Identification of BAP1-associated MicroRNAs and Implications in Cancer Development

Mapping Intimacies ◽

10.31487/j.ijcst.2019.01.01 ◽

2019 ◽

pp. 1-4

Author(s):

Tikam Chand ◽

Tikam Chand

Keyword(s):

Gene Regulation ◽

In Silico ◽

Mirna Target ◽

Target Prediction ◽

Differential Regulation ◽

Cancer Development ◽

Mirna Target Prediction ◽

Cancer Types ◽

In Silico Tools

Having role in gene regulation and silencing, miRNAs have been implicated in development and progression of a number of diseases, including cancer. Herein, I present potential miRNAs associated with BAP1 gene identified using in-silico tools such as TargetScan and Exiqon miRNA Target Prediction. I identified fifteen highly conserved miRNA (hsa-miR-423-5p, hsa-miR-3184-5p, hsa-miR-4319, hsa-miR125b-5p, hsa-miR-125a-5p, hsa-miR-6893-3p, hsa-miR-200b-3p, hsa-miR-200c-3p, hsa-miR-505-3p.1, hsa-miR-429, hsa-miR-370-3p, hsa-miR-125a-5p, hsa-miR-141-3p, hsa-miR-200a-3p, and hsa-miR-429) associated with BAP1 gene. We also predicted the differential regulation of these twelve miRNAs in different cancer types.

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Beyond seed match: Improving miRNA target prediction using PAR-CLIP data

2011 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS) ◽

10.1109/gensips.2011.6169461 ◽

2011 ◽

Author(s):

Mingzhu Lu ◽

C. L. Philip Chen ◽

Yufei Huang

Keyword(s):

Mirna Target ◽

Target Prediction ◽

Mirna Target Prediction ◽

Seed Match

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Comprehensive Overview and Assessment of microRNA Target Prediction Tools in Homo sapiens and Drosophila melanogaster

Current Bioinformatics ◽

10.2174/1574893614666190103101033 ◽

2019 ◽

Vol 14 (5) ◽

pp. 432-445 ◽

Cited By ~ 3

Author(s):

Muniba Faiza ◽

Khushnuma Tanveer ◽

Saman Fatihi ◽

Yonghua Wang ◽

Khalid Raza

Keyword(s):

Drosophila Melanogaster ◽

Mirna Target ◽

Homo Sapiens ◽

Target Prediction ◽

Computational Prediction ◽

Transcriptional Level ◽

Mirna Target Prediction ◽

Microrna Target ◽

Prediction Tools ◽

Mirna Targets

Background: MicroRNAs (miRNAs) are small non-coding RNAs that control gene expression at the post-transcriptional level through complementary base pairing with the target mRNA, leading to mRNA degradation and blocking translation process. Many dysfunctions of these small regulatory molecules have been linked to the development and progression of several diseases. Therefore, it is necessary to reliably predict potential miRNA targets. Objective: A large number of computational prediction tools have been developed which provide a faster way to find putative miRNA targets, but at the same time, their results are often inconsistent. Hence, finding a reliable, functional miRNA target is still a challenging task. Also, each tool is equipped with different algorithms, and it is difficult for the biologists to know which tool is the best choice for their study. Methods: We analyzed eleven miRNA target predictors on Drosophila melanogaster and Homo sapiens by applying significant empirical methods to evaluate and assess their accuracy and performance using experimentally validated high confident mature miRNAs and their targets. In addition, this paper also describes miRNA target prediction algorithms, and discusses common features of frequently used target prediction tools. Results: The results show that MicroT, microRNA and CoMir are the best performing tool on Drosopihla melanogaster; while TargetScan and miRmap perform well for Homo sapiens. The predicted results of each tool were combined in order to improve the performance in both the datasets, but any significant improvement is not observed in terms of true positives. Conclusion: The currently available miRNA target prediction tools greatly suffer from a large number of false positives. Therefore, computational prediction of significant targets with high statistical confidence is still an open challenge.

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