=Paper=
{{Paper
|id=Vol-1709/BMDID_2016_paper_8
|storemode=property
|title=BiDIP: a Biological Data Integration Platform for Transcriptome Analysis
|pdfUrl=https://ceur-ws.org/Vol-1709/BMDID_2016_paper_8.pdf
|volume=Vol-1709
|authors=Junho Park,Min-Ji Kim,Eung-Hee Kim,Sungkwon Yang,Sungin Lee,Jin-Muk Lim,Hyunwhan Joe,Kyung-Sik Ha,Hong-Gee Kim
|dblpUrl=https://dblp.org/rec/conf/semweb/ParkKKYLLJHK16
}}
==BiDIP: a Biological Data Integration Platform for Transcriptome Analysis==
https://ceur-ws.org/Vol-1709/BMDID_2016_paper_8.pdf
BiDIP: a Biological Data Integration Platform for
Transcriptome Analysis
Junho Park1, Min-Ji Kim2, Eung-Hee Kim1, Sungkwon Yang2, Sungin Lee2, Jin-Muk
Lim2, Hyunwhan Joe1, Kyung-Sik Ha1, and Hong-Gee Kim1
Biomedical Knowledge Engineering Lab. (BiKE),
Seoul National University, Seoul, Korea
1{naon,eungheekim,hyunwhanjoe,hakyung,hgkim}@snu.ac.kr
2{mingmaroo,syang0531,sunginlee,bikeljm}@gmail.com
Abstract. Many studies aimed to construct an automated gene expression analy-
sis platform for researchers. However, they lack an integrated data model for an-
alyzing heterogeneous data. In order to address this issue, we created a biological
data integration platform for transcriptome analysis (BiDIP) for managing vari-
ous kinds of databases. As part of this platform, we developed a biological inter-
action data model (BIM). Also we provide a Web application and OpenAPIs that
allow users to search for connections among multiple databases conformant to
the proposed data model. In this paper, we will present the current status of the
platform as well as its future research venues.
Keywords: Biological data integration, Biological ontology, Interaction data
model, Gene expression analysis, Transcriptomics
1 Introduction
Gene expression analysis has been a valuable research venue that provides for explan-
atory power to specific biological phenomena, given a biological context for a patient’s
histology or medicinal intervention. With the advent of microarray technology, tran-
scriptome analysis has greatly overcome the limitations resident in studying individual
genes, which offers fragmented and insufficient explanation for biological phenomena.
Microarray technology enables us to measure and identify compositions of transcrip-
tome under certain biological conditions. It, however, poses a significant challenge to
biologists, especially those who are not well-versed in using the technology. Seemingly
small, but still challenging to biologists, was the necessity for adequate amount of
knowledge in coding required for data processing and statistical analysis of the data
gained from microarray experiments. Almost 10 years after the introduction of the tech-
nology, we have acquired standard protocols for microarray analysis, though biologists
still quite often need helping hands of computer experts or of companies that provide
experiment tool kits.
We aimed to develop a platform that facilitates transcriptome analysis called
Online Transcriptome Analysis Pipeline (OnTAP). Two major components constitute
OnTAP: a Biological Data Integration Platform (BiDIP) and analysis modules includ-
ing data pre-processing. There are four sets of analysis modules: gene set enrichment
analysis, gene network analysis, drug target prioritization analysis, and miRNA target
prioritization analysis.
BiDIP consists of four main components: 1) a comprehensive database model,
BIM (Biological Interaction data Model) that encompasses 4 types of biological data-
bases; 2) 4 integrated databases, which deal with PPI (Protein-Protein Interaction) da-
tabases, DGI (Drug-Gene Interaction) databases, microRNA databases, and pathway
databases; 3) BiDIP browser, and 4) OpenAPI. BiDIP provides a unified view on vari-
ous biological databases facilitating and streamlining transcriptome analysis, alleviat-
ing some burden off biology researchers. This paper presents OnTAP in general, and
BiDIP in particular.
2 Related Work
Studies abound that aimed to provide streamlined services for microarray analysis: Ar-
rayPipe [1], Expression Profiler [2], L2L microarray analysis tool [3], RACE [4],
WebArray [5], MIDAW [6], CARMAweb [7], Asterias [8], MAGMA [9], GEPAS
[10], EzArray [11], ArrayMining [12], MAGNET [13] and GALAHAD [14]. The most
common services of these studies are data pre-processing, data normalization, gene
name conversion, gene or sample clustering, gene set enrichment analysis, and data
visualization. It should be noted that more recent studies used a higher volume of data,
offered more analysis services, such as network-based analysis [13] and drug-focused
analysis [14].
