# RNA-KG: An ontology-based knowledge graph for representing interactions
involving RNA molecules

Emanuele Cavalleri  AnacletoLab, Computer Science Department, University of
Milan, 20122, Italy  Alberto Cabri  AnacletoLab, Computer Science Department,
University of Milan, 20122, Italy  Mauricio Soto-Gomez  AnacletoLab, Computer
Science Department, University of Milan, 20122, Italy  Sara Bonfitto
AnacletoLab, Computer Science Department, University of Milan, 20122, Italy
Paolo Perlasca  AnacletoLab, Computer Science Department, University of Milan,
20122, Italy  Jessica Gliozzo  AnacletoLab, Computer Science Department,
University of Milan, 20122, Italy  Tiffany J. Callahan  Department of
Biomedical Informatics, Columbia University Irving Medical Center, New York,
NY 10032, USA  Justin Reese  Environmental Genomics and Systems Biology
Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
Peter N Robinson  Berlin Institute of Health - Charité, Universitätsmedizin,
Berlin, 13353, Germany  Elena Casiraghi  AnacletoLab, Computer Science
Department, University of Milan, 20122, Italy  Giorgio Valentini  AnacletoLab,
Computer Science Department, University of Milan, 20122, Italy  Marco Mesiti
AnacletoLab, Computer Science Department, University of Milan, 20122, Italy
corresponding author(s): Marco Mesiti<EMAIL_ADDRESS>
###### Abstract

The "RNA world" represents a novel frontier for the study of fundamental
biological processes and human diseases and is paving the way for the
development of new drugs tailored to the patient’s biomolecular
characteristics. Although scientific data about coding and non-coding RNA
molecules are continuously produced and available from public repositories,
they are scattered across different databases and a centralized, uniform, and
semantically consistent representation of the "RNA world" is still lacking. We
propose RNA-KG, a knowledge graph encompassing biological knowledge about RNAs
gathered from more than 50 public databases, integrating functional
relationships with genes, proteins, and chemicals and ontologically grounded
biomedical concepts. To develop RNA-KG, we first identified, pre-processed,
and characterized each data source; next, we built a meta-graph that provides
an ontological description of the KG by representing all the bio-molecular
entities and medical concepts of interest in this domain, as well as the types
of interactions connecting them. Finally, we leveraged an instance-based
semantically abstracted knowledge model to specify the ontological alignment
according to which RNA-KG was generated. RNA-KG can be downloaded in different
formats and also queried by a SPARQL endpoint. A thorough topological analysis
of the resulting heterogeneous graph provides further insights into the
characteristics of the "RNA world". RNA-KG can be both directly explored and
visualized, and/or analyzed by applying computational methods to infer bio-
medical knowledge from its heterogeneous nodes and edges. The resource can be
easily updated with new experimental data, and specific views of the overall
KG can be extracted according to the bio-medical problem to be studied.

## Background & Summary

The involvement of RNAs in various physiological processes has been
ascertained by several studies [1, 2, 3] that have revealed the pervasive
transcription of an unexpected variety of RNA molecules [4] that can lead to a
significant breakthrough in the treatment of cancer, genetic, and
neurodegenerative disorders, cardiovascular and infectious diseases [5]. The
study of RNA is also one of the most promising avenue of research in
therapeutics, as evidenced by the recent success of mRNA-based vaccines for
the COVID-19 pandemic [6], for the treatment of melanoma [7], for the
development of new drugs that can target both proteins and mRNA, as well as
other non-coding RNA, and for encoding missing or defective proteins,
regulating the transcriptome, and mediating DNA or RNA editing [8]. Thus, RNA
technology significantly broadens the set of druggable targets, and is also
less expensive than other technologies (e.g., drug synthesis based on
recombinant proteins), due to the relatively simple structure of RNA molecules
that facilitate their biochemical synthesis and chemical modifications [9].
Non coding RNAs (ncRNAs) comprise a large range of RNA species, and a large
set of scientific data representing different kinds of interactions among them
and with other bio-entities (e.g., genes, proteins, chemicals, diseases, and
phenotypes) are made publicly available by several genomics laboratories.

The possibility of integrating the interactions that they made available would
be of great relevance for knowledge discovery and also for the development of
new RNA-based drugs. However, these sources adopt different data models,
formats, and conventions for the representation of the bio-entities, and
different semantics can be assigned to the proposed interactions. The
extraction and integration of information from even two data sources for
conducting knowledge discovery activity would require a lot of effort from
researchers. To address these issues, KGs [10] have emerged as a compelling
abstraction for organizing interrelated knowledge in different domains and a
way for integrating heterogeneous information extracted from multiple data
sources with the aim of highlighting complex interdependencies and uncovering
hidden relationships. KGs can be represented both with property graphs (e.g.,
Neo4j [11]) or according to the Resource Description Framework (RDF [12]) with
different advantages and disadvantages [13]. When a KG is generated according
to an ontology, it contains a schema part (denoted TBOX or terminologies) and
a data part (denoted ABOX, facts, or assertions) on top of which different
kinds of reasoning activities can be conducted using expressive languages
(like OWL [14], DL [15], or SPARQL [16]). KGs have started to play a central
role also in the life sciences [17] for the representation of bio-entities and
their interactions and for the application of AI approaches for discovering
new knowledge and eventually for explaining it. Different ontologies have been
proposed for systematizing the corpus of terms used to describe the function
and localization of bio-entities and for offering a formal framework to
represent biological knowledge. Specific biological KGs (e.g., PrimeKG [18],
Human Disease benchmark KG [19], ReproTox-KG [20], Monarch Knowledge Graph
[21], and Knowledge Base of Biomedicine [22]) have been recently constructed
for conducting different kinds of analysis and supporting the research
activities.

In this paper we describe RNA-KG, the first ontology-based knowledge graph for
representing coding and non-coding RNA molecules and their interactions with
other biomolecular data as well as with pathways, abnormal phenotypes and
diseases to support the study and the discovery of the biological role of the
“RNA-world”. RNA-KG contains RDF triples extracted from more than 50 public
data sources and also integrates related bio-medical concepts. RNA-KG can be
exploited for the study of RNA molecules and the development of innovative
graph algorithms to support knowledge discovery in data science. A big effort
has been dedicated to the characterization of the data sources and to the
identification of the bio-medical ontological concepts that better represent
the information provided by the considered data sources and the interactions
involving RNA molecules. This work culminated in the construction of a meta-
graph that represents all the possible interactions that can be devised from
the considered data sources and that can be represented by means of the
Relation Ontology (RO [23]), which ensures common semantics for the different
relationships that can be extracted from the sources. Relying on the generated
meta-graph and exploiting the Phenotype Knowledge Translator (PheKnowLator
[19]) tool, we extracted 578,384 nodes and 8,768,582 edges of good quality
according to the metrics provided in each data source. We also experimentally
evaluated the main statistical and topological characteristics of the
generated KG. RNA-KG can be exported according to different knowledge models
and can be accessed through a SPARQL endpoint.

### Related Work

For a better understanding of the approach that we have followed in the
construction of RNA-KG, we first outline the methods developed for integrating
graph-based biomedical heterogeneous data sources and then summarize the main
characteristics of the different types of RNA molecules. Finally, we outline
the bio-ontologies that can be exploited for the characterization of RNA
molecules and the bio-entities with which they are related.

#### Approaches for the construction of bio-medical knowledge graphs

The data integration issue is a well-known problem in data management and many
approaches have been devised to deal with relational data [24]. However, the
explosion of data formats (like CSV, JSON, XML) and the variability in the
representation of the same types of information [25, 26] has pushed the need
to exploit ontologies as global common models both for accessing (OBDA –
Ontology-Based Data Access) and integrating (OBDI – Ontology-Based Data
Integration) data sources [27].

In OBDA, queries are expressed in terms of an ontology, and the mappings
between the ontology and the data sources’ schema are described in the form of
declarative rules. Two approaches are usually proposed to enable access and
integration of different data sources: materialization, where data are
converted from the local data format according to the ontology concepts and
relationships; virtualization, where the transformation is executed on the fly
during the evaluation of queries by exploiting the mapping rules and the
ontology. In this case, only the data from the original sources involved in
the query are accessed. Materialization can provide fast and accurate access
to data because already organized in a centralized repository. However, data
freshness can be compromised when data sources frequently change. On the other
hand, virtualization allows access to fresh data but requires the application
of transformations during query evaluation and can cause delay, and
inconsistency when the structures of the local sources change. Several
approaches are available for the specification of mapping rules like R2RML
[28] (a W3C standard for relational to RDF mapping), and RML [29] that extends
the standard for dealing with other formats. Moreover, SPARQL-Generate [30],
YARRRML [31], and ShExML [32] were also proposed for dealing with data
heterogeneity.

In the biological context, many efforts are nowadays devoted to the
construction of KGs by integrating different public sources that exploit the
materialization and virtualization approaches previously described. For
instance, Zhang and colleagues [33] applied a Connecting Ontology ($CO$) to
integrate all external ontologies that describe the data sources involved. By
exploiting algorithms for fusing and integrating annotations, an enriched KG
is obtained that spans multiple data sources and is annotated by the
integrated biological ontology obtained by gluing together the Gene [34],
Trait [35], Disease [36], and Plant [37] ontologies. The Precision Medicine KG
(PrimeKG) [18] was developed to represent holistic and multimodal views of
diseases. PrimeKG integrates more than 20 high-quality resources with more
than 4 million relations that capture information like disease-associated
perturbations in the proteome, biological processes, and molecular pathways.
The considered data were collected and annotated using diverse ontologies such
as Disease Gene Network, Mayo Clinical Knowledgebase, Mondo, Bgee, and
DrugBank. ReproTox-KG [20] combines information about genes, drugs, and
preclinical small molecules with knowledge about the association of genes and
drugs with birth defects with the aim of predicting the likelihood that
preclinical compounds induce specific birth abnormalities, and whether these
compounds are likely to cross the placental barrier. The information is
extracted from scientific publications by considering several ontologies
including HPO [38], CDC birth-defect terms [39], Geneshot [40] for connecting
genes with birth-defect terms, DrugCentral [41] for connecting drugs with
birth-defect terms, and LINCS L1000 data [42] for drug–gene associations. Sima
and colleagues [43] proposed a virtualization approach based on an ontology-
based federation of three data sources (Bgee, OMA, and UNIProtKB), i.e.,
starting from the GenEx semantic model for gene expression, the authors
proposed mapping rules to deal with the different formats of the three sources
and faced the issue of joint queries across the sources by leveraging SPARQL
endpoints. A preliminary version of the RNA-KG meta-graph [44] was recently
presented and here deeply enhanced with the description of the methodology and
the generation of RNA-KG. A fully automated Python 3 library named
PheKnowLator was recently proposed for the construction of semantically rich,
large-scale biomedical KGs that are Semantic Web compliant and amenable to
automatic OWL reasoning, and conform to contemporary property graph standards.
The library offers tools to download data, transform and/or pre-processing
resources into edge lists, construct knowledge graphs, and generate a wide-
range of outputs [19].

All these papers point out the difficulties that arise when trying to
integrate different data sources that exploit different data models, formats,
and ontologies. Specifically, data redundancies, data duplicates, and lack of
common identifier mechanisms must be properly addressed. In the case of RNA
data integration, we also have to consider the lack of specific ontologies for
the description of all possible non-coding RNA sequences, and the presence of
ontologies that are not well-recognized by the community because still in
their infancy. All these aspects must be properly addressed in the generation
of RNA-KG.

#### RNA molecules

The wide variety of RNA molecules, which can be classified as sketched in
Figure 1, can be translated into proteins, can regulate gene expression, have
enzymatic activity, and can modify or regulate other RNAs.

Coding RNA. In Eukaryotes, messenger RNA (mRNA) primary transcripts follow a
cascade of biological processes to transform them into mature functional mRNA
molecules that are read by ribosomes, translated into amino acid chains and
finally assembled in proteins through peptide bonds [45].

