Open Access

ChemiRs: a web application for microRNAs and chemicals

  • Emily Chia-Yu Su1,
  • Yu-Sing Chen2,
  • Yun-Cheng Tien2,
  • Jeff Liu3,
  • Bing-Ching Ho4,
  • Sung-Liang Yu4 and
  • Sher Singh2Email author
BMC BioinformaticsBMC series – open, inclusive and trusted201617:167

https://doi.org/10.1186/s12859-016-1002-0

Received: 8 October 2015

Accepted: 27 March 2016

Published: 18 April 2016

Abstract

Background

MicroRNAs (miRNAs) are about 22 nucleotides, non-coding RNAs that affect various cellular functions, and play a regulatory role in different organisms including human. Until now, more than 2500 mature miRNAs in human have been discovered and registered, but still lack of information or algorithms to reveal the relations among miRNAs, environmental chemicals and human health. Chemicals in environment affect our health and daily life, and some of them can lead to diseases by inferring biological pathways.

Results

We develop a creditable online web server, ChemiRs, for predicting interactions and relations among miRNAs, chemicals and pathways. The database not only compares gene lists affected by chemicals and miRNAs, but also incorporates curated pathways to identify possible interactions.

Conclusions

Here, we manually retrieved associations of miRNAs and chemicals from biomedical literature. We developed an online system, ChemiRs, which contains miRNAs, diseases, Medical Subject Heading (MeSH) terms, chemicals, genes, pathways and PubMed IDs. We connected each miRNA to miRBase, and every current gene symbol to HUGO Gene Nomenclature Committee (HGNC) for genome annotation. Human pathway information is also provided from KEGG and REACTOME databases. Information about Gene Ontology (GO) is queried from GO Online SQL Environment (GOOSE). With a user-friendly interface, the web application is easy to use. Multiple query results can be easily integrated and exported as report documents in PDF format. Association analysis of miRNAs and chemicals can help us understand the pathogenesis of chemical components. ChemiRs is freely available for public use at http://omics.biol.ntnu.edu.tw/ChemiRs.

Keywords

microRNA Gene ontology Chemical Genomics Disease

Background

The interactions between genetic factors and environmental factors have critical roles in determining the phenotype of an organism. In recent years, a number of studies have reported that the dysfunctions on microRNA (miRNAs), environmental factors or their interactions have strong effects on phenotypes and even may result in abnormal phenotypes and diseases [1]. Environmental chemicals have been shown to play a critical role in the etiology of many human diseases [2]. Studies have also demonstrated the link between specific miRNAs and aspects of pathogenesis [3]. The fact that a miRNA may regulate hundreds of targets and one gene might be regulated by more than one miRNAs makes the underlying mechanism of miRNA pathogenicity more complex. Many miRNA targets have been computationally predicted, but only a limited number of these were experimentally validated. Although a variety of miRNA target prediction methods are available, resulting lists of candidate target genes identified by these methods often do not overlap and thus show inconsistency. Hence, finding a functional miRNA target is still a challenging task [4]. Some integration methods and tools for comprehensive analysis of miRNA target prediction have been developed, such as miRGen [5], miRWalk [6], starBase [7], and ComiR [8]. However, it is rarely seen the consolidation and comparison of miRNA target prediction methods with chemicals, diseases, pathways and Gene Ontology (GO) related applications. Thus, it is crucial to develop the bioinformatics tools for more accurate prediction as it is equally important to validate the predicted target genes experimentally [9]. In this study, we develop a ChemiRs web server, in which various miRNA prediction methods and biological databases are integrated and relations between miRNAs, chemicals, genes, diseases and pathways are analyzed. First, we manually retrieved the associations of miRNAs and chemicals from biomedical literature, and downloaded toxicogenomics data from the comparative toxicogenomic database (CTD; http://ctd.mdibl.org) [10]. Then, our method integrated the latest versions of publicly available miRNA target prediction methods and curated databases, including DIANA-microT [11, 12], miRanda [13], miRDB [14], RNAhybrid [15], PicTar [16], PITA [17], RNA22 [18], TargetScan [19], miRWalk [6], miRecords [20], miR2Disease [21], and miRBase [22, 23]. A set of experimentally validated target genes integrated from the miRecords and mirTarBase [24] servers is also integrated in the ChemiRs server. In addition, information from KEGG [25], REACTOME [26], and Gene Ontology [27] databases were organized into ChemiRs manually. The logical restriction was also designed to compare different miRNA target prediction methods easily using R (http://www.r-project.org) for statistics.

