# InSillyClo, a User-Friendly Web Application to Assist Large-Scale Golden Gate Cloning and MoClo Workflows

Henri Galez, Bryan Brancotte, Juliette Bonche, Julien Fumey, Sara

Napolitano, Gregory Batt

► To cite this version:

Henri Galez, Bryan Brancotte, Juliette Bonche, Julien Fumey, Sara Napolitano, et al., InSillyClo, a User-Friendly Web Application to Assist Large-Scale Golden Gate Cloning and MoClo Workflows. ACS Synthetic Biology, 2025, 15 (1), pp.353-358. 10.1021/acssynbio.5c00553. [https://doi.org/10.1021/acssynbio.5c00553](https://doi.org/10.1021/acssynbio.5c00553).

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# InSillyClo, a User-Friendly Web Application to Assist Large-Scale Golden Gate Cloning and MoClo Workflows

*Published as part of ACS Synthetic Biology special issue "Bio Innovation via Synthetic Biology".*

Henri Galez, Bryan Brancotte, Juliette Bonche, Julien Fumey, Sara Napolitano, and Gregory Batt*

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Cite This: ACS Synth. Biol. 2026, 15, 353–358

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Supporting Information

**ABSTRACT:** Systems and synthetic biology developments often require the construction of many variants of a genetic circuit of interest, resulting in large-scale cloning campaigns. Golden Gate and Modular Cloning (MoClo), two powerful technologies enabling the scale-up of cloning workflows, play a central role for efficient circuit construction. These workflows include a number of dry-lab tasks, which are time-consuming and error-prone at scale. Currently, no software tool is available to handle these tasks in a dedicated, time-saving, and user-friendly manner. We present InSillyClo, an open-source web application to assist large-scale Golden Gate cloning and MoClo workflows. It supports an easy specification of genetic designs at any scale, followed by the automated generation of comprehensive workflow-related

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data. Moreover, InSillyClo leverages Modular Cloning with a versatile typing system of parts to generate user-defined workflows. InSillyClo is open source, accessible with or without user registration, and can also be used locally.

**KEYWORDS:** genetic circuit design, lab automation, cloning companion software, flexible MoClo support, golden gate, open-source software

## INTRODUCTION

Genetic engineering plays a pivotal role in synthetic and systems biology, either to empower living systems with new functions or to decipher their functioning. Despite decades of research works, engineering nature remains highly challenging. Design spaces have very high dimensionalities and knowledge of the functioning of cellular systems at the molecular level remains very partial and qualitative. In concrete terms, many variants of a target engineered system, or genetic circuit, have to be constructed to understand or optimize its functioning in its cellular context. This has motivated the development of a number of cloning technologies allowing efficient multipart, one-pot assemblies, such as Golden Gate¹ and Gibson² cloning. An extension of the Golden Gate assembly process is of special importance: Modular Cloning³ (MoClo). MoClo systems introduce standardized positions in the assembly (e.g., ‘part 2’, ‘part 3a’) and a biological significance to these positions (e.g., promoters are found as ‘part 2’). In computer science terms, MoClo systems are *typed* systems (e.g., promoters are of type ‘2’). This greatly simplifies the design of complex circuits. More than 50 MoClo kits are available on AddGene⁴ with some extending others with new parts and new part types, highlighting the interest of standardization.

Concretely, large cloning tasks are organized in cloning campaigns, in which one-pot assembly reactions are parallelized. These campaigns are typically composed of the

following sequence of tasks: circuit design, assembly reaction, bacterial transformation, plasmid purification and verification (PCR, restriction profile and/or sequencing). Automation with liquid handling robots has significantly reduced the burden of the wet-lab part.⁵,⁶ Yet, a number of tasks are conceptual in nature. The dry-lab part includes the specification of the genetic parts to assemble, the selection of the corresponding input plasmids (iP), the computation of the output plasmid (oP) map, calculations for equimolar mixes based on part concentrations, and the computation of fragment length for PCR/restriction digestion for construct verification. These tasks are time-consuming and error-prone, especially for large cloning campaigns. Most software tools available for assisting Golden Gate cloning support only the manual design of plasmids, one at a time. Therefore, they are not well suited for large cloning campaigns. The few tools enabling multiple plasmids specification offer limited support for cloning campaigns or lack flexibility and synergy with MoClo.

