Biochemistry Procedure-oriented Ontology: A Case Study

Mohammed Alliheedi

1,2

, Yetian Wang

and Robert E. Mercer

2,3

Department of Computer Science, Al Baha University, Prince Mohammad Bin Saud, Al Bahah 65527, Saudi Arabia

David Cheriton School of Computer Science, University of Waterloo, 200 University Ave., Waterloo, Ontario, Canada

Department of Computer Science, The University of Western Ontario, 1151 Richmond St., London, Ontario, Canada

Keywords:

Experimental Procedure, Procedural Steps, Sequence of Steps, Biomedical Ontology, Formal Ontology,

Knowledge Representation.

Abstract:

Ontologies must provide the entities, concepts, and relations required by the domain being represented. The

domain of interest in this paper is the biochemistry experimental procedure. The ontology language being

used is OWL-DL. OWL-DL was adopted due to its well-balanced ﬂexibility among expressiveness (e.g., class

description, cardinality restriction, etc.), completeness, and decidability. These procedures are composed of

procedure steps which can be represented as sequences. Sequences are composed of totally ordered, partially

ordered, and alternative subsequences. Subsequences can be represented with two relations, directlyFollows

and directlyPrecedes that are used to represent sequences. Alternative subsequences can be generated by

composing a oneOf function in OWL-DL, referred to it as optionalStepOf in this work, which is a simple

generalization of exclusiveOR. Alkaline Agarose Gel Electrophoresis, a biochemistry procedure, is described

and examples of these subsequences are provided.

1 INTRODUCTION

Ontologies provide entities (known as individuals in

some ontological languages) and concepts, and rela-

tions among those entities and concepts. Ontologies

must provide relations that are required by the domain

being represented. Our interest is centered on the bio-

chemistry domain, the experimental methodology as-

pect, in particular.

A number of biologically oriented ontologies have

been created, one of the best known is the Gene On-

tology (GO) (Ashburner et al., 2000). Others have

been developed for a variety of other purposes. They

are discussed in detail in the next section. Most of

these ontologies describe a set of concepts and cate-

gories in the biological domain that shows their prop-

erties and the relations between them.

The type of domain that we are attempting to

represent consists of procedures, experimental pro-

cedures, in particular. Procedures are sequences of

procedure steps (simply, steps, henceforth). Some

ontologies provide descriptions of steps (Soldatova

et al., 2013). To the best of our knowledge no current

biologically oriented ontology represents sequences

of steps. An important aspect of the steps in a pro-

cedure is that they immediately follow one another.

‘Directly follows’ (and ‘directly precedes’) is an in-

transitive relation (i.e., if B directly follows A, and if

C directly follows B, then C does not directly follow

A). Transitive relations are the norm in the current bi-

ologically oriented ontologies (e.g., the omnipresent

‘subclass’ relation; ‘proper part of’, ‘precedes’ and ‘is

causally related to’ ((Dumontier et al., 2014), Figures

6 and 9)).

Procedures can contain sequences of steps that are

totally ordered (i.e., the steps must be done one after

the other in the sequence speciﬁed), steps that can be

partially ordered (i.e., subsequences of steps that can

be done in any order), and alternative subsequences

of steps (i.e., only one of the alternatives is done). In

addition to the intransitive relations ‘directly follows’

and ‘directly precedes’ our contribution also includes

these three types of sequence orderings.

Descriptions of experimental procedures exist in

scientiﬁc writing. The scientiﬁc domain of interest to

us is biochemistry. An important type of information

contained in the Method section of biochemistry ar-

ticles are references to standard biochemistry exper-

iment procedures. These protocols, which typically

involve several steps, are described in detail in man-

uals of standard biochemistry experiment procedures

(Boyer, 2012; Sambrook and Russell, 2001). In this

164

Alliheedi, M., Wang, Y. and Mercer, R.

Biochemistry Procedure-oriented Ontology: A Case Study.

DOI: 10.5220/0008167101640173

In Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2019), pages 164-173

ISBN: 978-989-758-382-7

paper, we propose a biochemistry procedure-oriented

ontology that explicitly identiﬁes all of the steps of an

experimental procedure and provides the relations be-

tween the steps of an experimental procedure. A case

study investigates one experimental procedure, Alka-

line Agarose Gel Electrophoresis, that exists in the

manual of standard biochemistry experimental proce-

dures.