The notable drawbacks of the previous studies are: 1) limited coverage/volume of
data - it is arguably true that the higher volume of, and wider coverage of, data will
plug the gaps in our knowledge for biological phenomena; 2) absence of a comprehen-
sive and inclusive data model by which to view and analyze heterogeneous databases.
3 BiDIP
This section presents detailed explanation on BiDIP, with focus on Biological Interac-
tion data Model (BIM), integrated databases, Web-based browser, and Open API.
3.1 Snapshot of BiDIP
Fig. 1 shows the overall picture of OnTAP. In order to enable analysis on gene set
enrichment, gene network, drug target prioritization, and miRNA prioritization, four
types of databases are required: biological pathway, PPI (Protein-Protein interaction),
DGI (Drug-gene Interaction), and MGI (miRNA-gene interaction). BiDIP provides in
the main data groundwork for such analyses.
Fig. 1. The overall picture of OnTAP
3.2 Biological Interaction Data Model (BIM)
The core relations that BIM is designed to express are gene-gene, gene-drug, gene-
miRNA relations, and pathway data. As shown in Fig. 2, the top classes in BIM are
process, interaction and biochemical entity. Biochemical entity represents objects in-
volved in a biological interaction. An interaction connects two biochemical entities. A
sequence of interactions makes a process. In a process, the order of the interactions
carries significant meaning. Fig. 3 shows a more detailed overview of BIM.
Fig. 2. Top-level Classes of BIM
Fig. 3. Detailed View on BIM
3.3 BIM classes
Biochemical entity contains two main subclasses: gene and drug. Gene is defined as a
biological region with a specific function. Gene has three subclasses: 1) DNA-specific
region that will become a protein or functional RNA, 2) transcribed RNA from DNA-
specific region, 3) translated protein from transcribed RNA. RNA has two subclasses:
miRNA and mRNA. Drug represents any material used to treat or prevent diseases. In
transcriptome analysis, the distinction between gene and its subclasses has not been
used in general. However, we have made that distinction to reflect biological reality,
which allows BIM to link with other ontologies that require such distinction. Biochem-
ical entity is linked to EntityFeatures class with properties expressing full-name, ge-
nomic region, function annotation, and molecular weight.
Interaction has subclasses representing various unions of biochemical entities
such as Gene to Gene Interaction (GGI) and Drug to Gene Interaction (DGI). GGI has
miRNA to Gene Interaction (MGI) subclass, and Protein-Protein Interaction (PPI) sub-
class. GGI has two gene participants, MGI has mRNA and miRNA participants, PPI has
two protein participants and DGI has gene and drug participants. Interaction also has a
link to Publication class, each object of which is identified by PubMed id (PMID).
Process has a subclass pathway representing a sequence of GGI.
3.4 BIM properties
A process has a 1: N relationship with its interactions and each interaction has 2 bio-
chemical entities using a participant property. Participant property has two sub prop-
erties: participant gene and participant drug. participant gene has three sub properties:
participant_mRNA, participant_miRNA, and participant_ptn. Each interaction has a
datasource property to represent the origin of database. The xref (external reference)
property is used in two cases 1) to link publication objects to publication url 2) to link
a biochemical object to an external site for more information. To express a sequence in
a pathway we used the nextstep property, as shown in Fig. 5.
3.5 Integrated Databases
Biological databases are integrated based on BIM as shown in Table 1. We extracted
human species (Homo sapiens) records from the databases and then converted Gene ID
to official Gene Symbol by the HGNC standard.
Table 1. Integrated Databases
Database Type Data Source # of
instances
Pathway KEGG [15], Reactome [16] 833
Drug-Gene KEGG, DGIdb [17], PharmGKB [18], STITCH 793,405
Interaction [19]
Protein-Protein BIND [20], BioGrid[21], DIP [22], HPRD [23], In- 4,912,839
Interaction tAct [24], MINT [25], STRING [26]
miRNA-Gene miRTarBase [27], TarBase [28] 1,059,998
Interaction
3.6 BIM-based Representation Example
This section presents an example of how a gene is expressed in BIM. An ion channel
gene TRPC1 is used as an example in Fig. 4. TRPC1 is linked to Protein STIM1 with
PPI (PPI 307419), to miR-124-3p with MGI (MGI 235456), and to Dantrolene with
DGI (DGI 51715). Each interaction instance has a datasource property and an xref
property. Fig. 5 shows a serotonergic synapse pathway that includes TRPC1. A path-
way instance has more than one GGI component and a GGI instance has 2 participating
genes. The order of each GGI instance is determined by using the nextstep property to
represent the pathway flow.
