Knowledge-based Pathway Research Articles

PathDIP 5: improving coverage and making enrichment analysis more biologically meaningful.

Pathway Data Integration Portal (PathDIP) is an integrated pathway database that was developed to increase functional gene annotation coverage and reduce bias in pathway enrichment analysis. PathDIP 5 provides multiple improvements to enable more interpretable analysis: users can perform enrichment analysis using all sources, separate sources or by combining specific pathway subsets; they can select the types of sources to use or the types of pathways for the analysis, reducing the number of resulting generic pathways or pathways not related to users' research question; users can use API. All pathways have been mapped to seven representative types. The results of pathway enrichment can be summarized through knowledge-based pathway consolidation. All curated pathways were mapped to 53 pathway ontology-based categories. In addition to genes, pathDIP 5 now includes metabolites. We updated existing databases, included two new sources, PathBank and MetabolicAtlas, and removed outdated databases. We enable users to analyse their results using Drugst.One, where a drug-gene network is created using only the user's genes in a specific pathway. Interpreting the results of any analysis is now improved by multiple charts on all the results pages. PathDIP 5 is freely available at https://ophid.utoronto.ca/pathDIP.

Plant Reactome Knowledgebase: empowering plant pathway exploration and OMICS data analysis.

Plant Reactome (https://plantreactome.gramene.org) is a freely accessible, comprehensive plant pathway knowledgebase. It providescurated referencepathways from rice (Oryza sativa) andgene-orthology-based pathway projections to 129 additional species, spanning single-cell photoautotrophs, non-vascular plants, and higher plants, thus encompassing a wide-ranging taxonomic diversity. Currently, Plant Reactome houses a collection of 339 reference pathways, covering metabolic andtransport pathways, hormone signaling, genetic regulations ofdevelopmental processes, and intricate transcriptional networks that orchestrate a plant's response to abiotic and biotic stimuli. Beyond being a mere repository, Plant Reactome serves as a dynamic data discovery platform. Users can analyze andvisualize omics data, such as gene expression, gene-gene interaction, proteome,and metabolome data, all within the rich context of plant pathways. Plant Reactome is dedicated to fostering data interoperability, upholding global data standards, and embracing the tenets of the Findable, Accessible, Interoperableand Re-usable (FAIR) data policy.

Extending protein interaction networks using proteoforms and small molecules.

Biological network analysis for high-throughput biomedical data interpretation relies heavily on topological characteristics. Networks are commonly composed of nodes representing genes or proteins that are connected by edges when interacting. In this study, we use the rich information available in the Reactome pathway database to build biological networks accounting for small molecules and proteoforms modeled using protein isoforms and post-translational modifications to study the topological changes induced by this refinement of the network representation. We find that improving the interactome modeling increases the number of nodes and interactions, but that isoform and post-translational modification annotation is still limited compared to what can be expected biologically. We also note that small molecule information can distort the topology of the network due to the high connectedness of these molecules, which does not necessarily represent the reality of biology. However, by restricting the connections of small molecules to the context of biochemical reactions, we find that these improve the overall connectedness of the network and reduce the prevalence of isolated components and nodes. Overall, changing the representation of the network alters the prevalence of articulation points and bridges globally but also within and across pathways. Hence, some molecules can gain or lose in biological importance depending on the level of detail of the representation of the biological system, which might in turn impact network-based studies of diseases or druggability. Networks are constructed based on data publicly available in the Reactome Pathway knowledgebase: reactome.org.