2 RELATED WORK

Developing ontologies has become increasingly cru-

cial in the biomedical domain in general (Rosse and

Mejino Jr, 2003). Several ontologies have been de-

veloped in recent years such as the Gene Ontology

(Ashburner et al., 2000), the Ontology for Chem-

ical Entities of Biological Interest (ChEBI) (Degt-

yarenko et al., 2007), the Ontology for Biomedical In-

vestigations (OBI) (Bandrowski et al., 2016), and the

Foundational Model of Anatomy (FMA) (Rosse and

Mejino Jr, 2003). Mainly, the goal of these ontologies

is to provide deﬁnitive controlled terminologies that

describe entities in the biomedical genre.

The main aspect of Gene Ontology (GO) is to

provide information that describes gene products us-

ing precisely deﬁned vocabulary (Ashburner et al.,

2000). GO intially used three model organism

databases including FlyBase (FlyBase Consortium,

2003), Mouse Genome Informatics (Blake et al.,

2000; Ringwald et al., 2000), and the saccharomyces

Genome Database (Ball et al., 2000). Recently, the

number of model organism databases has increased

dramatically (Gene Ontology Consortium, 2011).

The Chemical Entities of Biological Interest on-

tology (ChEBI) is a lexicon of molecular entities

concerned with small molecules (Degtyarenko et al.,

2007). To create ChEBI, data from several resources

(e.g., IntEnz (Fleischmann et al., 2004), KEGG

COMPOUND (Kanehisa et al., 2006), and the Chem-

ical Ontology) were used. ChEBI used various rela-

tions to describe the relationships between ontology

entities. These relations include relations required

by ChEBI (e.g., ‘is conjugate acid of’, and ‘is tau-

tomer of’) as well as relations which are deﬁned by

the Relations Ontology

(e.g., ‘is a’ and ‘is part of’).

The Ontology for Biomedical Investigations (OBI),

http://purl.obolibrary.org/obo/obi, (Bandrowski et al.,

2016), a resource for annotating biomedical inves-

tigations, provides standard tools to represent study

design, protocols and instrumentation used, the data

generated and the types of analysis performed on

http://www.obofoundry.org/ontology/ro.html

the data. Several ontologies (Courtot et al., 2008),

(Brinkman et al., 2010), (Zheng et al., 2013), (Solda-

tova et al., 2013), (Dumontier et al., 2014) are based

on the OBI ontology. These ontologies are closest to

our interest in biochemistry procedures.

A work predating the above list, (Soldatova and

King, 2006), proposes EXPO, an ontology of scien-

tiﬁc experiments, in general. It remains a descriptive

ontology, providing a detailed description of various

aspects of scientiﬁc experiments and how they are re-

lated.

Descriptions of experimental processes are pro-

vided by OBI, and three real-world applications are

discussed in (Brinkman et al., 2010). Some of the

relations in these applications (e.g., inputs, outputs,

etc.) come very close to our purpose here. The beta

cell genomics application ontology (BCGO) (Zheng

et al., 2013) also uses OBI, but it tends to be a more

descriptive ontology than some of the others that use

OBI, but some of the relations in RO, the relation on-

tology (Smith et al., 2005), that are used (e.g., pro-

duces, translate to) do have an ordering sense.

The two ontologies that are most similar to the

work described below are EXACT (Soldatova et al.,

2013) and the Semanticscience Integrated Ontology

(Dumontier et al., 2014). Both are motivated by a

need to describe scientiﬁc protocols and experiments.

Where they differ from what we are proposing is that

they describe sets of actions in scientiﬁc protocols and

experiments, whereas we are proposing to represent

sequences of actions, or steps in a procedure, if you

like. Relations that describe orderings of actions (e.g.,

‘precedes’ (Dumontier et al., 2014)) are not applica-

ble to sequences since these relations are transitive.

The Molecular Methods Database (MolMeth) is a

database which contains scientiﬁc protocol ontologies

that conform to a set of laboratory protocol standards

(Klingstr

om et al., 2013).

Other ontologies describe general concepts that

are useful to a biochemistry procedure-oriented on-

tology include: Ontologies consist of process such as

(Lenat et al., 1985) and (Schlenoff et al., 2000), on-

tology for units of measure (Rijgersberg et al., 2013),

classiﬁcation of scenarios and plans (CLASP) (De-

vanbu and Litman, 1996), and materials ontology

(Ashino, 2010). Foundational theories such as pro-

cess calculus and regular grammar are essential for

the formalization of procedure-oriented ontologies.

