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Background In the last few years high-throughput analysis methods have become state-of-the-art in the life sciences. One of the latest developments is automated greenhouse systems for high-throughput plant phenotyping. Such systems allow the non-destructive screening of plants over a period of time by means of image acquisition techniques. During such screening different images of each plant are recorded and must be analysed by applying sophisticated image analysis algorithms. Results This paper presents an image analysis pipeline (HTPheno) for high-throughput plant phenotyping. HTPheno is implemented as a plugin for ImageJ, an open source image processing software. It provides the possibility to analyse colour images of plants which are taken in two different views (top view and side view) during a screening. Within the analysis different phenotypical parameters for each plant such as height, width and projected shoot area of the plants are calculated for the duration of the screening. HTPheno is applied to analyse two barley cultivars. Conclusions HTPheno, an open source image analysis pipeline, supplies a flexible and adaptable ImageJ plugin which can be used for automated image analysis in high-throughput plant phenotyping and therefore to derive new biological insights, such as determination of fitness.

Computational methods such as molecular docking or molecular dynamics (MD) simulations have been developed to simulate and explore the interactions between biomolecules. However, the interactions obtained using these methods are difficult to analyse and evaluate. Interaction fingerprints (IFPs) have been proposed to derive interactions from static 3D coordinates and transform them into 1D bit vectors. More recently, the concept has been applied to derive IFPs from MD simulations, which adds a layer of complexity by adding the temporal motion and dynamics of a system. As a result, many IFPs are obtained from one MD simulation, resulting in a large number of individual IFPs that are difficult to analyse compared to IFPs derived from static 3D structures. Scientific contribution : We introduce a new method to systematically aggregate IFPs derived from MD simulation data. In addition, we propose visualisations to effectively analyse and compare IFPs derived from MD simulation data to account for the temporal evolution of interactions and to compare IFPs across different MD simulations. This has been implemented as a freely available Python library and can therefore be easily adopted by other researchers and to different MD simulation datasets.

Data in behavioral research is often quantified with event-logging software, generating large data sets containing detailed information about subjects, recipients, and the duration of behaviors. Exploring and analyzing such large data sets can be challenging without tools to visualize behavioral interactions between individuals or transitions between behavioral states, yet software that can adequately visualize complex behavioral data sets is rare. TIBA (The Interactive Behavior Analyzer) is a web application for behavioral data visualization, which provides a series of interactive visualizations, including the temporal occurrences of behavioral events, the number and direction of interactions between individuals, the behavioral transitions and their respective transitional frequencies, as well as the visual and algorithmic comparison of the latter across data sets. It can therefore be applied to visualize behavior across individuals, species, or contexts. Several filtering options (selection of behaviors and individuals) together with options to set node and edge properties (in the network drawings) allow for interactive customization of the output drawings, which can also be downloaded afterwards. TIBA accepts data outputs from popular logging software and is implemented in Python and JavaScript, with all current browsers supported. The web application and usage instructions are available at tiba.inf.uni-konstanz.de . The source code is publicly available on GitHub: github.com/LSI-UniKonstanz/tiba .

Background Complex networks are studied across many fields of science and are particularly important to understand biological processes. Motifs in networks are small connected sub-graphs that occur significantly in higher frequencies than in random networks. They have recently gathered much attention as a useful concept to uncover structural design principles of complex networks. Existing algorithms for finding network motifs are extremely costly in CPU time and memory consumption and have practically restrictions on the size of motifs. Results We present a new algorithm (Kavosh), for finding k-size network motifs with less memory and CPU time in comparison to other existing algorithms. Our algorithm is based on counting all k-size sub-graphs of a given graph (directed or undirected). We evaluated our algorithm on biological networks of E. coli and S. cereviciae , and also on non-biological networks: a social and an electronic network. Conclusion The efficiency of our algorithm is demonstrated by comparing the obtained results with three well-known motif finding tools. For comparison, the CPU time, memory usage and the similarities of obtained motifs are considered. Besides, Kavosh can be employed for finding motifs of size greater than eight, while most of the other algorithms have restriction on motifs with size greater than eight. The Kavosh source code and help files are freely available at: http://Lbb.ut.ac.ir/Download/LBBsoft/Kavosh/ .

