A New System for Human MicroRNA functional Evaluation and Network

MicroRNAs are functionally important endogenous non-coding RNAs that silence host genes in animal and plant via destabilizing the mRNAs or preventing the translation. Given the far-reaching implication of microRNA regulation in human health, novel bioinformatics tools are desired to facilitate the mechanistic understanding of microRNA mediated gene regulation, their roles in biological processes, and the functional relevance among microRNAs. However, most state-of-the-art computational methods still focus on the functional study of microRNA targets and there is no e ective strategy to infer the functional similarity among microRNAs. In this study, we developed a new method to quantitatively measure the functional similarity among microRNAs based on the integrated functional annotation data from Gene Ontology, human pathways, and PFam databases. Through analyzing human microRNAs, we further demonstrated the use of the derived microRNA pairwise similarities to discover the cooperative microRNA modules and to construct the genome-scale microRNAmediated gene network in human. The complete results and the similarity assessment system can be freely accessed at (http://sbbi.unl.edu/microRNASim). Adviser: Juan Cu

Dynamic and Modularized MicroRNA Regulation and Its Implication in Human Cancers

MicroRNA is responsible for the fine-tuning of fundamental cellular activities and human disease development. The altered availability of microRNAs, target mRNAs, and other types of endogenous RNAs competing for microRNA interactions reflects the dynamic and conditional property of microRNA-mediated gene regulation that remains under-investigated. Here we propose a new integrative method to study this dynamic process by considering both competing and cooperative mechanisms and identifying functional modules where different microRNAs co-regulate the same functional process. Specifically, a new pipeline was built based on a meta-Lasso regression model and the proof-of-concept study was performed using a large-scale genomic dataset from ~4,200 patients with 9 cancer types. In the analysis, 10,726 microRNA-mRNA interactions were identified to be associated with a specific stage and/or type of cancer, which demonstrated the dynamic and conditional miRNA regulation during cancer progression. On the other hands, we detected 4,134 regulatory modules that exhibit high fidelity of microRNA function through selective microRNA-mRNA binding and modulation. For example, miR-18a-3p, −320a, −193b-3p, and −92b-3p co-regulate the glycolysis/gluconeogenesis and focal adhesion in cancers of kidney, liver, lung, and uterus. Furthermore, several new insights into dynamic microRNA regulation in cancers have been discovered in this study

Transcriptional and Post-transcriptional Regulation of Gene Expression

Regulation of gene expression includes a variety of mechanisms to increase or decrease specific gene products. Gene expression can be regulated at any stage from transcription to post-transcription and it\u27s essential to almost all living organisms, as it increases the versatility and adaptability by allowing the cell to express the needed proteins. In this dissertation, we comprehensively studied the gene regulation from both transcriptional and post-transcriptional points of view. Transcriptional regulation is by which cells regulate the transcription from DNA to RNA, thereby directing gene activity. Transcriptional factors (TFs) play a very important role in transcriptional regulation and they are proteins that bind to specific DNA sequences (regulatory elements) to regulate the gene expression. Current studies on TF binding are still very limited and thus, it leaves much to be improved on understanding the TF binding mechanism. To fill this gap, we proposed a variety of computational methods for predicting TF binding elements, which have been proved to be more efficient and accurate compared with other existing tools such as DREME and RSAT peaks-motif. On the other hand, studying only the transcriptional gene regulation is not enough for a comprehensive understanding. Therefore, we also studied the gene regulation at the post-transcriptional level. MicroRNAs (miRNAs) are believed to post-transcriptionally regulate the expression of thousands of target mRNAs, yet the miRNA binding mechanism is still not well understood. In this dissertation, we explored both the traditional and novel features of miRNA-binding and proposed several computational models for miRNA target prediction. The developed tools outperformed the traditional microRNA target prediction methods (.e.g miRanda and TargetScan) in terms of prediction accuracy (precision, recall) and time efficiency

MicroRNA modules prefer to bind weak and unconventional target sites

Motivation: MicroRNAs (miRNAs) play critical roles in gene regulation. Although it is well known that multiple miRNAs may work as miRNA modules to synergistically regulate common target mRNAs, the understanding of miRNA modules is still in its infancy. Results: We employed the recently generated high throughput experimental data to study miRNA modules. We predicted 181 miRNA modules and 306 potential miRNA modules. We observed that the target sites of these predicted modules were in general weaker compared with those not bound by miRNA modules. We also discovered that miRNAs in predicted modules preferred to bind unconventional target sites rather than canonical sites. Surprisingly, contrary to a previous study, we found that most adjacent miRNA target sites from the same miRNA modules were not within the range of 10–130 nucleotides. Interestingly, the distance of target sites bound by miRNAs in the same modules was shorter when miRNA modules bound unconventional instead of canonical sites. Our study shed new light on miRNA binding and miRNA target sites, which will likely ad-vance our understanding of miRNA regulation. Availability and implementation: The software miRModule can be freely downloaded a

Microrna Modules Prefer To Bind Weak And Unconventional Target Sites

Motivation: MicroRNAs (miRNAs) play critical roles in gene regulation. Although it is well known that multiple miRNAs may work as miRNA modules to synergistically regulate common target mRNAs, the understanding of miRNA modules is still in its infancy. Results: We employed the recently generated high throughput experimental data to study miRNA modules. We predicted 181 miRNA modules and 306 potential miRNA modules. We observed that the target sites of these predicted modules were in general weaker compared with those not bound by miRNA modules. We also discovered that miRNAs in predicted modules preferred to bind unconventional target sites rather than canonical sites. Surprisingly, contrary to a previous study, we found that most adjacent miRNA target sites from the same miRNA modules were not within the range of 10-130 nucleotides. Interestingly, the distance of target sites bound by miRNAs in the same modules was shorter when miRNA modules bound unconventional instead of canonical sites. Our study shed new light on miRNA binding and miRNA target sites, which will likely advance our understanding of miRNA regulation