Biochemistry Procedure-oriented Ontology: A Case Study

165

3 PROCEDURE-ORIENTED

ONTOLOGY

We propose a framework for procedure-oriented on-

tologies that explicitly identify all steps of an experi-

mental procedure and provide a set of relations to de-

scribe the relationships between the steps of an exper-

imental procedure. The novelty of this approach is to

allow creating a sequence of events (or steps in a pro-

cedure) using the ontological concept of “something

occurs before”. To accomplish this we need to have

an ontological concept of “sequence”. This is very

signiﬁcant concept because one cannot simply call a

sequence of events “a sequence” unless these events

happen step by step in some sort of ordering.

This approach will be used to provide the neces-

sary information about the experimental procedures

for Knowledge Base systems with the required knowl-

edge about experimental processes. There are man-

uals of standard procedures in biochemistry (Boyer,

2012; Sambrook and Russell, 2001) which in turn will

help in building ontologies.

3.1 Classes and Properties

The proposed ontology framework consists of three

core classes: Step, State, and Action.

3.1.1 Step

The Step class (see Figure 1) represents each step

within a procedure. Orderings of each step can be de-

scribed by object properties such as ‘precedes’, ‘fol-

lows’, ‘parallel’, all being transitive. The properties

‘precedes’ and ‘follows’, inverses of each other, indi-

cate the chronological order of the steps. The prop-

erty ‘parallel’ is symmetrical which indicates steps

can happen simultaneously. Intransitive properties

‘directlyPrecedes’ and ‘directlyFollows’ are also used

to describe the ordering of steps. They are subprop-

erties of ‘precedes’ and ‘follows’ respectively. Sim-

ilar to ‘precedes’ and ‘follows’, they are also in-

verses of each other. Therefore, by stating step1.1

‘directlyPrecedes’ step1.2 and step1.2 ‘directlyPre-

cedes’ step1.3, a reasoner will automatically infer that

step1.1 ‘precedes’ step1.2 as well as step1.3. Also,

step1.3 ‘directlyFollows’ step1.2 but only ‘follows’

step1.1, both being inferable by a reasoner. For clean-

liness, we indicate only the ‘precedes’ relation in the

ﬁgures presented in this paper.

The structure of the procedure is outlined by

the properties ‘subStepOf’ and ‘optionalStepOf’ in

which both domain and range of the properties are

Step. ‘subStepOf’ indicates that the step(s) must be

Figure 1: Step class and example instances.

Figure 2: State and Action classes.

completed for the completion of the parent step, e.g.,

the triples (step1.1, subStepOf, step1) and (step1.2,

subStepOf, step1) state that step1.1 and step1.2 must

be completed in order to consider step1 to be com-

pleted. Conversely, ‘optionalStepOf’ indicates that

one of the steps (not both) must be completed in or-

der to complete the parent step, e.g., (step1.1a, op-

tionalStepOf, step1.1) and (step1.1b, optionalStepOf,

step1.1) state that one and only one of step1.1a or

step1.1b needs to be completed to complete step1.1.

Figure 1 illustrates a scenario in which all individ-

uals are Step instances. Also, step1 is parallel to step2

while step1.1 must complete before step1.2. Note,

there are no ordering relations between step1.1.1 and

step1.1.2 since they are optional steps of step1.1.

3.1.2 State and Action

The class Step with corresponding properties outlines

the structure of a procedure. The actual process in

each step is represented as states and their associ-

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166

Figure 3: Demonstration of Entities class.

Figure 4: An example of alternative sub-sequences in steps

for preparing the Agarose solution.

ated actions. Each step involves a transition from

state to state via a single or a series of actions, rep-

resented by the classes State and Action (see Fig-

ure 2). State is connected to Step via the property

‘hasState’ and has three subclasses, InitialState, Mid-

State, and FinalState which are connected via proper-

ties such as ‘precedes’ and ‘follows’. InitialState can

only precede a state while FinalState can only follow

another state. Triples (StateX, precedes, StateY) im-

ply (StateY, follows, StateX), and vice versa, since

‘follows’ is an inverse property of ‘precedes’. Fig-

ures 1 and 2 omit ‘follows’ to keep the ﬁgures clean.