Non-coding RNA. Transcripts that are not translated into proteins are named
non-coding RNAs (ncRNAs). They can be further classified into long non-coding
RNAs (lncRNAs – with more than 200 nucleotides) and small non-coding RNAs
(sncRNAs – with less than 200 nucleotides) [46]. lncRNAs are the majority of
transcription products and play a pivotal role in disease development and
progression [47]. Circular RNAs (circRNAs) are lncRNAs produced from
alternative splicing events, and may play a role as splicing event regulators.
circRNAs have been involved in many human diseases, including cancer and
neurodegenerative disorders such as Alzheimer’s and Parkinson’s disease, due
to their aberrant expression in different pathological conditions [48].

Small non-coding RNA (sncRNA). sncRNAs are involved in several cellular
biological processes, including: translation processes; RNA interference
(RNAi) pathways; splicing and self-cleavage processes; catalysis of
biochemical reactions, and targeted gene editing.

sncRNAs involved in the translation process. Several sncRNAs are involved in
this process, including ribosomal RNAs (rRNAs), transfer RNAs (tRNAs), small
nuclear RNAs (snRNAs), small nucleolar RNAs (snoRNAs), and Small Cajal body-
specific RNAs (scaRNAs, snoRNAs specific to the Cajal body). While rRNA
constitutes the core structural and enzymatic framework of the ribosome, tRNAs
are characterized by a structure consisting of an acceptor stem that links to
a particular amino acid and of a specific anticodon sequence of 3 bases
complementary to the corresponding mRNA codon, thus assuring the translation
from the mRNA codon triplets to the corresponding sequence of amino acids.
snRNAs and snoRNAs primarily guide chemical modifications of other RNAs,
mainly rRNAs and tRNAs, and control chromatin compaction and accessibility.

sncRNAs associated with RNA interference pathways. RNA interference pathways
play a central role in gene expression and their misregulation is associated
with several diseases [49]. sncRNAs associated with RNAi pathways include:
microRNAs (miRNAs), short interfering RNAs (siRNAs), short hairpin RNAs
(shRNAs), antisense oligonucleotides (ASOs), piwi-interacting RNAs (piRNAs),
tRNA-derived fragments (tRFs), and tRNA-derived small RNAs (tsRNAs). Mature
miRNAs, siRNAs, shRNAs, and ASOs regulate the mRNA expression by blocking
translation or promoting degradation of the target mRNA (complementary base
pairings). Unlike siRNAs, each miRNA can simultaneously regulate the
expression and the activity of hundreds of protein-coding genes and
Transcription Factors (TFs). miRNAs from various exogenous sources, which are
present in human circulation, are named xeno-miRNAs [50]. By contrast, ASOs
are more effective to knock down nuclear targets, whereas siRNAs are superior
at suppressing mRNA cytoplasmic targets by recruiting, via Watson-Crick base
paring, the RNA-induced Silencing Complex (RISC) that catalyzes the mRNA
cleavage. Similarly, throughout RNAi, piRNAs and tRFs promote genome
integrity, avoiding potential threats to cellular homeostasis, by silencing
transposons, retrotransposons and repeat sequences [51].

Aptamers, riboswitches, ribozymes and guide-RNA. The tertiary structure of RNA
sequences can also be investigated to identify interferences. Aptamers are
short single-stranded nucleic acids that can bind to a variety of targets
(e.g., proteins, peptides, carbohydrates, DNA, and RNA) thanks to their 3D
conformation. Riboswitches are small non-coding RNAs involved in alternative
splicing and self-cleavage processes that cause gene expression control and
mRNA degradation, critical for survival of the cell [52]. Some RNAs, such as
ribozymes, even possess enzymatic activity therefore catalyzing biochemical
reactions (e.g., mRNA and protein cleavage). Synthetic ribozymes can and have
already been designed to target viral RNA. Synthetic guide RNAs (gRNAs) are
usually involved in the application of CRISPR-Cas9 technique, used for gene
editing and gene therapy [53].

#### Biomedical ontologies for the semantic characterization of RNA-KG

Several standard biomedical ontologies can be used to set up common semantics
in the considered data sources. Table 1 shows those considered during RNA-KG
construction (their specifications are made available in the web portals
ebi.ac.uk/ols4 and bioportal.bioontology.org). We selected these ontologies
because their terms and hierarchical structures are commonly accepted by the
scientific community to unequivocally describe biological classes and entities
such as diseases, phenotypes, chemicals, biological processes, proteins, and
relations between them. In the case of RNA-KG, we have also taken into account
the lack of specific ontologies for the description of all possible RNA
sequences (especially non-coding ones), and the presence of bio-ontologies
that are yet not well-recognized by the community.

### Results

RNA-KG is open-source and available through a SPARQL endpoint (hosted at
http://fievel.anacleto.di.unimi.it:9999). The code for generating and
maintaining RNA-KG is available on GitHub (https://github.com/AnacletoLAB/RNA-
KG). RNA-KG comprises a comprehensive collection of relationships involving
RNA molecules from various recognized RNA sources. The considered data sources
have been characterized from different perspectives and compared (see Tables
2-3). These tables also provide the different kinds of relationships that can
be established and their respective occurrences are summarized in Figure 6.
Data sources might contain similar relationships and their possible
overlapping has been visually highlighted in Figure 7. Relationships have been
represented according to the Relation Ontology (RO) and the resulting meta-
graph (Figure 8) has been used for the generation of an ontological
description of RNA-KG. Different analyses have been conducted to characterize
the types of nodes and interactions that are represented in RNA-KG (Figure
10), their distribution (Figures 11-12), and the topological structure
analysis (Table 5). Details on the obtained results are discussed in the Data
Records section.

The methodology we employed to construct RNA-KG enabled us to generate a high-
quality knowledge graph that includes reliable interactions, validated through
experimental methods and/or strongly endorsed by data providers, and whose
meaning was meticulously verified to ensure a consistent representation of
domain knowledge.

In the supplementary section, we have also included further figures and tables
that better describe RNA-KG. Specifically, Supplementary Table 1 delineates
the bio-ontologies that have been exploited for representing concepts in each
RNA source. Supplementary Tables 2-5 report the descriptive statistics of the
triples that have been extracted from the different data sources. Moreover,
Supplementary Figures 1-2 show bio-entities present in RNA sources and their
mapping to RO terms we used to represent relationships within the sources.
Supplementary Table 7 highlights the primary node types and their
corresponding identifiers with an instance sample. Finally, Supplementary
Listings 1\- 3 provide a few examples of SPARQL queries, illustrating the kind
of information that can be extracted from RNA-KG.

### Discussion

The structure of the obtained KG has been analyzed by computing various graph
metrics that provide a macroscopic description of the network topology derived
from the entity relations (see Table 5). By means of a t-SNE representation of
an embedding of the nodes/edges in RNA-KG (Figure 13) we have highlighted that
the embedding of the node type is able to effectively identify the
similarities among the nodes of the same type, thus capturing their function
in the network. Moreover, the edge embedding is able to capture, in most
cases, the similarity between edges. In our analysis we have also identified
the nodes having the highest out-degree and observed that the (undirected)
degree distribution follows a heavy-tailed distribution (see Figure 14).
Moreover, we computed bounds for the treewidth and the closeness centrality
distribution, which suggest a sparse yet well-connected structure. All of
these characteristics are commonly found in networks with a scale-free
structure, which is often seen in networks representing complex interactions.
Further investigation will be conducted in this direction to confirm such a
structure. This property would yield benefits in the network analysis both in
terms of structure understanding and algorithmic design. Finally, we have
investigated the presence of isomorphic node groups, that is nodes that are
topologically indistinguishable because they present exactly the same
neighbors. These nodes deserve further investigation to check whether the
involved molecules are duplicates and should thus be collapsed to improve the
information quality of RNA-KG. A detailed discussion of the results is
reported in Section Technical Validation.

RNA-KG can generate heterogeneous biomedical graphs in different formats that
can be processed by graph-based computational tools to infer biomedical
knowledge, provide insights into biomolecular mechanisms and biological
processes underlying diseases, support the discovery of new drugs, especially
those based on RNA, and evaluate biomedical hypotheses in silico. In
particular, RNA-KG is specifically designed to deal with computational tasks
involving RNAs, by e.g. exploiting the information about ncRNA interactions
for gene and protein expression regulation, collected from tens of publicly
available databases. By leveraging the biomedical concepts represented in the
biomedical ontologies embedded in the KG, RNA-KG can be also analyzed to
predict associations and causal relationships of the "RNA world" with diseases
and abnormal phenotypes. We also observe that the rich information embedded in
the RNA-KG can be leveraged for classical biomedical prediction tasks,
including e.g. gene-disease prioritization, drug-target prediction, and drug
repurposing.

Most of these biomedical tasks can be modeled as link or node-label prediction
problems in heterogeneous graphs. Even if, in principle we could apply methods
developed for homogeneous graphs [54], to leverage the rich information
scattered across the different types of modes and edges of the RNA-KG, we
suggest applying methods specifically designed for heterogeneous graphs [55].
To this end, several AI graph-based methods have been recently proposed to
deal with heterogeneous graphs, also in the context of biomedical Knowledge
Graphs [56]. In particular we foresee that Graph Representation Learning
methods, by leveraging the topology of the complex bio-medical heterogeneous
graphs to embed them into compact vectorial spaces, could be the most
promising choice to properly analyze the complex heterogeneous structure of
RNA-KG [57].

We are currently working on enhancing the proposed KG in different directions.
First, we are identifying key properties associated with RNA molecules and
their interactions to be included in RNA-KG. This is a tough problem because
we need to integrate and make uniform the information about the same bio-
entity obtained from different sources. However, we have already identified
the main properties characterizing the same molecule. Moreover, we have
established systematic rules for aligning the representation and fusing
records representing the same bio-entities. In this line of research, we are
also considering the possibility of proposing an RNA Ontology for describing
RNAs with a particular emphasis on non-coding RNA molecules. We aim to craft
this ontology on the data that we have identified in the sources considered in
this study. This could be a relevant contribution toward the definition of a
standard representation of RNA molecules and the interactions that might be
determined among them. Another research direction is the use of Transformers
[58] in combination with RNA-KG for the extraction of new triples from textual
documents. In this direction, we are currently leveraging the SPIRES engine
[59] which enables the user to guide OntoGPT on the kinds of interactions that
we are interested in, by specifying our meta-graph in terms of LinkML [60].
The identified triples can be validated by using RNA-KG as a gold standard in
this domain. This approach can limit the hallucination issue that is typical
of large language models, ensuring the reliability and accuracy of the
extracted information. We would like also to analyze RNA-KG with cutting-edge
AI graph representation learning algorithms [61] to support the discovery of
novel RNA drugs. Finally, we are developing graphical facilities for
supporting the user in the data acquisition and maintenance processes and thus
reducing the manual effort required for mapping the data available in the
different data sources into RNA-KG [62] and also for showing the graph at
different granularities [63, 64].

## Methods

The creation of a KG is a complex task that requires facing several phases
that can be organized in a workflow such as the one reported in Figure 2. In
the remainder of the section, we provide a detailed description of the
different issues and adopted solutions for each phase of the creation of our
RNA-based KG.

### RNA sources characterization

In this phase, we have identified and analyzed the characteristics of relevant
data sources from which the information for feeding the KG has been extracted.
This is a well recognized critical initial step in constructing a KG [65].

To this aim, an extensive literature review was carried out to identify
repositories dealing with RNA sequences and annotations developed by well-
reputed organizations, published in top journals, periodically updated, and
containing significant amounts of RNA molecules and relevant relationships
with other types of molecules and bio-entities. Furthermore, RNA sources that
are included in other bigger repositories have been considered, as well as
those that are used as collectors of other repositories. We have also
identified the presence of controlled vocabularies, thesauri, reference
ontologies that formally describe the repository content, and the presence of
well-recognized identification schemes.

Sources provide data in different formats (e.g., CSV, TSV, gaf, hpoa,
reactome, xlsx, JSON, and HTML) or by issuing queries on content management
systems. Once the data were downloaded, Pandas [66] DataFrames were used to
transform the data into a common format (TSV files) and remove syntactic
inconsistencies. The data obtained can be then processed through PheKnowLator
to extract the relationships among bio-entities.

