Excel-SBOL Converter: Creating SBOL from Excel Templates and Vice Versa

Excel-SBOL Converter: Creating SBOL from

Excel Templates and Vice Versa

Jeanet Mante,

†

Julian Abam,

†

Sai P. Samineni,

†

Isabel M. P¨otzsch,

‡

Prubhtej

Singh,

Jacob Beal,

and Chris J. Myers

∗,†

†University of Colorado Boulder

‡University of Cambridge

¶Guru Gobind Singh Indraprastha University

§Raytheon BBN Technologies

E-mail: chris.myers@colorado.edu

Abstract

Standards support synthetic biology research by enabling the exchange of compo-

nent information. However, using formal representations, such as the Synthetic Biology

Open Language (SBOL), typically requires either a thorough understanding of these

standards or a suite of tools developed in concurrence with the ontologies. Since these

tools may be a barrier for use by many practitioners, the Excel-SBOL Converter was

developed to allow easier use of SBOL and integration into existing workﬂows. The

converter consists of two Python libraries: one that converts Excel templates to SBOL,

and another that converts SBOL to an Excel workbook. Both libraries can be used

either directly or via a SynBioHub plugin. We illustrate the operation of the Excel-

SBOL Converter with two case studies: uploading experimental data with the study’s

metadata linked to the measurements and downloading the Cello part repository.

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Keywords

Excel, SBOL, conversion, Excel-to-SBOL, SBOL-to-Excel, ontologies

Introduction

Synthetic biology is bringing together engineers and biologists to design biological circuits

for a variety of applications in energy, medicine, and bio-manufacturing.

Associated with

this interdisciplinary movement is the need for tools that support reusability and supple-

ment the current understanding of genetic sequences. To satisfy this need, synthetic biology

communities across the world have developed tools and ontologies to help describe their

unique semantic annotations.

2–15

Shared representations for data and metadata, grounded

in well-deﬁned ontology terms, can help reduce confusion when sharing materials between

researchers.

The Synthetic Biology Open Language (SBOL)

has been developed to ad-

dress this challenge. SBOL provides a standardized format for the electronic exchange of

information on the structural and functional aspects of biological designs, supporting the use

of engineering principles such as abstraction, modularity, and standardization for synthetic

biology. Many tools have been created that work with SBOL,

17–26

including the SynBioHub

repository for storing and sharing designs.

The original SBOL has been developed further

to allow other data types to be represented and more information to be captured. This has

lead to SBOL2

and SBOL3.

Using formal representations such as SBOL, however, typically requires either a thorough

understanding of these standards or a suite of tools developed in concurrence with the

ontologies.

Unfortunately, this poses a signiﬁcant barrier for scientists not trained to work

with such abstractions. Not using standards makes it diﬃcult to share and reuse parts.

31–36

Therefore, the time and eﬀort required to ﬁnd parts is much greater, and the ability to use

the tools to automate design is limited.

One approach to lowering the barrier for use of ontologies was demonstrated by the Sys-

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tems Biology for Micro-Organisms (SysMO) consortium.

In SysMO, the MicroArray Gene

Expression Markup Language (Mage-ML) was set up as an XML schema,

and users were

expected to submit data to the SysMO Assets Catalogue (called SEEK) in XML format in

order to publish work. To allow the use of the Mage-ML language without having to un-

derstand XML, the RightField tool was created.

This tool is an ontology annotation and

information management application that can add constrained ontology term selection to Ex-

cel spreadsheets. It enables administrators to create templates with controlled vocabularies,

such that the scientists utilizing the tool would never actually see the raw RightField, only

the more familiar Excel spreadsheet interface. Spreadsheets are a popular interface as many

biological workﬂows already use spreadsheets and comma separated values (CSV) ﬁles. Fur-

thermore, several popular tools use spreadsheets and CSV ﬁles as inputs or outputs, including

Addgene (https://www.addgene.org/), and Opentrons (https://opentrons.com/).

Users of SBOL and SynBioHub have also faced a steep learning curve for understanding

the underlying ontology: as assessed in,

“For successful use and interpretation of metadata

presented in SynBioHub, the semantic annotation process should be biologist-friendly and

hide the underlying RDF predicates.” Recently, SynBio2Easy was published as a command

line tool to convert Excel spreadsheets of plasmids to SBOL.

The tool was designed to

enable several steps of a speciﬁc workﬂow for designing and depositing Synechocystis plasmids

into a public SynBioHub repository.

Embracing the same spreadsheet-based interface as these prior works, this paper presents

the Excel-SBOL Converter, a tool designed to provide a simple way for users to generate

and visualize SBOL data without needing a detailed understanding of the underlying on-

tology and associated technologies. Unlike SynBio2Easy, our converter generalizes beyond

plasmids to multiple kinds of SBOL data, as well as allowing customization of the tem-

plates. This converter thus provides a simple way for users to manage data by allowing users

to both download SBOL into Excel templates and to submit Excel templates for conver-

sion into SBOL. This paper presents the architecture and key engineering decisions for the

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Excel-SBOL Converter, as well as two case studies that illustrate its operation: uploading

experimental data and downloading a repository of parts.

Results

The Excel-SBOL Converter enables researchers that are more comfortable with Excel spread-

sheets to make use of SBOL repositories and tools without having to manipulate or under-

stand the SBOL data standard. The Excel-SBOL Converter is currently implemented as two

separate libraries: the Excel-to-SBOL library is used to convert Excel spreadsheets format-

ted using pre-designed templates into SBOL data, while the SBOL-to-Excel library is used

to convert SBOL data into Excel spreadsheets. These two libraries taken together enable

data to be converted between Excel spreadsheets and SBOL data as shown in Figure 1.