Low-dose radiation induces unstable gene expression in developing human iPSC-derived retinal ganglion organoids

The effects of low-dose radiation on undifferentiated cells carry important implications. However, the effects on developing retinal cells remain unclear. Here, we analyzed the gene expression characteristics of neuronal organoids containing immature human retinal cells under low-dose radiation and predicted their changes. Developing retinal cells generated from human induced pluripotent stem cells (iPSCs) were irradiated with either 30 or 180 mGy on days 4–5 of development for 24 h. Genome-wide gene expression was observed until day 35. A knowledge-based pathway analysis algorithm revealed fluctuations in Rho signaling and many other pathways. After a month, the levels of an essential transcription factor of eye development, the proportion of paired box 6 (PAX6)-positive cells, and the proportion of retinal ganglion cell (RGC)-specific transcription factor POU class 4 homeobox 2 (POU4F2)-positive cells increased with 30 mGy of irradiation. In contrast, they decreased after 180 mGy of irradiation. Activation of the “development of neurons” pathway after 180 mGy indicated the dedifferentiation and development of other neural cells. Fluctuating effects after low-dose radiation exposure suggest that developing retinal cells employ hormesis and dedifferentiation mechanisms in response to stress.

Illuminate the Functions of Dark Proteins Using the Reactome-IDG Web Portal.

Understudied or dark proteins have the potential to shed light on as-yet undiscovered molecular mechanisms that underlie phenotypes and suggest innovative therapeutic approaches for many diseases. The Reactome-IDG (Illuminating the Druggable Genome) project aims to place dark proteins in the context of manually curated, highly reliable pathways in Reactome, the most comprehensive, open-source biological pathway knowledgebase, facilitating the understanding functions and predicting therapeutic potentials of dark proteins. The Reactome-IDG web portal, deployed at https://idg.reactome.org, provides a simple, interactive web page for users to search pathways that may functionally interact with dark proteins, enabling the prediction of functions of dark proteins in the context of Reactome pathways. Enhanced visualization features implemented at the portal allow users to investigate the functional contexts for dark proteins based on tissue-specific gene or protein expression, drug-target interactions, or protein or gene pairwise relationships in the original Reactome's systems biology graph notation (SBGN) diagrams or the new simplified functional interaction (FI) network view of pathways. The protocols in this chapter describe step-by-step procedures to use the web portal to learn biological functions of dark proteins in the context of Reactome pathways. © 2023 Wiley Periodicals LLC. Basic Protocol 1: Search for interacting pathways of a protein Support Protocol: Interacting pathway results for an annotated protein Alternate Protocol: Use individual pairwise relationships to predict interacting pathways of a protein Basic Protocol 2: Using the IDG pathway browser to study interacting pathways Basic Protocol 3: Overlaying tissue-specific expression data Basic Protocol 4: Overlaying protein/gene pairwise relationships in the pathway context Basic Protocol 5: Visualizing drug/target interactions.

IGEM as a human iPS cell-based global epigenetic modulation detection assay provides throughput characterization of chemicals affecting DNA methylation

Chemical-induced dysregulation of DNA methylation during the fetal period is known to contribute to developmental disorders or increase the risk of certain diseases later in life. In this study, we developed an iGEM (iPS cell-based global epigenetic modulation) detection assay using human induced pluripotent stem (hiPS) cells that express a fluorescently labeled methyl-CpG-binding domain (MBD), which enables a high-throughput screening of epigenetic teratogens/mutagens. 135 chemicals with known cardiotoxicity and carcinogenicity were categorized according to the MBD signal intensity, which reflects the degree of nuclear spatial distribution/concentration of DNA methylation. Further biological characterization through machine-learning analysis that integrated genome-wide DNA methylation, gene expression profiling, and knowledge-based pathway analysis revealed that chemicals with hyperactive MBD signals strongly associated their effects on DNA methylation and expression of genes involved in cell cycle and development. These results demonstrated that our MBD-based integrated analytical system is a powerful framework for detecting epigenetic compounds and providing mechanism insights of pharmaceutical development for sustainable human health.