Fig. 4. BIM Representation of Entities Participating in PPI, MGI, and DGI of TRPC1
Fig. 5. BIM Representation of Serotonergic Synapse Pathway including TRPC1
3.7 BiDIP Browser and OpenAPI
A Web-based application, BiDIP Browser, and open-access APIs were developed. Both
services were designed and implemented to enable efficient retrieval of gene-centric
interaction information on drugs, miRNAs, pathways and proteins based on BIM. As
shown in Fig. 6, the browser has 5 panels: 1) Finding genes & Gene browsing history,
2) drug list, 3) miRNA list, 4) gene (on protein layer) list, and 5) pathway list. Given a
keyword entered in the Finding Genes box, a list of candidate genes is listed whose
symbols start with the keyword. Once a gene is selected from the search results, it is
first recorded and listed as a previously-selected gene in the Gene browsing history list.
The drug, miRNA, gene and pathway panels show elements interacting with the se-
lected gene. Clicking the ‘Save browsing history’ button will enable the user to save
his/her browsing history as an XML file. The browser is available at
http://147.47.41.58:8080/BiDIP_Browser/. Most of the information the user can access
from the browser can be obtained by using our RESTful web services, BiDIP OpenAPI;
and third parties can use the services to develop their own systems and applications that
make use of the BIM-based gene-centric interaction datasets. Table 2 gives a list of the
web services with examples.
Table 2. OpenAPI Web Services
Service Parameter syntax Example
getGenes keyword:KEY: baseURL/getGenes/keyword:AA:from:5to:
from:START 10
to:OFFSET
getDrugs geneIndex:GINDEX: baseURL/getDrugs/geneIndex:139:from:0t
from:START o:5
to:OFFSET
getMiRnas geneIndex:GINDEX: baseURL/getMiRnas/geneIndex:139:from:
from:START 0to:5
to:OFFSET
getPpis geneIndex:GINDEX: baseURL/getPpis/geneIndex:139:from:0to:
from:START 5
to:OFFSET
getPathways geneIndex:GINDEX: baseURL/getPathways/geneIndex:139:fro
from:START m:0to:5
to:OFFSET
* baseURL: http://147.47.41.58:8080/BiDIP_OpenAPI/openApi
Fig. 6. Screenshot of BiDIP Browser
4 Conclusion and Future Work
In this paper, a biological data integration platform, BiDIP, is presented. BiDIP inte-
grates four sets of interaction databases into a common data model. The differentiating
factors of this study that distinguish it from the extant similar gene expression analysis
studies are as follows: 1) development of comprehensive data model, BIM, for interac-
tion databases; 2) creation of consolidated databases for four types of heterogeneous
interaction databases; 3) two points of access, BiDIP Browser and open APIs, to the
database. BiDIP enables researchers to gain access to integrative biological information
about genes of interest.
The focus of this study has been to develop a solid basis for interaction-focused
databases. Hence, there are certain details left out for now such as epigenetic infor-
mation, DNA variant, and etc. These data will be progressively added to BIM.
Finally, BiDIP is part of a larger project called OnTAP. We are currently in
the process of developing data input modules, data pre-processing modules, and data
analysis modules for full deployment of OnTAP for general use.
Acknowledgements.
