SUMMARY: Long-read DNA sequencing is increasingly applied for whole-genome studies, yet experimental planning often lacks reliable estimates of target region coverage, leading to costly and time-consuming pilot studies and replicates. We present esloco, a Monte Carlo-based simulation framework for estimating local coverage in long-read sequencing experiments, including scenarios with unknown target regions (e.g. viral integration, CRISPR-Cas9) or PCR-free designs (e.g. base modifications). By modeling coverage as a function of sequencing depth and read length distribution, esloco enables informed predictions of local sequencing outcomes. Benchmarking across a 45-gene panel demonstrated close agreement with empirical data, underscoring the framework's reliability. AVAILABILITY AND IMPLEMENTATION: esloco is a Python package available on PyPI (https://pypi.org/project/esloco/), GitHub (https://github.com/aweich/esloco), and Zenodo (https://doi.org/10.5281/zenodo.17776161).

The accelerating growth of scientific literature overwhelms our capacity to manually distil complex phenomena like molecular networks linked to diseases. Moreover, biases in biomedical research and database annotation limit our interpretation of facts and generation of hypotheses. ENQUIRE (Expanding Networks by Querying Unexpectedly Inter-Related Entities) offers a time- and resource-efficient alternative to manual literature curation and database mining. ENQUIRE reconstructs and expands co-occurrence networks of genes and biomedical ontologies from user-selected input corpora and network-inferred PubMed queries. Its modest resource usage and the integration of text mining, automatic querying, and network-based statistics mitigating literature biases makes ENQUIRE unique in its broad-scope applications. For example, ENQUIRE can generate co-occurrence gene networks that reflect high-confidence, functional networks. When tested on case studies spanning cancer, cell differentiation, and immunity, ENQUIRE identified interlinked genes and enriched pathways unique to each topic, thereby preserving their underlying context specificity. ENQUIRE supports biomedical researchers by easing literature annotation, boosting hypothesis formulation, and facilitating the identification of molecular targets for subsequent experimentation.

We live in an unprecedented time in oncology. We have accumulated samples and cases in cohorts larger and more complex than ever before. New technologies are available for quantifying solid or liquid samples at the molecular level. At the same time, we are now equipped with the computational power necessary to handle this enormous amount of quantitative data. Computational models are widely used helping us to substantiate and interpret data. Under the label of systems and precision medicine, we are putting all these developments together to improve and personalize the therapy of cancer. In this review, we use melanoma as a paradigm to present the successful application of these technologies but also to discuss possible future developments in patient care linked to them. Melanoma is a paradigmatic case for disruptive improvements in therapies, with a considerable number of metastatic melanoma patients benefiting from novel therapies. Nevertheless, a large proportion of patients does not respond to therapy or suffers from adverse events. Melanoma is an ideal case study to deploy advanced technologies not only due to the medical need but also to some intrinsic features of melanoma as a disease and the skin as an organ. From the perspective of data acquisition, the skin is the ideal organ due to its accessibility and suitability for many kinds of advanced imaging techniques. We put special emphasis on the necessity of computational strategies to integrate multiple sources of quantitative data describing the tumour at different scales and levels.

BACKGROUND: Melanoma phenotype and the dynamics underlying its progression are determined by a complex interplay between different types of regulatory molecules. In particular, transcription factors (TFs), microRNAs (miRNAs), and long non-coding RNAs (lncRNAs) interact in layers that coalesce into large molecular interaction networks. Our goal here is to study molecules associated with the cross-talk between various network layers, and their impact on tumor progression. RESULTS: To elucidate their contribution to disease, we developed an integrative computational pipeline to construct and analyze a melanoma network focusing on lncRNAs, their miRNA and protein targets, miRNA target genes, and TFs regulating miRNAs. In the network, we identified three-node regulatory loops each composed of lncRNA, miRNA, and TF. To prioritize these motifs for their role in melanoma progression, we integrated patient-derived RNAseq dataset from TCGA (SKCM) melanoma cohort, using a weighted multi-objective function. We investigated the expression profile of the top-ranked motifs and used them to classify patients into metastatic and non-metastatic phenotypes. CONCLUSIONS: The results of this study showed that network motif UCA1/AKT1/hsa-miR-125b-1 has the highest prediction accuracy (ACC = 0.88) for discriminating metastatic and non-metastatic melanoma phenotypes. The observation is also confirmed by the progression-free survival analysis where the patient group characterized by the metastatic-type expression profile of the motif suffers a significant reduction in survival. The finding suggests a prognostic value of network motifs for the classification and treatment of melanoma.