Implementation

The workflow of ChemiRs server is illustrated in Fig. 1. Given different types of query inputs from the users, ChemiRs server extracts relevant search results from various prediction methods and databases. Then, the results are shown in an interactive viewer and available as downloadable files. Next, the data sources, implementation and components of ChemiRs are described as follows.
Fig. 1

The workflow of ChemiRs web server. Illustration of six analysis modules provided by ChemiRs

Input

To access ChemiRs web server, a user has to choose a search function from main menu for one or more searches as query processing. In the ‘Search by miRNA’ module, the user directly selects a miRNA of interest from a dropdown list of human miRNAs. For the other search modules (i.e., search by gene, genelist, chemical, disease and pathway), the user can submit a query keyword of interest to search for related topics. A graphical control checkbox permits the user to make multiple choices of both the search databases and topics of interest. Detailed descriptions of the inputs are given by scrollable tabboxes, checkboxes, radio buttons or type text. Then, the ChemiRs server processes the user query, generates the intersection of search results, and calculates the statistical significance level with p-value.

Output

The search results of target genes and related associations with chemicals, diseases, pathways and GO terms are shown in the ChemiRs server. The output results are presented to the user via both an interactive viewer and downloadable files.

Interactive viewer

Query results are shown in a tabbox and automatically made scrollable when the sum of their width exceeds the container width size. The listbox component can automatically generate checkboxes or radio buttons for selecting list items by user selected attributes. Checkboxes allow multiple selections to be made, unlike the radio buttons. It is easy to obtain results immediately with sorting functionalities built in the grid and listbox components.

Downloadable files

The results can also be downloaded as comma-separated value (CSV) files, which can be easily imported into Microsoft Excel. The CSV files include all features calculated by ChemiRs. In addition, a related reference represented by the Pubmed ID is also provided. Multiple query results can also be easily integrated and exported as report documents in PDF format.

Data sources

Schema of the client-server architecture of ChemiRs is shown in Fig. 2. ChemiRs incorporated miRNA target prediction methods and curated databases, including DIANA-microT, miRanda, miRDB, RNAhybrid, PicTar, PITA, RNA22, TargetScan, miRWalk, miRecords, miR2Disease and miRBase as shown in Table 1. Data from the latest versions of all dependent databases are collected and integrated into a relational database in the ChemiRs server. A set of experimentally validated target genes integrated from the miRecords and mirTarBase servers is also integrated in the ChemiRs server. In addition, biological information from CTD, KEGG, REACTOME and Gene Ontology databases were manually curated into ChemiRs. The information is stored in a remote PostgreSQL server which is accessed through a Java Model-View-Controller (MVC) web service design. MyBatis library is used to connect to databases, and data can be retrieved by clients in both text and PDF formats.
Fig. 2

System overview of ChemiRs core framework. All results generated by ChemiRs are deposited in PostgreSQL relational databases and displayed in the visual browser and web page

Table 1

The versions and links of dependent databases used in the ChemiRs server

Results and discussion

Data statistics in ChemiRs

The data statistics of ChemiRs are described in Table 2. All data were organized in ChemiRs.
Table 2

Data statistics in the ChemiRs server

Category

Total number

Unique miRNAs

2,588

Unique genes

36,817

Unique chemicals

161,394

Unique diseases

11,860

Unique pathways

292

Gene Ontology (GO) terms

41,468

miRNA-target genes associations

5,087,441

miRNA-disease associations

2,323

Chemical-gene interactions

500,105

Gene-disease associations

182,490

Chemical-disease associations

1,834,693

Gene-GO annotations

314,375

Case studies

The aim of ChemiRs web server is to provide integrated and comprehensive miRNA target prediction analysis via flexible search functions, including search by miRNAs, gene lists, chemicals, genes, diseases and pathways. Next, case study examples by six different search methods are described in the following sections.