Networks are employed to represent many nonlinear complex systems in the real world. The topological aspects and relationships between the structure and function of biological networks have been widely studied in the past few decades. However dynamic and control features of complex networks have not been widely researched, in comparison to topological network features. In this study, we explore the relationship between network controllability, topological parameters, and network medicine (metabolic drug targets). Considering the assumption that targets of approved anticancer metabolic drugs are driver nodes (which control cancer metabolic networks), we have applied topological analysis to genome-scale metabolic models of 15 normal and corresponding cancer cell types. The results show that besides primary network parameters, more complex network metrics such as motifs and clusters may also be appropriate for controlling the systems providing the controllability relationship between topological parameters and drug targets. Consequently, this study reveals the possibilities of following a set of driver nodes in network clusters instead of considering them individually according to their centralities. This outcome suggests considering distributed control systems instead of nodal control for cancer metabolic networks, leading to a new strategy in the field of network medicine.

Data visualization is of increasing importance in the Biosciences. During the past 15 years, a great number of novel methods and tools for the visualization of biological data have been developed and published in various journals and conference proceedings. As a consequence, keeping an overview of state-of-the-art visualization research has become increasingly challenging for both biology researchers and visualization researchers. To address this challenge, we have reviewed visualization research especially performed for the Biosciences and created an interactive web-based visualization tool, the BioVis Explorer. BioVis Explorer allows the exploration of published visualization methods in interactive and intuitive ways, including faceted browsing and associations with related methods. The tool is publicly available online and has been designed as community-based system which allows users to add their works easily.

Seeds provide the basis for many food, feed, and fuel products. Continued increases in seed yield, composition, and quality require an improved understanding of how the developing seed converts carbon and nitrogen supplies into storage. Current knowledge of this process is often based on the premise that transcriptional regulation directly translates via enzyme concentration into flux. In an attempt to highlight metabolic control, we explore genotypic differences in carbon partitioning for in vitro cultured developing embryos of oilseed rape (Brassica napus). We determined biomass composition as well as 79 net fluxes, the levels of 77 metabolites, and 26 enzyme activities with specific focus on central metabolism in nine selected germplasm accessions. Overall, we observed a tradeoff between the biomass component fractions of lipid and starch. With increasing lipid content over the spectrum of genotypes, plastidic fatty acid synthesis and glycolytic flux increased concomitantly, while glycolytic intermediates decreased. The lipid/starch tradeoff was not reflected at the proteome level, pointing to the significance of (posttranslational) metabolic control. Enzyme activity/flux and metabolite/flux correlations suggest that plastidic pyruvate kinase exerts flux control and that the lipid/starch tradeoff is most likely mediated by allosteric feedback regulation of phosphofructokinase and ADP-glucose pyrophosphorylase. Quantitative data were also used to calculate in vivo mass action ratios, reaction equilibria, and metabolite turnover times. Compounds like cyclic 3ʹ,5ʹ-AMP and sucrose-6-phosphate were identified to potentially be involved in so far unknown mechanisms of metabolic control. This study provides a rich source of quantitative data for those studying central metabolism.

Researchers around the world join forces to reconstruct the molecular processes of the virus-host interactions aiming to combat the cause of the ongoing pandemic.

More diverse data on animal ecology are now available. This “data deluge” presents challenges for both biologists and computer scientists; however, it also creates opportunities to improve analysis and answer more holistic research questions. We aim to increase awareness of the current opportunity for interdisciplinary research between animal ecology researchers and computer scientists. Immersive analytics (IA) is an emerging research field in which investigations are performed into how immersive technologies, such as large display walls and virtual reality and augmented reality devices, can be used to improve data analysis, outcomes, and communication. These investigations have the potential to reduce the analysis effort and widen the range of questions that can be addressed. We propose that biologists and computer scientists combine their efforts to lay the foundation for IA in animal ecology research. We discuss the potential and the challenges and outline a path toward a structured approach. We imagine that a joint effort would combine the strengths and expertise of both communities, leading to a well-defined research agenda and design space, practical guidelines, robust and reusable software frameworks, reduced analysis effort, and better comparability of results.

Dirk Koschützki1,2 and Falk Schreiber1,3 1Department of Molecular Genetics, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Corrensstraße 3, 06466 Gatersleben, Germany. 2Department of Computer and Electrical Engineering, Furtwangen University of Applied Sciences, Robert-Gerwig-Platz 1, 78120 Furtwangen, Germany. 3Institute for Computer Science, Martin-Luther-University Halle-Wittenberg, Von-Seckendorff-Platz 1, 06120 Halle, Germany. Abstract The structural analysis of biological networks includes the ranking of the vertices based on the connection structure of a network. To support this analysis we discuss centrality measures which indicate the importance of vertices, and demonstrate their applicability on a gene regulatory network. We show that common centrality measures result in different valuations of the vertices and that novel measures tailored to specific biological investigations are useful for the analysis of biological networks, in particular gene regulatory networks.