3
GFP4
4
GFP4
4
GFP1 NeoR1
GFP2
NeoR1
GFP2
GFP4
NeoR2
GFP3
GFP4
GFP4
GFP1
GFP2
GFP3
GFP2
GFP4
4
GFP3
GFP4
4
GFP4
4
GFP1 GFP2
NeoR2
GFP2
GFP4
NeoR2
GFP3
GFP4
GFP4
GFP1 NeoR1
GFP2
NeoR1
GFP2
GFP4
GFP3
GFP4
GFP4
GFP1
NeoR1
GFP2
NeoR1
GFP2
GFP4
4
GFP3
GFP4
4
GFP4
4
GFP1
GFP2
NeoR2
GFP2
GFP4
4
NeoR2
GFP3
GFP4
4
GFP4
4
GFP1
NeoR1
GFP2
NeoR1
GFP2
GFP4
GFP3
GFP4
GFP4
GFP1
GFP2
NeoR2
GFP2
GFP4
NeoR2
GFP3
GFP4
GFP4
GFP1 GFP2
NeoR2
GFP2
GFP4
4
NeoR2
GFP3
GFP4
4
GFP4
4
GFP1 NeoR1
GFP2
NeoR1
GFP2
GFP4
4
GFP3
GFP4
4
GFP4
4
GFP1 GFP2
GFP3
GFP2
GFP4
4
GFP3
GFP4
4
GFP4
4
GFP1
NeoR1
GFP2
NeoR1
GFP2
GFP4
NeoR2
GFP3
GFP4
GFP4
GFP1
GFP2
GFP3
GFP2
GFP4
GFP3
GFP4
GFP4
GFP1 NeoR1
GFP2
NeoR1
GFP2
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Fig. 5 Simulation of a Boolean logic LUT. a The six-input, one-output Boolean logic LUT of the BLADE system. Left, site-speciﬁc DNA recombinases (roundnodes) and their corresponding target DNA sites (round rectangles). Right, the genetic Boolean logic LUT circuit. b The wiring diagram of the genetic
circuit. c Annotated DNA sequence outcomes of each signal input pattern simulated by QUEEN. GFP expresses if one of the GFP-encoding genes is placedin the same direction as the upstream promoter without being prevented by a transcription terminator.

demonstrated that their quine codes and process histories could bederived from the output ﬁles (Supplementary Fig. 4).
Discussion
There have been several software packages developed to design
molecular cloning procedures and generate annotated plasmid
6ﬁles, such as ApE34, Benchling (https://www.benchling.com/),
Geneious35, j536, Pydna37, Raven38, and SnapGene (https://www.
snapgene.com/). While most of them are GUI-based softwaretools for local client computers, Benchling is a cloud-based
software tool and Pydna is a Python programming package thatenables the description of DNA construction processes. Notably,
the operational functions of these tools are all implemented for