Received: July 31, 2025

Revised: November 5, 2025

Accepted: December 4, 2025

Published: December 17, 2025

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<figure><img src="image_11.png" /><figcaption>**Figure 1.** Golden Gate cloning campaigns with InSillyClo. (A) Running example: designs for a combinatorial library of cellulose-degrading enzymes (EGII, CBHII and BGL1), signal peptides and display anchors, with the auxiliary modules made of the rtTA transcription factor for inducible expression of the library, the Aga1 cell-wall binder for Aga1-Aga2 based display (optional, only used with Aga2 anchor), and pUPR-Venus as reporter of secretion stress. (B) Design specification in a tabular format using a spreadsheet program to enter input DNA part names. (C) Upload of a database of input plasmids and of a mapping file linking DNA part name to plasmid identifier. (D) Algorithmic path from the scan of the cloning campaign file to the computation of output plasmid sequences. (E) Campaign data generated by the Assembly Simulator module. Specifying primers and/or restriction enzyme is needed to generate simulated agarose gels. Filling in reaction parameters allows users to generate files for iP dilutions.</figcaption></figure>

Here, we present InSillyClo, a user-friendly web application to assist large-scale Golden Gate and Modular Cloning campaigns. InSillyClo offers a unique combination of features. It supports the specification of multiple assemblies at once with automated retrieval of input plasmid sequences from a laboratory database, and the streamlined generation of valuable companion data, including plasmid maps, agarose gel simulation, dilution instructions and compatibility with automated cloning workflows. Moreover, InSillyClo leverages the notion of typed systems present in MoClo strategies to support campaign specification in a flexible and powerful manner. Lastly, InSillyClo is an open-source web application

that can be used anonymously or as a logged user that offers confidentiality (no data stored for anonymous users) and user-friendliness (simulation history stored in user profiles). A command line version available as python package is also proposed for in-house, custom use. A glossary of InSillyClo terms used below is provided in *Text S1*.

## RESULTS

**Support for Large-Scale Golden Gate Cloning Campaigns.** Typical cloning campaigns contain one or several combinatorial constructions together with a number of auxiliary modules. Here, we consider as a running example a
A Example of modular cloning system

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B Raw assembly versus typed assembly

<table><caption>Campaign_display_Raw.csv</caption><thead><tr><th></th><th>oP Type</th><th>iP1</th><th>iP2</th><th>iP3</th><th>iP4</th><th>iP5</th><th>iP6</th><th>iP7</th><th>iP8</th></tr></thead><tbody><tr><th>oP</th><td>IP Type</td><td>n.a.</td><td>n.a.</td><td>n.a.</td><td>n.a.</td><td>n.a.</td><td>n.a.</td><td>n.a.</td><td>n.a.</td></tr><tr><th>pIB001</th><td>n.a.</td><td>ConLS</td><td>pTET</td><td>SPsuc2</td><td>EGII</td><td>AGA2</td><td>tTDH1_4b</td><td>ConR1</td><td>AmpR</td></tr><tr><th>...</th><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td></tr><tr><th>pIB075</th><td>n.a.</td><td>ConLS</td><td>pUPR</td><td>Venus_3</td><td>tADH1_4</td><td>ConRE</td><td>AmpR</td><td>...</td><td>...</td></tr></tbody></table>