Figure 1: The Excel-SBOL Converter consists of two libraries: 1) Excel-to-SBOL allows the

use of Excel templates to create SBOL data, and 2) SBOL-to-Excel allows SBOL data to

be converted to Excel spreadsheets. Using the two libraries together allows the creation,

uploading, downloading, editing, and re-uploading of SBOL data. Additionally, it allows

collaboration between users who prefer spreadsheets and those using software tools that

require data to be provided in SBOL format.

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Excel-to-SBOL

The Excel-to-SBOL library converts Excel spreadsheet templates into SBOL data. While ini-

tially designed based on ﬁxed spreadsheet templates developed in the Defense Advanced Re-

search Projects Agency (DARPA) Synergistic Discovery and Design (SD2) program (https:

//sd2e.org/)), the library has since been generalized to allow more ﬂexibility. With this

approach, a user needs to have only minimal knowledge of SBOL, while a template designer

needs knowledge of both the SBOL data representation and the Excel-to-SBOL template

structure.

Excel Spreadsheet Templates

As an example, let us consider an Excel spreadsheet template that can organize genetic parts

into several part collection libraries. This example is composed of one Excel File containing

2 data sheets:

1. A sheet describing the part collection libraries (Figure 2).

2. A sheet describing the genetic parts (Figure 3).

Figure 2: Collections sheet. This is where the users add collections and a description of the

collection.

In order to use this template, a user would enter the library names and their descriptions

into the collections sheet. The user would then enter each genetic part name, the collection it

is a part of, any sequence alterations, part description, data source preﬁx (PubMed, GenBank

etc), Data Source (e.g. the PubMed Id), source organism, whether the part is circular, and

the sequence (the ﬁnal column is automatically generated).

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Figure 3: Parts sheet. This is where the users add parts and a description of the parts.

Additionally, parts can be added to collections speciﬁed in the collections sheet (Figure 2)

by adding collection names to the “Collection” column.

Excel Spreadsheet Template Programming

Excel-to-SBOL is designed to parse a variety of interlinking SBOL object types in a ﬂexible

manner, whilst maintaining code simplicity. This is achieved via two main innovations: an

initialization sheet, which lists all sheets to be processed, and a column deﬁnitions sheet,

which provides conversion parameters for each object property.

The initialization sheet is a list of all sheets to be processed (Figure 4). The initialization

sheet enables the conversion of multiple sheets. The ﬁrst column in the initialization sheet is

the sheet name, while the rest indicate how it is to be processed. Speciﬁcally, these columns

indicate whether a conversion should be performed (if True it contains SBOL objects, if

False it is ignored except to potentially convert cell values), which row sheet data starts on,

whether collection metadata is present (and if so, the number of rows and which columns

it can be found in), and whether a collection description is present (and if so, where it can

be found). Additionally, further columns may be added to this sheet which are applied to

all objects it references. For example, in Figure 4 Molecule Type has been added and ﬁlled

out for the library sheet. This indicates that all parts on the library sheet have the molecule

type DNA.

The column deﬁnitions sheet is used for three things: conversion of cell values to a ma-

chine readable format, format checking of the cells, and conversion of cell values to SBOL.

The column deﬁnition sheet for our example is shown in Figure 5. The ﬁrst two entries

identify the sheet and column that conversion is being deﬁned for. The SBOL Term ﬁeld

indicates the property that is found in this location, and how it should be converted into

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Figure 4: Example initialization sheet. The left most column contains the names of the

sheets to be processed. Next the ‘Convert’ column indicates whether the sheet contains

SBOL objects (contains SBOL objects if TRUE). The ‘Lib Start Row’ column indicates on

which zero-indexed row the information on the sheet starts. The next columns indicate if

collection metadata and description are provided or not, and if so, where they can be found.

All columns after Descript Cols are columns that contain information that is the same across

all items in the sheet.

SBOL or a custom data object. This deﬁnition begins with a short preﬁx for the names-

pace (e.g., “sbol”) followed by an underscore and the property name itself. Most terms

will lead to a simple addition or the property, however some (e.g. sbol sequence) call spe-

cialized functions that do more than just add a property (e.g. also creates a sequence

object). Additional ﬁelds indicate the namespace URL (Uniform Resource Locator) to use

for the property, the type of the value (e.g., String or URI (Uniform Resource Identiﬁer)),

whether the property can have multiple values and the character(s) on which the ﬁeld is

split for conversion into a list of values. Additional value checking can be performed with

a “pattern” ﬁeld that provides a series of regular expressions that the property value is

to be checked against, separated by quotations. For example: “ ∧ [a − zA − Z\s ∗] + $”

“https : \/\/www.ncbi.nlm.nih.gov/nuccore/. ∗” is used for checking sequence entries, indi-

cating that entries should either contain only alphabetical characters and spaces or be URLs

starting with https://www.ncbi.nlm.nih.gov/nuccore/.

The remaining ﬁelds are used to deﬁne mechanisms that can be used for conversion from

human readable to machine readable forms. The ﬁrst option is to use the Tyto software to

convert a string to an ontology term.

In this case, the ontology to look the terms up in

must also be given (“Ontology Name”). For example, in Figure 4, Role (on the Init Sheet)

is converted using the sequence Ontology (SO).

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Figure 5: Example column deﬁnitions sheet. The Sheet Name and Column Name entries

identify the sheet and column within the sheet that is being deﬁned. Next, the SBOL Term

indicates the encoding property for the cell. The Namespace URL is the namespace to be

used with the encoding property, and the Type column indicates the type of data expected

(i.e. URI or String). The Split On column is used to split a single column into multiple

values for a property. The Pattern column contains regular expressions to check the cell

values after they have undergone conversion. The following columns contain conversion

information to go from human to machine-readable values. Tyto Lookup uses the Tyto

library to perform lookups according to the “Ontology Name” Column. The library performs

lookups according to the “Ontology Name” Column. Sheet lookup takes the cell value and

converts it to another value like a lookup dictionary. The dictionary is created based on the

“Lookup Sheet Name”, “From Col”, and “To Col”. Replacement Lookup is a special case of

sheet lookup. In this case, the cell value is expected to be preﬁx:value. Object ID Lookup

converts the cell value to an SBOL Object URI. The “Parent Lookup” column indicates the

directionality of the object reference.

