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

Motivation: Functional 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.

Results: The FunCoup Cytoscape App integrates the FunCoup web service API with Cytoscape, allowing users to visualize and analyze gene interaction networks for 640 species.

New developments for the Quest for Orthologs benchmark service.

The Quest for Orthologs (QfO) orthology benchmark service (https://orthology.benchmarkservice.org) hosts a wide range of standardized benchmarks for orthology inference evaluation.

The Pfam protein families database: embracing AI/ML.

The Pfam protein families database is a comprehensive collection of protein domains and families used for genome annotation and protein structure and function analysis (https://www.ebi.ac.

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

FunCoup 6 (https://funcoup.org) 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.

Quest for Orthologs in the Era of Biodiversity Genomics.

The era of biodiversity genomics is characterized by large-scale genome sequencing efforts that aim to represent each living taxon with an assembled genome. Generating knowledge from this wealth of data has not kept up with this pace. We here discuss major challenges to integrating these novel genomes into a comprehensive functional and evolutionary network spanning the tree of life.

GeneSPIDER2: large scale GRN simulation and benchmarking with perturbed single-cell data.

Single-cell data is increasingly used for gene regulatory network (GRN) inference, and benchmarks for this have been developed based on simulated data. However, existing single-cell simulators cannot model the effects of gene perturbations. A further challenge lies in generating large-scale GRNs that often struggle with computational and stability issues.

Benchmarking enrichment analysis methods with the disease pathway network.

Enrichment analysis (EA) is a common approach to gain functional insights from genome-scale experiments. As a consequence, a large number of EA methods have been developed, yet it is unclear from previous studies which method is the best for a given dataset. The main issues with previous benchmarks include the complexity of correctly assigning true pathways to a test dataset, and lack of generality of the evaluation metrics, for which the rank of a single target pathway is commonly used.

InParanoiDB 9: Ortholog Groups for Protein Domains and Full-Length Proteins.

Prediction of orthologs is an important bioinformatics pursuit that is frequently used for inferring protein function and evolutionary analyses. The InParanoid database is a well known resource of ortholog predictions between a wide variety of organisms. Although orthologs have historically been inferred at the level of full-length protein sequences, many proteins consist of several independent protein domains that may be orthologous to domains in other proteins in a way that differs from the full-length protein case.

TOPAS, a network-based approach to detect disease modules in a top-down fashion.

A vast scenario of potential disease mechanisms and remedies is yet to be discovered. The field of Network Medicine has grown thanks to the massive amount of high-throughput data and the emerging evidence that disease-related proteins form 'disease modules'. Relying on prior disease knowledge, network-based disease module detection algorithms aim at connecting the list of known disease associated genes by exploiting interaction networks.

Knowledge of the perturbation design is essential for accurate gene regulatory network inference.

The gene regulatory network (GRN) of a cell executes genetic programs in response to environmental and internal cues. Two distinct classes of methods are used to infer regulatory interactions from gene expression: those that only use observed changes in gene expression, and those that use both the observed changes and the perturbation design, i.e.

Spatio-temporal analysis of prostate tumors in situ suggests pre-existence of treatment-resistant clones.

The molecular mechanisms underlying lethal castration-resistant prostate cancer remain poorly understood, with intratumoral heterogeneity a likely contributing factor. To examine the temporal aspects of resistance, we analyze tumor heterogeneity in needle biopsies collected before and after treatment with androgen deprivation therapy. By doing so, we are able to couple clinical responsiveness and morphological information such as Gleason score to transcriptome-wide data.

Optimal Sparsity Selection Based on an Information Criterion for Accurate Gene Regulatory Network Inference.

Accurate inference of gene regulatory networks (GRNs) is important to unravel unknown regulatory mechanisms and processes, which can lead to the identification of treatment targets for genetic diseases. A variety of GRN inference methods have been proposed that, under suitable data conditions, perform well in benchmarks that consider the entire spectrum of false-positives and -negatives. However, it is very challenging to predict which single network sparsity gives the most accurate GRN.

Corrigendum: Network Crosstalk as a Basis for Drug Repurposing.

[This corrects the article DOI: 10.3389/fgene.2022.

Benefits and Challenges of Pre-clustered Network-Based Pathway Analysis.

Functional analysis of gene sets derived from experiments is typically done by pathway annotation. Although many algorithms exist for analyzing the association between a gene set and a pathway, an issue which is generally ignored is that gene sets often represent multiple pathways. In such cases an association to a pathway is weakened by the presence of genes associated with other pathways.

GRNbenchmark - a web server for benchmarking directed gene regulatory network inference methods.