For the characterization of the relationships that can be extracted from the
different data sources, we applied the Relation Ontology. Moreover, the
hierarchical organization of concepts in RO allows the expression of different
kinds of relationships at different granularities (e.g., the general term
interacts with can be substituted with more specific terms such as molecularly
interacts with or genetically interacts with). Moreover, in case of a lack of
specific terms for describing relationships identified in a data source, two
strategies have been devised. The first one is to approximate the
concept/relationship type with a term already present in RO. This implies a
better coherence with RO semantics but misses details that the
concept/relationship can carry. The second strategy is to extend the RO
ontology with new terms specifically tailored for the representation of the
concept/relationship. In this case, non-standard, more precise terms can be
introduced for the representation of the concept/relationship. In the
construction of RNA-KG, we adopted the first strategy for a larger agreement
on the meaning of the used terms and uilized the interacts with relationship
for representing any connection among bio-entities when a more specific one is
missing.

The adoption of different types of molecular identifiers represents another
issue. Indeed, the identification scheme encountered in the considered data
may vary from the source and target of the relation and could be characterized
by different accuracy levels. Four levels have been detected: Well-Reputed
(denoted WR), when the identifiers are widely accepted by the scientific
community (e.g., NCBI Entrez Gene identifiers); Ontology-based (denoted O),
when the identifiers are directly represented with ontological terms; Mapping-
based (denoted M), when the identifiers can be obtained by exploiting look-up
tables; and Proprietary (denoted P), when all the previous techniques cannot
be applied. Once the identification scheme adopted in a source has been
classified, appropriate look-up tables for their mapping into the reference
ontology have been realized by analyzing synonyms in the reference ontology or
by examining the ones provided by the sources themselves to facilitate
interoperability with other sources dealing with the same entities. For
instance, we employed NCBI Gene Entrez identifiers to represent genes in RNA-
KG, but many sources provide the correspondent Gene Symbol. In this case, a
look-up table has been used to map gene identifiers into the chosen
representation (Figure 3).

To guarantee a high level of homogeneity in the KG, a few tuples have been
omitted when the mapping to the reference ontology was not possible. For some
types of RNA molecules (especially ncRNA sequences), the look-up tables cannot
be adopted because of the lack of a reference ontology with which these
molecules can be represented. In these cases, the NCBI Entrez Gene identifiers
of the gene from which the specific RNA is transcribed have been extended with
a suffix that corresponds to the type of non-coding RNA (e.g., in case of
small nucleolar RNA molecules the suffix is ?snoRNA). We remark that the lack
of a common ontological representation among heterogeneous sources can cause
the duplication of molecules. At the current stage of development, we decided
to admit the presence of duplicates for this kind of molecule, but we will
consider de-duplication techniques [67, 68] that rely on the use of similarity
measures on the molecule sequences in future releases.

To guarantee a high-level of reliability of the relationships to be included
in RNA-KG, only meaningful relationships have been considered, that is those
satisfying constraints that take into account p-values or FDR – False
Discovery Rate (e.g., $p_{val}<0.01$), experimental validation of results, or
scores (denoted with $\sigma$) defined as reliable in the considered data
source.

All of these activities led to the identification of the reference ontologies
for each data source and the relationships that can be extracted and
represented according to the RO ontology. Moreover, by studying the
organization and format of the data sources, the extraction patterns and the
look-up tables to apply have been realized. Finally, pruning strategies have
been devised specifically tailored to the characteristics of each data source.

### Ontological description of the KG

In this phase, we identified the classes of bio-entities that need to be
managed and of the kinds of relationships that can exist among them (schema
layer). Moreover, specific instances and the properties that need to be
maintained have been identified (data layer). This design activity plays a
fundamental role in the hierarchy, structure and content filling of the
knowledge graph, and it is the basis for determining the kind of reasoning
that can be supported.

Starting from the knowledge gained from the characterization of RNA sources,
we moved toward the construction of the ontological schema underlying RNA-KG.
A meta-graph was built to include all the kinds of bio-entities and
relationships between them outlined in the previous phase. The meta-graph
provides both direct and inverse relationships that are considered to
guarantee bi-directional navigation of the generated KG.

Once classes of bio-entities and their relationships have been identified, we
determined the properties that should be kept for them. At the current stage,
only fundamental properties of bio-entities have been collected (identifiers,
node types, and source provenance). This choice has the advantage of avoiding
the explosion of the KG size. However, in future implementations, we wish to
enhance the properties that can be stored within RNA-KG.

### Ontological alignment specification

In this phase, we identified the KG representation and the kind of storage
system to adopt. RDF triples have turned out to be suitable because of their
common, flexible, and uniform data model. These properties result in an
ontologically-grounded knowledge graph for conducting different kinds of
analysis and reasoning.

Since a standardized formal definition for the concept of a KG is still
lacking, we considered the one adopted by Callahan and colleagues [19] where a
KG is a pair $<T,A>$, where T is the TBox and A the ABox. The TBox represents
the taxonomy of a particular domain including classes,
properties/relationships, and assertions that are assumed to generally hold
within a domain (e.g., a miRNA is a small regulatory ncRNA located in an
exosome as depicted in Figure 4). The ABox describes attributes and roles of
class instances (i.e., individuals) and assertions about their membership in
classes within the TBox (e.g., hsa-miR-125b-5p is a type of miRNA that may
cause leukemia). Non-ontological entities (i.e., entities from a data source
that are not compliant to a given set of ontologies such as RNA molecules) can
be integrated with ontologies using either a TBox (i.e., class-based) or ABox
(i.e., instance-based) knowledge model. For the class-based approach, each
database entity is represented as subClassOf an existing ontology class, while
for the instance-based approach it is represented as instanceOf an existing
ontology class.

For the construction of the KG we have employed the PheKnowLator ecosystem
[19] because it offers both approaches for the representation of bio-entities
and their relationships, and also because of its simplicity in the
identification of the columns containing the molecules’ identifiers and for
the specification of their relationships in terms of the RO ontology.
PheKnowLator also provides tools to easily generate the ontology that better
describes the content of the KG that, besides the terms and relationships of
the meta-graph, also includes other ontological terms for supporting the
reasoning.

KGs can be easily exported according to different kinds of models offered by
PheKnowLator depending on the analyses to be conducted. Even if RNA-KG is made
available in all the supported knowledge models, we think that the instance-
based, inverse relation, semantically abstracted (OWL-NETS [69] without
harmonization) configuration is the most suitable to be processed by different
kinds of ML algorithms for node and link prediction. This solution ensures
that RNA molecules (which lack semantic characterizations in bio-ontologies)
and other non-ontological data can be specified as subClassOf specific
ontological classes. Moreover, this approach enables the automatic
specification of inverse relations among the involved bio-entities. Lastly,
OWL-NETS reversibly abstracts ontological biomedical knowledge into a network
representation containing only biologically meaningful concepts and relations.
Figure 5 shows a small toy-example subgraph extracted from RNA-KG according to
the proposed set-up. We can notice the presence of inverse relationships
(located in and its inverse location of), and the relation RDF subClassOf
connected to entities that do not have a corresponding term in a reference
ontology (miRNA molecules are specified as subClassOf the SO term miRNA).

By studying the characteristics of the data sources, specific mapping rules
have been devised through PheKnowLator to extract triples compliant with the
adopted ontologies. Mapping rules contain the position of the source and
object in the TSV file, the two human-readable labels for subject and object
(e.g., mRNA and disease), the type of relationship that holds/exists between
them according to RO (e.g., RO_0003302 corresponds to causes or contributes to
condition relation), and further detailed options (e.g., thresholds for
considering the tuple, row filtering options, transformation options according
to the look-up table). These rules will be exploited for the extraction of the
triples according to the reference ontology.

Since many ontologies are used in our context, we adopted the PheKnowLator
tools to clean ontology files (i.e., remove and normalize errors, eliminate
obsolete and/or deprecated entities, remove duplicate classes and class
concepts) and merge cleaned ontology files into a single ontology file. The
so-obtained merged ontology describes entirely the structure of RNA-KG and is
compliant with our meta-graph.

### RNA-KG generation

In this final phase, the PheKnowLator mapping rules have been issued on the
pre-processed data with the aim of generating a KG compliant with the meta-
graph identified in Phase 2 (ontological description of the KG).

In order to evaluate the characteristics of the generated KG, we used the
GRAPE library that we recently developed for fast and efficient graph
processing and embedding [70]. By importing RNA-KG into the GRAPE environment,
we were able to retrieve relevant topological information and topological
oddities that can be useful in identifying biological and biomedical KG
inconsistencies. Moreover, GRAPE can be exploited to implement different types
of graph embedding techniques that cannot be realized by means of other tools
because of the size of the generated KG.

Finally, a Blazegraph endpoint [71] has been realized to make RNA-KG freely
available and accessible. Using SPARQL, it is possible to extract portions of
the graph and use it for different kinds of analysis (see the examples
reported in the Supplementary Listings 1\- 3). Moreover, the entire RNA-KG can
be downloaded from our lab website.

## Data Records

### Identification and characterization of RNA entities and relationships

By applying the method discussed in the previous section, we identified more
than 50 public repositories. The papers describing these data sources were
published in top bioinformatics, bio-medical, and database focused journals
between 2008 and 2023 but the majority were published in the last 5 years. The
main characteristics of the identified repositories are reported in Tables
2-3, whose entries are organized according to the main type of RNA molecules
made available by the source. Sources with miRNA entities can contain hairpin
miRNA, xeno-miRNA, and mature miRNA molecules (last ones, in turn, can be
classified in -3p and -5p transcripts). Inter RNA sources are those that do
not focus on a single RNA type but propose multiple relationships among
different types of RNA molecules and bio-entities (e.g., disease in the case
of RNADisease or cellular component in the case of RNALocate). Note that no
species is present for aptamers because they are synthetic and none of the
databases are specific for piRNA molecules related to Homo sapiens (although
relationships involving piRNA sequences are stored in Inter RNA sources).
Regarding the format, the majority of the considered data sources ($\geq$
$80$%) export data in a flat-file format (CSV). Only a small fraction of them
(around $20$%) provide an API for accessing data stored in a relational
database. Only DrugBank offers a RDF data representation coupled with a SPARQL
endpoint.

Figure 6 summarizes the available relations involving RNA molecules and bio-
entities (i.e., gene, protein, chemical, and disease) that we have identified
in the different data sources. miRNA-lncRNA interactions are the most
numerous. We can retrieve around 150 million distinct relationships of this
type from public RNA-based data sources. In terms of cardinality, they are
followed by lncRNA-mRNA interactions ($\sim$28 million) and miRNA-mRNA
interactions ($\sim$12 million). Around 800 thousand distinct relationships
can also be identified for protein-lncRNA interactions. These categories of
molecules often interact with each other in specific diseases. RNA aptamer-
disease is the less represented one because at the current stage only two
approved (or under-investigation) RNA aptamer drugs are present in DrugBank
and, in general, RNA drugs are less represented than others because they are
synthetic (DrugBank siRNA and mRNA vaccine categories contain only 4 approved
or under-investigation drugs, ASO drugs are only 12, and RNA aptamer drugs
only 2). In addition to the so far discussed data sources, RNAcentral [72] is
a collector coordinated by the European Bioinformatics Institute (EBI [73]),
which imports non-coding RNA sequences from multiple databases and enables
integrated text search, sequence similarity search, bulk downloads, and
programmatic data access through a reliable API.

Starting from the need to understand whether the content of the different data
sources overlap, we examined the entities and relationships made available in
the considered data sources and identified containment (or overlapping) data
sources. The result of our study is reported in Figure 7 where bubbles
represent the relationships made available by the data sources. We can note
the presence of two prominent clusters (miRNet and RNAcentral) that properly
include or overlap the relationships made available by other data sources. The
identification of these containments has been exploited to reduce the issue of
semantic compatibility. Furthermore, many miRNA and lncRNA sources contain
relations that either overlap or are properly included within other sources.
For the sake of readability, we have included some of these RNA sources in a
legend. We remark that the Inter RNA sources RNAInter, RNALocate, RNAdisease,
ncRDeathDB, cncRNAdb, and ViRBase are nicknamed “Sister Projects” because they
are updated and maintained by the same research team. Common semantics in
“Sister Projects” result useful for data handling because they share a
practically identical structure.