<table><caption>iP_mapping_Raw.csv</caption><thead><tr><th>pID</th><th>Name</th></tr></thead><tbody><tr><td>...</td><td>...</td></tr><tr><td>pYTK056</td><td>tTDH1_4</td></tr><tr><td>pYTK066</td><td>tTDH1_4b</td></tr><tr><td>pYTK033</td><td>Venus_3</td></tr><tr><td>pYTK045</td><td>Venus_3b</td></tr><tr><td>pYTK058</td><td>Venus_4a</td></tr><tr><td>pCA0014</td><td>ConLS-pTET-Cas9-tENO1_4-ConR1-AmpR</td></tr><tr><td>...</td><td>...</td></tr></tbody></table>

<table><caption>DB_produced_Raw.csv</caption><thead><tr><th>oP</th><th>Name</th></tr></thead><tbody><tr><td>pIB001</td><td>ConLS-pTET-SPsuc2.EGII-AGA2-tENO1_4b-ConR1-AmpR</td></tr><tr><td>...</td><td>...</td></tr><tr><td>pIB075</td><td>ConLS-pUPR-Venus_3-tADH1_4-ConRE-AmpR</td></tr></tbody></table>

<table><caption>Campaign_display_L1.csv</caption><thead><tr><th></th><th>oP Type</th><th>ConL</th><th>Prom</th><th>CDS</th><th>Term</th><th>ConR</th><th>Backbone</th></tr></thead><tbody><tr><th>oP</th><td>IP Type</td><td>1</td><td>2</td><td>3, [3a,3b]</td><td>4, [4a,4b]</td><td>5</td><td>678, [6,7,8]</td></tr><tr><th>pIB001</th><td></td><td>TU1</td><td>ConLS</td><td>pTET</td><td>SPsuc2.EGII</td><td>AGA2.tTDH1</td><td>ConR1</td><td>AmpR</td></tr><tr><th>...</th><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td></tr><tr><th>pIB075</th><td></td><td>TU6</td><td>ConL5</td><td>pUPR</td><td>Venus</td><td>tADH1</td><td>ConRE</td><td>AmpR</td></tr></tbody></table>

<table><caption>iP_mapping_YTK.csv</caption><thead><tr><th>pID</th><th>Name</th><th>Type</th></tr></thead><tbody><tr><td>...</td><td>...</td><td>...</td></tr><tr><td>pYTK056</td><td>tTDH1</td><td>4</td></tr><tr><td>pYTK066</td><td>tTDH1</td><td>4b</td></tr><tr><td>pYTK033</td><td>Venus</td><td>3</td></tr><tr><td>pYTK045</td><td>Venus</td><td>3b</td></tr><tr><td>pYTK058</td><td>Venus</td><td>4a</td></tr><tr><td>pCA0014</td><td>pTET-Cas9-tENO1</td><td>TU1</td></tr><tr><td>...</td><td>...</td><td>...</td></tr></tbody></table>

<table><caption>DB_produced_L1.csv</caption><thead><tr><th>oP</th><th>Name</th><th>Type</th></tr></thead><tbody><tr><td>pIB001</td><td>pTET-SPsuc2.EGII-AGA2.tTDH1</td><td>TU1</td></tr><tr><td>...</td><td>...</td><td>...</td></tr><tr><td>pIB075</td><td>pUPR-Venus-tADH1</td><td>TU6</td></tr></tbody></table>

C InSillyClo Flowchart

<figure><img src="image_13.png" /><figcaption>**Figure 2.** MoClo campaigns with typed assemblies using InSillyClo. (A) Example of a MoClo grammar: the Yeast Tool Kit associates specific overhangs generated by Type IIs enzyme digestion to a biological meaning for the DNA parts. It forms a coherent assembly system to build (multi)transcriptional units. (B) Comparison between raw assembly (plain Golden Gate) and typed assembly (Moclo) for the campaign presented in Figure 1, underlying how the use of types clarifies the specifications. (C) Overview of the full InSillyClo workflow, including the initialization phase in which users generate the template file for their particular assembly with Assembly Designer, and compose iP mapping file containing identifiers and names of plasmids from their collection.</figcaption></figure>

combinatorial library to optimize the display of three cellulose degrading enzymes in yeast, together with the needed transcription factor, an optional anchor partner, and a stress reporter as auxiliary modules (Figure 1A).

