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Motivation: Boolean networks can serve as straightforward models for understanding processes such as gene regulation, and employing logical rules. These rules can either be derived from existing literature or by data-driven approaches. However, in the context of large networks, the exhaustive search for intervention targets becomes challenging due to the exponential expansion of a Boolean network's state space and the multitude of potential target candidates, along with their various combinations. Instead, we can employ the logical rules and resultant interaction graph as a means to identify targets of specific interest within larger-scale models. This approach not only facilitates the screening process but also serves as a preliminary filtering step, enabling the focused investigation of candidates that hold promise for more profound dynamic analysis. However, applying this method requires a working knowledge of R, thus restricting the range of potential users. We, therefore, aim to provide an application that makes this method accessible to a broader scientific community., Results: Here, we introduce GatekeepR, a graphical, web-based R Shiny application that enables scientists to screen Boolean network models for possible intervention targets whose perturbation is likely to have a large impact on the system's dynamics. This application does not require a local installation or knowledge of R and provides the suggested targets along with additional network information and visualizations in an intuitive, easy-to-use manner. The Supplementary Material describes the underlying method for identifying these nodes along with an example application in a network modeling pancreatic cancer., Availability and Implementation: https://www.github.com/sysbio-bioinf/GatekeepR https://abel.informatik.uni-ulm.de/shiny/GatekeepR/., (© The Author(s) 2024. Published by Oxford University Press.)

Motivation: Biological processes are complex systems with distinct behaviour. Despite the growing amount of available data, knowledge is sparse and often insufficient to investigate the complex regulatory behaviour of these systems. Moreover, different cellular phenotypes are possible under varying conditions. Mathematical models attempt to unravel these mechanisms by investigating the dynamics of regulatory networks. Therefore, a major challenge is to combine regulations and phenotypical information as well as the underlying mechanisms. To predict regulatory links in these models, we established an approach called CANTATA to support the integration of information into regulatory networks and retrieve potential underlying regulations. This is achieved by optimizing both static and dynamic properties of these networks., Results: Initial results show that the algorithm predicts missing interactions by recapitulating the known phenotypes while preserving the original topology and optimizing the robustness of the model. The resulting models allow for hypothesizing about the biological impact of certain regulatory dependencies., Availability and Implementation: Source code of the application, example files and results are available at https://github.com/sysbio-bioinf/Cantata., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2022. Published by Oxford University Press.)

Motivation: Interaction graphs are able to describe regulatory dependencies between compounds without capturing dynamics. In contrast, mathematical models that are based on interaction graphs allow to investigate the dynamics of biological systems. However, since dynamic complexity of these models grows exponentially with their size, exhaustive analyses of the dynamics and consequently screening all possible interventions eventually becomes infeasible. Thus, we designed an approach to identify dynamically relevant compounds based on the static network topology., Results: Here, we present a method only based on static properties to identify dynamically influencing nodes. Coupling vertex betweenness and determinative power, we could capture relevant nodes for changing dynamics with an accuracy of 75% in a set of 35 published logical models. Further analyses of the selected compounds' connectivity unravelled a new class of not highly connected nodes with high impact on the networks' dynamics, which we call gatekeepers. We validated our method's working concept on logical models, which can be readily scaled up to complex interaction networks, where dynamic analyses are not even feasible., Availability and Implementation: Code is freely available at https://github.com/sysbio-bioinf/BNStatic., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2021. Published by Oxford University Press.)

This article describes some use case studies and self-assessments of FAIR status of de.NBI services to illustrate the challenges and requirements for the definition of the needs of adhering to the FAIR (findable, accessible, interoperable and reusable) data principles in a large distributed bioinformatics infrastructure. We address the challenge of heterogeneity of wet lab technologies, data, metadata, software, computational workflows and the levels of implementation and monitoring of FAIR principles within the different bioinformatics sub-disciplines joint in de.NBI. On the one hand, this broad service landscape and the excellent network of experts are a strong basis for the development of useful research data management plans. On the other hand, the large number of tools and techniques maintained by distributed teams renders FAIR compliance challenging., (© The Author(s) 2021. Published by Oxford University Press.)