Systems medicine holds many promises, but has so far provided only a limited number of proofs of principle. To address this road block, possible barriers and challenges of translating systems medicine into clinical practice need to be identified and addressed. The members of the European Cooperation in Science and Technology (COST) Action CA15120 Open Multiscale Systems Medicine (OpenMultiMed) wish to engage the scientific community of systems medicine and multiscale modelling, data science and computing, to provide their feedback in a structured manner. This will result in follow-up white papers and open access resources to accelerate the clinical translation of systems medicine.

Motivation: Extracellular vesicles (EVs), including exosomes and microvesicles, are potent and clinically valuable tools for early diagnosis, prognosis and potentially the targeted treatment of cancer. The content of EVs is closely related to the type and status of the EV-secreting cell. Circulating exosomes are a source of stable RNAs including mRNAs, microRNAs and long non-coding RNAs (lncRNAs). Results: This review outlines the links between EVs, lncRNAs and cancer. We highlight communication networks involving the tumor microenvironment, the immune system and metastasis. We show examples supporting the value of exosomal lncRNAs as cancer biomarkers and therapeutic targets. We demonstrate how a system biology approach can be used to model cell-cell communication via exosomal lncRNAs and to simulate effects of therapeutic interventions. In addition, we introduce algorithms and bioinformatics resources for the discovery of tumor-specific lncRNAs and tools that are applied to determine exosome content and lncRNA function. Finally, this review provides a comprehensive collection and guide to databases for exosomal lncRNAs. Supplementary information: Supplementary data are available at Bioinformatics online.

The immune system is by definition multi-scale because it involves biochemical networks that regulate cell fates across cell boundaries, but also because immune cells communicate with each other by direct contact or through the secretion of local or systemic signals. Furthermore, tumor and immune cells communicate, and this interaction is affected by the tumor microenvironment. Altogether, the tumor-immunity interaction is a complex multi-scale biological system whose analysis requires a systemic view to succeed in developing efficient immunotherapies for cancer and immune-related diseases. In this review we discuss the necessity and the structure of a systems medicine approach for the design of anticancer immunotherapies. We support the idea that the approach must be a combination of algorithms and methods from bioinformatics and patient-data-driven mathematical models conceived to investigate the role of clinical interventions in the tumor-immunity interaction. For each step of the integrative approach proposed, we review the advancement with respect to the computational tools and methods available, but also successful case studies. We particularized our idea for the case of identifying novel tumor-associated antigens and therapeutic targets by integration of patient's immune and tumor profiling in case of aggressive melanoma.

There was evidence that RNAs are a functionally rich class of molecules not only since the arrival of the next-generation sequencing technology. Non-coding RNAs (ncRNA) could be the key to accelerated diagnosis and enhanced prediction of disease and therapy outcomes as well as the design of advanced therapeutic strategies to overcome yet unsatisfactory approaches.In this review, we discuss the state of the art in RNA systems biology with focus on the application in the systems biomedicine field. We propose guidelines for analysing the role of microRNAs and long non-coding RNAs in human pathologies. We introduce RNA expression profiling and network approaches for the identification of stable and effective RNomics-based biomarkers, providing insights into the role of ncRNAs in disease regulation. Towards this, we discuss ways to model the dynamics of gene regulatory networks and signalling pathways that involve ncRNAs. We also describe data resources and computational methods for finding putative mechanisms of action of ncRNAs. Finally, we discuss avenues for the computer-aided design of novel RNA-based therapeutics.