### Construction of the RNA meta-graph and ontological description of the KG

Besides the sequences, these data sources also contain different kinds of
relationships that can be exploited for the KG construction. Table 4 reports
the main relationships that have been identified in the considered data
sources according to the RO ontology. For each relation, Table 4 reports the
RO identifier, the corresponding meaning, and, whenever feasible, we have
introduced the inverse relationships in case only a unidirectional
relationship is available in the data source (e.g., develops from and develops
into). The general relationships interacts with available in RO with the
meaning “A relationship that holds between two entities in which the processes
executed by the two entities are causally connected" have been specified in
the most specific relationships molecularly interacts with in our
classification to represent the situation in which the two partners are
molecular entities that directly physically interact with each other (e.g.,
via a stable binding interaction or a brief interaction during which one
modifies the other). We use this relationship to represent a specific
interaction process at the molecular level (e.g., aptamer-protein binding
interaction or tRNA molecule charged with a specific amino acid). We remark
that some authors [74, 75] suggest that miRNA molecules are involved in
negative regulation of complementary miRNA molecules by forming base-pairing
interactions. However, this kind of relationship is not present in the
considered data sources.

The content of Tables 2–4 is the groundwork for the generation of the meta-
graph reported in Figure 8. The graphical representation provides a global
overview of the richness of information that is currently provided. To
simplify the visualization of the meta-graph, we omitted most of the non-RNA
bio-entities that are known to play an important role in studying the biology
(and supporting the discovery) of novel RNA drugs. Moreover, we have omitted
some of the relationships extracted from the Inter RNA data sources (see Table
3) because of the limitation of their occurrences. The meta-graph in Figure 8
can be further extended to include other nodes representing other bio-entities
(e.g., diseases, epigenetic modifications, small molecules, tissues,
biological pathways, and cellular components) and relationships relevant to
the analysis. This "enlarged" meta-graph is quite complex and difficult to be
graphically represented. Figure 9 shows a very abstract representation by
clustering in a single RNA node all the kinds of RNA molecules described in
Figure 8. Then, this node is connected with various bio-entities based on
insights extracted from RNA sources and literature. It is worth noting that
RNA-KG has the potential for expansion by integrating additional KGs, and to
set the basis for an RNA ontology thanks to the hierarchical structure
introduced by the subClassOf relationship.

### RNA-KG statistical analysis

The current version of RNA-KG has a single connected component containing
578,384 nodes and 8,768,582 edges. The number of nodes and edges has been
deeply reduced by considering only the relationships with a high reliability.
The construction process of the graph is designed to be periodically updated,
including data from other public RNA and related biomedical sources. Moreover,
thresholds can be tuned for enlarging or reducing the KG size. Table 5 depicts
the main macroscopic topological and structural properties of the current RNA-
KG.

Figure 10(a) shows the distribution of nodes contained in RNA-KG. Nodes can be
classified into nodes representing bio-entities and those that represent the
ontological terms. Bio-entities have been further subdivided into RNA nodes
(gathering together sncRNA, mRNA, lncRNA, viral RNA, and unclassified RNA
nodes), and non-RNA nodes (named other bio-entities) that contain, for
instance, gene and variant (SNP)-typed nodes. Furthermore, Figure 11(a)
presents the distribution of nodes according to the main type of RNA
molecules, detailing the different categories of sncRNA. mRNA, lncRNA, and
miRNA available in RNA-KG. These RNA molecules are the most represented in
RNA-KG because they are well-studied (many RNA sources have been
categorized/typed as lncRNA and miRNA, and mRNA are in relationships with many
other ncRNAs as already discussed in the characterization of the data
sources). Also tRF molecules (that are classified in the sncRNA category) are
very numerous because they are “fragments” of tRNAs (one tRNA can generate
more than one tRF or tsRNA). The unclassified RNA category includes 692 RNA
nodes for which a better semantic characterization cannot be assigned because,
in the original sources, they are specified as “other RNA”, “miscellaneous
RNA”, “unknown RNA”, “ncRNA”, or “RNA molecules to be experimentally
confirmed". Finally, the other category includes sncRNA molecules whose
distribution is negligible in RNA-KG (64 sncRNA molecules among ribozymes,
piRNAs, enhancer RNAs, vault RNAs, Y RNAs, retained introns, mitochondrial
RNAs, small conditional RNAs, and scaRNAs). The total number of mRNA, and in
general, RNA, is consistent with experimental studies regarding the number of
genes in human ($\sim 22$-$25K$ protein coding genes and more than 100K total
genes [76]). Ontological terms shown in Figure 10(a) are introduced in the
generation of the KG for supporting reasoning activities and can be further
classified according to the specific bio-ontology from which they are
extracted (e.g., ChEBI for chemicals and HPO for phenotypes). Among them,
chemical and protein nodes cover around 42.5% of the total amount of nodes in
RNA-KG. This is due to the fact that ChEBI and PRO both contain many terms
representing chemical entities and proteins for Homo sapiens. Figure 11(b)
further details the distribution of ontological terms. Since the considered
ontologies contain also terms that do not follow the usual pattern for their
identification (e.g., terms representing glycans belong to PRO but their
identifier starts with the prefix GNO which differs from the usual one adopted
for identifying proteins), we have introduced the category species, with the
terms representing the species (all species start with the prefix NCBITaxon),
and the category other terms, generally containing all the others.

Figure 10(b) shows the distribution of edges in RNA-KG. Edges have been
subdivided into three categories: $i)$ edges representing RO terms that have
been further classified in those that describe the interactions among RNA
molecules and RO terms introduced by the integrations of the bio-ontologies;
$ii)$ edges representing the subClassOf relationships; and $iii)$ edges
representing other kinds of relationships not included in RO (e.g., has gene
template belonging to PRO). Figure 12(a) details the distribution of the types
of edges involving RNA molecules. As reflected by the organization of the
meta-graph, interacts with is the most represented edge type because it is
symmetric, whereas the presence of many regulates activity of edges is
justified by the vast majority of miRNA molecules within RNA-KG that indeed
regulate the activity of, for example, pseudogene and mRNA molecules.
Moreover, Figure 12(b) shows the distribution of the remaining edges included
in RNA-KG. We can notice the prevalence of many subClassOf due to the
integration of the bio-ontologies, and because we specified each RNA molecule
as subClassOf an appropriate class within SO (e.g., SO_0000276 for miRNA
molecules).

## Technical Validation

In order to evaluate the quality of the generated KG, several analyses have
been performed whose results are reported in the following paragraphs.

##### t-SNE representation.

Figure 13 shows the t-SNE representation of an embedding of the nodes/edges in
RNA-KG by using the GRAPE implementation of Node2Vec with CBOW, a random walk-
based second-order embedding algorithm [77], with walk length equal to 5.
Figure 13(a) shows how the embedding of the node type is able to effectively
identify the similarities among the nodes of the same type, thus capturing
their function in the network. On the other hand, Figure 13(b) depicts the
edge embedding for RNA-KG. Also in this case, the embedding is able to capture
the similarity between edges with the only exception of the interacts with
relation which seems to overlap several other edge types. This fact is not so
surprising considering that this relation is also used to denote a generic
relation between nodes.

##### Topological analysis.

The topological analysis led to the identification of top-5 nodes with the
highest degree centrality: microvesicle (GO_1990742) with degree 27.11K (whose
type is GO); nucleus (GO_0005634, degree 20.15K), hcmv-miR-US25-1-5p (human
cytomegalovirus hcmv-miR-US25-1-5p mature miRNA, degree 18.18K and node type
miRNA), hFOXA1 (PR_P55317, degree 31.80K and node type protein), and cytosol
(GO_0005829, degree 17.26K). We remark that the nodes cytosol, nucleus, and
microvescicle represent cellular components used for aggregating different
bio-entities existing in the context of a cell and this is the main reason for
the high node degree within RNA-KG. Moreover, the RNA relationships with these
kinds of cellular components are enhanced by the semantics contained in and
location of together with their respective RO inverse contains and located in.
On the other hand, hcmv-miR-US25-1-5p is a human cytomegalovirus
(HCMV)-encoded -5p miRNA transcript, whose diagnostic and prognostic value has
been proved valid for several human diseases and their clinical implications
[78]. Finally, hFOXA1 is a forkhead TF known to be the main target of insulin
signaling, to regulate metabolic homeostasis in response to oxidative stress,
and to interact with chromatin. The central role assumed by hFOXA1 in RNA-KG
is quite interesting since this TF is implicated in various human malignancies
characterized by altered expression of ncRNAs [79].

##### Degree distribution.

As can be seen in Table 5, the average degree of the undirected version of
RNA-KG is relatively small (9.65). Despite the graph sparsity, the diameter of
the KG is also relatively small (36). On the other hand, as shown in Figure
14(a), the degree distribution suggests a heavy-tailed distribution. All of
these properties are usually associated with scale-free graphs, which is a
common structure in real-world complex systems. These properties motivate the
computation of the empirical complementary cumulative distribution function
(CCDF) for the degree reported in Figure 14(b). This curve approximates the
probability distribution that a randomly selected node has a degree greater
than or equal to $x$. The linear trend in the plot is usually associated with
a powerlaw distribution, where the CCDF is given by a function proportional to
$x^{1-\alpha}$. We estimate the power of the distribution using [80],
obtaining a value of $\alpha=1.832$. The theoretical powerlaw obtained for the
degrees is shown in Figure 14(b) together with other common heavy-tailed
distributions. Among these alternatives, we found that the powerlaw
distribution fits better according to the log-likelihood ratio criterion [81]
with $p$-values smaller or equal than $10^{-6}$. Further exploration should be
made to confirm the powerlaw properties of the graph since they are usually
associated with a hierarchical modular structure of the network, entailing
algorithmic advantages for its analysis. For instance, the closeness
centrality distribution in Figure 14(c) presents a bimodal behavior, which
could be explained by the existence of a well-connected core usually present
in heavy-tail degree distribution networks.

##### Treewidth.

Treewidth is a graph parameter measuring the structural similarity between a
graph and a tree. It is based on the construction of a tree decomposition
which captures how subset of nodes can be grouped to form a tree structure
that maintains the global structure of the former graph. For instance, graphs
having treewidth equal to one are trees, cycles have treewidth two, and clique
graphs have a treewidth equal to the number of nodes minus one. The
computation of the treewidth is in NP, but several approximation strategies
can be used [82]. The upper bound ($8,554$ in Table 5), computed on the
undirected version of the KG, can be considered relatively small because it
represents about $1.5\%$ of the KG size. This result is consistent with a
tree-like hierarchical structure of RNA-KG that has a small and well-connected
core.

##### Isomorphic node groups.

RNA-KG contains 829 isomorphic node groups, that is nodes with exactly the
same neighbours, node and edge types. Nodes in such groups are topologically
indistinguishable, that is, swapping their identifiers would not change the
graph topology. These groups involve a total of 14.30K nodes (2.47%) and
398.32K edges (4.54%), with the largest one containing 1.04K nodes and 19.78K
edges. This particular group has degree 19 and is composed of riboswitches,
specifically putative Ile (GAU) T-box riboswitches, and contains sequences
that are all located upstream of an isoleucine–tRNA ligase. Other isomorphic
node groups involve other riboswitches, tRNAs, tsRNAs, and tRFs. The detected
isomorphic group components involve sncRNAs all interacting with amino acids
at a molecular level or that originate from tRNA with molecular interactions
tied to specific amino acids. For example, these groups may include sequences
such as Aspartic acid-GTC tRNA sequences or tsRNA-Leucines. All these
isomorphic node groups deserve further investigation to check whether the
involved molecules correspond to the same tRNA, riboswitch, tsRNA, or tRF and
thus pruning them to improve the information quality of RNA-KG. Indeed, these
groups derive from different RNA sources and contain molecules presenting
proprietary identifiers that might collapse.

## Usage Notes

While every effort has been made to provide complete, up-to-date and correct
information, we make no warranty about the completeness and accuracy of the
RNA-KG content. The use of the data contained in this database is limited to
research-oriented and non-profit activities only (as requested by the data
sources from which the data have been extracted).

## Code Availability

The RNA-KG ’s project website is at http://fievel.anacleto.di.unimi.it:9999.
The code to reproduce results, together with documentation and tutorials, is
available in RNA-KG ’s GitHub repository at
https://github.com/AnacletoLAB/RNA-KG. In addition, the repository contains
information and Python scripts to build new versions of RNA-KG as the
underlying primary resources get updated and new data become available. RNA-KG
data resource is hosted on Zenodo under a persistent identifier
https://doi.org/10.5281/zenodo.10078877. We have deposited the KG and all
relevant intermediate files in this repository.