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speciﬁc cloning methods, such as Gibson Assembly, Golden Gate
Assembly, and traditional RE digestion and ligation cloning.
These speciﬁed functions seem intuitive and convenient for users
as long as they design DNA materials with the prepared set of
methods, but lack the elasticity to incorporate new DNA building
methods. In contrast, we hypothesized that DNA construction
processes can be generalized by the combination of four basic
operations, “cut,” “ﬂip,” “join,” and “modify ends.” We demonstrated that this system could easily describe the equivalent
operations of the previous tools in various examples. Although arange of currently available genomic resources are genetically
modiﬁed by gene deletion and transgene insertion throughhomologous DNA repair, Cre-loxP, and genome editing, nosoftware tool to describe all these processes has been developed.
The simulation of the BLADE circuit showed that QUEEN is also
capable of ﬂexibly describing a process of dynamic DNA
sequence alterations. In combination with the existing simulation
platforms for transcription unit (TU)-based genetic circuits39–41,
QUEEN could also accelerate the construction of genetic circuits
and cells having those.
Two major contrivances were implemented in QUEEN topermeate its use in the life science community: (1) the use of gbk
ﬁle format and (2) the self-reproducibility of process codes. We
ﬁrst determined that the gbk ﬁle format is the foremost format
that QUEEN needed to adopt for the life science community thatwidely uses gbk ﬁles to share DNA maps. Furthermore, while
SBOL has been proposed to enable organized annotation of
structural and functional aspects of DNA sequences in contrast to
the gbk ﬁle format, we considered it important to demonstrate theconcept of QUEEN in the more unorganized semantic system of
gbk to showcase its versatility in ﬁle format. Recording of selfreproducible operational history in the output gbk ﬁle and theirinheritance by descendant gbk ﬁles are also unique features of
QUEEN that have great potential to change the ways of building
DNA. The ability to generate a quine code from a QUEENgenerated gbk ﬁle not only certiﬁes its reproducibility but alsoenables the accompaniment of protocols and those of parental
DNA materials with the generating gbk ﬁle. This is the key featureof QUEEN that enables the community to share DNA materials
and protocols together without asking for the additional cost of
consciously managing these two separately. A protocol retrieved
from a QUEEN-generated gbk ﬁle can be edited to generate a new
DNA construct. This process can also be recorded and passed to a
newly producing gbk ﬁle. Benchling and SnapGene are also
capable of recording DNA construction processes. However, they
are unable to easily share the construction protocols outside of
the software environments nor track the deep inheritance ofmaterials and protocols from one DNA construct to another.
If the life science community starts generating and sharing gbk
ﬁles using QUEEN or QUEEN-compliant software tools, there
will be new ways of evaluating resources and developers’ contributions. The impacts of materials and protocols can be evaluated not only by a “trending” information of how many times
they are requested by other researchers, like shown in Addgene,but also by how they are inherited in successive products evenover multiple generations. However, the current implementationof QUEEN for the recovery of process histories from their gbk
ﬁles has two potential issues. First, as the community starts utilizing QUEEN-generated gbk ﬁles, the sizes of new QUEENgenerated gbk ﬁles will keep increasing. Second, the current
QUEEN framework highly depends on the community’s goodwill
and cannot certify original developers of DNA materials or thatthe utilization of previously created resources is properly recorded
in QUEEN-generated gbk ﬁles. For cell strains, CellRepo has
proposed for the deposition of engineered strains with speciﬁc
DNA barcode identiﬁers where CellRepo serves as a certiﬁcation

ARTICLEauthority42. While the current issues of QUEEN could be resolved
by a similar cloud-based certiﬁcation authority system that
authorizes, stores, and traces DNA material and protocol
resources together with QUEEN-compliant client software tools,
such a framework would not be most effective until the sharing ofboth DNA material and protocol resources is widely communizedwith the current QUEEN framework.
Once QUEEN is widely adopted in the life science community,
it would also accelerate the development of an efﬁcient DNA
design system. There have been algorithms proposed to compute
efﬁcient DNA assembly steps for target DNA products from agiven synthetic DNA library resource36,38,43. The wealth ofpractical DNA construction process knowledge made available by
QUEEN will greatly contribute to such an automated design of
DNA construction processes, where a building process of userrequested DNA can be autonomously designed with the most
optimal recycling of available DNA materials and reagents in auser’s environment, and with practically the best DNA cloning
strategy chosen based on the knowledge of how many times thecommunity has succeeded in similar methods. This idea could
also be implemented to gene synthesis and assembly automation
systems44.
While challenging, it is also important to establish similar systems to share materials and reproducible protocols in otherexperimental domains that require more complex descriptions.
Laboratory automation of natural science experiments in general
requires the full semantic description of reproducible protocols in arobot-executable manner. Ideally, this would resolve many of thecurrent issues in life science, including the reproducibility crisis45.
However, signiﬁcant technical and social contrivances would beneeded to realize this goal primarily because the development ofrobotic systems requires tremendous investments, and laboratory
automation communities have not been coherently progressing(different projects develop their own systems which cannot easily be
integrated). As represented by Protocol Activity Modeling Language(PAML) (https://github.com/Bioprotocols/paml), several processontologies have been proposed to describe life science experimental
processes. However, we believe good practices like the ones
implemented in QUEEN are important to motivate the current life
science community to adopt a new system without feeling a substantial cost. A potential direction for reproducible process sharing
and laboratory automation of various experimental domains could
be the development of a standardized “multiscale” process semantics, where any experimental process of any resolution can be
programmed, from the resolution of current material and methods
descriptions with researchers’ best efforts to that of robotic executions with their APIs. It may be possible to encourage the community to ﬁrst use such a semantics for daily experiments with thesupport of GUI-assisted editors, whose description can be shared,
reused, and elaborated later for laboratory automation with robotics. If widespread, this could accelerate the momentum of laboratory automation towards a similar vision that we propose here withQUEEN. Accordingly, we report QUEEN as a framework toaccelerate describing and sharing of DNA material and protocol
resources, and as a starting point to ponder a similar system forentire life sciences domains with laboratory automation.
Methods
Implementation of QUEEN. QUEEN was implemented as a Python 3.7 module. It
requires BioPython46 mainly for GenBank ﬁle parsing. The visualization ofannotated DNA sequence maps (visualizemap) and operational process ﬂow charts(visualizeﬂow) from QUEEN objects are dependent on Python Matplotlib47 andGraphviz48 modules, respectively. The detailed usage of each function implemented
in the QUEEN module is described in the GitHub repository (https://github.com/
yachielab/QUEEN/blob/master/README.md). Example QUEEN scripts are provided as Jupyter Notebook ﬁles at https://github.com/yachielab/QUEEN/tree/
master/demo/tutorial.