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The second option is to lookup the ontology term using another sheet within the spread-

sheet as a dictionary (see for example Figure 6). When this option is selected, the user must

also specify the sheet to use for the lookup (“Lookup Sheet Name”), the column in which

the human readable value is found (“From Col”), and the column in which the machine

readable ontology term is found (“To Col”). For example, following the dictionary shown

in Figure 6), TRUE in the Circular column is converted to http://purl.obolibrary.org/

obo/SO_0000988 (the Sequence Ontology Role for circular) and false means no additional

role is added.

Figure 6: Lookup Sheet (circular types). This spreadsheet is used in a Lookup replacement

and converts human readable terms to machine readable ones. In this case the value TRUE

in the circular column leads to the Sequence Ontology term for circular whilst FALSE leads

to no addtion.

Replacement Lookup is a special case of sheet lookup. In this case, the cell value is

expected to be preﬁx:value, e.g. PMID:24295448, representing the data source is a PubMed

id with the value 24295448. Here the lookup works by using the preﬁx as the key and pulls a

value from the “To Col” and inserts the cell suﬃx in the place of {REPLACE HERE}

(Figure 7). So for the case PMID:24295448, the PMID key gives the string https://

pubmed.ncbi.nlm.nih.gov/{REPLACE_HERE}, which is changed to https://pubmed.ncbi.

nlm.nih.gov/24295448. This kind of lookup is useful in the case where several diﬀerent

kinds of information are put in one column, e.g., a data source might be GenBank, PubMed,

AddGene, etc. Additionally, it allows the formatting of a URL rather than being a direct

remapping in the way a simple sheet look up is.

The ﬁnal kind of lookup is Object ID lookup. The Object ID Lookup means that the

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Figure 7: Replacement Lookup Sheet. This is an example of a replacement lookup sheet

referenced by the column deﬁnitions sheet. In this case the preﬁx found in column B is

used to identify the correct URL in column and the suﬃx is placed in the URL at the

location indicated by {REPLACE HERE}. So for the case PMID:24295448, the PMID key

gives the string https://pubmed.ncbi.nlm.nih.gov/{REPLACE_HERE}, which is changed to

https://pubmed.ncbi.nlm.nih.gov/24295448.

cell value is converted to a URI. For example, a part called GFP promoter can be referenced

using that name, but this reference can then be converted to the URI http://www.examples.

org/gfp_promoter using this lookup. This type of lookup only works for SBOL objects.

If the parent lookup column is also true, then it indicates that the current object is added

as a property to the referenced object rather than vice versa. For example, in a row for a

component deﬁnition called pAOX1 (Figure 3), there may be a collection column with the

collection name: Promoter Collection. If the Parent Lookup is true, then rather than adding

Promoter Collection to pAOX1 using the sbol member attribute, the pAOX1 URI is added

to the Promoter Collection object using the sbol member attribute.

Excel-to-SBOL Conversion Algorithm

The initialization and column deﬁnition sheets are used by the Excel-to-SBOL conversion

algorithm shown in Algorithm 1. The full code for the Excel-to-SBOL converter can be found

at https://github.com/SynBioDex/Excel-to-SBOL. This algorithm proceeds as follows.

First, it reads in the initialization, column deﬁnition, and data sheets. It then adds any

additional properties speciﬁed on the Init sheet to the column deﬁnitions sheet as well as the

appropriate data sheet. Next, it creates an SBOL Document, and it begins parsing all the

sheets that have been marked in the initialization sheet as containing SBOL content. To do

this, it identiﬁes the displayId and SBOL object type columns, and it creates an SBOL object

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of the speciﬁed type for each entry found with a URI constructed from the displayId found.

Finally, it parses all the other columns in each sheet as speciﬁed in the column deﬁnitions

sheet. First, it splits the entries found using the Split On ﬁeld. Next, it converts the value

found, if needed, using the lookup method speciﬁed in the column deﬁnition sheet for that

entry. Then, it checks the converted cell value against the regular expression provided in

the Pattern ﬁeld. Finally, it adds the property and value to the corresponding SBOL object

using the speciﬁed SBOL Term and Namespace URL.

Algorithm 1: Excel-to-SBOL

Input: Filled Excel Template

Output: SBOL File

Initialization

Read in Init Sheet and create a list of sheets with SBOL objects in them

Read in SBOL Version to Convert to based on Init Sheet

Read in all Sheets based on Init Sheet

Read in Column Deﬁnitions Sheet

Add any columns deﬁned on the Init Sheet to the appropriate sheets and add

deﬁnitions for them in the Column Deﬁnitions Sheet

Parse Objects

Create SBOL Document

for sheet in SBOL Object Sheets do

Find Display ID Column and SBOL Object Type Column

for SBOL Object in sheet do

Create SBOL Object

Save SBOL Object in a dictionary common name:URI

Parse Columns

for sheet in SBOL Object Sheets do

for Row in sheet do

for Column in Row do

Split the cell value based on Column Deﬁnitions Sheet split column

Convert cell value using Ontology Lookups found in Column Deﬁnitions

Sheet

Check that the converted cell value match the pattern speciﬁed in the

Column Deﬁnitions Sheet

Convert the cell value based on SBOL Term, Namespace URL and

Parental Lookup values from the Column Deﬁnitions Sheet

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SBOL-to-Excel

The SBOL-to-Excel converter performs the opposite task of taking SBOL data and con-

verting it into an Excel spreadsheet. This converter reads an SBOL ﬁle in any standard

RDF format (e.g., XML, JSON-LD, Turdle, N-triples), and for every SBOL object found,

it creates a row in a spreadsheet that has a column for each property of the object. In this

case, the RDFlib python library is used rather than the more specialized SBOL libraries in

order to allow the conversion of diﬀerent SBOL object types in any version of SBOL whilst

maintaining code simplicity.