Search by a miRNA

As an example, we applied ChemiRs to analyze the hsa-let-7a-5p miRNA. We selected the miRNA ‘hsa-let-7a-5p’ in ‘Search by miRNA’ module and chose ‘pictar(5way),’ ‘PITA,’ ‘RNA22,’ and ‘TargetScan’ as miRNA target prediction methods; ‘4 minimum predicted methods’ as restrictions; and ‘Targets,’ ‘Chemicals,’ ‘Diseases,’ ‘Pathways,’ and ‘GO terms’ as the output functions, respectively. This example can be referred by clicking ‘Tip#2 logical analysis’ on the start page of ChemiRs. As shown in Fig. 3, a PDF report including top ten results can be easily downloaded. We checked ‘target genes,’ the top ten ‘related chemicals,’ ‘related diseases,’ ‘related pathways,’ and ‘related GO terms’ returned by ChemiRs, which were sorted according to their significance of activity changes denoted by -log(p-value). The p-value represents the probability of a random intersection of two different gene sets, and the p-value calculations are based on hypergeometric distribution. The probability to randomly obtain an intersection of certain size between user’s set and a network/pathway follows hypergeometric distribution. The lower the p-value, the higher is the non-randomness of finding such intersection. By taking log of p-value, the higher the -log(p-value), the higher is the non-randomness. Generally, when p-value is considered as 0.05, the -log(p-value) greater than 2.995 denotes statistically significant. As shown in Fig. 4, our system identified 37 miRNAs within the intersection of the 4-way Venn diagram. Notably, the top one related pathway, ‘Bladder cancer,’ has already been reported to be associated with the hsa-let-7a miRNA in biomedical literature [28]. This demonstrates that our proposed method is able to identify important features that correspond well with biological insights.
Fig. 3

Query result of ‘hsa-let-7a-5p’ by ‘Search by miRNA’ module in ChemiRs. Given a miRNA as query, ChemiRs identifies related a Targets, b Chemicals, c Diseases, d Pathways and e GO terms as output, respectively

Fig. 4

The four-way Venn diagram of hsa-let-7a-5p target genes using a pictar(5way), b PITA, c RNA22 and d TargetScan as the miRNA target prediction methods in ChemiRs

Search by a gene list

We applied ChemiRs to analyze a gene list data reported by Naciff et al. [29], in which the gene set was selected according to expression changes induced by Bisphenol A (BPA) and 17alpha-ethynyl estradiol in human Ishikawa cells. We downloaded the gene list with 76 genes in Table 6 [29] under the accession number GSE17624. We used the 76 genes gene symbols as input in ChemiRs by choosing ‘Search by gene list’ module, and ‘miRNAs,’ ‘Chemicals,’ ‘Diseases,’ ‘Pathways,’ and ‘GO terms’ as the output functions; all ten methods as miRNA target prediction methods; and ‘5 minimum predicted methods’ as restrictions, respectively.

We analyzed the top ten related chemicals returned by ChemiRs, which were sorted according to their significance of activity changes (i.e., −log(p-value)). Interestingly, we found that these chemicals have already been well-known to be associated with estrogens or Endocrine Disrupting Chemicals (EDCs). In fact, many industrially made estrogenic compounds and other EDCs are potential risk factors of cancer. Moreover, estrogen and progesterone receptor status have already been reported to be associated with breast cancer [30]. For example, BPA was linked to breast cancer tumor growth [31]. It is expected that other chemicals might also be involved in ‘Pathways in cancer’ returned by ChemiRs, and these chemicals might be potential candidates for further investigation.