MidState can be connected to another state with both

‘precedes’ and ‘follows’ properties. Note that a step

has at most one instance of InitialState or FinalState

but may have multiple instances of MidState. For ex-

ample, an instance of Step, step1, may involve two

instances of State, i.e., step1 state1 and step1 state2,

represented by the following triples: (step1, has-

State, step1 state1), (step1, hasState, step1 state2),

(step1 state1, precedes, step1 state2).

3.1.3 Biochemistry Domain Knowledge

States are connected to the Action class via

‘beforeState’ and ‘afterState’, representing the states

before and after an action, respectively. The State

class is also connected to the Entities class (see Figure

3) via the property ‘involves’ which can be expanded

to describe instruments, materials, and devices in-

volved in a speciﬁc state. Thus, domain knowledge of

biochemistry can be described by extending the Enti-

ties class. For demonstration purposes, we have only

included selected general concepts related to experi-

mental procedures described in the Case Study. In-

strument includes Container and Device where Con-

tainer ‘contains’ Material which is a class for Chemi-

cal and Non-Chemical materials used in biochemistry

experiment procedures. Compound materials and as-

sembled instruments are represented using the prop-

erty ‘consistsOf’. Instrument and Material can be

connected to the class Measure which is a combina-

tion of numerical values and Unit of Measure, e.g.,

‘10m’ is a measure where the value is 10 with a unit

of measure of ‘meter’ (Rijgersberg et al., 2013). The

Measure class was extended with subclasses to repre-

sent absolute measures (e.g., 10m), range values (e.g.,

5m-10m), and ratio (e.g., 1/2).

3.2 Relations

We ﬁrst need to examine the types of features that an

experimental procedure needs for its deﬁnition.

A procedure is a sequence of steps. These steps

can be totally ordered or partially ordered. Total

ordering needs a means to represent the concept that

one event precedes another event and this relation

needs to be transitive. Because a procedure is a

sequence of steps, there needs to be a means to rep-

resent the relation that one step immediately follows

another step and this relation needs to be intransi-

tive. These relations have been deﬁned for OWL

(McGuinness et al., 2004) and are available from

http://www.ontologydesignpatterns.org/cp/owl/seque

nce.owl. Partial ordering is accomplished simply by

allowing more than one step to follow or to precede

another step.

Finally, we would like to be able to represent a

subsequence of steps and the choice of a subsequence

from one or more possible subsequences. This ‘op-

tionalStepOf’ relation would need to be crafted de-

Biochemistry Procedure-oriented Ontology: A Case Study

167

Figure 5: Instances related to Step3 which involves initiating the electrophoresis.

pending on how many choices are available. If two

choices, this relation is simply equivalent to exclu-

sive or otherwise it is simply a generalization of the

exclusive or. We have developed the concept of “pro-

cedure” based on these underlying relations.

4 CASE STUDY

We have designed a procedure-oriented ontology for

Alkaline Agarose Gel Electrophoresis (Sambrook and

Russell, 2001) using the set of relations described in

Section 3. Our motivation is analyzing the text in the

Method section of biochemistry articles. Since the

Method section in biochemistry articles is describing

experimental procedures, these procedures use some

steps that are not explicitly mentioned in the text be-

cause the article is intended for readers who have prior

knowledge of the ﬁeld. Thus, without knowing this

implicit information, one cannot fully understand all

the steps of experimental procedures. For example, in

order to understand fully the sentence fragment, “the

resulting ca. 900 bp piece was gel puriﬁed and lig-

ated using T4 ligase into pUC19” (Carenbauer et al.,

2002), one needs to access the information involved

in gel puriﬁcation and ligation. Thus, we have moved

to build an ontology that satisﬁes this requirement.

Figure 10 (see Appendix) shows the ﬁrst steps

of Alkaline Agarose Gel Electrophoresis that are in-

volved in preparing both the agarose solution and

the DNA samples. Figure 4 describes step1.1, the

preparation of the agarose solution. Basically, step1.1

“adding the appropriate amount of powdered agarose

to a measured quantity of H2O” has two options ei-

ther: step1.1.1 “an Erlenmeyer ﬂask” ‘exclusiveOR’

step1.1.2 “a glass bottle”. So we have a relation

that conveys the choice of using one container or an-

other. So, there is a choice of two sequences of steps:

If step1.1.1 “an Erlenmeyer ﬂask” is selected then

‘directlyFollows’ step1.1.1.1 “loosely plug the neck

of the Erlenmeyer ﬂask with Kimwipes” which in-

volves both initial and ﬁnal states, action and con-

tainer as seen in Figure 4; else if step1.1.2 “a glass

bottle” is selected then ‘directlyFollows’ step1.1.2.1

“make sure that the cap is loose”

. In future steps of

the ontology, the instance Container1 appropriately

refers to the instances of either Erlenmeyer ﬂask or

the glass bottle and material1 refers to the instances

of kimwipes or glass bottle cap. The two main steps

(step1, and step2) shown in Figure 1 are meant to be

partially ordered, that is, they can be performed in any

order (i.e., step1 then step2 or vice versa). In addition,

each one of these main steps consists of several steps

(mini-steps or sub-steps). Note that we only include

describe step 1.1.1 and step 3 in Figures 4 and 5 be-

cause these steps are representative of all other steps

in the procedure.

As one can see, Figure 4 shows a total ordered

sequence. Another example, shown in Figure 5, de-

scribes the instances of step3, step3.1 and step3.2

that are concerned with initiating the electrophore-

sis. Step3.1 is straightforward.

Since step3.2 in-

volves a condition to ensure the gel reaches a certain

length, this step requires several MidStates in addi-

tion to both the initial and ﬁnial states as is shown

in Table 1. All entities for step3.2 are described in

Table 1. Note that Step3.2 consists of a number of

MidStates which represents waiting until the desired

Due to the limited space of the paper, the option

step1.1.2 and its substeps are not included in Figure 4.

Due to the limited space of the paper, step3.1 and its

substeps are not described in Table 1.

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Table 1: Description of the entities involved in Step3.2.

Subject Property Object Description

step3.2 state initial rdf:type InitialState

involves electrophoresis

involves electrophoresis measure

precedes step3.2 state m1

step3.2 action initial m1 rdf:type TurnOn TurnOn is a subclass of Action

beforeState step3.2 state initial

afterState step3.2 state m1

step3.2 state m1 rdf:type MidState

involves electrophoresis

involves electrophoresis measure

involves bg migrate measure

measure for the migration of

bromocresol green

involves bromocresol green

involves gel

precedes step3.2 state m2

step3.2 action m1 m2 rdf:type DoNothing DoNothing is a subclass of Action

beforeState step3.2 state m1

afterState step3.2 state m2

step3.2 state m2 rdf:type MidState

involves bg migrate measure



a measure for the migration of

bromocresol green

involves bromocresol green

involves gel

involves gel length portion







a measure of current length of gel

that the bromocresol green has

migrated to

precedes step3.2 state m3

step3.2 action m2 m3 rdf:type TurnOff

beforeState step3.2 state m2

afterState step3.2 state m3

step3.2 state m3 rdf:type MidState

involves electrophoresis

involves electrophoresis measure

involves gel length portion







a measure of current length of gel

that the bromocresol green has

migrated to, less than 2/3

precedes step3.2 state m4

precedes step3.2 state ﬁnal

step3.2 action m3 m4 rdf:type Action Put glass plate on gel

beforeState step3.2 state m3

afterState step3.2 state m4

step3.2 state m4 rdf:type MidState

involves gel

involves gel length portion

involves glass plate

step3.2 action m4 m1 rdf:type TurnOn

beforeState step3.2 state m4

afterState step3.2 state m1

step3.2 action m3 ﬁnal rdf:type Action Put glass plate on gel

beforeState step3.2 state m3

afterState step3.2 state ﬁnal

step3.2 state ﬁnal rdf:type FinalState

involves electrophoresis

involves electrophoresis measure

involves gel length portion2











a measure of current length of gel

that the bromocresol green has

migrated to, equal to or more than

2/3

involves bromocresol green

involves gel

Biochemistry Procedure-oriented Ontology: A Case Study

169

amount of migration has been reached (i.e., 2/3 of gel

length). The instance step3.2 state initial and

step3.2 state final are instances of InitialState

and FinalState, respectively. The instances of Mid-

States are step3.2 state m1 to step3.2 state m4,

each representing a middle state described below:

• step3.2 state m1: Electrophoresis power is on

• step3.2 state m2: The state where bromocresol

green is migrating into gel

• step3.2 state m3: Bromocresol green has mi-

grated into gel approximately 0.5-1 cm, the power

of the electrophoresis has been turned off.