An example Excel spreadsheet generated by the SBOL-to-Excel converter from the SBOL

generated for the library example presented earlier is shown in Figure 8. This spreadsheet

looks similar to the templates described earlier, but has a few key diﬀerences. First, all

column names are based on the property name rather than human readable column names.

Second, no Init or Column Deﬁnitions sheet is present in the output. Finally, many of the

properties are full URLs rather than human readable names. Future work will address these

diﬀerences to allow full round-tripping of data from Excel to SBOL and back again.

The algorithm used by the SBOL-to-Excel Converter is shown in Algorithm 2. The full

code for the SBOL-to-Excel Converter can be found at https://github.com/SynBioDex/

SBOL-to-Excel.

The algorithm ﬁrst loads SBOL data into a dictionary with a dictionary of predicates

containing a list of objects for each subject. This is then converted to a pandas DataFrame.

Next, the predicate preﬁxes are converted to more human readable forms (e.g. “http:

//purl.org/dc/terms” to ‘dcterms’). Then the columns are reordered according to a hard-

coded priority system and any columns in a hard-coded removal list are removed. Finally,

each SBOL object type is output to its own sheet.

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(a) Collection Sheet

(b) ComponentDeﬁnition Sheet

Figure 8: Spreadsheet output by the SBOL-to-Excel Converter. This is an example output

for the SBOL example shown in the Excel-to-SBOL section. Note that each SBOL object

type is split into separate sheets.

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Algorithm 2: SBOL to Excel

Input: SBOL File

Output: Excel Filled Template

Initialization

Load SBOL document

Initialize output path

Parse Graph Subjects

Initialize Data Frame

for Subject, Predicate, Object in SBOL document do

Add items to Data Frame

Process Column Names

for Column Name in Predicates

Process the property URLs into human readable values to be set as the

column names

return processed Column Name

Reorder and Drop Columns

Any Data Frame columns found in the column list are reordered according to the

order in the column list and are moved to the front. The rest of the columns

maintain the same order.

Get Data Frame with list of new columns

Drop unnecessary columns from Data Frame.

return Data Frame with list of dropped columns

Dataframe to Excel Processing

for SBOL Object Type in Data Frame

Create a new Work Sheet

Add all cell values to the Work Sheet

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SynBioHub Plugins

SynBioHub plugins for the Excel-SBOL Converter were developed to enable the converter

to be integrated into a data processing workﬂow using the SynBioHub repository (see Fig-

ure 9). SynBioHub plugins are a way to create modular extensions to the capabilities of the

SynBioHub repository.

The plugins developed as part of this project allow the integration

of SynBioHub into synthetic biology workﬂows with spreadsheets. A submit plugin was de-

veloped for the Excel-to-SBOL converter. This plugin takes in Excel spreadsheet templates,

converts them to SBOL, and uploads the converted SBOL to SynBioHub. Similarly, a down-

load plugin was developed using the SBOL-to-Excel library that allows a user to download

SBOL stored in SynBioHub in the form of an Excel spreadsheet.

Figure 9: Integration of the Excel-SBOL Converter with SynBioHub via plugins. Submit:

When a user uploads an Excel spreadsheet template to the submit endpoint, SynBioHub

sends it to the Excel-to-SBOL plugin, which returns SBOL to be deposited in SynBioHub.

Download: If a user requests an Excel spreadsheet template to be downloaded from Syn-

BioHub, SynBioHub sends the appropriate SBOL to the SBOL-to-Excel plugin. The plugin

returns an Excel spreadsheet to SynBioHub, which is then returned to the user.

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Case Studies

We now illustrate the operation of the Excel-SBOL Converter with two case studies, one

focused on the upload of experimental data, the other focused on downloading a part library.

Experimental Data Sheets

Capturing experimental data with Excel-to-SBOL allows researchers to more easily associate

measurement metadata with genetic part information. The experimental metadata may in-

clude descriptions of experimental samples, study conditions, assay types, and equipment.

All this information can be captured in the SBOL data standard. To process experimen-

tal data, an Excel spreadsheet template was created that includes sheets for Studies (i.e.,

experimental replicates), Assays (i.e., experiments), Sample Designs (i.e., the media, strain,

and vector used), Samples (i.e., an individually measured instance of a sample design), and

Measurements (i.e., the data collected).

In the example, shown in Figure 10, each experimental study is conducted over a period

of twenty-ﬁve hours with measurements taken every 15 minutes. This experimental study

is repeated two times, with the study composed of two assays and each assay comprising of

two samples with two sample designs, for a total of four samples. Each of these four samples

then has two signals (i.e., optical density (OD), green ﬂuorescent protein (GFP)) measured

for 100 time points, resulting in a total of 800 measurements.

The information in this spreadsheet can be uploaded for storage in the SynBioHub repos-

itory

after processing by the Excel-to-SBOL converter. The Excel-to-SBOL converter con-

verts each study into an SBOL Collection object, each assay into an SBOL Experiment

object, each sample design into an SBOL Module Deﬁnition object, and each sample into an

SBOL ExperimentalData object. However, Measurements are not converted to SBOL and

are not uploaded to SynBioHub. In the future, we plan to upload the measurement data to

the Flapjack repository

(a repository for the storage of experimental measurement data).