Genetic designs are specified by filling in a template file using a spreadsheet program (Figure 1B). Each line of the resulting *campaign* file represents a one-pot Golden Gate reaction. Specifications can be made using part names instead of plasmid identifiers (e.g., pTET instead of pMYT015; Text S2).

This campaign file is loaded on the Assembly Simulator module of the web application, along with a database of Genbank files and one or several *iP\_mapping* files (Figure 1C). These latter files are mapping part names with plasmid identifiers corresponding to Genbank file names. This InSillyClo specification process has been designed to combine simplicity, flexibility and efficiency. Guidance for input file generation is provided in Text S3. The Assembly Simulator algorithm performs the assemblies, one line at a time, by first
gathering the part names, then retrieving the corresponding
Genbank files through *iP_mapping*, and finally assembling the
desired sequence (Figure 1D). The assembly is made by
digesting input sequences with the chosen type IIS enzyme,
retrieving the fragments inside the enzyme recognition sites,
and joining them by matching overhangs.

Data produced by the Assembly Simulator module are
represented in Figure 1E and detailed in Text S4. They include
the assembled output plasmid maps and the *DB_produced* file,
which contains automatically computed output plasmid names
for easy update of the *iP_mapping* database. It also generates
the instruction file for automated cloning with Opentrons
liquid handling robots using the Slowpoke software tool.⁷
Additional data can be generated depending on user needs.
Automated computation of master mix and input plasmid
dilutions is provided. Assistance for assembly verification is
possible with the simulation of agarose gels after PCR and
restriction digestion. An SBOL file containing the history of
the assembly can be generated too.

InSillyClo was designed to assist experimentalists in their daily lives by automating repetitive, time-consuming tasks. Interestingly, users will also get other benefits coming with automation, notably the automated computation of generic names and tables recapitulating mapping between names and plasmid identifiers. In that sense, InSillyClo can be seen as a minimal laboratory information management system, which could easily be integrated into more complete environments.

**Support for Typed Assemblies Coming from MoClo Systems.** Modular Cloning approaches extend the Golden Gate technology by providing a standardization of the assemblies. In a given MoClo system, parts have specific positions in the assemblies. They are typed. One refers to them as parts of type 2, 3 or 4a for example. For each MoClo system, part types are specified by a particular combination of overhangs. The way parts assemble in constructs of different levels formally defines a grammar. This standardization considerably streamlines cloning processes. MoClo remains flexible thanks to the possibility of using spacers and subtyping. MoClo has become instrumental in the genetic engineering of many organisms.⁸ Figure 2A depicts the grammar of a popular MoClo system, the Yeast Tool Kit, which allows users to build transcriptional units and assemble them.⁹

InSillyClo takes advantage of the standardization offered by
MoClo to provide a second mode for campaign specification
that is more compact and clearer. To illustrate the benefits at
the design phase of MoClo over plain Golden Gate, we
represent in Figure 2B the specification of the same cloning
campaign with the plain Golden Gate format (raw assembly)
or with the MoClo format (typed assembly). Several biological
sequences are often present in part databases with different
overhangs, that is, with different types, to be used at different
positions. In our example, taken from the YTK and its
extensions,¹⁰,¹¹ this is the case for the Venus reporter and the
tTHD1 terminator. In the typed version, each part is
represented by a part-name/part-type pair. In the raw version,
the user needs to employ ad hoc name extensions to
distinguish the otherwise identical parts, leading to error-
prone situations. To embrace MoClo modularity, it is possible
to define subparts by adding a separator character between two
subparts as in the example with the dot "." separating AGA2
and tTDH1. Other advantages of the typed assembly
specification are the design comfort with shorter standardized
names and more homogeneous files.