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## Acknowledgements

This research was supported by the National Center for Gene Therapy and Drugs
based on RNA Technology, PNRR-NextGenerationEU program (G43C22001320007).

## Author contributions statement

M.M. and G.V. designed the study. Em.C. retrieved, processed, and harmonized
datasets. T.J.C., M.M. defined the methodology for the construction of the KG.
Em.C. and S.B. worked on the specification of the mapping alignment. Em.C.
generated the KG that was analyzed by A.C. and M.S.G. S.B. and P.P. set up the
SPARQL endpoint and developed the SPARQL queries on the generated KG. G.V.,
E.C., J.G., J.R., P.N.R. identified the possible applications of the KG in
conducting knowledge discovery in life science. M.M., Em.C. and G.V. wrote the
initial draft of the paper. All authors reviewed the final version of the
manuscript and approved it.

## Competing interests

The authors declare no competing interests.

## Figures & Tables

Figure 1: Schematic representation of the RNA network within a cell.

1\. RNA sources characterization 1. RNA sources identification, data download,
and pre-processing 2. For each RNA source: (a) Extraction and characterization
of relationships according to the RO (b) Identification scheme for source and
target relationships (c) Set up of thresholds for the extraction of meaningful
relationships 2\. Ontological description of the KG 1. Schema layer: (a)
Identification of a meta-graph for representing the types of bio-entities and
their relationships (b) Selection of bio-ontologies for describing the bio-
entities 2. Data layer: identification of the attributes to maintain for the
bio-entities 4\. RNA-KG generation 1. Application of PheKnowLator for the
generation of the integrated ontology and RDF triples 2. Extraction of RNA-KG
properties 3. Set up of SPARQL endpoint and analysis of RNA-KG 3\. Ontological
Alignment Specification 1. Choice of knowledge model 2. Construction of
PheKnowLator mapping rules for the extraction of triples according to previous
steps 3. Identification of the knowledge storage

Figure 2: Workflow for the construction of RNA-KG.

CHEMICAL-GENE……CHEBI:16113CETP……CHEBI:167559ALG1……LOOK-UP
TABLE……CETP1071……ALG156052……GENE-miRNA……1071hsa-miR-124-3p……56052hsa-
miR-21-5p……

Figure 3: The relationship between chemicals and miRNA cannot be decoded
directly because of the use of different identification schemes. However, by
means of a look-up table the relationship can be highlighted.  Figure 4: An
example of the use of Description Logic (DL) for knowledge modeling. The TBox
includes classes (i.e., miRNA, small regulatory ncRNA, and exosome), and the
assertions between classes (i.e., “miRNA subClassOf small regulatory ncRNA”
and “miRNA is located in exosome”). The ABox includes instances of classes
(i.e., hsa-miR-125b-5p) represented in the TBox and assertions about those
instances (i.e., “hsa-miR-125b-5p instanceOf miRNA” and “hsa-miR-125b-5p
causes leukemia”).  Figure 5: Example of a RNA-KG subgraph realized according
to the instance-based, inverse relations, semantically abstracted (OWL-NETS
without harmonization) parameters. Black nodes and edges come from the
integration of RNA sources, whereas red ones from the integration of selected
ontologies.
miRNAmRNAdiseasechemicallncRNAcircRNAsiRNAshRNAproteinaptamerASOgRNAgeneribozymeviral
RNAriboswitchtRF &
tsRNAsnoRNApseudogenerRNAsnRNAtRNAscaRNAmiRNAmRNAdiseasechemicallncRNAcircRNAsiRNAshRNAproteinaptamerASOgRNAgeneribozymeviral
RNAriboswitchtRF &
tsRNAsnoRNApseudogenerRNAsnRNAtRNAscaRNA$10^{1}$$10^{2}$$10^{3}$$10^{4}$$10^{5}$$10^{6}$$10^{7}$$10^{8}$Count
Figure 6: Number of relationships involving RNA molecules and relevant bio-
entities (gene, protein, chemical, and disease) within the considered RNA
sources. Colors represent the ranges of relationships in log scale, as
reported in the legend.

$miRNet$$NONCODE$ $Lnc$ $RNA$ $Wiki$
$miRandola$$Vesciclepedia$$RNAcentral$$LncBook$$miRBase$$Modomics$$Ribocentre$$snoDB$$TarBase$$miRecords$$miRTarBase$$tRNAdb$
$miRDB\cup TargetScan$ $SomamiR$ $Lnc$ $Exp$ $DB$ $miRCancer\cup miRdSNP\cup
PolymiRTS\cup dbDEMC$ $PuTmiR$$EpimiR$$SM2miR$$tsRFun$$tRFdb$$MINTbase$ $Drug$
$Bank$ $ICBP$ $siRNA$ $Apta$–$Index$ $eSkip$–$Finder$
$dbEssLnc$$lncATLAS$$viroidDB$$TBDB$$RSwitch$$Addgene$$HMDD$$miRPathDB$$TAM$$TransmiR$$miR2Disease$$LncRNADisease$$Lnc2Cancer$

Figure 7: Bubbles represent the relationships made available from the
considered data sources. Overlapping and inclusions of bubbles show the
presence of common relationships among the considered data sources. Figure 8:
RNA-KG meta-graph. Most non-RNA entities are not represented to simplify the
visualization.

Figure 9: The complete conceptual RNA-KG meta-graph.

chemical25.9%(150,080)protein16.6%(96,197)cell7.6%(44,175)GO7.6%(43,869)sncRNA13.4%(77,741)other
bio-entities7.7%(44,399)disease 4.0%
(23,270)phenotype 2.9%
(16,865)anatomy 2.5%
(14,232)vaccine 1.1%
(6,246)pathway 0.5%
(2,606)other terms 0.4%
(2,427)sequence 0.4%
(2,363)species 0.4%
(2,148)lncRNA 4.3%
(24,749)mRNA 3.6%
(20,632)viral RNA 1.0%
(5,693)unclassified RNA 0.1%
(692)ontological terms 69.9% (404,478)RNA 22.3% (129,507)other 7.7% (44,399)

(a) Distribution of nodes.

RO termsinvolvingRNA molecules85.9% (7,528,124)other RO termsintroduced bythe
integration2.4% (213,259)subClassOf9.2%(806,681)other terms2.5% (220,518)non-
RO 11.7% (1,027,199)RO 88.3% (7,741,383)

(b) Distribution of edges.

Figure 10: Pie-chart of: (a) node distribution according to node types. (b)
edge distribution according to edge types.

$\displaystyle{10^{0}}$$\displaystyle{10^{1}}$$\displaystyle{10^{2}}$$\displaystyle{10^{3}}$$\displaystyle{10^{4}}$$\displaystyle{10^{5}}$tRFmRNAriboswitchlncRNAmiRNAtsRNApseudogenecircRNAviral
RNAASOsnRNAtRNAunclassified RNAsnoRNArRNAaptamers(i/h)RNAgRNAotherCount

(a) Distribution of RNA molecules.

$\displaystyle{10^{0}}$$\displaystyle{10^{1}}$$\displaystyle{10^{2}}$$\displaystyle{10^{3}}$$\displaystyle{10^{4}}$$\displaystyle{10^{5}}$chemicalproteincellGOdiseasegenephenotypevariant
(SNP)anatomysmall proteinvaccinepathwayother termssequencespecieshistone
modificationepigenetic modificationCount

(b) Distribution of other bio-entities.

Figure 11: Node distribution according to node types.

$\displaystyle{10^{0}}$$\displaystyle{10^{1}}$$\displaystyle{10^{2}}$$\displaystyle{10^{3}}$$\displaystyle{10^{4}}$$\displaystyle{10^{5}}$$\displaystyle{10^{6}}$$\displaystyle{10^{7}}$interacts
withregulates activity ofdevelops fromis downstream of sequence ofcauses or
contributes to conditioncontained inlocation ofcausally influenced
byparticipates inoverlaps sequence ofgene product ofubiquitously expressed
inCount

(a) Distribution of edges involving RNA molecules.

$\displaystyle{10^{0}}$$\displaystyle{10^{1}}$$\displaystyle{10^{2}}$$\displaystyle{10^{3}}$$\displaystyle{10^{4}}$$\displaystyle{10^{5}}$$\displaystyle{10^{6}}$$\displaystyle{10^{7}}$subClassOfpart
ofonly in taxonhas rolederives fromhas quality at some timehas gene
templatehas functional parentis disease model forhas modifieris conjugate acid
ofhas qualitycharacteristic ofCount

(b) Distribution of other edges in RNA-KG.

Figure 12: Edges distribution. Only direct edges with more than 5,000
occurrences are reported.

(a) Node types embedding.

(b) Edge types embedding.

Figure 13: Bidimensional view of RNA-KG embeddings.

$0$$0.2$$0.4$$0.6$$0.8$$1$$1.2$$1.4$$\cdot
10^{5}$$\displaystyle{10^{0}}$$\displaystyle{10^{1}}$$\displaystyle{10^{2}}$$\displaystyle{10^{3}}$$\displaystyle{10^{4}}$$\displaystyle{10^{5}}$$\displaystyle{10^{6}}$DegreeCountDegree
distribution

(a)

$\displaystyle{10^{0}}$$\displaystyle{10^{1}}$$\displaystyle{10^{2}}$$\displaystyle{10^{3}}$$\displaystyle{10^{4}}$$\displaystyle{10^{5}}$$\displaystyle{10^{-6}}$$\displaystyle{10^{-5}}$$\displaystyle{10^{-4}}$$\displaystyle{10^{-3}}$$\displaystyle{10^{-2}}$$\displaystyle{10^{-1}}$$\displaystyle{10^{0}}$Degree
(log scale)CCDFCCDF Degree distributionempirical
distributionpowerlawlognormalexponential

(b)

12345$\cdot
10^{-7}$$\displaystyle{10^{0}}$$\displaystyle{10^{1}}$$\displaystyle{10^{2}}$$\displaystyle{10^{3}}$$\displaystyle{10^{4}}$$\displaystyle{10^{5}}$Closeness
Centrality CountApprox. Closeness Centrality distribution

(c)

Figure 14: (a) Node degree distribution (semi-log). (b) Complementary
cumulative distribution (CCDF) for the node degree. (c) Approximated closeness
centrality distribution.

