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ChemiRs: a web application for microRNAs and chemicals.

Su EC, Chen YS, Tien YC, Liu J, Ho BC, Yu SL, Singh S - BMC Bioinformatics (2016)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, 110, Taiwan.

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 .

No MeSH data available.


Related in: MedlinePlus

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
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Fig4: 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

Mentions: 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


ChemiRs: a web application for microRNAs and chemicals.

Su EC, Chen YS, Tien YC, Liu J, Ho BC, Yu SL, Singh S - BMC Bioinformatics (2016)

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
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4836156&req=5

Fig4: 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
Mentions: 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

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, 110, Taiwan.

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 .

No MeSH data available.


Related in: MedlinePlus