Templates tailored to specific MoClo systems are generated with the Assembly Designer module of InSillyClo. The user can set the number of input parts, their generic names, the possibility to have subparts, the associated type IIs enzyme, and the naming conventions. Templates are already available on the Web App for popular MoClo kits. New users need to perform the initialization step (Figure 2C, Text S3). It involves downloading or generating the template (Text S5), preparing the repository of plasmid sequences, and providing the file mapping part names and types with plasmid unique identifiers (*iP_mapping* file). The corresponding files for several widely used MoClo systems (YTK,⁹ Plant Part Kit,¹² Cidar,¹³ Vnat,¹⁴ and EcoFlex¹⁵) are available in the InSillyClo Gitlab repository (Text S6). Then, for each campaign, the user will fill in the template thus obtaining the *campaign* file. Running the Assembly Simulator module will generate the campaign data. InSillyClo is also available as a command line tool in which the *template* and *simulate* commands mirror the Assembly Designer and Assembly Simulator Web App modules (Text S7). The tool consists in a python package available in Pypi.

■ DISCUSSION

This work presents InSillyClo, an open-source web application
supporting Golden Gate Cloning and Moclo workflows. It
supports simple and flexible specification of genetic designs,
even for large-scale campaigns, with automated retrieval of
input sequences and assembly of output sequences. In
addition, the application generates data helping users in wet
lab tasks and in reporting. InSillyClo embraces the typing
system present in MoClo to improve user experience. The tool
is free of charge and runs on servers hosted by the Institut
Pasteur, a nonprofit organization. It is possible to use the web
application anonymously (without registration). Alternatively,
a command line tool can be used locally. Tutorials are available
on the web application for a smooth introduction, and
templates for many MoClo kits are already provided.

Golden Gate Cloning is supported by many software tools.
Several tools guide users for manual plasmid construction with
an intuitive interface (Ape,¹⁶ OpenCloning,¹⁷ SnapGene,¹⁸
Benchling,¹⁹ Geneious,²⁰ Teselagen²¹). With such tools,
plasmids are built one at a time. This approach is therefore
not suitable for large cloning campaigns. Some tools allow
users to specify multiple plasmids to be constructed at the
same time. This is notably the case for Benchling,¹⁹
Geneious,²⁰ Teselagen,²¹ Diva,²² Cuba,²³ and the Google
Sheets-based CloneCoordinate²⁴ with its companion tool
GGQueuer.¹⁴ Cuba²³ is the only one in which the retrieval
of input sequences is automated, as in InSillyClo, which is
important for large campaigns. The specification phase is done
in a tabular format for both Cuba²³ and InSillyClo. However,
InSillyClo users can refer to input plasmids directly with part
names, whereas Cuba users need to fetch the corresponding
Genbank file name or the Genbank internal ID field. While
many of the tools presented above provide calculations for
equimolar reaction mixes based on part concentrations and/or
support assembly verification via simulation of agarose gels
after PCR and restriction digestion, InSillyClo streamlines data
generation thanks to its campaign-oriented approach. This
approach differs from other pipelines dedicated to high-
throughput Golden Gate cloning combining several software
tools and modules.²⁵ In addition, InSillyClo uniquely supports
MoClo by leveraging its typing system and letting users tailor
the workflow to their MoClo system. Several improvements are
envisioned. They notably include the support of Golden Gate
assemblies with two restriction enzymes, the integration of
genetic part homing by PCR, the connection with popular
databases, and the compliance of the generated SBOL files
with a recent standard.²⁶

Large biofoundries are equipped with comprehensive
software ecosystems as demonstrated by the applications
developed by the Edinburgh Genome Foundry.²⁵,²⁷⁻³⁰ Yet,
automation of Golden Gate and MoClo workflows is amenable
for small companies or academic laboratories, via the
standardization of manual protocols or the use of liquid
handling robots, increasingly accessible.⁵,⁷ We therefore believe
that by providing support for the design, verification, reporting,
and wet-lab automation, InSillyClo will usefully serve the
bioengineering and synthetic biology communities.