Predicting Response to FLT3 Inhibitors Using Genomics-Based Computational Bio-Simulation Platform

Background Despite rapid advances in targeted therapies, Acute Myeloid Leukemia (AML) is still suffering from drug resistance and relapse, leading to poor outcomes. That is partly because the traditional "one drug, one target” paradigm lacks consideration of the molecular complexity and heterogeneity in AML. Thus a comprehensive modeling system, integrating all known-effect mutant/drug mechanisms, is needed, which is more aligned to the characteristics of AML, providing an improved prediction accuracy of drug response. Methods We develop a computational bio-simulation platform to predict drug response based on genomics data. In brief, a static cellular signaling network is extracted from a public signaling pathway knowledge base (Reactome), then it is transformed into a computational graph based on the biochemistry knowledge in PubMed. That is used to represent a normal hematic progenitor cell (WT Digital Cell). After applying the patient specimen genomic changes to WT Digital Cell, a patient-specific disease cell is built (Disease Digital Cell). Then drug response simulation is executed based on the Disease Digital Cell. The phenotype reverse degree of Disease Digital Cell determines the final drug response score. A patient drug response dataset from the Beat AML project, is selected from ex vivo drug sensitivity assays of FLT3 inhibitors, which is used to validate the prediction performance of this computational bio-simulation platform. After generating patient-specific Disease Digital Cell based on WES sequencing data, drugs response simulation of each FLT3 inhibitor is executed. Finally, the computational bio-simulation result is compared to the BEAT AML ex vivo sensitivity assays. Results For all 533 selected sample-drug pairs the computational bio-simulation platform achieves an 84.4% accuracy with 76.1% sensitivity, 94.0% specificity, 93.5% PPV and 77.5% NPV, respectively. When stratified by risk classification (ELN2017), the adverse group (n=196) has the best performance with an accuracy of 91.3%. As for different FLT3 inhibitors, gilteritinib, which is indicated for the treatment of adult patients who have relapsed or refractory AML with FLT3 mutations, has the highest 89.3% accuracy with 81.3% sensitivity, 100.0% specificity, 100.0% PPV and 80.0% NPV. Detailed analysis shows that the model predicts correctly for cases carrying FLT3 inhibitors resistant mutations (i.e. FLT3-TKD, NRAS, PTPN11, KIT, etc.) and the inconsistent cases are mostly due to the lack of drug target mutations. Conclusions We develop a genomics-based computational bio-simulation platform for drug response prediction and validate it in the FLT3 inhibitors dataset from the Beat AML project. The comprehensive results show the computational bio-simulation platform can achieve good consistency with ex vivo sensitivity assays in drug response. Furthermore, it may contribute to choosing personalized precision therapies for AML patients. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

Uncovering the effect of responsible leadership on employee creative behaviour: from the perspective of knowledge-based pathway

PurposeThe study aims to explore ‘motivational climate', which designs the recurring patterns associated with employees' attitudes, behaviour, and feelings. If organizations successfully adopt a motivational climate, such climate influences the performance and behavior of employees to a great extent. Responsible leadership plays a constructive role in injecting a motivational climate in an organization to ensure information flow. In a motivational climate, top management or leaders reward their employees for individual progress, improvement and mastery. Knowledge sharing is supported in a mastery climate because such a climate can reduce the motive of knowledge hiding and instead further help in stimulating creativity.Design/methodology/approachStudy was to scrutinize a moderated-mediation model, a quantitative hypothetic deductive approach to verify the hypotheses of the study. The data were gathered from employees and supervisors of advertising agencies and marketing departments in metropolitan cities of Punjab, Pakistan. Such firms and departments are considered because they offer a great opportunity to relevant variables and their relations. These organizations and departments are the most creativity-seeking domains and involve frequent interactions (for instance, regular meetings) between leaders with their employees and among peers. Data were primarily gathered from managerial employees performing their duties in the areas mentioned above.FindingsCurrent study reveals that RL has a positive and significant relation with employee creative behaviour. Increasing RL characteristics can ultimately boost employee performance in the creativity domain. Being a responsible leader becomes mandatory for leaders to foster employee creativity to maintain the sustainability of an organization. It is confirmed from the results that responsible leadership articulates the mind thinking of employees, which creates an open environment of information while persuading creative and similar behaviour.Originality/valueThe current research investigates how responsible leadership can efficiently leverage the stakeholder approach in influencing employees through a knowledge-based pathway to boost their creative behaviour. The current study tends to uncover the mediating effect of the basic construct of knowledge management, which is knowledge sharing. Knowledge sharing enables employees to exchange their information while creating mutual understanding, which helps in the smooth flow of knowledge within the organization; this flow enriches employees to think openly in a creative and appreciative environment.