Motivation: Cancer is a complex and heterogeneous disease involving multiple somatic mutations that accumulate during its progression. In the past years, the wide availability of genomic data from patients' samples opened new perspectives in the analysis of gene mutations and alterations. Hence, visualizing and further identifying genes mutated in massive sets of patients are nowadays a critical task that sheds light on more personalized intervention approaches., Results: Here, we extensively review existing tools for visualization and analysis of alteration data. We compare different approaches to study mutual exclusivity and sample coverage in large-scale omics data. We complement our review with the standalone software AVAtar ('analysis and visualization of alteration data') that integrates diverse aspects known from different tools into a comprehensive platform. AVAtar supplements customizable alteration plots by a multi-objective evolutionary algorithm for subset identification and provides an innovative and user-friendly interface for the evaluation of concurrent solutions. A use case from personalized medicine demonstrates its unique features showing an application on vaccination target selection., Availability: AVAtar is available at: https://github.com/sysbio-bioinf/avatar., Contact: hans.kestler@uni-ulm.de, phone: +49 (0) 731 500 24 500, fax: +49 (0) 731 500 24 502., (© The Author(s) 2020. Published by Oxford University Press.)

Summary: Mathematical models and their simulation are increasingly used to gain insights into cellular pathways and regulatory networks. Dynamics of regulatory factors can be modeled using Boolean networks (BNs), among others. Text-based representations of models are precise descriptions, but hard to understand and interpret. ViSiBooL aims at providing a graphical way of modeling and simulating networks. By providing visualizations of static and dynamic network properties simultaneously, it is possible to directly observe the effects of changes in the network structure on the behavior. In order to address the challenges of clear design and a user-friendly graphical user interface (GUI), ViSiBooL implements visual representations of BNs. Additionally temporal extensions of the BNs for the modeling of regulatory time delays are incorporated. The GUI of ViSiBooL allows to model, organize, simulate and visualize BNs as well as corresponding simulation results such as attractors. Attractor searches are performed in parallel to the modeling process. Hence, changes in the network behavior are visualized at the same time., Availability and Implementation: ViSiBooL (Java 8) is freely available at http://sysbio.uni-ulm.de/?Software:ViSiBooL ., Contact: hans.kestler@uni-ulm.de., (© The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com)

Unlabelled: Over the past years growing knowledge about biological processes and pathways revealed complex interaction networks involving many genes. In order to understand these networks, analysis of differential expression has continuously moved from single genes towards the study of gene sets. Various approaches for the assessment of gene sets have been developed in the context of gene set analysis (GSA). These approaches are bridging the gap between raw measurements and semantically meaningful terms.We present a novel approach for assessing uncertainty in the definition of gene sets. This is an essential step when new gene sets are constructed from domain knowledge or given gene sets are suspected to be affected by uncertainty. Quantification of uncertainty is implemented in the R-package GiANT. We also included widely used GSA methods, embedded in a generic framework that can readily be extended by custom methods. The package provides an easy to use front end and allows for fast parallelization., Availability and Implementation: The package GiANT is available on CRAN., Contacts: hans.kestler@leibniz-fli.de or hans.kestler@uni-ulm.de., (© The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.)

Motivation: When processing gene expression profiles or other biological data, it is often required to assign measurements to distinct categories (e.g. 'high' and 'low' and possibly 'intermediate'). Subsequent analyses strongly depend on the results of this quantization. Poor quantization will have potentially misleading effects on further investigations. We propose the BiTrinA package that integrates different multiscale algorithms for binarization and for trinarization of one-dimensional data with methods for quality assessment and visualization of the results. By identifying measurements that show large variations over different time points or conditions, this quality assessment can determine candidates that are related to the specific experimental setting., Availability and Implementation: BiTrinA is freely available on CRAN., Contact: hans.kestler@leibniz-fli.de or hans.kestler@uni-ulm.de, Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.)

Motivation: In bioinformatic applications, computationally demanding algorithms are often parallelized to speed up computation. Nevertheless, setting up computational environments for distributed computation is often tedious. Aim of this project were the lightweight ad hoc set up and fault-tolerant computation requiring only a Java runtime, no administrator rights, while utilizing all CPU cores most effectively., Results: The Sputnik framework provides ad hoc distributed computation on the Java Virtual Machine which uses all supplied CPU cores fully. It provides a graphical user interface for deployment setup and a web user interface displaying the current status of current computation jobs. Neither a permanent setup nor administrator privileges are required. We demonstrate the utility of our approach on feature selection of microarray data., Availability and Implementation: The Sputnik framework is available on Github http://github.com/sysbio-bioinf/sputnik under the Eclipse Public License., Contact: hkestler@fli-leibniz.de or hans.kestler@uni-ulm.de, Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.)

