Browsing by Author "Sun, Liang"
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Item PathRings: a web-based tool for exploration of ortholog and expression data in biological pathways(BioMed Central Ltd., 2015-05-19) Zhu, Yongnan; Sun, Liang; Garbarino, Alexander; Schmidt, Carl; Fang, Jinglong; Chen, Jian; Yongnan Zhu, Liang Sun, Alexander Garbarino, Carl Schmidt, Jinglong Fang and Jian Chen; Sun, Liang; Schmidt, CarlBackground High-throughput methods are generating biological data on a vast scale. In many instances, genomic, transcriptomic, and proteomic data must be interpreted in the context of signaling and metabolic pathways to yield testable hypotheses. Since humans can interpret visual information rapidly, a means for interactive visual exploration that lets biologists interpret such data in a comprehensive and exploratory manner would be invaluable. However, humans have limited memory capacity. Current visualization tools have limited viewing and manipulation capabilities to address complex data analysis problems, and visual exploratory tools are needed to reduce the high mental workload imposed on biologists. Results We present PathRings, a new interactive web-based, scalable biological pathway visualization tool for biologists to explore and interpret biological pathways. PathRings integrates metabolic and signaling pathways from Reactome in a single compound graph visualization, and uses color to highlight genes and pathways affected by input data. Pathways are available for multiple species and analysis of user-defined species or input is also possible. PathRings permits an overview of the impact of gene expression data on all pathways to facilitate visual pattern finding. Detailed pathways information can be opened in new visualizations while maintaining the overview, that form a visual exploration provenance. A dynamic multi-view bubbles interface is designed to support biologists’ analytical tasks by letting users construct incremental views that further reflect biologists’ analytical process. This approach decomposes complex tasks into simpler ones and automates multi-view management. Conclusions PathRings has been designed to accommodate interactive visual analysis of experimental data in the context of pathways defined by Reactome. Our new approach to interface design can effectively support comparative tasks over substantially larger collection than existing tools. The dynamic interaction among multi-view dataset visualization improves the data exploration. PathRings is available free at http://raven.anr.udel.edu/~sunliang/PathRings webcite and the source code is hosted on Github: https://github.com/ivcl/PathRings webcite.Item Transcriptome analysis of heat stressed LMH cells and related gene enrichment tools(University of Delaware, 2015) Sun, LiangHuge economic loss is caused by heat-related stress in poultry industries in the United States. Studying the molecular mechanism underlying heat stress is important for improving chicken egg and meat production. In the first study, we have used RNA-Seq to identify heat stress responsive genes in the male white-leghorn chicken hepatocellular (LMH) cell line. High throughput technologies are being used to simultaneously study the expression patterns of large numbers of genes. Typically these studies involve examining how genes are expressed in two or more biological states (e.g. heat stress vs. normal condition), with the goal of understanding how biological differences affect transcription. This can generate lists containing hundreds to thousands of differentially expressed genes that are challenging to interpret. One approach to understanding the underlying biology of large gene lists is to group the responsive genes to knowledge bases such as pathways. However, no popular pathway databases, such as KEGG and Reactome, have complete chicken pathway data. Reactome is a popular human centric metabolic and signaling pathway database that relies on orthology between genomic sequences to predict pathways in other species. This approach is valuable, but will miss genes identified in transcriptomes that have not yet been identified in the respective genomic sequence. My second study extends the Reactome approach to use orthology information based on transcriptome data to annotate chicken pathways and create a web-based chicken pathway analysis and visualization tool ( http://raven.anr.udel.edu/~sunliang/pathway/ ). Second approach to help the researcher interpret large gene datasets is to associate genes and informative terms (iTerm) that are obtained from the biomedical literature using the eGIFT text-mining system. However, examining large lists of iTerm and gene pairs is a daunting task. My third study is to develop WebGIVI, an interactive web-based visualization tool to explore gene:iTerm pairs ( http://raven.anr.udel.edu/~sunliang/webgivi/index.php ). The transcriptome analysis of heat stressed LMH cells help us further understand heat stress mechanism. The limitation of bioinformatics analysis tools in this study also encouraged us to create two bioinformatics enrichment tools, WebCHRIP and WebGIVI. These two tools can facilitate the enrichment of large gene lists, and help biologists to generate integrated biological hypotheses.Item WebGIVI: a web-based gene enrichment analysis and visualization tool(BioMed Central, 2017-05-04) Sun, Liang; Zhu, Yongnan; Mahmood, A. S. M. Ashique; Tudor, Catalina O.; Ren, Jia; Vijay-Shanker, K.; Chen, Jian; Schmidt, Carl J.; Liang Sun, Yongnan Zhu, A. S. M. Ashique Mahmood, Catalina O. Tudor, Jia Ren, K. Vijay-Shanker, Jian Chen and Carl J. Schmidt; Sun, Liang; Mahmood, A. S. M. Ashique; Tudor, Catalina O.; Ren, Jia; Vijay-Shanker, K.; Schmidt, Carl J.BACKGROUND: A major challenge of high throughput transcriptome studies is presenting the data to researchers in an interpretable format. In many cases, the outputs of such studies are gene lists which are then examined for enriched biological concepts. One approach to help the researcher interpret large gene datasets is to associate genes and informative terms (iTerm) that are obtained from the biomedical literature using the eGIFT text-mining system. However, examining large lists of iTerm and gene pairs is a daunting task. RESULTS: We have developed WebGIVI, an interactive web-based visualization tool (http://raven.anr.udel.edu/webgivi/) to explore gene:iTerm pairs. WebGIVI was built via Cytoscape and Data Driven Document JavaScript libraries and can be used to relate genes to iTerms and then visualize gene and iTerm pairs. WebGIVI can accept a gene list that is used to retrieve the gene symbols and corresponding iTerm list. This list can be submitted to visualize the gene iTerm pairs using two distinct methods: a Concept Map or a Cytoscape Network Map. In addition, WebGIVI also supports uploading and visualization of any two-column tab separated data. CONCLUSIONS: WebGIVI provides an interactive and integrated network graph of gene and iTerms that allows filtering, sorting, and grouping, which can aid biologists in developing hypothesis based on the input gene lists. In addition, WebGIVI can visualize hundreds of nodes and generate a high-resolution image that is important for most of research publications. The source code can be freely downloaded at https://github.com/sunliang3361/WebGIVI. The WebGIVI tutorial is available at http://raven.anr.udel.edu/webgivi/tutorial.php.