■ METHODS

InSillyClo is distributed under the LGPL-3 license. It is
organized as a python command line tool wrapped in a web
application. The command line tool performs the computa-
tions and produces the data. It can be used independently as a
python package available on Pypi (Text S7). InSillyClo
leverages biopython for handling of sequence files, pandas
for template parsing, and pycairo for image generation. The
web application is built on Django, Bootstrap, Docker and is
hosted on a Kubernetes cluster at Institut Pasteur. Current
version of the software tool was deposited on Software
Heritage at https://archive.softwareheritage.org/browse/direc-
tory/1d5af55927d908325c14d599f9f1fa08abca905e/.

■ ASSOCIATED CONTENT

Data Availability Statement

InSillyClo can be accessed at https://insillyclo.pasteur.cloud.
The source code is available at https://gitlab.pasteur.fr/
insillyclo. Documentation for the Web App is available in the
Tutorial section of the application. Documentation for the
command line tool is available at https://insillyclo.pages.-
pasteur.fr/insillyclo-cli/.

SL Supporting Information

The Supporting Information is available free of charge at
https://pubs.acs.org/doi/10.1021/acssynbio.5c00553.

Definitions and explanations of terms; guidelines for
quick cloning with InSillyClo; guidelines for generation
of InSillyClo input files; description of the campaign
data produced by InSillyClo; standard typed cloning
 workflow; InSillyClo with other MoClo systems; and
local implementation with the command line tool
(PDF)

■ AUTHOR INFORMATION

Corresponding Author

**Gregory Batt** – Institut Pasteur, Inria, Université Paris Cité, 75015 Paris, France; ● orcid.org/0000-0003-1697-139X;
Email: gregory.batt@inria.fr

Authors

Henri Galez – Institut Pasteur, Inria, Université Paris Cité, 75015 Paris, France

Bryan Brancotte – Institut Pasteur, Université Paris Cité, Bioinformatics and Biostatistics Hub, 75015 Paris, France

Juliette Bonche – Institut Pasteur, Université Paris Cité, Bioinformatics and Biostatistics Hub, 75015 Paris, France

Julien Fumey - Institut Pasteur, Université Paris Cité,
Bioinformatics and Biostatistics Hub, 75015 Paris, France;
● orcid.org/0000-0002-6272-5619

Sara Napolitano – Institut Pasteur, Inria, Université Paris Cité, 75015 Paris, France; ● orcid.org/0000-0001-5040-3391

Complete contact information is available at:
https://pubs.acs.org/10.1021/acssynbio.5c00553

Author Contributions

H.G. proposed the project and developed prototype code.
H.G. and G.B. cosupervised the project, with help from S.N.
J.B. designed the software front-end. B.B. developed the back-
end and implemented the front-end of the software, with help
from J.F. H.G. and G.B. wrote the paper.

Funding

This work was supported by ANR grants Anoruti (ANR-20-PAMR-0001), Seq2Diag (ANR-20-PAMR-0010), SmartSec (ANR-21-CE44-0033) and TrojanYeast (ANR-24-CE18-2885).

Notes

The authors declare no competing financial interest.

■ ACKNOWLEDGMENTS

We want to thank the Biofoundry team from Lesaffre, notably
François Bertaux. Our collaboration led to the idea of
developing InSillyClo software. We also thank Esteban Lebrun
and Alicia Da Silva, who tested early versions of the software
and provided useful feedbacks. Lastly, we thank Rachel
Torchet who helped for the front-end design.

■ ABBREVIATIONS

MoClo, modular cloning; YTK, yeast tool kit; TU, transcriptional unit; iP, input plasmid; oP, output plasmid; SBOL, synthetic biology open language

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