Molecular profiling of ginsenoside metabolites to identify estrogen receptor alpha activity.

20(S)-Protopanaxadiol (PPD) and 20(S)-Protopanaxatriol (PPT) are major metabolites of ginseng in humans and are considered to have estrogenic activity in cellular bioassays. In this study, we conducted in silico analyses to determine whether PPD and PPT interact with estrogen receptor alpha (ERα) and compared them with ERα agonists, partial agonists, and antagonists to identify their ERα activity. The transcriptome profile of 17β-estradiol (E2), PPD, and PPT in MCF-7 cells expressing ERα was further compared to understand the ERα activity of ginsenoside metabolites. The results showed that PPD and PPT interacted with the 1ERE, 1GWR, and 3UUD ERα proteins in the E2 interaction model, the 3ERD protein in the diethylstilbestrol (DES) interaction model, and the 1X7R protein in the genistein (GEN) interaction model. Conversely, neither the 4PP6 protein of the interaction model with the antagonist resveratrol (RES) nor the 1ERR protein of the interaction model with the antagonist raloxifene (RAL) showed the conformation of amino acid residues. When E2, PPD, and PPT were exposed to MCF-7 cells, cell proliferation and gene expression were observed. The transcriptomic profiles of E2, PPD, and PPT were compared using a knowledge-based pathway. PPD-induced transcription profiling was similar to that of E2, and the neural transmission pathway was detected in both compounds. In contrast, PPT-induced transcription profiling displayed characteristics of gene expression associated with systemic lupus erythematosus. These results suggest that ginsenoside metabolites have ERα agonist activity and exhibit neuroprotective effects and anti-inflammatory actions. However, a meta-analysis using public microarray data showed that the mother compounds GRb1 and GRg1 of PPD and PPT showed metabolic functions in insulin signaling pathways, condensed DNA repair and cell cycle pathways, and immune response and synaptogenesis. These results suggest that the ginsenoside metabolites have potent ERα agonist activity; however, their gene expression profiles may differ from those of E2.

The reactome pathway knowledgebase 2022.

The Reactome Knowledgebase (https://reactome.org), an Elixir core resource, provides manually curated molecular details across a broad range of physiological and pathological biological processes in humans, including both hereditary and acquired disease processes. The processes are annotated as an ordered network of molecular transformations in a single consistent data model. Reactome thus functions both as a digital archive of manually curated human biological processes and as a tool for discovering functional relationships in data such as gene expression profiles or somatic mutation catalogs from tumor cells. Recent curation work has expanded our annotations of normal and disease-associated signaling processes and of the drugs that target them, in particular infections caused by the SARS-CoV-1 and SARS-CoV-2 coronaviruses and the host response to infection. New tools support better simultaneous analysis of high-throughput data from multiple sources and the placement of understudied (‘dark’) proteins from analyzed datasets in the context of Reactome’s manually curated pathways.

Network Meta-analysis on the Changes of Amyloid Precursor Protein Expression Following SARS-CoV-2 Infection.