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Figure 10: Excel-to-SBOL example for a collection of experiments. Top: Diagram showing

the design of an example experiment comprising a study with measurements every 15 minutes

over a period of twenty-ﬁve hours with two repeats. For each repeat, there is one plate per

repeat with one assay per plate. Each plate has two samples per assay for a total of four

samples at two diﬀerent sample design setups. Measuring each sample at each of the 100

time points results in 800 data points. Bottom: Given this experimental setup, ﬁve diﬀerent

sheets are used to describe and connect information for Studies, Assays, Sample Designs,

Samples, and Measurements. Note the way the sheets are linked via IDs. For example, assays

identify the study they are part of using the “Study ID” column and similarly, samples to

assay with “Assay ID”, and measurements to sample with “Sample ID”.

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Cello

The Cello library

was chosen as a case study for the SBOL-to-Excel Converter due the

variety of SBOL objects that it contains, including objects with a diverse set of custom

annotations. In order to achieve compliance with these varied SBOL types, it is key that

the SBOL-to-Excel Converter has a way of dynamically manipulating these types. With

the XML parsing capabilities oﬀered by RDFLib, the SBOL-to-Excel Converter is able to

successfully process and organize all of the data into a 15 sheets in an Excel document. The

conversion to Excel facilitates the analysis of the SBOL document for a user that prefers

spreadsheets. The results of the Cello conversion can be seen in Figure 11.

Figure 11: Example of a part of a sheet (ComponentDeﬁnition) output by the SBOL-to-

Excel Converter from the conversion of the Cello part Library.

In total 15 sheets are output

for: Range, Collection, Interaction, ComponentDeﬁnition, ModuleDeﬁnition, Participation,

FunctionalComponent, Sequence, Attachment, SequenceAnnotation, Component, Activity,

Agent, Association, and Usage. Note the human readable column names.

Discussion

We have presented two Python converter libraries: Excel-to-SBOL and SBOL-to-Excel. The

development of these libraries simpliﬁes the incorporation of SBOL into existing synthetic

biology workﬂows that make use of spreadsheets for data storage and exchange. The Excel-

to-SBOL converter allows multiple sheets with diﬀerent SBOL object types to be processed.

The converter can be used to convert spreadsheet columns into any RDF properties, and

can output either SBOL Version 2 or 3 documents, as well as validating information using

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user supplied regular expressions and converting user-friendly names into ontology terms.

Its complement, the SBOL-to-Excel Converter, can process any RDF ﬁle and turn it into

a spreadsheet. This allows both SBOL2 and SBOL3 documents to be converted to spread-

sheets. The conversion makes the values more human readable, and splits diﬀerent SBOL

object types across sheets.

While the Excel-SBOL Converter is already useful for many applications, there are several

improvements planned for the future. For the Excel-to-SBOL Converter, real-time sheet

checking using Excel plugins would enable errors to be detected and ﬁxed more eﬃciently.

Second, more standard Excel spreadsheet templates with example data should be created

to provide a starting point for both users and template creators. For the SBOL-to-Excel

Converter, the next step is to record the conversion process into a column deﬁnition sheet

in order to support round-trip conversion back to SBOL.

Finally, the case studies presented here both indicate that more thought needs to be

given to development of core standards regarding the information to be collected and shared

about experiments and parts. There is currently no consensus regarding what information

is desirable to share or how to organize it. Excel templates can be made available using

the Excel-SBOL Converter, however, it may be possible to help in developing a de facto

standard for information to be collected, and such a minimum information standard should

be further considered.

Methods

Below is a more in depth description of the methods used by the Excel-to-SBOL and SBOL-

to-Excel Converters, as well as the methods used in the case studies. Note that all code was

written in Python.

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Excel-to-SBOL

This converter relies on a set of Python modules to function. The modules used and their

functionality in the converter is explained below.

• Openpyxl is a Python library to read and write Excel ﬁles. This library is used

together with Pandas to read in the data from the Excel workbook and store it as a

data frame.

• pandas is a data analysis library. It is used for the reading in of the Excel workbook

and the further storage and manipulation of the data.

• pySBOL2 and pySBOL3 are libraries for the reading, writing, and manipulation of

SBOL data. These libraries are used to create the SBOL objects required and write

them out to an SBOL document.

• Tyto is a tool to make the semantic web more accessible. It is used to convert cell

values to ontology terms.

SBOL-to-Excel

SBOL-to-Excel relies on a set of libraries to ensure the smooth processing of a given SBOL

document. These libraries facilitate advanced processing procedures, and ensure that the

data is properly output to Excel for the user to analyze.

• RDFLib is a python package that enables the user to work with the Resource De-

scription Framework (RDF). RDF serves as an important proponent to this project.

Its functionality includes the ability to parse through RDF triples within an Excel ﬁle.

With access to the subject, object, and predicate triples, the library made it possible

to extract these values, and convert them into a form that can be easily interpreted in

order to eventually be output to Excel.

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• Openpyxl is a Python library to read and write Excel ﬁles. The Openpyxl library is

crucial to the Converter, as its functionality allows the writing of SBOL data into the

Excel spreadsheet. It is then also used to format the spreadsheet (including creating

the hyperlinks).

• Pandas is a python library that provides data structures with capabilities for the

accessing and manipulating of data that it holds, and does so with the information

processed from the SBOL collection. This occurs speciﬁcally when the data is being

read in. The subjects, objects, and predicates are set into the pandas data frame in

order to be processed by various modules, before being output into Excel.

• Validators is used to check items expected to be URLs are valid URLS.

• Pytest makes it easy to write small tests, as well as, scales to support testing for large

applications. This library enabled modular testing, ensuring that the correct forms of

data were being passed through the converter at the appropriate points.

Acknowledgement

JA, JM, and CM are supported by the National Science Foundation under Grant No.