Search by a chemical

Here, we exemplify the application of ChemiRs to search by chemicals. We applied ChemiRs to analyze diethylhexyl phthalate (DEHP) by submitting ‘DEHP’ in ‘Search by chemical’ module. After pressing the ‘Refresh’ button, we clicked the Medical Subject Heading (MeSH) ID ‘D004051, Diethylhexyl Phthalate’ and chose ‘None’ as the filter; ‘miRNAs,’ ‘Genes,’ ‘Diseases,’ ‘Pathways,’ and ‘GO terms’ as the output functions; all ten methods as miRNA target prediction methods, and ‘10 minimum predicted methods’ as restrictions, respectively. As shown in Fig. 5, the results can be easily downloaded as CSV files.
Fig. 5

Query result of ‘DEHP’ by ‘Search by chemical’ module in ChemiRs. Related miRNAs of MeSH ID ‘D004051, Diethylhexyl Phthalate’ are listed

We checked ‘Candidate miRNAs,’ the top ten ‘related genes,’ ‘related diseases,’ ‘related pathways,’ and ‘related GO terms’ returned by ChemiRs, which were sorted according to their significance of activity changes (i.e., −log(p-value)). The 93 related human genes and their associated references are listed in Table 3. The top one related pathway is ‘Pathways in cancer,’ and the top one related disease is ‘Brest-Ovarian Cancer, Familiar, Susceptibility To, 1; BROVCA1 (OMIM: 604370).’ DEHP is converted by intestinal lipases to mono-(2-ethylhexyl) phthalate (MEHP), which is then preferentially absorbed [2]. It has already been reported that exposure to the parent compound of the phthalate metabolite MEHP might be associated with breast cancer [32].
Table 3

Ninety-three related human genes and associated PubMed references of searching by chemical for MeSH ID (D004051, Diethylhexyl Phthalate)