SARS-CoV-2 infection begins with the attachment of its spike (S) protein to angiotensin-converting enzyme-2 (ACE2) followed by complex host immune responses with cardiovascular and neurological implications. Our meta-analyses used QIAGEN Ingenuity Pathway Analysis (IPA) and Knowledge Base (QKB) to investigate how the expression of amyloid precursor protein (APP) was modulated by attachment of SARS-CoV-2 S protein in the brain microvascular endothelial cells (BMVECs) and during COVID-19 in progress. Published 80 host response genes reported to be modulated in BMVECs following SARS-CoV-2 S protein binding were used to identify key canonical pathways and intermediate molecules mediating the regulation of APP production following the attachment of S protein to endothelial cells. This revealed that the attachment of SARS-CoV-2 S protein may inhibit APP expression in the BMVECs. Our results shed light on the molecular mechanisms by which SARS-CoV-2 infection may potentiate the incidence of stroke by inhibiting the production of APP in the BMVECs. We also analyzed molecules associated with COVID-19, which revealed six upstream regulators, TNF, IFNG, STAT1, IL1β, IL6, and STAT3. The upstream regulators mediate the increased production of APP via intermediators, with eleven regulated by all six upstream regulators. These COVID-19 upstream regulators increased APP expression with a statistically significant Z-score of 3.705 (p value = 0.000211). These findings have revealed molecular mechanisms by which COVID-19 disease may lead to long-term neurological manifestations resulting from the elevated APP expression in line with immune response in the host. Altogether, our study revealed two distinct scenarios which may have differential impact on APP expression.Graphic Supplementary InformationThe online version contains supplementary material available at 10.1007/s11481-021-10012-9.

A Convergent Functional Genomics Analysis to Identify Biological Regulators Mediating Effects of Creatine Supplementation.

Creatine (Cr) and phosphocreatine (PCr) are physiologically essential molecules for life, given they serve as rapid and localized support of energy- and mechanical-dependent processes. This evolutionary advantage is based on the action of creatine kinase (CK) isozymes that connect places of ATP synthesis with sites of ATP consumption (the CK/PCr system). Supplementation with creatine monohydrate (CrM) can enhance this system, resulting in well-known ergogenic effects and potential health or therapeutic benefits. In spite of our vast knowledge about these molecules, no integrative analysis of molecular mechanisms under a systems biology approach has been performed to date; thus, we aimed to perform for the first time a convergent functional genomics analysis to identify biological regulators mediating the effects of Cr supplementation in health and disease. A total of 35 differentially expressed genes were analyzed. We identified top-ranked pathways and biological processes mediating the effects of Cr supplementation. The impact of CrM on miRNAs merits more research. We also cautiously suggest two dose–response functional pathways (kinase- and ubiquitin-driven) for the regulation of the Cr uptake. Our functional enrichment analysis, the knowledge-based pathway reconstruction, and the identification of hub nodes provide meaningful information for future studies. This work contributes to a better understanding of the well-reported benefits of Cr in sports and its potential in health and disease conditions, although further clinical research is needed to validate the proposed mechanisms.

Algorithmic Annotation of Functional Roles for Components of 3,044 Human Molecular Pathways.

Current methods of high-throughput molecular and genomic analyses enabled to reconstruct thousands of human molecular pathways. Knowledge of molecular pathways structure and architecture taken along with the gene expression data can help interrogating the pathway activation levels (PALs) using different bioinformatic algorithms. In turn, the pathway activation profiles can characterize molecular processes, which are differentially regulated and give numeric characteristics of the extent of their activation or inhibition. However, different pathway nodes may have different functions toward overall pathway regulation, and calculation of PAL requires knowledge of molecular function of every node in the pathway in terms of its activator or inhibitory role. Thus, high-throughput annotation of functional roles of pathway nodes is required for the comprehensive analysis of the pathway activation profiles. We proposed an algorithm that identifies functional roles of the pathway components and applied it to annotate 3,044 human molecular pathways extracted from the Biocarta, Reactome, KEGG, Qiagen Pathway Central, NCI, and HumanCYC databases and including 9,022 gene products. The resulting knowledgebase can be applied for the direct calculation of the PALs and establishing large scale profiles of the signaling, metabolic, and DNA repair pathway regulation using high throughput gene expression data. We also provide a bioinformatic tool for PAL data calculations using the current pathway knowledgebase.