Name | Abbr. |  Description
---|---|---
Human Phenotype Ontology [38] | HPO |  Terms representing medically relevant phenotypes and disease-phenotype annotations.
Gene Ontology [34] | GO |  Terms representing attributes of gene products in all organisms. Cellular component, molecular function, and biological process domains are covered.
Monarch Merged Disease Ontology [83] | Mondo |  Terms representing human diseases.
Vaccine Ontology [84] | VO |  Terms in the domain of vaccine and vaccination.
Chemical Entities of Biological Interest [85] | ChEBI |  Structured classification of molecular entities of biological interest focusing on “small” chemical compounds.
Uber-anatomy Ontology [86] | Uberon |  Terms representing body parts, organs and tissues in a variety of animal species, with a focus on vertebrates.
Cell Line Ontology [87] | CLO |  Terms representing publicly available cell lines.
PRotein Ontology [88]* | PRO |  Terms representing protein-related entities (including specific modified forms, orthologous isoforms, and protein complexes).
Sequence Ontology [89] | SO |  Terms representing features and properties of nucleic acid used in biological sequence annotation.
Pathway Ontology [90] | PW |  Terms for annotating gene products to pathways.
Relation Ontology [23] | RO |  Terms and properties representing relationships used across a wide variety of biological ontologies.
Table 1: Main biomedical ontologies employed for RNA-KG construction (* modified to exclude all non-human proteins). Type | Data source | Species | # RNAs | Format | API | Threshold | SI | Relation with | TI | # Relation
---|---|---|---|---|---|---|---|---|---|---
miRNA |  miRBase [91] | 271 | 87,474 | rel/CSV | no |  | WR | premiRNA | WR | 48,885
miRDB [92] | 5 | 7,086 | CSV | no | $\sigma>80$ | WR | mRNA | WR | 3,519,884
miRNet [93] | 10 | 7,928 | rel/CSV | yes |  | WR |  SNP mRNA snoRNA chemical TF epi. mod. lncRNA pseudogene circRNA disease |  WR WR WR M M M WR WR WR M |  67,532 3,025,487 9,738 4,935 3,311 1,955 31,345 59,417 804,086 32,004
miRecords [94] | 9 | 384 | CSV | no | validated | WR | mRNA | M | 1,529
HMDD [95] | HS | 1,206 | CSV | no |  | WR | disease | M | 35,547
EpimiR [96] | 7 | 617 | CSV | no |  | WR | epi. mod. | M | 1,974
miR2Disease [97] | HS | 349 | CSV | no |  | WR | disease | O | 3,273
TargetScan [98] | 5 | 5,168 | CSV | no | validated | WR | mRNA | WR | 2,850,014
SomamiR DB [99] | HS | 1,078 | CSV | no | validated | WR |  mRNA circRNA lncRNA disease |  WR WR WR M |  2,313,416 428,237 127,025 2,424
TarBase [100] | 18 | 2,156 | rel/CSV | no |  | WR | mRNA | WR | 665,843
miRTarBase [101] | 28 | 4,630 | CSV | no |  | WR | mRNA | WR | 2,200,449
SM2miR [102] | 21 | 1,658 | CSV | no |  | WR | chemical | M | 4,989
TransmiR [103] | 19 | 785 | CSV | no | validated | WR | TF | M | 3,730
PolymiRTS [104] | HS | 11,182 | rel/CSV | no |  | WR | disease | M | 83,516
dbDEMC [105] | HS | 3,268 | CSV | no |  $p_{val}$<0.01 | WR | disease | M | 160,800
TAM [106] | HS | 1,209 | CSV | no |  | WR |  mol. function miRNA TF disease anatomy |  M WR M M M |  2,538 1,218 165 12,516 58
PuTmiR [107] | HS | 1,296 | CSV | no |  | WR | TF | M | 12,097
miRPathDB [108] | HS, MM | 29,430 | CSV | no | FDR<0.01 | WR | mol. function | M | 3,063
miRCancer [109] | HS | 57,984 | CSV | no |  | WR | disease | M | 9,080
miRdSNP [110] | HS | 249 | CSV | no |  | WR |  disease SNP |  WR M |  786 758
|  miRandola [111] | 14 | 1,002 | CSV | no |  | WR |  extracell. form chemical |  M M |  3,262 25
mRNA vaccine |  DrugBank [112] |  | 4 | rel/RDF |  yesS |  | P | disease | M | 7
s(i/h)RNA |  ICBP siRNA [113] | HS, MM | 147 | HTML | no |  | P | mRNA | WR | 147
DrugBank [112] |  | 4 | rel |  yesS |  | P |  mRNA disease |  WR M |  3 3
RNA aptamer |  Apta-Index [114] |  | 230 | rel | no |  | P |  chemical protein |  M M |  77 153
DrugBank [112] |  | 2 | rel/RDF |  yesS |  | P |  protein disease |  M M |  2 2
ASO |  eSkip-Finder [115] | 4 | 2,196 | rel | no |  | P | mRNA | WR | 11,778
DrugBank [112] |  | 12 | rel/RDF |  yesS |  | P |  protein mRNA disease |  M WR M |  12 7 11
gRNA |  Addgene [116] | 29 | 296 | HTML | no |  | P | gene | WR | 321
Table 2:  Main data sources (Part I). For each type of RNA molecule, the table reports the corresponding data sources. Moreover, for each data source, Species and #RNAs columns specify the number of species and distinct sequences (HS and MM tags refer to specific species Homo sapiens and Mus musculus); Relation with and #Relation columns specify the distinct relationships with bio-entities and their number; Format column refers to the data format (CSV for flatfiles, rel for relational tables, RDF, or HTML for web pages); API column reports the availability of API or SPARQL endpoints (the last one denoted with the superscript s) for data access; Threshold column provides identified quality threshold within the source. SI and TI columns contain the class of the identification schemes (WR – Well-Reputed, O – Ontology-based, M – Mapping\- based, and P – Proprietary) adopted respectively by source and target(s) within a specific resource (the source is the RNA molecule specified in the Type column, whereas target(s) are specified in the Relation with column). Type | Data source | Species | # RNAs | Format | API | Threshold | SI | Relation with | TI | # Relation
---|---|---|---|---|---|---|---|---|---|---
lncRNA |  LncBook [117] | HS | 323,950 | rel/CSV | no |  | WR |  miRNA small protein disease biological context |  WR WR M M |  146,092,274 772,745 34,536 95,243
LncRNADisease [118] | 4 | 6,066 | CSV | no |  | WR | disease | M | 20,277
LncExpDB [119] | HS | 101,293 | rel/CSV | no |  | WR | mRNA | WR | 28,443,865
dbEssLnc [120] | HS, MM | 207 | JSON | no |  | WR |  biological role biological process |  P O |  207 28
lncATLAS [121] | HS | 6,768 | CSV | no |  | WR | cell. comp. | M | 2,429,368
NONCODE [122] | 39 | 644,510 | rel | no |  | WR | disease | O | 32,226
Lnc2Cancer [123] | HS | 3,402 | CSV | no |  | WR | disease | O | 9,254
LncRNAWiki [124] | HS | 106,063 | rel/CSV | no |  | WR |  small protein disease biological context cell. comp. gene miRNA TF biological process mol. function chemical pathway |  WR M M M WR WR M M M M M |  9,387 7,634 18,453 4,969 509 210 232 10,806 1,800 789 571
Ribozyme |  Ribocentre [125] | 1,195 | 21,084 | rel | no |  | P | biological process | M | 34
Viral RNA |  ViroidDB [126] | 9 | 9,891 | CSV | no |  | WR | ribozyme | P | 17,460
Riboswitch |  TBDB [127] | 3,621 | 23,497 | CSV | no |  | P | protein | M | 23,535
RSwitch [128] | 50 | 215 | rel/CSV | no |  | P | bact. strain | WR | 215
tRF & tsRNA |  tRFdb [129] | 7 | 863 | CSV | no |  | P | tRNA | P | 792
tsRFun [130] | HS | 3,940 | CSV | no |  FDR<0.01 | P |  miRNA tRNA disease |  WR P M |  45,165 46,798 4,620
MINTbase [131] | HS | 28,824 | CSV | no |  | P | tRNA | P | 125,285
snoRNA |  snoDB [132] | HS | 751 | CSV | no |  | WR |  gene mRNA lncRNA miRNA pseudogene rRNA snoRNA snRNA tRNA scaRNA |  WR WR WR WR WR P WR WR P WR |  763 276 45 17 10 735 670 164 164 34
tRNA |  tRNAdb [133] | 681 | 9,758 | rel | no |  | P | amino acid | M | 8,872
Inter RNA |  RNAInter [134] | 156 | 455,887 | CSV | yes | $\sigma\geq 0.2886$ | WR |  chemical histone mod. RBP TF protein gene |  M P M M M WR |  10,890 1,060,685 5,200,067 9,323,690 22,543,829 119,377
RNALocate [135] | 104 | 123,592 | CSV | yes |  | WR | cell. comp. | M | 213,429
RNADisease [136] | 117 | 91,245 | CSV | yes | $\sigma\geq 0.95$ | WR | disease | O | 343,273
ncRDeathDB [137] | 12 | 648 | CSV | yes |  | WR | prog. cell death | M | 4,615
cncRNADB [138] | 21 | 2,002 | CSV | yes |  | WR | anatomy | M | 2,598
ViRBase [139] | 152 | 41,718 | CSV | yes | $\sigma\geq 0.7$ | WR |  viral RNA viral protein |  WR M |  719,214 195
Vesiclepedia [140] | 41 | 20,490 | CSV | no |  | WR | extracell. form | M | 388,154
DirectRMDB [141] | 25 | 19,702 | CSV | no |  | WR | epi. mod. | WR | 904,712
Modomics [142] | 32 | 225 | rel/RDF | yes |  | WR | epi.mod. | WR | 276
Table 3: Main data sources (Part II). Relation ID | Name | Inverse Relation ID | Inverse Name
---|---|---|---
RO:0000056 | participates in | RO:0000057 | has participant
RO:0000079 | function of | RO:0000085 | has function
RO:0001015 | location of | RO:0001025 | located in
RO:0001018 | contained in | RO:0001019 | contains
RO:0002202 | develops from | RO:0002203 | develops into
RO:0002204 | gene product of | RO:0002205 | has gene product
RO:0002245 | over-expressed in |  |
RO:0002260 | has biological role |  |
RO:0002246 | under-expressed in |  |
RO:0002291 | ubiquitously expressed in | RO:0002293 | ubiquitously expresses
RO:0002302 | is treated by substance | RO:0002606 | is substance that treats
RO:0002430 | involved in regulation of |  |
RO:0002430 | involved in negative regulation of |  |
RO:0002434 | interacts with* |  |
RO:0002436 | molecularly interacts with* |  |
RO:0002526 | overlaps sequence of* |  |
RO:0002528 | is upstream of sequence of | RO:0002529 | is downstream of sequence of
RO:0002559 | causally influenced by | RO:0002566 | causally influences
RO:0003002 | represses expression of |  |
RO:0003302 | causes or contributes to condition |  |
RO:0010001 | generically depends on | RO:0010002 | is carrier of
RO:0011002 | regulates activity of |  |
RO:0011007 | decreases by repression quantity of |  |
Table 4: Main relations among bio-entities involving RNA with the RO identifier (* symmetric relationship). Graph parameter |
---|---
Number of nodes | 578,384
Number of directed edges | 8,768,582
Max out degree | 27,109
Max in degree | 117,135
Number of edges* | 5,583,802
Max degree* | 117,142
Min degree* | 1
Mean degree* | 9.65
Diameter* | 36
Upper bound Treewidth* | 8,554
Mean closeness centrality* | $3.38\times 10^{-7}$
Table 5: Basic topological properties of RNA-KG (* calculated on the
undirected version of the KG).

## Supplementary material

Supplementary Table 1 specifies the bio-ontologies that can be exploited for
representing concepts in RNA sources. RNA sources are categorized according to
the main treated molecules of RNA (whose characteristics are reported in
Tables 2-3).

Supplementary Tables 2-6 detail the interactions present in RNA-KG by showing
the two interactors with their numerosity (Subject column refers to the
numerosity of the first entity while Object column refers to the numerosity of
the second entity in the Edge column), the RO relation we used to semantically
describe the interaction (Relation column) with its cardinality (Direct
Relation(s) column), and the sources that have been processed to include the
interaction in RNA-KG (Source(s) column). We omit inverse relationships
because they can be retrieved by means of Table 4. Supplementary Figure 1-2
show bio-entities present in RNA sources and the mapping on RO terms we used
to represent relationships within the sources. Each source is annotated with a
red spot if the bioentity or the RO term is present in the source. Confirming
our previous analysis, lncRNA, miRNA, mRNA, and protein are among the most
represented molecules, whereas interacts with, regulates activity of, and
causes or contributes to condition are among the most represented RO terms.
Supplementary Table 7 shows the primary node types and their corresponding
identifiers with an instance sample. Finally, Supplementary Listings 1\- 3
show three examples of queries on RNA-KG that can be executed through the
SPARQL endpoint’s query tab.

aptamerASOchemicalcircRNAdiseasegenegRNAlncRNAmiRNAmRNApiRNAproteinpseudogeneriboswitchribozymerRNAscRNAshRNAsiRNAsnoRNAsnRNAtRFtRNAtsRNAviral
RNAAddgeneApta-IndexcncRNADBdbDEMCdbEssLncDirectRMDBDrugBankEpimiReSkip-
FinderHMDDICBP
siRNALnc2CancerLncATLASLncBookLncExpDBLncRNADiseaseLncRNAWikiMINTbasemiR2DiseasemiRandolamiRBasemiRCancermiRDBmiRdSNPmiRecordsmiRNetmiRPathDBmiRTarBaseModomicsncRDeathDBPolymiRTSPuTmiRRibocentreRNADiseaseRNAInterRNALocateRSwitch
databaseSM2miRsnoDBSomamiRTAMTarBaseTargetScanTBDBTransmiRtRFdbtRNAdbtsRFunVesiclepediaViRBaseViroidDB
Supplementary Figure 1: Relevant bio-entities involved in RNA sources. Columns
represent bio-entities involved in the RNA sources (rows) included in RNA-KG.

causally influenced bycauses or contributes to conditioncontained indecreases
by repression quantity ofdevelops fromfunction ofgene product ofgenerically
depends onhas biological roleinteracts withinvolved in negative regulation
ofinvolved in regulation ofis treated by substanceis upstream of sequence
oflocation ofmolecularly interacts withover-expressed inoverlaps sequence
ofparticipates inregulates activity ofrepresses expression ofubiquitously
expressed inAddgeneApta-
IndexcncRNADBdbDEMCdbEssLncDirectRMDBDrugBankEpimiReSkip-FinderHMDDICBP
siRNALnc2CancerLncATLASLncBookLncExpDBLncRNADiseaseLncRNAWikiMINTbasemiR2DiseasemiRandolamiRBasemiRCancermiRDBmiRdSNPmiRecordsmiRNetmiRPathDBmiRTarBaseModomicsncRDeathDBPolymiRTSPuTmiRRibocentreRNADiseaseRNAInterRNALocateRSwitch
databaseSM2miRsnoDBSomamiRTAMTarBaseTargetScanTBDBTransmiRtRFdbtRNAdbtsRFunVesiclepediaViRBaseViroidDB

Supplementary Figure 2: Overlap of of RO terms in RNA sources. Columns
represent the RO terms involved in the RNA sources (rows) included in RNA-KG.