1939892. JM is additionally supported by a Dean’s Graduate Assistantship at the Univer-

sity of Colorado Boulder. JA is additionally supported by a CU Boulder Discovery Learning

Apprenticeship. IP was supported by the Google Summer of Code and the Bisch¨oﬂiche Stu-

dienf¨orderung Cusanuswerk. PS was supported by the SBOL Industrial Consortium. JB and

CM are partially supported by Air Force Research Laboratory (AFRL) contracts FA8750-

17-C-0184 and FA8750-17-C-0229. SynBioHub and the Excel-SBOL Converter plugins are

run on a Microsoft Azure Server provided by Microsoft Research. This document does not

contain technology or technical data controlled under either U.S. International Traﬃc in

Arms Regulation or U.S. Export Administration Regulations. Any opinions, ﬁndings, and

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(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made

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conclusions or recommendations expressed in this material are those of the author(s) and

do not necessarily reﬂect the views of the funding agencies. All authors contributed to the

writing of this manuscript.

Author Contributions

All authors contributed to the writing of this manuscript. JM worked on Excel-to-SBOL,

supervising IP in the initial design and then taking over. JA worked on SBOL-to-Excel

with the help of JM. SS created the experimental data case study. PS is working on Tyto

integration into Excel. CM supervised the project. JB contributed guidance and the initial

DARPA spreadsheets.

Conﬂicts of Interest

The authors declare no conﬂicts of interest.

Supporting Information Available

Excel2SBOL

• GitHub: https://github.com/SynBioDex/Excel-to-SBOL

• Documentation: https://github.com/SynBioDex/Excel-to-SBOL/wiki

• Plugin: https://github.com/SynBioHub/Plugin-Submit-Excel2SBOL

• PyPI: https://pypi.org/project/excel2sbol/

SBOL2Excel

• GitHub: https://github.com/SynBioDex/SBOL-to-Excel

• Documentation: https://github.com/SynBioDex/SBOL-to-Excel/wiki

• Plugin: https://github.com/SynBioHub/Plugin-Download-SBOL2Excel

• PyPI: https://pypi.org/project/sbol2excel/

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Suplemental Filesl

• Template for Excel to SBOL Conversion SBOL2 simple parts template.xlsx

• Output of SBOL to Excel sbol2excel output.xlsx

• Case Study Excel to SBOL (Experimental Data) ﬂapjack compiler sbol3 v023.xlsx

• Case Study SBOL to Excel (Cello) cello.xlsx

References

(1) Khalil, A. S.; Collins, J. J. Synthetic biology: applications come of age. Nature Reviews

Genetics 2010, 11, 367–379.

(2) Hucka, M.; Finney, A.; Sauro, H. M.; Bolouri, H.; Doyle, J. C.; Kitano, H.; Arkin, A. P.;

Bornstein, B. J.; Bray, D.; Cornish-Bowden, A. et al. The systems biology markup

language (SBML): a medium for representation and exchange of biochemical network

models. Bioinformatics (Oxford, England) 2003, 19, 524–531.

(3) Hucka, M.; Nickerson, D. P.; Bader, G. D.; Bergmann, F. T.; Cooper, J.; Demir, E.;

Garny, A.; Golebiewski, M.; Myers, C. J.; Schreiber, F. et al. Promoting Coordinated

Development of Community-Based Information Standards for Modeling in Biology:

The COMBINE Initiative. Frontiers in Bioengineering and Biotechnology 2015, 3, 19.

(4) Waltemath, D.; Adams, R.; Bergmann, F. T.; Hucka, M.; Kolpakov, F.; Miller, A. K.;

Moraru, I. I.; Nickerson, D.; Sahle, S.; Snoep, J. L. et al. Reproducible computational

biology experiments with SED-ML–the Simulation Experiment Description Markup

Language. BMC systems biology 2011, 5, 198.

(5) Nov`ere, N. L.; Hucka, M.; Mi, H.; Moodie, S.; Schreiber, F.; Sorokin, A.; Demir, E.;

Wegner, K.; Aladjem, M. I.; Wimalaratne, S. M. et al. The Systems Biology Graphical

Notation. Nature Biotechnology 2009, 27, 735–741.

.CC-BY-NC-ND 4.0 International licenseavailable under a

(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made

The copyright holder for this preprintthis version posted August 31, 2022. ; https://doi.org/10.1101/2022.08.31.505873doi: bioRxiv preprint

(6) Galdzicki, M.; Clancy, K. P.; Oberortner, E.; Pocock, M.; Quinn, J. Y.; Ro-

driguez, C. A.; Roehner, N.; Wilson, M. L.; Adam, L.; Anderson, J. C. et al. The

Synthetic Biology Open Language (SBOL) provides a community standard for commu-

nicating designs in synthetic biology. Nature Biotechnology 2014, 32, 545–550.

(7) Gleeson, P.; Crook, S.; Cannon, R. C.; Hines, M. L.; Billings, G. O.; Farinella, M.;

Morse, T. M.; Davison, A. P.; Ray, S.; Bhalla, U. S. et al. NeuroML: A Language

for Describing Data Driven Models of Neurons and Networks with a High Degree of

Biological Detail. PLOS Computational Biology 2010, 6, e1000815.

(8) Cannon, R. C.; Gleeson, P.; Crook, S.; Ganapathy, G.; Marin, B.; Piasini, E.; Sil-

ver, R. A. LEMS: a language for expressing complex biological models in concise and

hierarchical form and its use in underpinning NeuroML 2. Frontiers in Neuroinformatics

2014, 8, 79.

(9) Faeder, J. R.; Blinov, M. L.; Hlavacek, W. S. In Systems Biology; Maly, I. V., Ed.;

Methods in Molecular Biology; Humana Press, 2009; Vol. 500; p 113–167.

(10) Bergmann, F. T.; Adams, R.; Moodie, S.; Cooper, J.; Glont, M.; Golebiewski, M.;

Hucka, M.; Laibe, C.; Miller, A. K.; Nickerson, D. P. et al. COMBINE archive and

OMEX format: one ﬁle to share all information to reproduce a modeling project. BMC

bioinformatics 2014, 15, 369.

(11) Gennari, J. H.; Neal, M. L.; Galdzicki, M.; Cook, D. L. Multiple ontologies in action:

composite annotations for biosimulation models. Journal of Biomedical Informatics

2011, 44, 146–154.