LimeMap: a comprehensive map of lipid mediator metabolic pathways

Lipid mediators are major factors in multiple biological functions and are strongly associated with disease. Recent lipidomics approaches have made it possible to analyze multiple metabolites and the associations of individual lipid mediators. Such systematic approaches have enabled us to identify key changes of biological relevance. Against this background, a knowledge-based pathway map of lipid mediators would be useful to visualize and understand the overall interactions of these factors. Here, we have built a precise map of lipid mediator metabolic pathways (LimeMap) to visualize the comprehensive profiles of lipid mediators that change dynamically in various disorders. We constructed the map by focusing on ω-3 and ω-6 fatty acid metabolites and their respective metabolic pathways, with manual curation of referenced information from public databases and relevant studies. Ultimately, LimeMap comprises 282 factors (222 mediators, and 60 enzymes, receptors, and ion channels) and 279 reactions derived from 102 related studies. Users will be able to modify the map and visualize measured data specific to their purposes using CellDesigner and VANTED software. We expect that LimeMap will contribute to elucidating the comprehensive functional relationships and pathways of lipid mediators.

The reactome pathway knowledgebase.

The Reactome Knowledgebase (https://reactome.org) provides molecular details of signal transduction, transport, DNA replication, metabolism and other cellular processes as an ordered network of molecular transformations in a single consistent data model, an extended version of a classic metabolic map. Reactome functions both as an archive of biological processes and as a tool for discovering functional relationships in data such as gene expression profiles or somatic mutation catalogs from tumor cells. To extend our ability to annotate human disease processes, we have implemented a new drug class and have used it initially to annotate drugs relevant to cardiovascular disease. Our annotation model depends on external domain experts to identify new areas for annotation and to review new content. New web pages facilitate recruitment of community experts and allow those who have contributed to Reactome to identify their contributions and link them to their ORCID records. To improve visualization of our content, we have implemented a new tool to automatically lay out the components of individual reactions with multiple options for downloading the reaction diagrams and associated data, and a new display of our event hierarchy that will facilitate visual interpretation of pathway analysis results.

Plant Reactome: a knowledgebase and resource for comparative pathway analysis.

Plant Reactome (https://plantreactome.gramene.org) is an open-source, comparative plant pathway knowledgebase of the Gramene project. It uses Oryza sativa (rice) as a reference species for manual curation of pathways and extends pathway knowledge to another 82 plant species via gene-orthology projection using the Reactome data model and framework. It currently hosts 298 reference pathways, including metabolic and transport pathways, transcriptional networks, hormone signaling pathways, and plant developmental processes. In addition to browsing plant pathways, users can upload and analyze their omics data, such as the gene-expression data, and overlay curated or experimental gene-gene interaction data to extend pathway knowledge. The curation team actively engages researchers and students on gene and pathway curation by offering workshops and online tutorials. The Plant Reactome supports, implements and collaborates with the wider community to make data and tools related to genes, genomes, and pathways Findable, Accessible, Interoperable and Re-usable (FAIR).

Perform Pathway Enrichment Analysis Using ReactomeFIViz.

Modern large-scale biological data analysis often generates a set of significant genes, frequently associated with scores. Pathway-based approaches are routinely performed to understand the functional contexts of these genes. Reactome is the most comprehensive open-access biological pathway knowledge base, widely used in the research community, providing a solid foundation for pathway-based data analysis. ReactomeFIViz is a Cytoscape app built upon Reactome pathways to help users perform pathway- and network-based data analysis and visualization. In this chapter we describe procedures on how to perform pathway enrichment analysis using ReactomeFIViz for a gene score file. We describe two types of analysis: pathway enrichment based on a set of significant genes and GSEA analysis using gene scores without cutoff. We also describe a feature to overlay gene scores onto pathway diagrams, enabling users to understand the underlying mechanisms for up- or down- regulated pathways collected from pathway analysis.