• step3.2 state m4: A glass plate has been

placed on top of the gel, bromocresol green has

migrated less than 2/3 of the gel length.

The process is a loop since step3.2 state m4

precedes step3.2 state m1. step3.2 state m4

differs with step3.2 state final in that the

bromocresol green has migrated to the targeted

amount in the latter state. step3.2 state m3

precedes both step3.2 state m4 and

step3.2 state final. An instance of Measure

could be used to track the amount that bromocresol

green has migrated.

4.1 Ontology Queries using SPARQL

We have used SPARQL to extract some domain

knowledge about the experimental procedure of Al-

kaline Agarose Gel Electrophoresis from our frame-

work. Figures 6, 8, 9, and 7 (see Appendix) show

the true power of knowledge representation by auto-

matically extracting the essential information that a

biochemist would use to perform experimental pro-

cedures in a lab. These ﬁgures show in a few ex-

amples how much information can be mined from

such a framework with only one experimental proce-

dure. What if all standard experimental procedures

in biochemistry (Boyer, 2012; Sambrook and Rus-

sell, 2001), for example, are modeled and built, one

simply cannot imagine how much time and effort will

be saved, knowing all essential information is just a

few clicks away. Figure 8 shows all of the instru-

ments involved in any state for all steps of the Alka-

line Agarose Gel Electrophoresis procedure whereas

Figure 7 shows a query that returned all materials in-

volved in the procedure. Figure 9 shows a query that

returned the states of step3 and its substeps which

are concerned with measuring the gel length and re-

turned their target values. The ontology was veri-

ﬁed to be consistent using HermiT 1.3.8.3 reasoner

(Shearer et al., 2008).

5 CONCLUSIONS

We have proposed a framework that describes the re-

lations and steps of experimental procedures. This

framework will enrich the knowledge based systems

with necessary information about experimental proce-

dures that a scientist would automatically access such

as instruments (e.g., laboratory centrifuge) and mate-

rials (e.g., buffers). Most importantly, this approach

is an important step toward our ultimate goal to ana-

lyze biomedical articles. This work will be publicly

available for the research community to enhance and

expand upon. Such a work could be beneﬁcial for

various genres that have similar procedure-oriented

characteristics. We also aim to expand our work by

incorporating existing ontologies that are essential to

this domain such as the ontology for units of measure

(Rijgersberg et al., 2013) and the materials ontology

(Ashino, 2010). Certain theoretical ontological mod-

elling of states and empirical observations in science

can be fruitfully incorporated into our ontology in the

future (Masolo et al., 2018).

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Appendix

SPARQL Queries

Query1. Return all devices involved in a state of all

steps (1.1, 1.2, 3)

SELECT ? s t e p ? s t a t e ? i te m

WHERE { ? s t e p r d f : t y p e : S t e p .

? s t e p : h a s S t a t e ? s t a t e .

? s t a t e : i n v o l v e s ? it em .

? i te m r d f : t y p e : D ev ic e }

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171

Query2. Return all materials involved in all steps

SELECT ? s t e p ? s t a t e ? i te m

WHERE { ? s t e p r d f : t y p e : S t e p .

? s t e p : h a s S t a t e ? s t a t e .

? s t a t e : i n v o l v e s ? it em .

? i te m r d f : t y p e / r d f s : s u b C la ss Of :

M a t e r i a l }

Query3. Return all instruments involved in all steps

SELECT ? s t e p ? s t a t e ? i te m

WHERE { ? s t e p r d f : t y p e : S t e p .

? s t e p : h a s S t a t e ? s t a t e .

? s t a t e : i n v o l v e s ? it em .

? i te m r d f : t y p e / r d f s : s u b C la ss Of :

I n s t r u m e n t }

Query4. Which states of step 3 and its substeps mea-

sure the gel length, and what is the target value?

SELECT ? s t e p ? s t a t e ? x

WHERE {

: s t e p 3 ˆ : s ub S t e p ? s t e p .

? s t e p : h a s S t a t e ? s t a t e .

? s t a t e : i n v o l v e s : g e l .

: g e l : h a sM ea su re / : hasNumValue ? x }

Figure 6: Result of Query1: extract all devices involved in

all steps of the Alkaline Agarose Gel Electrophoresis pro-

cedure.