(12) Wolstencroft, K.; Krebs, O.; Snoep, J. L.; Stanford, N. J.; Bacall, F.; Golebiewski, M.;

Kuzyakiv, R.; Nguyen, Q.; Owen, S.; Soiland-Reyes, S. et al. FAIRDOMHub: a reposi-

tory and collaboration environment for sharing systems biology research. Nucleic Acids

Research 2017, 45, D404–D407.

.CC-BY-NC-ND 4.0 International licenseavailable under a

(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made

The copyright holder for this preprintthis version posted August 31, 2022. ; https://doi.org/10.1101/2022.08.31.505873doi: bioRxiv preprint

(13) Wittig, U.; Kania, R.; Golebiewski, M.; Rey, M.; Shi, L.; Jong, L.; Algaa, E.; Wei-

demann, A.; Sauer-Danzwith, H.; Mir, S. et al. SABIO-RK–database for biochemical

reaction kinetics. Nucleic Acids Research 2012, 40, D790–796.

(14) Nickerson, D.; Atalag, K.; de Bono, B.; Geiger, J.; Goble, C.; Hollmann, S.; Lonien, J.;

M¨uller, W.; Regierer, B.; Stanford, N. J. et al. The Human Physiome: how standards,

software and innovative service infrastructures are providing the building blocks to

make it achievable. Interface Focus 2016, 6, 20150103.

(15) Yu, T.; Lloyd, C. M.; Nickerson, D. P.; Cooling, M. T.; Miller, A. K.; Garny, A.;

Terkildsen, J. R.; Lawson, J.; Britten, R. D.; Hunter, P. J. et al. The Physiome Model

Repository 2. Bioinformatics (Oxford, England) 2011, 27, 743–744.

(16) Wolstencroft, K.; Owen, S.; Horridge, M.; Jupp, S.; Krebs, O.; Snoep, J.; Du Preez, F.;

Mueller, W.; Stevens, R.; Goble, C. Stealthy annotation of experimental biology by

spreadsheets. Concurrency and Computation: Practice and Experience 2013, 25, 467–

480.

(17) Watanabe, L.; Nguyen, T.; Zhang, M.; Zundel, Z.; Zhang, Z.; Madsen, C.; Roehner, N.;

Myers, C. IBIOSIM 3: A Tool for Model-Based Genetic Circuit Design. ACS Synthetic

Biology 2019, 8, 1560–1563.

(18) Myers, C. J.; Barker, N.; Jones, K.; Kuwahara, H.; Madsen, C.; Nguyen, N.-P. D.

iBioSim: A Tool for the Analysis and Design of Genetic Circuits. Bioinformatics 2009,

25, 2848–2849.

(19) Roehner, N.; Myers, C. J. Directed Acyclic Graph-Based Technology Mapping of Ge-

netic Circuit Models. ACS Synthetic Biology 2014, 3, 543–555.

(20) Chen, Y.; Zhang, S.; Young, E. M.; Jones, T. S.; Densmore, D.; Voigt, C. A. Genetic

circuit design automation for yeast. 5, 1349–1360.

.CC-BY-NC-ND 4.0 International licenseavailable under a

(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made

The copyright holder for this preprintthis version posted August 31, 2022. ; https://doi.org/10.1101/2022.08.31.505873doi: bioRxiv preprint

(21) Nielsen, A. A. K.; Der, B. S.; Shin, J.; Vaidyanathan, P.; Paralanov, V.; Strychal-

ski, E. A.; Ross, D.; Densmore, D.; Voigt, C. A. Genetic circuit design automa-

tion. 352, Publisher: American Association for the Advancement of Science eprint:

https://science.sciencemag.org/content/352/6281/aac7341.full.pdf.

(22) Baig, H.; Madsen, J. A Top-down Approach to Genetic Circuit Synthesis and Opti-

mized Technology Mapping. 9th International Workshop on Bio-Design Automation

(Pittsburgh, PA). 2017; pp 1–2.

(23) Gaspar, P.; Oliveira, J. L.; Frommlet, J.; Santos, M. A. S.; Moura, G. EuGene: Max-

imizing Synthetic Gene Design for Heterologous Expression. Bioinformatics 2012, 28,

2683–2684.

(24) Chen, J.; Densmore, D.; Ham, T. S.; Keasling, J. D.; Hillson, N. J. DeviceEditor Visual

Biological CAD Canvas. Journal of Biological Engineering 2012, 6, 1.

(25) Terry, L.; Earl, J.; Thayer, S.; Bridge, S.; Myers, C. J. SBOLCanvas: A Visual Editor

for Genetic Designs. ACS Synthetic Biology 2021, 10, 1792–1796.

(26) Czar, M. J.; Cai, Y.; Peccoud, J. Writing DNA with GenoCADTM. Nucleic Acids

Research 2009, 37, W40–W47.

(27) McLaughlin, J. A.; Myers, C. J.; Zundel, Z.; Mısırlı, G.; Zhang, M.; Oﬁteru, I. D.;

Go˜ni-Moreno, A.; Wipat, A. SynBioHub: A Standards-Enabled Design Repository for

Synthetic Biology. ACS Synthetic Biology 2018, 7, 682–688.

(28) Roehner, N.; Beal, J.; Clancy, K.; Bartley, B.; Misirli, G.; Gr¨unberg, R.; Oberortner, E.;

Pocock, M.; Bissell, M.; Madsen, C. et al. Sharing Structure and Function in Biological

Design with SBOL 2.0. ACS Synthetic Biology 2016, 5, 498–506.