CDKN2A, CDK1, and CCNE1 overexpression in sebaceous gland carcinoma of eyelid.

To investigate the overexpression of genes in sebaceous gland carcinoma (SGC) of the eyelid compared to sebaceous adenoma of the eyelid in order to elucidate the molecular mechanism underlying pathogenesis. We performed histopathological examination of eyelid tissues surgically removed from four patients diagnosed with SGC (cases 1-3) and sebaceous adenoma (case 4) of the eyelid. Next, we performed global gene expression analysis of surgical tissue samples using a GeneChip® system and the Ingenuity Pathways Knowledge Base. The results of the GeneChip® analysis were explored with quantitative real-time polymerase chain reaction (qRT-PCR) analysis. In the SGC samples, we found that 211, 199, and 199 genes, respectively, showed ≥ 2.0-fold higher expression than those in the sebaceous adenoma sample (case 4); 194 genes were common to all three SGC samples. For the 194 genes with upregulated expression, functional category analysis showed that SGC of the eyelid employed a unique gene network, including cyclin-dependent kinase inhibitor 2A (CDKN2A), cyclin-dependent kinase 1 (CDK1), and cyclin E1 (CCNE1), which are related to cell cycle progression, incidence of tumor, and cell viability. Furthermore, qRT-PCR analysis showed that the expression levels of CDKN2A, CDK1, and CCNE1 were significantly upregulated in all SGC cases compared to those in the sebaceous adenoma case. These data were similar to the results of microarray analysis. Overexpression of cell cycle-related genes CDKN2A, CDK1, CCNE1, and their gene network may help elucidate the pathogenic pathway of SGC of the eyelid at the molecular level.

PathwayMatcher: proteoform-centric network construction enables fine-granularity multiomics pathway mapping.

BackgroundMapping biomedical data to functional knowledge is an essential task in bioinformatics and can be achieved by querying identifiers (e.g., gene sets) in pathway knowledge bases. However, the isoform and posttranslational modification states of proteins are lost when converting input and pathways into gene-centric lists.FindingsBased on the Reactome knowledge base, we built a network of protein-protein interactions accounting for the documented isoform and modification statuses of proteins. We then implemented a command line application called PathwayMatcher (github.com/PathwayAnalysisPlatform/PathwayMatcher) to query this network. PathwayMatcher supports multiple types of omics data as input and outputs the possibly affected biochemical reactions, subnetworks, and pathways.ConclusionsPathwayMatcher enables refining the network representation of pathways by including proteoforms defined as protein isoforms with posttranslational modifications. The specificity of pathway analyses is hence adapted to different levels of granularity, and it becomes possible to distinguish interactions between different forms of the same protein.

Visualization of drug target interactions in the contexts of pathways and networks with ReactomeFIViz

The precision medicine paradigm is centered on therapies targeted to particular molecular entities that will elicit an anticipated and controlled therapeutic response. However, genetic alterations in the drug targets themselves or in genes whose products interact with the targets can affect how well a drug actually works for an individual patient. To better understand the effects of targeted therapies in patients, we need software tools capable of simultaneously visualizing patient-specific variations and drug targets in their biological context. This context can be provided using pathways, which are process-oriented representations of biological reactions, or biological networks, which represent pathway-spanning interactions among genes, proteins, and other biological entities. To address this need, we have recently enhanced the Reactome Cytoscape app, ReactomeFIViz, to assist researchers in visualizing and modeling drug and target interactions. ReactomeFIViz integrates drug-target interaction information with high quality manually curated pathways and a genome-wide human functional interaction network. Both the pathways and the functional interaction network are provided by Reactome, the most comprehensive open source biological pathway knowledgebase. We describe several examples demonstrating the application of these new features to the visualization of drugs in the contexts of pathways and networks. Complementing previous features in ReactomeFIViz, these new features enable researchers to ask focused questions about targeted therapies, such as drug sensitivity for patients with different mutation profiles, using a pathway or network perspective.