Genomic research emerges from collaborative work within and across different scientific disciplines. A diverse range of visualisation techniques has been employed to aid this research, yet relatively little is known as to how these techniques facilitate collaboration. We conducted a case study of collaborative research within a biomedical institute to learn more about the role visualisation plays in genomic mapping. Interviews were conducted with molecular biologists (N = 5) and bioinformaticians (N = 6). We found that genomic research comprises a variety of distinct disciplines engaged in complex analytic tasks that each resist simplification, and their complexity influences how visualisations were used. Visualisation use was impacted by group-specific interactions and temporal work patterns. Visualisations were also crucial to the scientific workflow, used for both question formation and confirmation of hypotheses, and acted as an anchor for the communication of ideas and discussion. In the latter case, two approaches were taken: providing collaborators with either interactive or static imagery representing a viewpoint. The use of generic software for simplified visualisations, and quick production and curation was also noted. We discuss these findings with reference to group-specific interactions and present recommendations for improving collaborative practices through visual analytics.

Novel visualization methods and strategies are necessary to cope with the deluge of datasets present in any scientific field to make discoveries and find answers to previously unanswered questions. These methods and strategies should not only present scientific findings as images in a concise way but also need to be effective and expressive, which often remain untested. Here, we present Versus, a tool to enable easy image quality assessment and image ranking, utilizing a two-alternative forced choice methodology (2AFC) and an efficient ranking algorithm based on a binary search. The tool provides a systematic way of setting up evaluation experiments via the web without the necessity to install any additional software or require any programming skills. Furthermore, Versus can easily interface with crowdsourcing platforms, such as Amazon’s Mechanical Turk, or can be used as a stand-alone system to carry out evaluations with experts. We demonstrate the use of Versus by means of an image evaluation study, aiming to determine if hue, saturation, brightness, and texture are good indicators of uncertainty in three-dimensional protein structures. Drawing from the power of crowdsourcing, we argue that there is demand and also great potential for this tool to become a standard for simple and fast image evaluations, with the aim to test the effectiveness and expressiveness of scientific visualizations.

We investigate task performance and reading characteristics for scatterplots (Cartesian coordinates) and parallel coordinates. In a controlled eye-tracking study, we asked 24 participants to assess the relative distance of points in multidimensional space, depending on the diagram type (parallel coordinates or a horizontal collection of scatterplots), the number of data dimensions (2, 4, 6, or 8), and the relative distance between points (15%, 20%, or 25%). For a given reference point and two target points, we instructed participants to choose the target point that was closer to the reference point in multidimensional space. We present a visual scanning model that describes different strategies to solve this retrieval task for both diagram types, and propose corresponding hypotheses that we test using task completion time, accuracy, and gaze positions as dependent variables. Our results show that scatterplots outperform parallel coordinates significantly in 2 dimensions, however, the task was solved more quickly and more accurately with parallel coordinates in 8 dimensions. The eye-tracking data further shows significant differences between Cartesian and parallel coordinates, as well as between different numbers of dimensions. For parallel coordinates, there is a clear trend toward shorter fixations and longer saccades with increasing number of dimensions. Using an area-of-interest (AOI) based approach, we identify different reading strategies for each diagram type: For parallel coordinates, the participants’ gaze frequently jumped back and forth between pairs of axes, while axes were rarely focused on when viewing Cartesian coordinates. We further found that participants’ attention is biased: toward the center of the whole plotfor parallel coordinates and skewed to the center/left side for Cartesian coordinates. We anticipate that these results may support the design of more effective visualizations for multidimensional data.

Abstract While many measures of viewpoint goodness have been proposed in computer graphics, none have been evaluated for ribbon representations of protein secondary structure. To fill this gap, we conducted a user study on Amazon's Mechanical Turk platform, collecting human viewpoint preferences from 65 participants for 4 representative superfamilies of protein domains. In particular, we evaluated viewpoint entropy, which was previously shown to be a good predictor for human viewpoint preference of other, mostly non‐abstract objects. In a second study, we asked 7 experts in molecular biology to find the best viewpoint of the same protein domains and compared their choices with viewpoint entropy. Our results indicate that viewpoint entropy overall is a significant predictor of human viewpoint preference for ribbon representations of protein secondary structure. However, the accuracy depends on the type and composition of the structure: while most participants agree on good viewpoints for structures with mainly beta sheets, viewpoint preference varies considerably for complex arrangements of alpha helices. Finally, experts tend to choose viewpoints of both low and high viewpoint entropy to emphasize different aspects of the respective structure.

BACKGROUND: To understand the molecular mechanisms that give rise to a protein's function, biologists often need to (i) find and access all related atomic-resolution 3D structures, and (ii) map sequence-based features (e.g., domains, single-nucleotide polymorphisms, post-translational modifications) onto these structures. RESULTS: To streamline these processes we recently developed Aquaria, a resource offering unprecedented access to protein structure information based on an all-against-all comparison of SwissProt and PDB sequences. In this work, we provide a requirements analysis for several frequently occuring tasks in molecular biology and describe how design choices in Aquaria meet these requirements. Finally, we show how the interface can be used to explore features of a protein and gain biologically meaningful insights in two case studies conducted by domain experts. CONCLUSIONS: The user interface design of Aquaria enables biologists to gain unprecedented access to molecular structures and simplifies the generation of insight. The tasks involved in mapping sequence features onto structures can be conducted easier and faster using Aquaria.

Abstract Summary: In this paper we present SRS 3D, a new service that allows users to easily and rapidly find all related structures for a given target sequence; structures can then be viewed together with sequences, alignments and sequence features (currently from UniProt, InterPro and PDB). Extensive user feedback confirms that SRS 3D is intuitive and useful especially for those not expert in structures. Availability: An SRS 3D server is provided at http://srs3d.ebi.ac.uk/ Supplementary information: Complete documentation is available (click the ‘Help’ button after login). Product information can be found at http://www.lionbioscience.com/SRS3D/products/srs/srs-3d

The problem of predicting protein structure from the sequence remains fundamentally unsolved despite more than three decades of intensive research effort. However, new and promising methods in three-dimensional (3D), 2D and 1D prediction have reopened the field. Mean-force-potentials derived from the protein databases can distinguish between correct and incorrect models (3D). Inter-residue contacts (2D) can be detected by analysis of correlated mutations, albeit with low accuracy. Secondary structure, solvent accessibility and transmembrane helices (1D) can be predicted with significantly improved accuracy using multiple sequence alignments. Some of these new prediction methods have proven accurate and reliable enough to be useful in genome analysis, and in experimental structure determination. Moreover, the new generation of theoretical methods is increasingly influencing experiments in molecular biology.

Fluorescence resonance-energy transfer (FRET) has been widely used to determine distance information in macromolecular systems. However, little has been written about methods for combining FRET distances into coherent structural models. I argue that the methods used so far are inappropriate. This paper describes an algorithm specifically tailored for finding structures from FRET measurements. This algorithm finds structures which fit the experimentally measured parameter, the efficiency of energy transfer, rather than derived distances. The algorithm was implemented in Mathematica and applied to FRET distances obtained for the contractile protein actin. The approach used is applicable to other experimental techniques which measure distances between a relatively small number of loci.