Municipal authorities have, among other tasks, a great interest in supporting their local economy. For this purpose, they provide consulting offices that advise companies and mediate cooperation partners.
The city administration of Leipzig created a business register in which companies can provide their competences in free text fields. This business register contains over 1000 entries and it is not straight forward to find and compare companies based on their self-descriptions.
In this paper, we propose a new visualization to analyze the distribution of local companies and exploring the competence profiles of the companies. In order to visualize connections between companies, we perform a semantical analysis. In detail, we use the management staff listing and the core competence descriptions to link the entries. The company location and the connections between the companies are visualized on a map or as a graph. The visualization provides several filtering and interaction mechanisms on demand. From a governance perspective, this leads to insights into company and industry sector networks within a local economic zone.
Sierra Platinum is a fast and robust peak-caller for replicated ChIP-seq experiments with visual quality-control and -steering. The required computing resources are optimized but still may exceed the resources available to researchers at biological research institutes. Sierra Platinum Service provides the full functionality of Sierra Platinum: using a web interface, a new instance of the service can be generated. Then experimental data is uploaded and the computation of the peaks is started. Upon completion, the results can be inspected interactively and then downloaded for further analysis, at which point the service terminates.
Bioinformaticians judge the likelihood of the overall RNA secondary structure based on comparing its base pair probabilities. These probabilities can be calculated by various tools and are frequently displayed using dot plots for further analysis. However, most tools produce only static dot plot images which restricts possible interactions to the capabilities of the respective viewers (mostly PostScript-viewers). Moreover, this approach does not scale well with larger RNAs since most PostScript viewers are not designed to show a huge number of elements and have only legacy support for PostScript. Therefore, we developed iDotter, an interactive tool for analyzing RNA secondary structures. iDotter overcomes the previously described limitations providing multiple interaction mech- anisms facilitating the interactive analysis of the displayed data. According to the biologists and bioinformaticians that regularly use out interactive dot plot viewer, iDotter is superior to all previous approaches with respect to facilitating dot plot based analysis of RNA secondary structures.
iDotter is available under the GNU GPL v3 on https://git.gurkware.de/biovis/idotter.git
iDotter is hosted at https://idotter.sca-ds.de
DNA bound proteins such as transcription factors and modified histone proteins play an important role in gene regulation. Therefore, their genomic locations are of great interest. Usually, the location is measured using ChIP-seq and analyzed using a peak-caller. While replicated ChIP-seq experiments become more and more available, they are still mostly analyzed using methods based on peak-callers for single replicates. The only exception is PePr, which allows peak calling of several replicates. However, PePr does not provide quality measures to assess the result of the peak-calling process. Moreover, its underlying model might not be suitable for the conditions under which the experiments are performed. We propose a new peak-caller called `Sierra Platinum' that not only allows to call peaks for several replicates but also provides a variety of quality measures. Together with integrated visualizations, the quality measures support the assessment of the replicates and the resulting peaks. We show that Sierra Platinum outperforms methods based on single-replicate peak-callers as well as PePr using a newly generated benchmark data set and using real data from the NIH Roadmap Epigenomics Project.
Cognitive abilities, such as memory, learning, language, problem solving, and planning, involve the frontal lobe and other brain areas. Not much is known yet about the molecular basis of cognitive abilities, but it seems clear that cognitive abilities are determined by the interplay of many genes. One approach for analyzing the genetic networks involved in cognitive functions is to study the coexpression networks of genes with known importance for proper cognitive functions, such as genes that have been associated with cognitive disorders like intellectual disability (ID) or autism spectrum disorders (ASD). Because many of these genes are gene regulatory factors (GRFs) we aimed to provide insights into the gene regulatory networks active in the human frontal lobe. Using genome wide human frontal lobe expression data from 10 independent data sets, we first derived 10 individual coexpression networks for all GRFs including their potential target genes. We observed a high level of variability among these 10 independently derived networks, pointing out that relying on results from a single study can only provide limited biological insights. To instead focus on the most confident information from these 10 networks we developed a method for integrating such independently derived networks into a consensus network. This consensus network revealed robust GRF interactions that are conserved across the frontal lobes of different healthy human individuals. Within this network, we detected a strong central module that is enriched for 166 GRFs known to be involved in brain development and/or cognitive disorders. Interestingly, several hubs of the consensus network encode for GRFs that have not yet been associated with brain functions. Their central role in the network suggests them as excellent new candidates for playing an essential role in the regulatory network of the human frontal lobe, which should be investigated in future studies.