miRTarVis is an interactive visual analysis tool for microRNA-mRNA expression profile data.
Daekyoung Jung, Sehi L'Yi, Bohyoung Kim, Robert J. Freishtat, Mamta Giri, Eric Hoffman, and Jinwook Seo / 2015
- Background: MicroRNAs (miRNA) are short nucleotides that down-regulate its target genes. Various miRNA target prediction algorithms have used sequence complementarity between miRNA and its targets. Recently, other algorithms tried to improve sequence-based miRNA target prediction by exploiting miRNA-mRNA expression profile data. Some web-based tools are also introduced to help researchers predict targets of miRNAs from miRNA-mRNA expression profile data. A demand for a miRNA-mRNA visual analysis tool that features novel miRNA prediction algorithms and more interactive visualization techniques exists.
- Results: We designed and implemented miRTarVis, which is an interactive visual analysis tool that predicts targets of miRNAs from miRNA-mRNA expression profile data and visualizes the resulting miRNA-target interaction network. miRTarVis has intuitive interface design in accordance with the analysis procedure of load, filter, predict, and visualize. It predicts targets of miRNA by adopting Bayesian inference and MINE analyses, as well as conventional correlation and mutual information analyses. It visualizes a resulting miRNA-mRNA network in an interactive Treemap, as well as a conventional node-link diagram. miRTarVis is available at hcil.snu.ac.kr/~rati/miRTarVis/index.html.
- Conclusions: We reported findings from miRNA-mRNA expression profile data of asthma patients using miRTarVis in a case study. miRTarVis helps to predict and understand targets of miRNA from miRNA-mRNA expression profile data.
- Daekyoung Jung, Bohyoung Kim, Robert J. Freishtat, Mamta Giri, Eric Hoffman, and Jinwook Seo, miRTarVis: An interactive visual analysis tool for microRNA-mRNA expression profile data, [PDF], BioVis 2015
- Daekyoung Jung, Sehi L'Yi, Bohyoung Kim, and Jinwook Seo, "Interactive Visual Analysis of miRNA Target Prediction Results", [PDF], BigComp 2017