Recent developments in molecular biology have led to the massive discovery of new marker candidates for the prediction of patient survival. To evaluate the predictive value of these markers, statistical tools for measuring the performance of survival models are needed. We consider estimators of discrimination measures, which are a popular approach to evaluate survival predictions in biomarker studies. Estimators of discrimination measures are usually based on regularity assumptions such as the proportional hazards assumption. Based on two sets of molecular data and a simulation study, we show that violations of the regularity assumptions may lead to over-optimistic estimates of prediction accuracy and may therefore result in biased conclusions regarding the clinical utility of new biomarkers. In particular, we demonstrate that biased medical decision making is possible even if statistical checks indicate that all regularity assumptions are satisfied., (© The Author 2013. Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.)

Incorporating secondary structure information into the alignment process improves the quality of RNA sequence alignments. Instead of using fixed weighting parameters, sequence and structure components can be treated as different objectives and optimized simultaneously. The result is not a single, but a Pareto-set of equally optimal solutions, which all represent different possible weighting parameters. We now provide the interactive graphical software tool RNA-Pareto, which allows a direct inspection of all feasible results to the pairwise RNA sequence-structure alignment problem and greatly facilitates the exploration of the optimal solution set.

Motivation: The calculation of reliable alignments for structured RNA is still considered as an open problem. One approach is the incorporation of secondary structure information into the optimization criteria by using a weighted sum of sequence and structure components as an objective function. As it is not clear how to choose the weighting parameters, we use multi-objective optimization to calculate a set of Pareto-optimal RNA sequence-structure alignments. The solutions in this set then represent all possible trade-offs between the different objectives, independent of any previous weighting., Results: We present a practical multi-objective dynamic programming algorithm, which is a new method for the calculation of the set of Pareto-optimal solutions to the pairwise RNA sequence-structure alignment problem. In selected examples, we show the usefulness of this approach, and its advantages over state-of-the-art single-objective algorithms., Availability and Implementation: The source code of our software (ISO C++11) is freely available at http://sysbio.uni-ulm.de/?Software and is licensed under the GNU GPLv3., Supplementary Information: Supplementary data are available at Bioinformatics online.

Motivation: Accurate, context-specific regulation of gene expression is essential for all organisms. Accordingly, it is very important to understand the complex relations within cellular gene regulatory networks. A tool to describe and analyze the behavior of such networks are Boolean models. The reconstruction of a Boolean network from biological data requires identification of dependencies within the network. This task becomes increasingly computationally demanding with large amounts of data created by recent high-throughput technologies. Thus, we developed a method that is especially suited for network structure reconstruction from large-scale data. In our approach, we took advantage of the fact that a specific transcription factor often will consistently either activate or inhibit a specific target gene, and this kind of regulatory behavior can be modeled using monotone functions., Results: To detect regulatory dependencies in a network, we examined how the expression of different genes correlates to successive network states. For this purpose, we used Pearson correlation as an elementary correlation measure. Given a Boolean network containing only monotone Boolean functions, we prove that the correlation of successive states can identify the dependencies in the network. This method not only finds dependencies in randomly created artificial networks to very high percentage, but also reconstructed large fractions of both a published Escherichia coli regulatory network from simulated data and a yeast cell cycle network from real microarray data.

Motivation: As the study of information processing in living cells moves from individual pathways to complex regulatory networks, mathematical models and simulation become indispensable tools for analyzing the complex behavior of such networks and can provide deep insights into the functioning of cells. The dynamics of gene expression, for example, can be modeled with Boolean networks (BNs). These are mathematical models of low complexity, but have the advantage of being able to capture essential properties of gene-regulatory networks. However, current implementations of BNs only focus on different sub-aspects of this model and do not allow for a seamless integration into existing preprocessing pipelines., Results: BoolNet efficiently integrates methods for synchronous, asynchronous and probabilistic BNs. This includes reconstructing networks from time series, generating random networks, robustness analysis via perturbation, Markov chain simulations, and identification and visualization of attractors., Availability: The package BoolNet is freely available from the R project at http://cran.r-project.org/ or http://www.informatik.uni-ulm.de/ni/mitarbeiter/HKestler/boolnet/ under Artistic License 2.0., Contact: hans.kestler@uni-ulm.de, Supplementary Information: Supplementary data are available at Bioinformatics online.