BACKGROUND: Non-coding RNAs gain more attention as their diverse roles in many cellular processes are discovered. At the same time, the need for efficient computational prediction of ncRNAs increases with the pace of sequencing technology. Existing tools are based on various approaches and techniques, but none of them provides a reliable ncRNA detector yet. Consequently, a natural approach is to combine existing tools. Due to a lack of standard input and output formats combination and comparison of existing tools is difficult. Also, for genomic scans they often need to be incorporated in detection workflows using custom scripts, which decreases transparency and reproducibility. RESULTS: We developed a Java-based framework to integrate existing tools and methods for ncRNA detection. This framework enables users to construct transparent detection workflows and to combine and compare different methods efficiently. We demonstrate the effectiveness of combining detection methods in case studies with the small genomes of Escherichia coli, Listeria monocytogenes and Streptococcus pyogenes. With the combined method, we gained 10% to 20% precision for sensitivities from 30% to 80%. Further, we investigated Streptococcus pyogenes for novel ncRNAs. Using multiple methods--integrated by our framework--we determined four highly probable candidates. We verified all four candidates experimentally using RT-PCR. CONCLUSIONS: We have created an extensible framework for practical, transparent and reproducible combination and comparison of ncRNA detection methods. We have proven the effectiveness of this approach in tests and by guiding experiments to find new ncRNAs. The software is freely available under the GNU General Public License (GPL), version 3 at http://www.sbi.uni-rostock.de/moses along with source code, screen shots, examples and tutorial material.

UNLABELLED: In this application note, we present an Systems biology markup language (SBML) export interface for the Systems Biology Toolbox for MATLAB. This interface allows modelers to automatically convert models, represented in the toolbox's own format (SBmodels) to SBML files. Since SBmodels do not explicitly contain all the information that is required to generate SBML, the necessary information is gathered by parsing SBmodels. The export can be done in two different ways. First, it is possible to call the export from the command line, thereby directly converting a model to an SBML file. The second option is to inspect and edit the conversion results with the help of a graphical user interface and to subsequently export the model to SBML. AVAILABILITY: The SBML export interface has been integrated into the Systems Biology Toolbox for MATLAB, which is open source and freely available from http://www.sbtoolbox.org. The website also contains a tutorial, extensive documentation and examples.

MOTIVATION: A very promising approach in drug discovery involves the integration of available biomedical data through mathematical modelling and data mining. We have developed a method called optimization program for drug discovery (OPDD) that allows new enzyme targets to be identified in enzymopathies through the integration of metabolic models and biomedical data in a mathematical optimization program. The method involves four steps: (i) collection of the necessary information about the metabolic system and disease; (ii) translation of the information into mathematical terms; (iii) computation of the optimization programs prioritizing the solutions that propose the inhibition of a reduced number of enzymes and (iv) application of additional biomedical criteria to select and classify the solutions. Each solution consists of a set of predicted values for metabolites, initial substrates and enzyme activities, which describe a biologically acceptable steady state of the system that shifts the pathologic state towards a healthy state. RESULTS: The OPDD was used to detect target enzymes in an enzymopathy, the human hyperuricemia. An existing S-system model and bibliographic information about the disease were used. The method detected six single-target enzyme solutions involving dietary modification, one of them coinciding with the conventional clinical treatment using allopurinol. The OPDD detected a large number of possible solutions involving two enzyme targets. All except one contained one of the previously detected six enzyme targets. The purpose of this work was not to obtain solutions for direct clinical implementation but to illustrate how increasing levels of biomedical information can be integrated together with mathematical models in drug discovery. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Abstract Summary: PLMaddon is a General Public License (GPL) software module designed to expand the current version of the SBToolbox (a Matlab ™ toolbox for systems biology; www.sbtoolbox.org) with a set of functions for the analysis of power-law models, a specific class of kinetic models, set in ordinary differential equations (ODE) and in which the kinetic orders can have positive/negative non-integer values. The module includes functions to generate power-law Taylor expansions of other ODE models (e.g. Michaelis-Menten type models), as well as algorithms to estimate steady-states. The robustness and sensitivity of the models can also be analysed and visualized by computing the power-law's logarithmic gains and sensitivities. Availability: PLMaddon is an open source module for the analysis of power-law models based on the SBToolbox. The latest version of PLMaddon is freely available from: www.sbi.uni-rostock.de/plmaddon The website contains a tutorial with examples, as well as an interactive introductory course on power-law models in systems biology. Contact: [email protected]