MOTIVATION: Understanding RNA folding at the level of secondary structures can give important insights concerning the function of a molecule. We are interested to learn how secondary structures change dynamically during transcription, as well as whether particular secondary structures form already during or only after transcription. While different approaches exist to simulate cotranscriptional folding, the current strategies for visualization are lagging behind. New, more suitable approaches are necessary to help with exploring the generated data from cotranscriptional folding simulations. RESULTS: We present DrForna, an interactive visualization app for viewing the time course of a cotranscriptional RNA folding simulation. Specifically, users can scroll along the time axis and see the population of structures that are present at any particular time point. AVAILABILITY AND IMPLEMENTATION: DrForna is a JavaScript project available on Github at https://github.com/ViennaRNA/drforna and deployed at https://viennarna.github.io/drforna.

SUMMARY: The visualization and analysis of genomic repeats is typically accomplished using dot plots; however, the emergence of telomere-to-telomere assemblies with multi-megabase repeats requires new visualization strategies. Here, we introduce StainedGlass, which can generate publication-quality figures and interactive visualizations that depict the identity and orientation of multi-megabase tandem repeat structures at a genome-wide scale. The tool can rapidly reveal higher-order structures and improve the inference of evolutionary history for some of the most complex regions of genomes. AVAILABILITY AND IMPLEMENTATION: StainedGlass is implemented using Snakemake and available open source under the MIT license at https://mrvollger.github.io/StainedGlass/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

We present Scalable Insets, a technique for interactively exploring and navigating large numbers of annotated patterns in multiscale visualizations such as gigapixel images, matrices, or maps. Exploration of many but sparsely-distributed patterns in multiscale visualizations is challenging as visual representations change across zoom levels, context and navigational cues get lost upon zooming, and navigation is time consuming. Our technique visualizes annotated patterns too small to be identifiable at certain zoom levels using insets, i.e., magnified thumbnail views of the annotated patterns. Insets support users in searching, comparing, and contextualizing patterns while reducing the amount of navigation needed. They are dynamically placed either within the viewport or along the boundary of the viewport to offer a compromise between locality and context preservation. Annotated patterns are interactively clustered by location and type. They are visually represented as an aggregated inset to provide scalable exploration within a single viewport. In a controlled user study with 18 participants, we found that Scalable Insets can speed up visual search and improve the accuracy of pattern comparison at the cost of slower frequency estimation compared to a baseline technique. A second study with 6 experts in the field of genomics showed that Scalable Insets is easy to learn and provides first insights into how Scalable Insets can be applied in an open-ended data exploration scenario.

This paper presents an interactive visualization interface-HiPiler-for the exploration and visualization of regions-of-interest in large genome interaction matrices. Genome interaction matrices approximate the physical distance of pairs of regions on the genome to each other and can contain up to 3 million rows and columns with many sparse regions. Regions of interest (ROIs) can be defined, e.g., by sets of adjacent rows and columns, or by specific visual patterns in the matrix. However, traditional matrix aggregation or pan-and-zoom interfaces fail in supporting search, inspection, and comparison of ROIs in such large matrices. In HiPiler, ROIs are first-class objects, represented as thumbnail-like "snippets". Snippets can be interactively explored and grouped or laid out automatically in scatterplots, or through dimension reduction methods. Snippets are linked to the entire navigable genome interaction matrix through brushing and linking. The design of HiPiler is based on a series of semi-structured interviews with 10 domain experts involved in the analysis and interpretation of genome interaction matrices. We describe six exploration tasks that are crucial for analysis of interaction matrices and demonstrate how HiPiler supports these tasks. We report on a user study with a series of data exploration sessions with domain experts to assess the usability of HiPiler as well as to demonstrate respective findings in the data.

MOTIVATION: The secondary structure of RNA is integral to the variety of functions it carries out in the cell and its depiction allows researchers to develop hypotheses about which nucleotides and base pairs are functionally relevant. Current approaches to visualizing secondary structure provide an adequate platform for the conversion of static text-based representations to 2D images, but are limited in their offer of interactivity as well as their ability to display larger structures, multiple structures and pseudoknotted structures. RESULTS: In this article, we present forna, a web-based tool for displaying RNA secondary structure which allows users to easily convert sequences and secondary structures to clean, concise and customizable visualizations. It supports, among other features, the simultaneous visualization of multiple structures, the display of pseudoknotted structures, the interactive editing of the displayed structures, and the automatic generation of secondary structure diagrams from PDB files. It requires no software installation apart from a modern web browser. AVAILABILITY AND IMPLEMENTATION: The web interface of forna is available at http://rna.tbi.univie.ac.at/forna while the source code is available on github at www.github.com/pkerpedjiev/forna. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: Modern DNA sequencing methods produce vast amounts of data that often requires mapping to a reference genome. Most existing programs use the number of mismatches between the read and the genome as a measure of quality. This approach is without a statistical foundation and can for some data types result in many wrongly mapped reads. Here we present a probabilistic mapping method based on position-specific scoring matrices, which can take into account not only the quality scores of the reads but also user-specified models of evolution and data-specific biases. RESULTS: We show how evolution, data-specific biases, and sequencing errors are naturally dealt with probabilistically. Our method achieves better results than Bowtie and BWA on simulated and real ancient and PAR-CLIP reads, as well as on simulated reads from the AT rich organism P. falciparum, when modeling the biases of these data. For simulated Illumina reads, the method has consistently higher sensitivity for both single-end and paired-end data. We also show that our probabilistic approach can limit the problem of random matches from short reads of contamination and that it improves the mapping of real reads from one organism (D. melanogaster) to a related genome (D. simulans). CONCLUSION: The presented work is an implementation of a novel approach to short read mapping where quality scores, prior mismatch probabilities and mapping qualities are handled in a statistically sound manner. The resulting implementation provides not only a tool for biologists working with low quality and/or biased sequencing data but also a demonstration of the feasibility of using a probability based alignment method on real and simulated data sets.