Biology subjects

Sonnhammer, E. L. L.

Publications and source records attributed to Sonnhammer, E. L. L..

5 recordsLinked to original sources

bioRxiv2025

GeneSNAKE: a Python package for benchmarking and simulation of gene regulatory networks and perturbation-induced expression data

Understanding how genes interact with and regulate each other is a key challenge in systems biology. One of the primary methods to study this is through gene regulatory networks (GRNs). The field of GRN inference however faces many challenges, which necessitates effective tools for evaluating inference methods. For this purpose, data that corresponds to a known GRN, from various conditions and experimental setups is necessary, which is only possible to attain via simulation. Today, most existing tools for GRN-based simulation are limited either in network or data properties, with few or no options to modify these properties. To address these limitations we present GeneSNAKE, a Python package designed to allow users to generate biologically realistic GRNs, and expression data for benchmarking purposes. GeneSNAKE allows the user to control a wide range of network and data properties, including several distinct noise models. GeneSNAKE improves on previous work in the field by adding a perturbation model and a wide range of perturbation schemes along with the ability to control the noise and the perturbation strength. For benchmarking, GeneSNAKE offers a number of functions both for comparing network similarity, and properties in data and GRNs. These functions can further be used to study properties of biological data to produce simulated data with more realistic properties. GeneSNAKE is an open-source, comprehensive simulation and benchmarking package with powerful capabilities that are not combined in any other single package, and thanks to the Python implementation it can be extended and modified by users.

bioinformatics↗

bioRxiv2024

The FunCoup Cytoscape App: multi-species network analysis and visualization

SummaryFunctional association networks, such as FunCoup, are crucial for analyzing complex gene interactions. To facilitate the analysis and visualization of such genome-wide networks, there is a need for seamless integration with powerful network analysis tools like Cytoscape. The FunCoup Cytoscape App integrates the FunCoup web service API with Cytoscape, allowing users to visualize and analyze gene interaction networks for 640 species. Users can input gene identifiers and customize search parameters, employing various network expansion algorithms like group or independent gene search, MaxLink, and TOPAS. The app maintains consistent visualizations with the FunCoup website, providing detailed node and link information, including tissue and pathway gene annotations. The integration with Cytoscape plugins, such as ClusterMaker2, enhances the analytical capabilities of FunCoup, as exemplified by the identification of the Myasthenia gravis disease module along with potential new therapeutic targets. Availability and implementationThe FunCoup Cytoscape App is developed using the Java OSGi framework, with UI components implemented in Java Swing and build support from Maven. The App is available as a JAR file at https://bitbucket.org/sonnhammergroup/funcoup_cytoscape/ repo, and can be downloaded from the Cytoscape App store https://apps.cytoscape.org/. Contacterik.sonnhammer@scilifelab.se

bioinformatics↗

bioRxiv2024

FunCoup 6: advancing functional association networks across species with directed links and improved user experience

FunCoup 6 (https://funcoup6.scilifelab.se/, will be https://funcoup.org after publication) represents a significant advancement in global functional association networks, aiming to provide researchers with a comprehensive view of the functional coupling interactome. This update introduces novel methodologies and integrated tools for improved network inference and analysis. Major new developments in FunCoup 6 include vastly expanding the coverage of gene regulatory links, a new framework for bin-free Bayesian training, and a new website. FunCoup 6 integrates a new tool for disease and drug target module identification using the TOPAS algorithm. To expand the utility of the resource for biomedical research, it incorporates pathway enrichment analysis using the ANUBIX and EASE algorithms. The unique comparative interactomics analysis in FunCoup provides insights of network conservation, now allowing users to align orthologs only or query each species network independently. Bin-free training was applied to 23 primary species, and in addition networks were generated for all remaining 618 species in InParanoiDB 9. Accompanying these advancements, FunCoup 6 features a new redesigned website, together with updated API functionalities, and represents a pivotal step forward in functional genomics research, offering unique capabilities for exploring the complex landscape of protein interactions. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=66 SRC="FIGDIR/small/612391v1_ufig1.gif" ALT="Figure 1"> View larger version (21K): org.highwire.dtl.DTLVardef@1cf674eorg.highwire.dtl.DTLVardef@18737f0org.highwire.dtl.DTLVardef@699d88org.highwire.dtl.DTLVardef@1e57910_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2024

Comprehensive analysis of the RBP regulome reveals functional modules and drug candidates in liver cancer

RNA binding proteins (RBPs) are essential components of the transcriptomic regulome. Identifying the RBP regulome in cancer cells is crucial to discovering and understanding carcinogenesis mechanisms and providing new therapeutic targets. Here, we aimed to reveal the regulome of liver cancer upon specific perturbations. To this end, we applied a consensus Gene Regulatory Network (GRN) approach using knockdown data for the liver cancer cell line HepG2. By incorporating multiple GRNs from diverse inference methods, we constructed a highly precise GRN. To validate our results, we comprehensively evaluated the consensus GRN, focusing on characterizing the most relevant aspects of the liver cancer regulome. This included utilizing eCLIP-seq and RAPseq data to verify RBP interactions and binding sites. In addition, we performed an enrichment analysis of network modules and drug repurposing based on the inferred GRN. Taken together, our findings demonstrate the critical roles of RBP regulatory interactions in liver cancer that can be employed to improve treatment strategies.

cancer biology↗

bioRxiv2019

Perturbation-based gene regulatory network inference to unravel oncogenic mechanisms

The gene regulatory network (GRN) of human cells encodes mechanisms to ensure proper functioning. However, if this GRN is dysregulated, the cell may enter into a disease state such as cancer. Understanding the GRN as a system can therefore help identify novel mechanisms underlying disease, which can lead to new therapies. Reliable inference of GRNs is however still a major challenge in systems biology.\n\nTo deduce regulatory interactions relevant to cancer, we applied a recent computational inference framework to data from perturbation experiments in squamous carcinoma cell line A431. GRNs were inferred using several methods, and the false discovery rate was controlled by the NestBoot framework. We developed a novel approach to assess the predictiveness of inferred GRNs against validation data, despite the lack of a gold standard. The best GRN was significantly more predictive than the null model, both in crossvalidated benchmarks and for an independent dataset of the same genes under a different perturbation design. It agrees with many known links, in addition to predicting a large number of novel interactions from which a subset was experimentally validated. The inferred GRN captures regulatory interactions central to cancer-relevant processes and thus provides mechanistic insights that are useful for future cancer research.\n\nData available at GSE125958Inferred GRNs and inference statistics available at https://dcolin.shinyapps.io/CancerGRN/ Software available at https://bitbucket.org/sonnhammergrni/genespider/src/BFECV/\n\nAuthor SummaryCancer is the second most common cause of death globally, and although cancer treatments have improved in recent years, we need to understand how regulatory mechanisms are altered in cancer to combat the disease efficiently. By applying gene perturbations and inference of gene regulatory networks to 40 genes known or suspected to have a role in cancer due to interactions with the oncogene MYC, we deduce their underlying regulatory interactions. Using a recent computational framework for inference together with a novel method for cross validation, we infer a reliable regulatory model of this system in a completely data driven manner, not reliant on literature or priors. The novel interactions add to the understanding of the progressive oncogenic regulatory process and may provide new targets for therapy.

systems biology↗