(29) McLaughlin, J. A.; Beal, J.; Mısırlı, G.; Gr¨unberg, R.; Bartley, B. A.; Scott-Brown, J.;

Vaidyanathan, P.; Fontanarrosa, P.; Oberortner, E.; Wipat, A. et al. The Synthetic Bi-

.CC-BY-NC-ND 4.0 International licenseavailable under a

(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made

The copyright holder for this preprintthis version posted August 31, 2022. ; https://doi.org/10.1101/2022.08.31.505873doi: bioRxiv preprint

ology Open Language (SBOL) Version 3: Simpliﬁed Data Exchange for Bioengineering.

Frontiers in Bioengineering and Biotechnology 2020, 8 .

(30) Maccagnan, A.; Riva, M.; Feltrin, E.; Simionati, B.; Vardanega, T.; Valle, G.; Can-

nata, N. Combining ontologies and workﬂows to design formal protocols for biological

laboratories. Automated Experimentation 2010, 2, 3.

(31) Curty, R. G.; Crowston, K.; Specht, A.; Grant, B. W.; Dalton, E. D. Attitudes and

norms aﬀecting scientists’ data reuse. PLOS ONE 2017, 12, e0189288.

(32) Pasquetto, I. V.; Borgman, C. L.; Woﬀord, M. F. Uses and Reuses of Scientiﬁc Data:

The Data Creators’ Advantage. Harvard Data Science Review 2019, 1 .

(33) Zuiderwijk, A.; Shinde, R.; Jeng, W. What drives and inhibits researchers to share and

use open research data? A systematic literature review to analyze factors inﬂuencing

open research data adoption. PLOS ONE 2020, 15, e0239283.

(34) Costello, M. J.; Michener, W. K.; Gahegan, M.; Zhang, Z.-Q.; Bourne, P. E. Biodiversity

data should be published, cited, and peer reviewed. Trends in Ecology & Evolution

2013, 28, 454–461.

(35) Borgman, C. L. The conundrum of sharing research data. Journal of the American

Society for Information Science and Technology 2012, 63, 1059–1078.

(36) Frey, K.; Hafner, A.; Pucker, B. The Reuse of Public Datasets in the Life Sciences:

Potential Risks and Rewards; 2020.

(37) Booth, I. R. SysMO: back to the future. Nature Reviews Microbiology 2007, 5, 566–566.

(38) Spellman, P. T.; Miller, M.; Stewart, J.; Troup, C.; Sarkans, U.; Chervitz, S.; Bern-

hart, D.; Sherlock, G.; Ball, C.; Lepage, M. et al. Design and implementation of mi-

croarray gene expression markup language (MAGE-ML). Genome Biology 2002, 3,

research0046.1.

.CC-BY-NC-ND 4.0 International licenseavailable under a

(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made

The copyright holder for this preprintthis version posted August 31, 2022. ; https://doi.org/10.1101/2022.08.31.505873doi: bioRxiv preprint

(39) Wolstencroft, K.; Owen, S.; Horridge, M.; Krebs, O.; Mueller, W.; Snoep, J. L.;

du Preez, F.; Goble, C. RightField: embedding ontology annotation in spreadsheets.

Bioinformatics 2011, 27, 2021–2022.

(40) Urquiza-Garc´ıa, U.; Zieli´nski, T.; Millar, A. J. Better research by eﬃcient sharing:

evaluation of free management platforms for synthetic biology designs. Synthetic Biology

2019, 4, ysz016.

(41) Zieli´nski, T.; Hay, J.; Romanowski, A.; Nenninger, A.; McCormick, A.; Millar, A. J.

SynBio2Easy—a biologist-friendly tool for batch operations on SBOL designs with Ex-

cel inputs. Synthetic Biology 2022, 7, ysac002.

(42) Bartley, B. A. Tyto: A Python Tool Enabling Better Annotation Practices for Synthetic

Biology Data-Sharing. ACS Synthetic Biology 2022,

(43) Eilbeck, K.; Lewis, S. E.; Mungall, C. J.; Yandell, M.; Stein, L.; Durbin, R.; Ash-

burner, M. The Sequence Ontology: a tool for the uniﬁcation of genome annotations.

Genome Biology 2005, 6, R44.

(44) Mante, J.; Zundel, Z.; Myers, C. Extending SynBioHub’s Functionality with Plugins.

ACS Synthetic Biology 2020, 9, 1216–1220.

(45) McLaughlin, J. A.; Myers, C. J.; Zundel, Z.; Mısırlı, G.; Zhang, M.; Oﬁteru, I. D.;

Goni-Moreno, A.; Wipat, A. SynBioHub: a standards-enabled design repository for

synthetic biology. ACS synthetic biology 2018, 7, 682–688.

(46) Yanez Feliu, G.; Earle Gomez, B.; Codoceo Berrocal, V.; Mu˜noz Silva, M.; Nu˜n˜ez, I. N.;

Matute, T. F.; Arce Medina, A.; Vidal, G.; Vidal C´e´sspedes, C.; Dahlin, J. et al.

Flapjack: Data management and analysis for genetic circuit characterization. ACS

Synthetic Biology 2020, 10, 183–191.

.CC-BY-NC-ND 4.0 International licenseavailable under a

(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made

The copyright holder for this preprintthis version posted August 31, 2022. ; https://doi.org/10.1101/2022.08.31.505873doi: bioRxiv preprint

(47) Jones, T. S.; Oliveira, S.; Myers, C. J.; Voigt, C. A.; Densmore, D. Genetic circuit

design automation with Cello 2.0. Nature Protocols 2022, 1–17.

.CC-BY-NC-ND 4.0 International licenseavailable under a

(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made

The copyright holder for this preprintthis version posted August 31, 2022. ; https://doi.org/10.1101/2022.08.31.505873doi: bioRxiv preprint

Graphical TOC Entry

.CC-BY-NC-ND 4.0 International licenseavailable under a

(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made

The copyright holder for this preprintthis version posted August 31, 2022. ; https://doi.org/10.1101/2022.08.31.505873doi: bioRxiv preprint