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Abstract Summary We present a new graphical tool for RNA secondary structure analysis. The central feature is the ability to visually compare/contrast up to three base pairing configurations for a given sequence in a compact, standardized circular arc diagram layout. This is complemented by a built-in CT-style file viewer and radial layout substructure viewer which are directly linked to the arc diagram window via the zoom selection tool. Additional functionality includes the computation of some numerical information, and the ability to export images and data for later use. This tool should be of use to researchers seeking to better understand similarities and differences between structural alternatives for an RNA sequence. Availability and implementation https://github.com/gtDMMB/RNAStructViz/wiki. 

Abstract Motivation When learning to subtype complex disease based on next-generation sequencing data, the amount of available data is often limited. Recent works have tried to leverage data from other domains to design better predictors in the target domain of interest with varying degrees of success. But they are either limited to the cases requiring the outcome label correspondence across domains or cannot leverage the label information at all. Moreover, the existing methods cannot usually benefit from other information available a priori such as gene interaction networks. Results In this article, we develop a generative optimal Bayesian supervised domain adaptation (OBSDA) model that can integrate RNA sequencing (RNA-Seq) data from different domains along with their labels for improving prediction accuracy in the target domain. Our model can be applied in cases where different domains share the same labels or have different ones. OBSDA is based on a hierarchical Bayesian negative binomial model with parameter factorization, for which the optimal predictor can be derived by marginalization of likelihood over the posterior of the parameters. We first provide an efficient Gibbs sampler for parameter inference in OBSDA. Then, we leverage the gene-gene network prior information and construct an informed and flexible variational family to infer the posterior distributions of model parameters. Comprehensive experiments on real-world RNA-Seq data demonstrate the superior performance of OBSDA, in terms of accuracy in identifying cancer subtypes by utilizing data from different domains. Moreover, we show that by taking advantage of the prior network information we can further improve the performance. Availability and implementation The source code for implementations of OBSDA and SI-OBSDA are available at the following link. https://github.com/SHBLK/BSDA. Supplementary information Supplementary data are available at Bioinformatics online. 

Abstract Summary The need for an efficient and cost-effective method is compelling in biomolecular NMR. To tackle this problem, we have developed the Poky suite, the revolutionized platform with boundless possibilities for advancing research and technology development in signal detection, resonance assignment, structure calculation, and relaxation studies with the help of many automation and user interface tools. This software is extensible and scalable by scripting and batching as well as providing modern graphical user interfaces and a diverse range of modules right out of the box. Availability Poky is freely available to non-commercial users at https://poky.clas.ucdenver.edu. Supplementary information Supplementary data are available at Bioinformatics online.