Accurate inference of gene regulatory networks (GRN) is an essential component of systems biology, and there is a constant development of new inference methods. The most common approach to assess accuracy for publications is to benchmark the new method against a selection of existing algorithms. This often leads to a very limited comparison, potentially biasing the results, which may stem from tuning the benchmark's properties or incorrect application of other methods.

InParanoid-DIAMOND: faster orthology analysis with the InParanoid algorithm.

Summary: Predicting orthologs, genes in different species having shared ancestry, is an important task in bioinformatics. Orthology prediction tools are required to make accurate and fast predictions, in order to analyze large amounts of data within a feasible time frame. InParanoid is a well-known algorithm for orthology analysis, shown to perform well in benchmarks, but having the major limitation of long runtimes on large datasets.

The Quest for Orthologs orthology benchmark service in 2022.

The Orthology Benchmark Service (https://orthology.benchmarkservice.org) is the gold standard for orthology inference evaluation, supported and maintained by the Quest for Orthologs consortium.

Network Crosstalk as a Basis for Drug Repurposing.

The need for systematic drug repurposing has seen a steady increase over the past decade and may be particularly valuable to quickly remedy unexpected pandemics. The abundance of functional interaction data has allowed mapping of substantial parts of the human interactome modeled using functional association networks, favoring network-based drug repurposing. Network crosstalk-based approaches have never been tested for drug repurposing despite their success in the related and more mature field of pathway enrichment analysis.

PathwAX II: network-based pathway analysis with interactive visualization of network crosstalk.

Motivation: Pathway annotation tools are indispensable for the interpretation of a wide range of experiments in life sciences. Network-based algorithms have recently been developed which are more sensitive than traditional overlap-based algorithms, but there is still a lack of good online tools for network-based pathway analysis.

Results: We present PathwAX II-a pathway analysis web tool based on network crosstalk analysis using the BinoX algorithm.

Generation of Realistic Gene Regulatory Networks by Enriching for Feed-Forward Loops.

The regulatory relationships between genes and proteins in a cell form a gene regulatory network (GRN) that controls the cellular response to changes in the environment. A number of inference methods to reverse engineer the original GRN from large-scale expression data have recently been developed. However, the absence of ground-truth GRNs when evaluating the performance makes realistic simulations of GRNs necessary.

Fast and accurate gene regulatory network inference by normalized least squares regression.

Motivation: Inferring an accurate gene regulatory network (GRN) has long been a key goal in the field of systems biology. To do this, it is important to find a suitable balance between the maximum number of true positive and the minimum number of false-positive interactions. Another key feature is that the inference method can handle the large size of modern experimental data, meaning the method needs to be both fast and accurate.

Drug repurposing improves disease targeting 11-fold and can be augmented by network module targeting, applied to COVID-19.

This analysis presents a systematic evaluation of the extent of therapeutic opportunities that can be obtained from drug repurposing by connecting drug targets with disease genes. When using FDA-approved indications as a reference level we found that drug repurposing can offer an average of an 11-fold increase in disease coverage, with the maximum number of diseases covered per drug being increased from 134 to 167 after extending the drug targets with their high confidence first neighbors. Additionally, by network analysis to connect drugs to disease modules we found that drugs on average target 4 disease modules, yet the similarity between disease modules targeted by the same drug is generally low and the maximum number of disease modules targeted per drug increases from 158 to 229 when drug targets are neighbor-extended.

PathBIX-a web server for network-based pathway annotation with adaptive null models.

Motivation: Pathway annotation is a vital tool for interpreting and giving meaning to experimental data in life sciences. Numerous tools exist for this task, where the most recent generation of pathway enrichment analysis tools, network-based methods, utilize biological networks to gain a richer source of information as a basis of the analysis than merely the gene content. Network-based methods use the network crosstalk between the query gene set and the genes in known pathways, and compare this to a null model of random expectation.

Inferring the experimental design for accurate gene regulatory network inference.

Motivation: Accurate inference of gene regulatory interactions is of importance for understanding the mechanisms of underlying biological processes. For gene expression data gathered from targeted perturbations, gene regulatory network (GRN) inference methods that use the perturbation design are the top performing methods. However, the connection between the perturbation design and gene expression can be obfuscated due to problems, such as experimental noise or off-target effects, limiting the methods' ability to reconstruct the true GRN.

Ten Years of Collaborative Progress in the Quest for Orthologs.

Accurate determination of the evolutionary relationships between genes is a foundational challenge in biology. Homology-evolutionary relatedness-is in many cases readily determined based on sequence similarity analysis. By contrast, whether or not two genes directly descended from a common ancestor by a speciation event (orthologs) or duplication event (paralogs) is more challenging, yet provides critical information on the history of a gene.