This research was supported by Basic Science Research Program through the National
Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT and fu-
ture Planning (NRF-2014R1A2A1A11049728)
References
1. Hokamp, K., Roche, F., Acab, M., Rousseau, M., Kuo, B., Goode, D., Aeschliman, D.,
Bryan, J., Babiuk, L., Hancock, R., Brinkman, F.S.: ArrayPipe: a flexible processing pipe-
line for microarray data. Nucleic Acids Res. 32 (Web Server), W457 (2004)
2. Kapushesky, M., Kemmeren, P., Culhane, A., Durinck, S., Ihmels, J., Korner, C., Kull, M.,
Torrente, A., Sarkans, U., Vilo, J., Brazma, A.: Expression Profiler: next generation-an
online platform for analysis of microarray data. Nucleic Acids Res. 32 (Web Server), W465
(2004)
3. Newman, J.C., Weiner, A.M.: L2L: a simple tool for discovering the hidden significance in
microarray expression data. Genome Biol. 6(9), R81 (2005)
4. Psarros, M., Heber, S., Sick, M., Thoppae, G., Harshman, K., Sick, B.: RACE: remote anal-
ysis computation for gene expression data. Nucleic Acids Res. 33 (Web Server), W638
(2005)
5. Xia, X., McClelland, M., Wang, Y.: WebArray: an online platform for microarray data anal-
ysis. BMC Bioinformatics. 6, 306 (2005)
6. Romualdi, C., Vitulo, N., Favero, M., Lanfranchi, G.: MIDAW: a web tool for statistical
analysis of microarray data. Nucleic Acids Res. 33 (Web Server), W644 (2005)
7. Rainer, J., Sanchez-Cabo, F., Stocker, G., Sturn, A., Trajanoski, Z.: CARMAweb: compre-
hensive R-and bioconductor-based web service for microarray data analysis. Nucleic Acids
Res. 34 (Web Server), W498 (2006)
8. Diaz-Uriarte, R., Alibes, A., Morrissey, E., Canada, A., Rueda, O.M., Neves, M.L.: Asterias:
integrated analysis of expression and aCGH data using an open-source, web-based, parallel-
ized software suite. Nucleic Acids Res. 35 (Web Server), W75 (2007)
9. Rehrauer, H., Zoller, S., Schlapbach, R.: MAGMA: analysis of two-channel microarrays
made easy. Nucleic Acids Research. 35 (Web Server), W86 (2007)
10. Tárraga, J., Medina, I., Carbonell, J., Huerta-Cepas, J., Minguez, P., Alloza, E., Al-Shahrour,
F., Vegas-Azcárate, S., Goetz, S., Escobar, P., Garcia-Garcia, F., Conesa, A., Montaner, D.,
Dopazo, J.: GEPAS, a web-based tool for microarray data analysis and interpretation. Nu-
cleic Acids Res. 31(13), 3461-3467 (2008)
11. Zhu, Y., Zhu, Y., Xu, W.: EzArray: A web-based highly automated Affymetrix expression
array data management and analysis system. BMC Bioinformatics. 9(46) (2008)
12. Glaab, E., Garibaldi, J.M., Krasnogor, N.: ArrayMining: a modular web-application for mi-
croarray analysis combining ensemble and consensus methods with cross-study normaliza-
tion. BMC Bioinformatics. 10(358) (2009)
13. Linderman, G.C., Chance, M.R., Bebek, G.: MAGNET: Microarray gene expression and
network evaluation toolkit. Nucleic Acids Res. 40 (Web server), W152-156 (2012)
14. Laenen, G., Ardeshirdavani, A., Moreau, Y., Thorrez, L.: Galahad: a web server for drug
effect analysis from gene expression. Nucleic Acids Res. 43 (Web server), W208-212 (2015)
15. KEGG: Kyoto Encyclopedia of Genes and Genomes, http://www.genome.jp/kegg
16. Reactome, http://www.reactome.org
17. DGIdb: The Drug Gene Interaction Database, http://dgidb.genome.wustl.edu
18. PharmGKB: The Pharmacogenomic Knowledgebase, https://www.pharmgkb.org
19. STITCH: Chemical-Protein Interactions, http://stitch.embl.de
20. BINDTranslation, http://baderlab.org/BINDTranslation
21. BioGRID: The Biological General Repository for Interaction Datasets, http://thebiogrid.org
22. DIP: Database of Interacting Proteins, http://dip.doe-mbi.ucla.edu/dip/Main.cgi
23. HPRD: Human Protein Reference Databse, http://www.hprd.org
24. IntAct, http://www.ebi.ac.uk/intact
25. MINT: The Molecular INTeraction database, http://mint.bio.uniroma2.it
26. STIRNG: functional protein association networks, http://www.string-db.org
27. miRTarBase: the experimentally validated microRNA-target interactions database,
http://mirtarbase.mbc.nctu.edu.tw
28. TarBase, http://diana.imis.athena-innovation.gr/DianaTools/index.php?r=tarbase/index