报告题目：Identifying interaction clusters and visualizing functional annotation for miRNA and mRNA pairs in a complex network
Among potential new diagnostic and therapeutic targets, microRNAs (hereinafter miRNAs) are increasingly studied because they play a well-conserved and crucial role in normal biological processes, such as cellular differentiation, proliferation and apoptosis through a complicated gene regulation networking. MiRNAs are an abundant non-coding RNAs that participate in posttranscriptional regulation through binding to the 3’ UTRs of messenger RNAs (mRNAs). Although there is a large number of studies on the characteristics of miRNA-mRNA interactions using miRNA and mRNA sequencing data, the complexity of the change of the correlation coefficients and expression values of the miRNA-mRNA pairs between tumor and normal samples is still not resolved, and this hinders the potential clinical applications. There is an urgent need to develop innovative methodologies and tools that can cluster mRNA-miRNA interaction pairs into groups and characterize functional consequences of cancer risk genes.
We developed an innovative bioinformatics tool for identifying gene and miRNA interaction clusters and visualizing functional annotation of miRNA-mRNA pairs in a network, known as MMiRNA-Viewer2. The tool contains a module of scoring and identifying significant clusters from mRNA and miRNA interaction pairs and can decipher mRNA and miRNA regulation network. Moreover, our MMiRNA-Viewer2 web server integrates and displays the mRNA and miRNA gene annotation information, signaling cascade pathways and direct cancer association between miRNAs and mRNAs. Functional annotation and gene regulatory information can be directly retrieved from our web server, which can help users quickly identify important interaction sub-network and report possible disease or cancer association.
Our MMiRNA-Viewer2 serves as a multitasking platform in which users can identify significant interaction clusters and retrieve functional and cancer-associated information for miRNA-mRNA pairs between tumor and normal samples. Our tool is applicable across a range of diseases and cancers and has uniquely distinctive advantages over existing tools.
Dr. Yongsheng Baireceived his B.S. degree in Animal Science from the China (Beijing) Agricultural University. After obtaining his M.Sc. degree in Poultry Science from the University of Arkansas and his M.Sc. degree in Computer Science from The University of Texas at Arlington, respectively, he received his Ph.D. in Quantitative Biology from The University of Texas at Arlington in 2007. Dr. Bai worked as an independent senior bioinformatics research scientist for the Human Genome Sequencing Center at Baylor College of Medicine between 2007-2008, where he populated the Genboree Database for the Bioinformatics Research Laboratory at Baylor College of Medicine. From 2008-2013, Dr. Bai worked as a senior research scientist in the Department of Computational Medicine and Bioinformatics, University of Michigan and as an adjunct faculty member in the Biology Department at Eastern Michigan University during his tenure in Michigan. Dr. Bai helped to establish the Bioinformatics Core at University of Michigan from scratch and developed several cutting-edge next-generation sequencing data analysis methods/tools for the Core and community to use. From 2013-2014, Dr. Bai worked as a computational biologist in Morgridge Institute for Research at University of Wisconsin-Madison and contributed his expertise in de novo assembly methods evaluation algorithm - DETONATE.
Dr. Yongsheng Bai joined the Department of Biology at Indiana State University as an assistant professor in 2014 and is an affiliated faculty member in the Math & Computer Science Department. He oversees the bioinformatics core in The Center for Genomic Advocacy at Indiana State University. Dr. Bai published his research work in many scientific journals and conferences and served as reviewers and editors for many prestigious national international journals. His current research interests lie in the development and refinement of bioinformatics algorithms/software and databases on next-generation sequencing data, development of statistical model for solving biological and agricultural problems, bioinformatics analysis of clinical data, as well as other topics including, but not limited to, large-scale genome annotation and comparative “Omics”.