Gene

Chemical

Reference PubMed ID

NR1I2

Diethylhexyl Phthalate

23899473;16054614;11581012;22206814;17003290;21227907

PPARG

mono-(2-ethylhexyl)phthalate

21561829;10581215;16326050;12927354;23118965

PPARA

Diethylhexyl Phthalate

10581215;20123618;21354252;16455614

CYP3A4

mono-(2-ethylhexyl)phthalate

23545481;18332045;22186153

CYP19A1

mono-(2-ethylhexyl)phthalate

22401849;19501113;19822197

ESR1

Diethylhexyl Phthalate

20382090;16756374;15840436

CYP3A4

Diethylhexyl Phthalate

11581012;18332045;21742782

CASP3

Diethylhexyl Phthalate

22155658;23220035;21864672

CASP3

mono-(2-ethylhexyl)phthalate

12927354;19165384;23360888

PPARA

mono-(2-ethylhexyl)phthalate

10581215;20123618;16326050

CYP1A1

Diethylhexyl Phthalate

8242868;16954067

NR1I3

Diethylhexyl Phthalate

21227907;23899473

NR4A1

mono-(2-ethylhexyl)phthalate

23118965;19822197

CYP2C9

mono-(2-ethylhexyl)phthalate

22186153;23545481

AR

Diethylhexyl Phthalate

19643168;20943248

AKR1B1

Diethylhexyl Phthalate

20943248;19643168

AKT1

Diethylhexyl Phthalate

19956873;23793038

IL4

Diethylhexyl Phthalate

20082445

HEXB

Diethylhexyl Phthalate

20082445

HEXA

Diethylhexyl Phthalate

20082445

ESR2

Diethylhexyl Phthalate

15840436

CYP1B1

Diethylhexyl Phthalate

16040568

CXCL8

Diethylhexyl Phthalate

23724284

CDO1

Diethylhexyl Phthalate

16223563

CASP9

Diethylhexyl Phthalate

22155658

CASP8

Diethylhexyl Phthalate

22155658

CASP7

Diethylhexyl Phthalate

21864672

BCL2

Diethylhexyl Phthalate

22155658

BAX

Diethylhexyl Phthalate

22155658

AHR

Diethylhexyl Phthalate

23220035

ACADVL

Diethylhexyl Phthalate

21354252

ACADM

Diethylhexyl Phthalate

21354252

ABCB1

Diethylhexyl Phthalate

17003290

ZNF461

mono-(2-ethylhexyl)phthalate

19822197

VCL

mono-(2-ethylhexyl)phthalate

22321834

TXNRD1

mono-(2-ethylhexyl)phthalate

23360888

TP53

mono-(2-ethylhexyl)phthalate

21515331

STAR

mono-(2-ethylhexyl)phthalate

22401849

SREBF2

mono-(2-ethylhexyl)phthalate

23118965

SREBF1

mono-(2-ethylhexyl)phthalate

23118965

SQLE

mono-(2-ethylhexyl)phthalate

23118965

SLC22A5

mono-(2-ethylhexyl)phthalate

23118965

SCD

mono-(2-ethylhexyl)phthalate

23118965

SCARA3

mono-(2-ethylhexyl)phthalate

23360888

PTGS2

mono-(2-ethylhexyl)phthalate

23360888

PRNP

mono-(2-ethylhexyl)phthalate

23360888

PPARGC1A

mono-(2-ethylhexyl)phthalate

20123618

NR4A3

mono-(2-ethylhexyl)phthalate

19822197

NR4A2

mono-(2-ethylhexyl)phthalate

19822197

NR1I2

mono-(2-ethylhexyl)phthalate

16054614

NR1H3

mono-(2-ethylhexyl)phthalate

23118965

NCOR1

mono-(2-ethylhexyl)phthalate

20123618

MYC

mono-(2-ethylhexyl)phthalate

22321834

MMP2

mono-(2-ethylhexyl)phthalate

22321834

MED1

mono-(2-ethylhexyl)phthalate

20123618

MBD4

mono-(2-ethylhexyl)phthalate

20123618

MARS

mono-(2-ethylhexyl)phthalate

22321834

LHCGR

mono-(2-ethylhexyl)phthalate

22401849

LFNG

mono-(2-ethylhexyl)phthalate

22321834

IL17RD

mono-(2-ethylhexyl)phthalate

22321834

ID1

mono-(2-ethylhexyl)phthalate

22321834

HSD11B2

mono-(2-ethylhexyl)phthalate

19786001

HMGCR

mono-(2-ethylhexyl)phthalate

23118965

GUCY2C

mono-(2-ethylhexyl)phthalate

22401849

GLRX2

mono-(2-ethylhexyl)phthalate

23360888

GJA1

mono-(2-ethylhexyl)phthalate

22321834

FSHR

mono-(2-ethylhexyl)phthalate

22401849

FSHB

mono-(2-ethylhexyl)phthalate

19501113

FASN

mono-(2-ethylhexyl)phthalate

23118965

EP300

mono-(2-ethylhexyl)phthalate

20123618

DHCR24

mono-(2-ethylhexyl)phthalate

23360888

DDIT3

mono-(2-ethylhexyl)phthalate

22321834

CYP2C19

mono-(2-ethylhexyl)phthalate

22186153

CYP1A1

mono-(2-ethylhexyl)phthalate

15521013

CTNNB1

mono-(2-ethylhexyl)phthalate

22321834

CSNK1A1

mono-(2-ethylhexyl)phthalate

16484285

CLDN6

mono-(2-ethylhexyl)phthalate

22321834

CGB

mono-(2-ethylhexyl)phthalate

22461451

CGA

mono-(2-ethylhexyl)phthalate

19501113

CELSR2

mono-(2-ethylhexyl)phthalate

16484285

CDKN1A

mono-(2-ethylhexyl)phthalate

21515331

CASP7

mono-(2-ethylhexyl)phthalate

23360888

BCL2

mono-(2-ethylhexyl)phthalate

12927354

BAX

mono-(2-ethylhexyl)phthalate

12927354

AOX1

mono-(2-ethylhexyl)phthalate

23360888

VEGFA

Diethylhexyl Phthalate

18252963

AMH

mono-(2-ethylhexyl)phthalate

19165384

TNF

Diethylhexyl Phthalate

20082445

TIMP2

Diethylhexyl Phthalate

