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. A way to counteract this is to cluster the gene set into more homogenous parts before performing pathway analysis on each module. We explored whether network-based pre-clustering of a query gene set can improve pathway analysis. The methods MCL, Infomap, and MGclus were used to cluster the gene set projected onto the FunCoup network. We characterized how well these methods are able to detect individual pathways in multi-pathway gene sets, and applied each of the clustering methods in combination with four pathway analysis methods: Gene Enrichment Analysis, BinoX, NEAT, and ANUBIX. Using benchmarks constructed from the KEGG pathway database we found that clustering can be beneficial by increasing the sensitivity of pathway analysis methods and by providing deeper insights of biological mechanisms related to the phenotype under study. However, keeping a high specificity is a challenge. For ANUBIX, clustering caused a minor loss of specificity, while for BinoX and NEAT it caused an unacceptable loss of specificity. GEA had very low sensitivity both before and after clustering. The choice of clustering method only had a minor effect on the results. We show examples of this approach and conclude that clustering can improve overall pathway annotation performance, but should only be used if the used enrichment method has a low false positive rate.

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. It offers several new features compared with the first version, including interactive graphical network visualization of the crosstalk between a query gene set and an enriched pathway, and the addition of Reactome pathways.

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. Moreover, our results highlight that drug repurposing is more dependent on target proteins being shared between diseases than on polypharmacological properties of drugs. We apply our drug repurposing and network module analysis to COVID-19 and show that Fostamatinib is the drug with the highest module coverage.

Biological mechanisms stem from complex intracellular interactions spanning across different levels of regulation. Mapping these interactions is fundamental for the understanding of all types of biological conditions, including complex diseases. Each experimental approach carries its own bias and noise. Combining heterogeneous data sources reduces noise and gives a broader sense of the interactions between genes known as functional association, where both direct and indirect interactions are captured.

FunCoup is one of the most comprehensive functional association databases, providing networks for 22 organisms in all domains of life. FunCoup uses a naïve Bayesian integration approach to combine 11 different data types and increases the coverage by transferring associations between species via orthologs. Additional insights into the mechanisms of a gene network are provided through tissue specificity filtering and directed regulatory links.

Even though FunCoup provides a comprehensive map of the intracellular machinery, gaining insights into conditions such as diseases requires a functional level analysis rather than a gene level analysis. Thus, studying genes that are involved in a condition from a functional perspective requires the usage of pathway enrichment analysis. Several approaches exist, from basic gene overlap to more elaborate analyses that use functional association networks. ANUBIX is a novel network-based analysis (NBA) method that overcomes the high false positive rate issue that previous state-of-the-art NBA approaches have. Additionally, even with accurate methods, a commonly ignored problem is that gene sets derived from experiments are often noisy or contain multiple mechanisms, mixing different pathways which weakens their association to the condition under study. To increase the sensitivity of pathway analysis, we developed a pipeline to cluster gene sets into more homogeneous parts with the aim of unraveling all the mechanisms activated in the studied condition. To facilitate the usage of these tools, we built a web server called PathBIX, a user-friendly platform that allows interactive analysis of all species in FunCoup against multiple pathway databases.

FunCoup (https://funcoup.sbc.su.se) is one of the most comprehensive functional association networks of genes/proteins available. Functional associations are inferred by integrating different types of evidence using a redundancy-weighted naïve Bayesian approach, combined with orthology transfer. FunCoup's high coverage comes from using eleven different types of evidence, and extensive transfer of information between species. Since the latest update of the database, the availability of source data has improved drastically, and user expectations on a tool for functional associations have grown. To meet these requirements, we have made a new release of FunCoup with updated source data and improved functionality. FunCoup 5 now includes 22 species from all domains of life, and the source data for evidences, gold standards, and genomes have been updated to the latest available versions. In this new release, directed regulatory links inferred from transcription factor binding can be visualized in the network viewer for the human interactome. Another new feature is the possibility to filter by genes expressed in a certain tissue in the network viewer. FunCoup 5 further includes the SARS-CoV-2 proteome, allowing users to visualize and analyze interactions between SARS-CoV-2 and human proteins in order to better understand COVID-19. This new release of FunCoup constitutes a major advance for the users, with updated sources, new species and improved functionality for analysis of the networks.

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.

Results: We developed PathBIX, a novel web application for network-based pathway analysis, based on the recently published ANUBIX algorithm which has been shown to be more accurate than previous network-based methods. The PathBIX website performs pathway annotation for 21 species, and utilizes prefetched and preprocessed network data from FunCoup 5.0 networks and pathway data from three databases: KEGG, Reactome, and WikiPathways.

Pathway enrichment analysis is the most common approach for understanding which biological processes are affected by altered gene activities under specific conditions. However, it has been challenging to find a method that efficiently avoids false positives while keeping a high sensitivity. We here present a new network-based method ANUBIX based on sampling random gene sets against intact pathway. Benchmarking shows that ANUBIX is considerably more accurate than previous network crosstalk based methods, which have the drawback of modelling pathways as random gene sets. We demonstrate that ANUBIX does not have a bias for finding certain pathways, which previous methods do, and show that ANUBIX finds biologically relevant pathways that are missed by other methods.

Cell cultures are often used to study physiological processes in health and disease. It is well-known that cells change their gene expression in vitro compared to in vivo, but it is rarely experimentally addressed. High glucose is a known trigger of apoptosis in proximal tubular cells (PTC). Here we used RNA-seq to detect differentially expressed genes in cultures of primary rat PTC, 3 days old, compared to cells retrieved directly from rat outer renal cortex and between PTC exposed to 15 mM glucose and control for 8 h. The expression of 6,174 genes was significantly up- or downregulated in the cultures of PTC compared to the cells in the outer renal cortex. Most altered were mitochondrial and metabolism related genes. Gene expression of proapoptotic proteins were upregulated and gene expression of antiapoptotic proteins were downregulated in PTC. Expression of transporter related genes were generally downregulated. After 8 h, high glucose had not altered the gene expression in PTC. The current study provides evidence that cells alter their gene expression in vitro compared to in vivo and suggests that short-term high glucose exposure can trigger apoptosis in PTC without changing the gene expression levels of apoptotic proteins.

Motivation: 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. A way to counteract this is to cluster the gene set into more homogenous parts before performing pathway analysis on each cluster.

Results: We explored whether network-based pre-clustering of a query gene set can improve pathway analysis. The methods MCL, Infomap, and MGclus were used to cluster the gene set projected onto the FunCoup network. We characterized how well these methods are able to detect individual pathways in multi-pathway gene sets, and applied each of the clustering methods in combination with four pathway analysis methods: Gene Enrichment Analysis, BinoX, NEAT, and ANUBIX. Using benchmarks constructed from the KEGG pathway database we found that clustering substantially increased the sensitivity of pathway analysis methods. For ANUBIX this came with almost no loss of specificity, while for BinoX and NEAT the specificity decreased roughly as much as the sensitivity increased. GEA had very low sensitivity both before and after clustering. The choice of clustering method only had a minor effect on the results. We conclude that clustering can improve overall pathway annotation performance, but only if the used enrichment method has a low false positive rate. 

Availability and Implementation: https://bitbucket.org/sonnhammergroup/clustering-and-pathway-enrichment/