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Browsing by Subject "Databases, Genetic"
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Item A mathematical model of bimodal epigenetic control of miR-193a in ovarian cancer stem cells(PLoS, 2014-12-29) Cheng, Frank H.C.; Aguda, Baltazar; Tsai, Je-Chiang; Kochanczyk, Marek; Lin, Jora M.J.; Chen, Gary C.W.; Lai, Hung-Cheng; Nephew, Kenneth P.; Hwang, Tzy-Wei; Chan, Michael W.Y.; Department of Cellular and Integrative Physiology, IU School of MedicineAccumulating data indicate that cancer stem cells contribute to tumor chemoresistance and their persistence alters clinical outcome. Our previous study has shown that ovarian cancer may be initiated by ovarian cancer initiating cells (OCIC) characterized by surface antigen CD44 and c-KIT (CD117). It has been experimentally demonstrated that a microRNA, namely miR-193a, targets c-KIT mRNA for degradation and could play a crucial role in ovarian cancer development. How miR-193a is regulated is poorly understood and the emerging picture is complex. To unravel this complexity, we propose a mathematical model to explore how estrogen-mediated up-regulation of another target of miR-193a, namely E2F6, can attenuate the function of miR-193a in two ways, one through a competition of E2F6 and c-KIT transcripts for miR-193a, and second by binding of E2F6 protein, in association with a polycomb complex, to the promoter of miR-193a to down-regulate its transcription. Our model predicts that this bimodal control increases the expression of c-KIT and that the second mode of epigenetic regulation is required to generate a switching behavior in c-KIT and E2F6 expressions. Additional analysis of the TCGA ovarian cancer dataset demonstrates that ovarian cancer patients with low expression of EZH2, a polycomb-group family protein, show positive correlation between E2F6 and c-KIT. We conjecture that a simultaneous EZH2 inhibition and anti-estrogen therapy can constitute an effective combined therapeutic strategy against ovarian cancer.Item PAGER: constructing PAGs and new PAG-PAG relationships for network biology(Oxford University Press, 2015-06-15) Yue, Zongliang; Kshirsagar, Madhura M.; Nguyen, Thanh; Suphavilai, Chayaporn; Neylon, Michael T.; Zhu, Liugen; Ratliff, Timothy; Chen, Jake Yue; Department of Computer & Information Science, School of ScienceIn this article, we described a new database framework to perform integrative "gene-set, network, and pathway analysis" (GNPA). In this framework, we integrated heterogeneous data on pathways, annotated list, and gene-sets (PAGs) into a PAG electronic repository (PAGER). PAGs in the PAGER database are organized into P-type, A-type and G-type PAGs with a three-letter-code standard naming convention. The PAGER database currently compiles 44 313 genes from 5 species including human, 38 663 PAGs, 324 830 gene-gene relationships and two types of 3 174 323 PAG-PAG regulatory relationships-co-membership based and regulatory relationship based. To help users assess each PAG's biological relevance, we developed a cohesion measure called Cohesion Coefficient (CoCo), which is capable of disambiguating between biologically significant PAGs and random PAGs with an area-under-curve performance of 0.98. PAGER database was set up to help users to search and retrieve PAGs from its online web interface. PAGER enable advanced users to build PAG-PAG regulatory networks that provide complementary biological insights not found in gene set analysis or individual gene network analysis. We provide a case study using cancer functional genomics data sets to demonstrate how integrative GNPA help improve network biology data coverage and therefore biological interpretability. The PAGER database can be accessible openly at http://discovery.informatics.iupui.edu/PAGER/.Item PROGgeneV2: enhancements on the existing database(Springer (Biomed Central Ltd.), 2014) Goswami, Chirayu Pankaj; Nakshatri, Harikrishna; Department of Surgery, IU School of MedicineBACKGROUND: We recently published PROGgene, a tool that can be used to study prognostic implications of genes in various cancers. The first version of the tool had several areas for improvement. In this paper we present some major enhancements we have made on the existing tool in the new version, PROGgeneV2. RESULTS: In PROGgeneV2, we have made several modifications to enhance survival analysis capability of the tool. First, we have increased the repository of public studies catalogued in our tool by almost two folds. We have also added additional functionalities to perform survival analysis in a variety of new ways. Survival analysis can now be performed on a) single genes b) multiple genes as a signature, c) ratio of expression of two genes, and d) curated/published gene signatures in new version. Users can now also adjust the survival analysis models for available covariates. Users can study prognostic implications of entire gene signatures in different cancer types, which are searchable by keywords. Also, unique to our tool, in the new version, users will be able to upload and use their own datasets to perform survival analysis on genes of interest. CONCLUSIONS: We believe, like its predecessor, PROGGeneV2 will continue to be useful for the scientific community for formulating research hypotheses and designing mechanistic studies. With added datasets PROGgeneV2 is the most comprehensive survival analysis tool available. PROGgeneV2 is available at http://www.compbio.iupui.edu/proggene.Item SPOT-Seq-RNA: Predicting protein-RNA complex structure and RNA-binding function by fold recognition and binding affinity prediction(Springer, 2014) Yang, Yuedong; Zhao, Huiying; Wang, Jihua; Zhou, Yaoqi; Department of BioHealth InformaticsRNA-binding proteins (RBPs) play key roles in RNA metabolism and post-transcriptional regulation. Computational methods have been developed separately for prediction of RBPs and RNA-binding residues by machine-learning techniques and prediction of protein-RNA complex structures by rigid or semiflexible structure-to-structure docking. Here, we describe a template-based technique called SPOT-Seq-RNA that integrates prediction of RBPs, RNA-binding residues, and protein-RNA complex structures into a single package. This integration is achieved by combining template-based structure-prediction software, SPARKS X, with binding affinity prediction software, DRNA. This tool yields reasonable sensitivity (46 %) and high precision (84 %) for an independent test set of 215 RBPs and 5,766 non-RBPs. SPOT-Seq-RNA is computationally efficient for genome-scale prediction of RBPs and protein-RNA complex structures. Its application to human genome study has revealed a similar sensitivity and ability to uncover hundreds of novel RBPs beyond simple homology. The online server and downloadable version of SPOT-Seq-RNA are available at http://sparks-lab.org/server/SPOT-Seq-RNA/.