Type | Data source | GO | Mondo/HPO | VO | ChEBI | Uberon | CLO | PRO | SO | PW
---|---|---|---|---|---|---|---|---|---|---
miRNA | miRBase |  |  |  |  |  |  |  | x |
miRDB |  |  |  |  |  |  |  | x |
miRNet | x | x |  | x |  |  | x | x |
miRecords |  |  |  |  |  |  |  | x |
EpimiR | x |  |  |  |  |  |  | x |
HMDD |  | x |  |  |  |  |  | x |
miR2Disease |  | x |  |  |  |  |  | x |
TargetScan |  |  |  |  |  |  |  | x |
SomamiR DB |  | x |  |  |  |  |  | x |
TarBase |  |  |  |  |  |  |  | x |
miRTarBase |  |  |  |  |  |  |  | x |
SM2miR |  |  |  | x |  |  |  | x |
TransmiR |  |  |  |  |  |  | x | x |
PolymiRTS |  | x |  |  |  |  |  | x |
dbDEMC |  | x |  |  |  |  |  | x |
TAM | x | x |  |  | x |  | x | x |
PuTmiR |  |  |  |  |  |  | x | x |
miRPathDB | x |  |  |  |  |  |  | x |
miRCancer |  | x |  |  |  |  |  | x |
| miRdSNP |  | x |  |  |  |  |  | x |
| miRandola | x |  |  | x |  |  |  | x |
mRNA vaccine |  DrugBank |  | x | x |  |  |  |  |  |
siRNA | ICBP siRNA |  |  |  |  |  |  |  | x |
DrugBank |  | x |  | x |  |  |  | x |
RNA aptamer | Apta-Index |  |  |  | x |  |  | x |  |
DrugBank |  | x |  | x |  |  | x |  |
ASO | eSkip-Finder |  |  |  |  |  |  |  | x |
DrugBank |  | x |  | x |  |  | x |  |
gRNA | Addgene |  |  |  |  |  |  |  | x |
lncRNA | LncBook | x | x |  |  | x | x |  | x |
LncRNADisease |  | x |  |  |  |  |  | x |
LncExpDB |  |  |  |  |  |  |  | x |
dbEssLnc | x |  |  | x |  |  |  | x |
lncATLAS | x |  |  |  |  |  |  | x |
NONCODE |  | x |  |  |  |  |  | x |
Lnc2Cancer |  | x |  |  |  |  |  | x |
LncRNAWiki | x | x |  | x |  |  | x | x | x
Ribozyme | Ribocentre | x |  |  |  |  |  |  | x |
Viral RNA |  ViroidDB |  |  |  |  |  |  |  | x |
Riboswitch | TBDB |  |  |  |  |  |  | x | x |
RSwitch |  |  | x |  |  |  |  | x |
tRF | tRFdb |  |  |  |  |  |  |  | x |
tsRFun |  | x |  |  |  |  |  | x |
MINTbase |  |  |  |  |  |  |  | x |
snoRNA | snoDB |  |  |  |  |  |  |  | x |
tRNA | tRNAdb |  |  |  | x |  |  |  | x |
Inter RNA | RNAInter |  |  |  | x |  |  |  | x |
RNALocate | x |  |  |  |  |  |  | x |
RNADisease |  | x |  |  |  |  |  | x |
ncRDeathDB | x |  |  |  |  |  |  | x |
cncRNADB |  |  |  |  |  |  | x | x |
ViRBase |  |  |  |  |  |  | x | x |
Vesiclepedia | x |  |  |  |  |  |  | x |
DirectRMDB |  |  |  |  |  |  |  | x |
Modomics |  |  |  |  |  |  | x | x |
Supplementary Table 1: Bio-ontologies that can be exploited for the characterization of data sources. Edge | Relation | Source(s) | Subjects | Objects | Direct Relation(s)
---|---|---|---|---|---
premiRNA-miRNA | develops into | miRBase | 1,917 | 2,656 | 2,879
premiRNA-pseudogene | interacts with | RNAInter | 1 | 1 | 1
miRNA-protein | interacts with | RNAInter | 10 | 98 | 105
premiRNA-protein | interacts with | RNAInter | 20 | 22 | 26
premiRNA-mRNA | regulates activity of |  | miRDB
---
miRecords
TarBase
miRTarBase
TargetScan
SomamiR
miRdSNP
11 | 107 | 117
miRNA-mRNA | regulates activity of |  | miRDB
---
miRecords
TarBase
miRTarBase
TargetScan
SomamiR
miRdSNP
2,761 | 19,125 | 1,373,343
miRNA-mRNA | interacts with | RNAInter | 2,599 | 16,718 | 580,001
premiRNA-mRNA | interacts with | RNAInter | 133 | 175 | 222
miRNA-miRNA | interacts with | RNAInter | 4 | 4 | 4
miRNA-pseudogene | regulates activity of | miRNet | 642 | 3,743 | 59,377
miRNA-pseudogene | interacts with | RNAInter | 466 | 720 | 1,894
mRNA-pseudogene | interacts with | RNAInter | 856 | 1,043 | 1,173
pseudogene-pseudogene | interacts with | RNAInter | 79 | 77 | 85
rRNA-pseudogene | interacts with | RNAInter | 5 | 3 | 5
miRNA-circRNA | interacts with | RNAInter | 42 | 139 | 322
miRNA-othersRNA | interacts with | RNAInter | 6 | 12 | 13
miRNA-epigenetic modification | interacts with |  | EpimiR
---
RNAInter
DirectRMDB
499 | 22 | 1,911
premiRNA-epigenetic modification | interacts with |  | EpimiR
---
RNAInter
DirectRMDB
421 | 27 | 773
protein-epigenetic modification | interacts with | Modomics | 32 | 7 | 33
scaRNA-epigenetic modification | interacts with | Modomics | 21 | 4 | 21
snoRNA-epigenetic modification | interacts with | Modomics | 6 | 3 | 6
miRNA-disease | causes or contributes to condition |  | miR2Disease
---
HMDD
miRNet
dbDEMC
miRdSNP
TAM
RNADisease
PolymiRTS
miRCancer
2,627 | 611 | 38,258
premiRNA-disease | causes or contributes to condition |  | miR2Disease
---
HMDD
miRNet
dbDEMC
miRdSNP
TAM
RNADisease
PolymiRTS
miRCancer
608 | 193 | 5,034
miRNA-lncRNA | interacts with |  | miRNet
---
LncRNAWiki
SomamiR
RNAInter
2,534 | 777 | 13,893
premiRNA-lncRNA | interacts with |  | miRNet
---
LncRNAWiki
SomamiR
RNAInter
111 | 535 | 3,450
Supplementary Table 2: RNA-KG Descriptive statistics by primary edge type (Part I). Edge | Relation | Source(s) | Subjects | Objects | Direct Relation(s)
---|---|---|---|---|---
variant-miRNA | causally influences |  | miRNet
---
miRdSNP
300 | 164 | 343
variant-premiRNA | causally influences |  | miRNet
---
miRdSNP
10,756 | 251 | 11,281
variant-gene | causally influences | miRNet | 357,872 | 16,755 | 357,872
variant-disease | causally influences | miRdSNP | 321 | 26 | 346
variant-TF | causally influences | miRNet | 2,080,335 | 113 | 2,082,657
tsRNA-miRNA | interacts with | tsRFun | 8,044 | 103 | 121,236
tsRNA-disease | causes or contributes to condition | tsRFun | 449 | 31 | 26,602
tRF-tRNA | develops from |  | tRFdb
---
MINTbase
20,628 | 882 | 114,851
tRF-mRNA | interacts with | RNAInter | 269 | 11,150 | 26,314
tRF-lncRNA | interacts with | RNAInter | 18 | 6 | 18
tRF-pseudogene | interacts with | RNAInter | 20 | 13 | 26
piRNA-lncRNA | interacts with | RNAInter | 1 | 1 | 1
piRNA-mRNA | interacts with | RNAInter | 1 | 1 | 1
snoRNA-gene | interacts with | snoDB | 373 | 249 | 379
snoRNA-premiRNA | interacts with |  | snoDB
---
miRNet
1 | 1 | 1
snoRNA-miRNA | interacts with |  | snoDB
---
miRNet
RNAInter
249 | 509 | 1,385
snoRNA-snoRNA | interacts with | snoDB | 74 | 86 | 344
snoRNA-lncRNA | interacts with |  | snoDB
---
RNAInter
146 | 31 | 196
snoRNA-snRNA | interacts with |  | snoDB
---
RNAInter
432 | 329 | 717
snoRNA-rRNA | interacts with | snoDB | 427 | 318 | 672
snoRNA-mRNA | interacts with |  | snoDB
---
RNAInter
99 | 142 | 180
snoRNA-tRNA | interacts with | snoDB | 4 | 4 | 4
snoRNA-retained intron | interacts with | snoDB | 25 | 8 | 33
snoRNA-scaRNA | interacts with |  | snoDB
---
RNAInter
24 | 9 | 34
snoRNA-pseudogene | interacts with |  | snoDB
---
RNAInter
8 | 8 | 9
lncRNA-gene | interacts with |  | LncRNAWiki
---
RNAInter
185 | 620 | 1,138
othersRNA-gene | interacts with | RNAInter | 1 | 2 | 2
mRNA-gene | interacts with | RNAInter | 3 | 10 | 10
lncRNA-disease | causes or contributes to condition |  | LncRNADisease
---
Lnc2Cancer
LncRNAWiki
LncBook
RNADisease
1,331 | 347 | 6,557
circRNA-disease | causes or contributes to condition |  | LncRNADisease
---
Lnc2Cancer
RNADisease
14 | 8 | 15
snRNA-disease | causes or contributes to condition | RNADisease | 7 | 13 | 14
pseudogene-disease | causes or contributes to condition | RNADisease | 101 | 68 | 397
snoRNA-disease | causes or contributes to condition | RNADisease | 14 | 6 | 14
scRNA-disease | causes or contributes to condition | RNADisease | 1 | 3 | 3
mRNA-disease | causes or contributes to condition | RNADisease | 412 | 93 | 502
lncRNA-chemical | interacts with |  | LncRNAWiki
---
RNAInter
822 | 80 | 1,173
small protein-lncRNA | gene product of | LncBook | 8,178 | 810 | 8,417
lncRNA-protein | interacts with |  | LncBook
---
LncRNAWiki
RNAInter
8,412 | 294 | 16,212
lncRNA-biological context | over-expressed in | LncBook | 822 | 9 | 1,395
lncRNA-biological context | under-expressed in | LncBook | 1,742 | 9 | 3,128
Supplementary Table 3: RNA-KG Descriptive statistics by primary edge type (Part II). Edge | Relation | Source(s) | Subjects | Objects | Direct Relation(s)
---|---|---|---|---|---
lncRNA-biological role | has biological role | dbEssLnc | 173 | 3 | 173
lncRNA-biological context | ubiquitously expressed in | LncBook | 2,221 | 9 | 6,610
lncRNA-cellular component | contained in | LncATLAS | 10,207 | 6 | 27,287
lncRNA-pathway | participates in | LncRNAWiki | 30 | 11 | 43
lncRNA-biological process | participates in | LncRNAWiki | 21 | 2 | 23
miRNA-TF | is upstream of sequence of | PuTmiR | 4 | 17 | 19
miRNA-TF | is downstream of sequence of | PuTmiR | 4 | 10 | 10
premiRNA-TF | is upstream of sequence of | PuTmiR | 484 | 66 | 1,839
premiRNA-TF | is downstream of sequence of | PuTmiR | 488 | 65 | 1,949
premiRNA-TF | involved in regulation of |  | miRNet
---
TransmiR
TAM
330 | 320 | 1,869