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ARTICLE

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Reporting summary. Further information on research design is available in the Nature
Research Reporting Summary linked to this article.

Data availability
Of the gbk ﬁles used as input ﬁles for the simulation of base editor plasmid constructions,pLV-eGFP, pCMV-ABE7.10, pcDNA3.1_pCMV-nCas-PmCDA1-ugi pH1-gRNA(HPRT),
pCMV-BE4max, and pCMV-ABEmax were obtained from Addgene (Plasmid IDs: 36083,
102919, 79620, 112903, and 112905, respectively). The sequence ﬁle for pLV-SIN-CMV-Puro
was obtained from Takara Bio, Inc. (Japan, https://catalog.takara-bio.co.jp/DNA_seq/
pLVSIN-CMV_pur.zip). The gbk ﬁle for pRS112 and pUC-optimized-PmCDA1-ugi
encoding the codon-optimized PmCDA1-UGI was created using Benchling. Some detailsequence feature annotations of input ﬁles were added manually before using them for thedemonstration (the modiﬁed ﬁles are available at https://github.com/yachielab/QUEEN/tree/
master/demo/sakata_et_al_2020). The gbk ﬁle used for the simulation of the Boolean logic
LUT circuit was downloaded from Addgene (Plasmid ID 87554), to which sequence feature
annotations for the site-speciﬁc recombination sites were added manually before thedemonstration (the modiﬁed ﬁle is available at https://github.com/yachielab/QUEEN/tree/
master/demo/Weinberg_et_aL_2017).

Code availability
QUEEN is an open-source software package distributed with MIT License. The entire
package, installation, and user’s manual are available at the GitHub repository (https://github.com/yachielab/QUEEN/). All of the source codes for QUEEN are placed in
https://github.com/yachielab/QUEEN/tree/master/QUEEN. The QUEEN scripts used toconstruct the base editor plasmids and the simulation of the Boolean logic LUT areprovided as Jupyter Notebook ﬁles at https://github.com/yachielab/QUEEN/tree/master/
demo/sakata_et_al_2020 and https://github.com/yachielab/QUEEN/tree/master/demo/
Weinberg_et_aL_2017, respectively. All of the Jupyter Notebook ﬁles for thedemonstrations in this study are available in GitHub and made executable in Google
Colaboratory (Supplementary Table 1).

Received: 11 November 2021; Accepted: 10 May 2022;

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Acknowledgments
We thank the members of the Yachie laboratory at the University of British Columbia
and the University of Tokyo for useful comments and discussions, especially Soh Ishiguro for testing the code and Samuel King for proofreading and providing feedback on
the manuscript. This study was performed under the Canada Research Chair programsupported by the Canadian Institutes for Health Research (CIHR). H.M. was supportedby the JSPS DC2 Fellowship and TTCK Fellowship (from the Yamagata prefectural
government and Tsuruoka city).

ARTICLE

Correspondence and requests for materials should be addressed to Nozomu Yachie.
Peer review information Nature Communications thanks Natalio Krasnogor and theother, anonymous, reviewer(s) for their contribution to the peer review of this work. Peerreviewer reports are available.
Reprints and permission information is available at http://www.nature.com/reprints
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Supplementary information The online version contains supplementary material
available at https://doi.org/10.1038/s41467-022-30588-x.

© The Author(s) 2022

Author contributions
H.M. and N.Y. conceived the study and designed QUEEN. H.M. implemented QUEEN.
H.M. and N.Y. wrote the manuscript together.

Competing interests
The authors declare no competing interests.

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