Aims: Clinical parameters are weak predictors of outcome in patients with idiopathic dilated cardiomyopathy (IDC). We assessed the prognostic value of cardiac magnetic resonance (CMR) parameters in addition to conventional clinical and electrocardiographic characteristics., Methods and Results: One hundred and forty-one IDC patients were studied. QRS and QTc intervals were measured in 12-lead surface electrocardiogram. Patients were followed for median 1339 days, including 483 patient-years. The primary endpoint-cardiac death or sudden death-occurred in 25 (18%) patients, including 16 patients with cardiac death, 3 patients with sudden cardiac death (SCD), and 6 patients with ICD shock. Late gadolinium enhancement (LGE) was detected in 36 patients (26%). Kaplan-Meier survival analysis displayed QRS >110 ms (P = 0.010), the presence of LGE (P = 0.037), and diabetes mellitus (P < 0.001) as significant parameters for a worse outcome. Multivariable analysis revealed cardiac index (P < 0.001), right ventricular end-diastolic volume index (RVEDVI) (P = 0.006) derived from CMR imaging, the presence of diabetes mellitus (P = 0.006), and QRS >110 ms (P = 0.045) as significant predictors for the primary endpoint., Conclusion: Cardiac index and RVEDVI derived from CMR imaging in addition to QRS duration >110 ms from conventional surface ECG and diabetes mellitus provide prognostic impact for cardiac death and SCD in patients with IDC.

Motivation: DNA copy number aberrations are frequently found in different types of cancer. Recent developments of microarray-based approaches have broadened the knowledge on number and structure of such aberrations. High-density single nucleotide polymorphism (SNP) microarrays provide an extremely high resolution with up to 500,000 SNPs per genome. Owing to the enormous amount of data the detection of common aberrations in large datasets is a great challenge. We describe a novel open source software tool--IdeogramBrowser--which was specifically designed for use with the Affymetrix SNP arrays. It provides an interactive karyotypic visualization of multiple aberration profiles and direct links to GeneCards. Visualization of consensus regions together with gene representation allows the explorative assessment of the data., Availability: IdeogramBrowser and its source code are freely available under a creative commons license and can be obtained from http://www.informatik.uni-ulm.de/ni/staff/HKestler/ideo/. IdeogramBrowser is a platform independent Java application.

Motivation: Microarray experiments generate vast amounts of data. The unknown or only partially known functional context of differentially expressed genes may be assessed by querying the Gene Ontology database via GOMiner. Resulting tree representations are difficult to interpret and are not suited for visualization of this type of data. Methods are needed to effectively visualize these complex set relationships., Results: We present a visualization approach for set relationships based on Venn diagrams. The proposed extension enhances the usual notion of Venn diagrams by incorporating set size information. The cardinality of the sets and intersection sets is represented by their corresponding circle (polygon) sizes. To avoid local minima, solutions to this problem are sought by evolutionary optimization. This generalized Venn diagram approach has been implemented as an interactive Java application (VennMaster) specifically designed for use with GOMiner in the context of the Gene Ontology database., Availability: VennMaster is platform-independent (Java 1.4.2) and has been tested on Windows (XP, 2000), Mac OS X, and Linux. Supplementary information and the software (free for non-commercial use) are available at http://www.informatik.uni-ulm.de/ni/mitarbeiter/HKestler/vennm together with a user documentation., Contact: hans.kestler@medizin.uni-ulm.de.

Aims: Because of its high spatial resolution and tissue contrast, magnetic resonance imaging (MRI) was used to assess cardiac structure and function in a large population of patients with acute myocardial infarction (AMI)., Methods and Results: One hundred and ten patients were studied by MRI 6.1 +/- 2.2 days after AMI. Infarct size (IS), persistent microvascular obstruction (PMO), left and right ventricular (LV/RV) volumes, and functions were measured. The same MRI measurements were repeated in 89 patients after a mean follow-up period of 225 +/- 92 days. IS was 11.9 +/- 7.3% of total LV muscle mass. PMO was detected in 51/110 (46.4%) patients and comprised 15.6 +/- 8.5% of IS and 2.8 +/- 2.3% of LV muscle mass. Papillary muscle infarct was seen in 26%, RV infarction in 16%, pericarditis in 40%, and pericardial effusion in 66% of the patients. During follow-up, there were 16 major adverse cardiac events (MACE) including seven deaths. IS, PMO, and amount of transmural infarction were predictive for LV adverse remodelling defined as > 20% increase in LV end-diastolic volume. Multivariable analysis revealed LV end-diastolic volume, LV ejection fraction, and PMO as significant predictors for the occurrence of MACE., Conclusion: MRI is a highly sensitive and reliable tool to detect morphologic and functional sequelae of AMI providing baseline MRI parameters with relevant predictive power for LV adverse remodelling and occurrence of MACE.