Figure 7: Result of Query2: return all materials involved in

all steps of the Alkaline Agarose Gel Electrophoresis pro-

cedure.

Figure 8: Result of Query3: extract all instruments involved

in all steps of the Alkaline Agarose Gel Electrophoresis pro-

cedure.

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Figure 9: Result of Query4: return which states of step3 and its substeps measure the gel length, and what is the target value.

Alkaline Agarose Gel Electrophoresis

1. Prepare the agarose solution

1.1 Adding the appropriate amount of powdered agarose to a measured

quantity of H2O in either:

1.1.1 An Erlenmeyer flask (Container 1)

1.1.1.1 Loosely plug the neck of the Erlenmeyer

flask with Kimwipes

1.1.2 OR a glass bottle (Container 1)

1.1.1.2 Make sure that the cap is loose

1.2 Heat the slurry (Item1) in (Conatiner1) for the minimum time

required to allow all of the grains of agarose to dissolve using

either:

1.2.1 A boiling-water bath

1.1.1.3 Check that the volume of the solution (Item

1) has not been decreased by evaporation

during boiling in (Container 1):

1.1.1.3.1 if yes: replenish with

H2O in (Container 1)

1.1.1.3.2 If no: do not add H2O in

(Container 1)

1.2.2 OR a microwave oven

1.2.2.1 Check that the volume of the solution (Item

1) has not been decreased by evaporation

during boiling in (Container 1):

1.2.2.1.1 if yes: replenish with

H2O in (Container 1)

1.2.2.1.2 If no: do not add H2O in

(Container 1)

1.3 Cool the clear solution (Item 1) to 55 C.

1.3.1 Add 0.1 volume of 10x alkaline agarose gel

electrophoresis buffer in (Container 1)

1.3.2 And immediately pour the gel (Item 1) into mold

(Container 2)

1.4 After the gel (Item 1) is completely set

1.4.1 Mount it (Item 1) in the electrophoresis tank (Container

1.4.2 Add freshly made 1x alkaline electrophoresis buffer until

the gel (Item 1) is just covered.

2. Prepare DNA samples

2.1 Collect the DNA samples (Item 2) by standard precipitation with

ethanol

2.2 Dissolve the damp precipitates of DNA (Item 2) in 10-20 μl of 1x

gel buffer. (Item 3)

2.3 Add 0.2 volume of 6x alkaline gel-loading buffer.

2.3.1 It is important to chelate all Mg2+ with EDTA before

adjusting the electrophoresis samples to alkaline

conditions.

3. Initiate the electrophoresis

3.1 Load the DNA samples dissolved in 6x alkaline gel-loading buffer

into the wells of the gel (container 3)

3.2 Start the electrophoresis at <3.5 V/cm when the bromocresol green

has migrated into the gel approx. 0.5-1 cm; Turn off the power supply,

and place a glass plate on top of the gel in (Container 3) and then

continue electrophoresis until the bromocresol green has migrated

approximately two thirds of the length of the gel in (container 3).

4. Finalize the experiment

4.1 Process the gel according to one of the procedures either Southern

hybridization by:

4.1.1 Transfer the DNA either:

4.1.1.1 Directly (without soaking the gel) from the

alkaline agarose gel to a charged nylon

membrane. Please see Southern Blotting:

Capillary Transfer of DNA to Membranes

4.1.1.2 OR after soaking the gel in neutralizing

solution for 45 minutes at room

temperature to either:

4.1.1.2.1 An uncharged nitrocellulose as

described in Southern Blotting:

Capillary Transfer of DNA to

Membranes

4.1.1.2.2 OR nylon membrane as

described in Southern Blotting:

Capillary Transfer of DNA to

Membranes

4.1.2 Detect the target sequences in the immobilized DNA by

hybridization to an appropriate labeled probe. Please see Southern

Hybridization of Radiolabeled Probes to Nucleic Acids Immobilized

on Membranes

4.2 OR Staining

4.2.1 Soak the gel in neutralizing solution for 45 minutes at

room temperature.

4.2.1.1 Stain the neutralized gel with 0.5 μg/ml

ethidium bromide in 1x TAE or with SYBR

Gold.

4.2.1.1.1 A band of interest can be

sliced from the gel and

subsequently eluted by one of

the procedures described

Recovery of DNA from

Agarose Gels

Figure 10: The ﬁrst steps of Alkaline Agarose Gel Electrophoresis.

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