19956873

SUOX

Diethylhexyl Phthalate

16223563

RPS6KB1

Diethylhexyl Phthalate

23793038

PPARD

Diethylhexyl Phthalate

16455614

PIK3CA

Diethylhexyl Phthalate

23793038

PAPSS2

Diethylhexyl Phthalate

16223563

PAPSS1

Diethylhexyl Phthalate

16223563

NCOA1

Diethylhexyl Phthalate

11581012

MYC

Diethylhexyl Phthalate

16455614

MTOR

Diethylhexyl Phthalate

23793038

MMP9

Diethylhexyl Phthalate

19956873

MMP2

Diethylhexyl Phthalate

19956873

MAPK3

Diethylhexyl Phthalate

16455614

MAPK1

Diethylhexyl Phthalate

16455614

LAMP3

Diethylhexyl Phthalate

20678512

Search by a gene

We applied ChemiRs to analyze the CXCR4 gene using ‘Search by gene’ module. After pressing the ‘Refresh’ button, we clicked ‘CXCR4,’ chose all output system functions, and pressed the ‘Query’ button. All the ‘related miRNAs,’ ‘related chemicals,’ ‘related diseases,’ ‘related pathways,’ and ‘related GO terms’ will be returned by ChemiRs.

Search by a disease

We applied ChemiRs to analyze Schizophrenia in ‘Search by disease’ module. We used ‘Schizophrenia’ as query and pressed the ‘Refresh’ button. A simple tree data model is used to represent a disease tree, and we pressed the light blue line’MeSH: D012559 Schizophrenia.’ All disease annotations included ‘MeSH Heading’ (i.e., controlled term in the MeSH thesaurus), ‘Tree Number’ (i.e., tree number of the MeSH term), ‘Scope Note’ (i.e., the scope notes that define the subject heading), and ‘MeSH Tree Structures’ (i.e., tree structure of the MeSH term) will be returned by ChemiRs.

Search by a pathway

We applied ChemiRs to analyze a cell cycle pathway using ‘Search by pathway’ module. We entered ‘cell cycle’ and pressed the ‘Refresh’ button, then five relevant pathways are listed. After we pressed the light blue line ‘KEGG: 04110 Cell cycle,’ all the hsa04110 pathway information will be returned.

Future extensions

In the future, we will continuously develop and enhance the interactive analysis module and adjust the web service for better user-experience. An automatic update will also be carried out monthly to keep pace with the latest database versions. It is also planned to incorporate more applications for gene expression data and allow users to customize their own visualization.

Conclusion

The ChemiRs web server integrates and compares ten miRNA target prediction methods of interest. The server provides comprehensive features to facilitate both experimental and computational target predictions. In addition, ChemiRs incorporates flexible search modules including (i) search by miRNA, (ii) search by gene, (iii) search by gene list, (iv) search by chemical, (v) search by disease and (vi) search by pathway. Moreover, ChemiRs can make predictions for Homo sapiens miRNAs of interest, and also allow fast search of query results for multiple miRNA selection and logical restriction, which can be easily integrated and exported as report documents in PDF format. The service is unique in that it integrates a large number of miRNA target prediction methods, experiment results, genes, chemicals, diseases and GO terms with instant and visualization functionalities.

Availability and requirements

Home page: http://omics.biol.ntnu.edu.tw

Tip: http://omics.biol.ntnu.edu.tw: Welcome

Demo: http://omics.biol.ntnu.edu.tw: Video

Tutorial: http://omics.biol.ntnu.edu.tw: Help

Operating system(s): Both portal and clients are platform independent.

Programming language: JAVA, JavaScript

Any restrictions to use by non-academics: None

Abbreviations

BPA: 

bisphenol A

DEHP: 

diethylhexyl phthalate

GO: 

gene ontology

MEHP: 

mono-(2-ethylhexyl) phthalate

MeSH: 

medical subject heading

miRNA: 

microRNA

MVC: 

Model-View-Controller

Declarations

Acknowledgements

We thank the NTNU BISBE Lab for supporting computational resources for this work.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University
(2)
Department of Life Science, College of Science, National Taiwan Normal University
(3)
Department of Civil Engineering, College of Engineering, National Taiwan University College of Engineering
(4)
Department of Clinical Laboratory Sciences and Medical Biotechnology, College of Medicine, National Taiwan University

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© Su et al. 2016