premiRNA-molecular function | has function |  | TAM
---
miRPathDB
26 | 2 | 29
premiRNA-premiRNA | interacts with | TAM | 320 | 646 | 820
premiRNA-anatomy | located in | TAM | 57 | 7 | 70
lncRNA-anatomy | located in | cncRNADB | 4 | 5 | 6
mRNA-anatomy | located in | cncRNADB | 3 | 3 | 3
lncRNA-cell | located in | cncRNADB | 49 | 7 | 52
mRNA-cell | located in | cncRNADB | 41 | 23 | 70
mRNA-chemical | interacts with | RNAInter | 92 | 12 | 97
TEC-chemical | interacts with | RNAInter | 2 | 1 | 2
snoRNA-chemical | interacts with | RNAInter | 15 | 2 | 15
pseudogene-chemical | interacts with | RNAInter | 82 | 7 | 84
premiRNA-chemical | interacts with |  | SM2miR
---
RNAInter
miRNet
miRandola
17 | 11 | 22
miRNA-chemical | interacts with |  | SM2miR
---
RNAInter
miRNet
miRandola
724 | 147 | 3,067
ASO drug-disease | is substance that treats | DrugBank | 10 | 8 | 12
ASO drug-protein | decreases by repression quantity of | DrugBank | 5 | 11 | 11
ASO drug-protein | is carrier of | DrugBank | 1 | 1 | 1
siRNA drug-mRNA | involved in negative regulation of | DrugBank | 3 | 3 | 3
siRNA-mRNA | involved in negative regulation of | ICBP siRNA | 77 | 54 | 79
shRNA-mRNA | involved in negative regulation of | ICBP siRNA | 40 | 42 | 42
siRNA drug-disease | is substance that treats | DrugBank | 4 | 4 | 6
aptamer-protein | molecularly interacts with | Apta-Index | 33 | 76 | 85
aptamer-chemical | molecularly interacts with | Apta-Index | 114 | 67 | 114
aptamer drug-protein | molecularly interacts with | DrugBank | 2 | 4 | 4
aptamer drug-disease | is substance that treats | DrugBank | 1 | 4 | 4
mRNA vaccine-disease | is substance that treats | DrugBank | 8 | 1 | 8
lncRNA-mRNA | interacts with |  | RNAInter
---
LncExpDB
2,214 | 18,228 | 2,429,654
lncRNA-lncRNA | interacts with | RNAInter | 1,431 | 1,431 | 3,187
lncRNA-rRNA | interacts with | RNAInter | 10 | 8 | 17
lncRNA-pseudogene | interacts with | RNAInter | 136 | 2,239 | 2,577
lncRNA-ncRNA | interacts with | RNAInter | 4 | 6 | 6
lncRNA-scaRNA | interacts with | RNAInter | 7 | 8 | 11
lncRNA-TF | interacts with | RNAInter | 4,833 | 370 | 173,842
lncRNA-ribozyme | interacts with | RNAInter | 1 | 1 | 1
circRNA-protein | interacts with | RNAInter | 1 | 2 | 2
ncRNA-protein | interacts with | RNAInter | 12 | 2 | 12
othersRNA-protein | interacts with | RNAInter | 12 | 2 | 12
pseudogene-protein | interacts with | RNAInter | 44 | 17 | 54
ribozyme-protein | interacts with | RNAInter | 1 | 2 | 2
Supplementary Table 4: RNA-KG Descriptive statistics by primary edge type (Part III). Edge | Relation | Source(s) | Subjects | Objects | Direct Relation(s)
---|---|---|---|---|---
scRNA-protein | interacts with | RNAInter | 1 | 5 | 5
snRNA-protein | interacts with | RNAInter | 3 | 11 | 11
snoRNA-protein | interacts with | RNAInter | 2 | 3 | 3
snoRNA-miscRNA | interacts with | snoDB | 2 | 2 | 2
unknownRNA-protein | interacts with | RNAInter | 1 | 1 | 1
vRNA-protein | interacts with | RNAInter | 1 | 1 | 1
riboswitch-protein | is downstream of sequence of | TBDB | 9,384 | 1,405 | 148,262
riboswitch-bacterial strain | interacts with | RSwitch database | 215 | 77 | 215
riboswitch-biological process | participates in | TBDB | 13,072 | 111 | 13,072
viralRNA-ribozyme | overlaps sequence of | ViroidDB | 5,654 | 6 | 5,896
ribozyme-biological process | participates in | Ribocentre | 2 | 6 | 11
miRNA-viralmRNA | interacts with | ViRBase | 80 | 38 | 250
snRNA-viralmRNA | interacts with | ViRBase | 1 | 1 | 1
lncRNA-viralmRNA | interacts with | ViRBase | 3 | 8 | 8
pseudogene-viralmRNA | interacts with | ViRBase | 1 | 3 | 3
snRNA-viralmiRNA | interacts with | ViRBase | 2 | 1 | 2
pseudogene-viralmiRNA | interacts with | ViRBase | 351 | 4 | 352
snoRNA-viralmiRNA | interacts with | ViRBase | 8 | 1 | 8
premiRNA-viralmiRNA | interacts with | ViRBase | 1 | 1 | 1
unknownRNA-viralmiRNA | interacts with | ViRBase | 3 | 1 | 3
lncRNA-viralmiRNA | interacts with | ViRBase | 263 | 1 | 263
othersRNA-viralmiRNA | interacts with | ViRBase | 1 | 1 | 1
scRNA-viralmiRNA | interacts with | ViRBase | 1 | 1 | 1
protein-viralmiRNA | interacts with | ViRBase | 11 | 18 | 21
mRNA-viralmiRNA | interacts with | ViRBase | 17,564 | 63 | 17,923
miRNA-viralmiRNA | interacts with | ViRBase | 1 | 1 | 1
lncRNA-viral protein | interacts with | ViRBase | 1 | 1 | 1
miRNA-viral protein | interacts with | ViRBase | 51 | 15 | 59
miRNA-ribozyme | interacts with | RNAInter | 1 | 1 | 1
miRNA-unknownRNA | interacts with | RNAInter | 14 | 4 | 14
miRNA-scRNA | interacts with | RNAInter | 1 | 1 | 1
othersRNA-mRNA | interacts with | RNAInter | 58 | 68 | 70
othersRNA-lncRNA | interacts with | RNAInter | 41 | 16 | 49
othersRNA-pseudogene | interacts with | RNAInter | 8 | 8 | 8
othersRNA-rRNA | interacts with | RNAInter | 1 | 1 | 1
snRNA-snRNA | interacts with | RNAInter | 21 | 21 | 33
snRNA-lncRNA | interacts with | RNAInter | 34 | 14 | 52
snRNA-mRNA | interacts with | RNAInter | 39 | 96 | 100
snRNA-pseudogene | interacts with | RNAInter | 9 | 11 | 15
eRNA-mRNA | interacts with | RNAInter | 2 | 2 | 4
scRNA-mRNA | interacts with | RNAInter | 1 | 23 | 23
mRNA-mRNA | interacts with | RNAInter | 4,459 | 4,459 | 7,035
mRNA-rRNA | interacts with | RNAInter | 120 | 4 | 133
mRNA-ncRNA | interacts with | RNAInter | 7 | 5 | 7
mRNA-scaRNA | interacts with | RNAInter | 4 | 2 | 4
mRNA-protein | interacts with | RNAInter | 179 | 130 | 227
rRNA-rRNA | interacts with | RNAInter | 2 | 3 | 3
circRNA-RBP | interacts with | RNAInter | 1,027 | 31 | 1,105
mRNA-RBP | interacts with | RNAInter | 8,856 | 130 | 17,754
ribozyme-RBP | interacts with | RNAInter | 1 | 13 | 13
scaRNA-RBP | interacts with | RNAInter | 11 | 17 | 35
scRNA-RBP | interacts with | RNAInter | 1 | 5 | 5
othersRNA-RBP | interacts with | RNAInter | 19 | 26 | 54
snRNA-RBP | interacts with | RNAInter | 44 | 42 | 158
lncRNA-RBP | interacts with | RNAInter | 1,011 | 140 | 6,136
snoRNA-RBP | interacts with | RNAInter | 200 | 70 | 753
ncRNA-RBP | interacts with | RNAInter | 5 | 45 | 48
rRNA-RBP | interacts with | RNAInter | 19 | 9 | 30
pseudogene-RBP | interacts with | RNAInter | 1,014 | 92 | 1,907
Supplementary Table 5: RNA-KG Descriptive statistics by primary edge type (Part IV). Edge | Relation | Source(s) | Subjects | Objects | Direct Relation(s)
---|---|---|---|---|---
premiRNA-RBP | interacts with | RNAInter | 32 | 18 | 37
unknownRNA-TF | interacts with | RNAInter | 1 | 74 | 74
circRNA-TF | interacts with | RNAInter | 33 | 6 | 33
ribozyme-TF | interacts with | RNAInter | 3 | 64 | 94
scaRNA-TF | interacts with | RNAInter | 17 | 83 | 429
othersRNA-TF | interacts with | RNAInter | 541 | 152 | 11,851
snRNA-TF | interacts with | RNAInter | 1,124 | 109 | 24,489
snoRNA-TF | interacts with | RNAInter | 419 | 108 | 11,932
scRNA-TF | interacts with | RNAInter | 1 | 74 | 74
tRNA-TF | interacts with | RNAInter | 8 | 10 | 77
ncRNA-TF | interacts with | RNAInter | 22 | 110 | 716
rRNA-TF | interacts with | RNAInter | 26 | 55 | 713
pseudogene-TF | interacts with | RNAInter | 9,524 | 245 | 188,443
mRNA-TF | interacts with | RNAInter | 5,019 | 222 | 8,629
premiRNA-TF | interacts with | RNAInter | 1,839 | 140 | 71,662
miRNA-TF | interacts with | RNAInter | 1,522 | 272 | 4,038
circRNA-extracellular vesicle | contained in | miRandola | 6 | 1 | 6
lipid-extracellular vesicle | contained in | Vesiclepedia | 35 | 3 | 47
mRNA-extracellular vesicle | contained in | Vesiclepedia | 13,907 | 2 | 14,116
snRNA-extracellular vesicle | contained in | Vesiclepedia | 1 | 1 | 1
protein-extracellular vesicle | contained in | Vesiclepedia | 11,924 | 5 | 20,081
miRNA-extracellular vesicle | contained in | Vesiclepedia | 435 | 2 | 554
premiRNA-extracellular vesicle | contained in | Vesiclepedia | 26 | 2 | 29
circRNA-miRNA | interacts with |  | SomamiR
---
miRNet
4,309 | 2,590 | 302,197
circRNA-premiRNA | interacts with |  | SomamiR
---
miRNet
8,755 | 110 | 129,990
unknownRNA-histone modification | interacts with | RNAInter | 1 | 18 | 18
mRNA-histone modification | interacts with | RNAInter | 42 | 20 | 486
othersRNA-histone modification | interacts with | RNAInter | 20 | 20 | 263
lncRNA-histone modification | interacts with | RNAInter | 4,816 | 20 | 51,375
ncRNA-histone modification | interacts with | RNAInter | 12 | 20 | 130
pseudogene-histone modification | interacts with | RNAInter | 43 | 20 | 460
premiRNA-histone modification | interacts with | RNAInter | 1,842 | 20 | 20,754
premiRNA-subcellular location | located in | RNALocate | 578 | 7 | 926