The Experts below are selected from a list of 2925 Experts worldwide ranked by ideXlab platform
Henning Hermjakob - One of the best experts on this subject based on the ideXlab platform.
-
using Reactome to build an autophagy mechanism knowledgebase
2021Co-Authors: Thawfeek M Varusai, Lincoln Stein, Peter Deustachio, Marc Gillespie, Lisa Matthews, Cristoffer Sevilla, S Jupe, Emmanouil Metzakopian, Henning HermjakobAbstract:The 21st century has revealed much about the fundamental cellular process of autophagy. Autophagy controls the catabolism and recycling of various cellular components both as a constitutive process...
-
exploring leishmania host interaction with Reactome a database of biological pathways and processes
2021Co-Authors: Julieth Murillo, B Jassal, Maria Adelaida Gomez, Henning HermjakobAbstract:Abstract Leishmaniasis is a parasitic disease with a wide range of clinical manifestations. Multiple aspects of the Leishmania-host interaction, such as genetic factors and modulation of microbicidal functions in host cells, influence pathogenesis, disease severity and treatment outcome. How do scientists contend with this complexity? Here, we work towards representing detailed, contextual knowledge on Leishmania-host interactions in the Reactome pathway database to facilitate the extraction of novel mechanistic insights from existing datasets. The Reactome database uses a hierarchy of abstractions that allows for the incorporation of detailed contextual knowledge on biological processes matched to differentially expressed genes. It also includes tools for enhanced over-representation analysis that exploits this extra information. We conducted a systematic curation of published studies documenting different aspects of the Leishmania-host interaction. The “Leishmania infection pathway” included four sub-pathways: phagocytosis, killing mechanisms, cell recruitment, and Leishmania parasite growth and survival. As proof-of-principle of the usefulness of the released pathway, we used it to analyze two previously released transcriptomic datasets of human and murine macrophages infected with Leishmania. Our results provide insights on the participation of ADORA2B signaling pathway in the modulation of IL10 and IL6 in infected macrophages. This work opens the way for other researchers to contribute to, and make use of, the Reactome database. Importance Leishmaniasis is a neglected disease infectious disease which affects more than 1.5 million people annually. Many researchers in the field apply -omic technologies to dissect the basis of clinical and therapeutic outcomes and access drug targetable features in the host-parasite interaction, among others. However, getting mechanistic insights from -omics data to such end is not an easy task. The most common approach is to use the -omics data to inquire pathways databases. The retrieved list of pathways often contains vague names that lack the biological context. In this study, we worked to create the Leishmania infection pathway in the Reactome database. With two practical examples from transcriptomics and microarray data, we demonstrated how this pathway facilitates the analysis of such data. In both datasets, we found a common mechanism of IL10 and IL6 production that the authors did not advert in their previous analysis, providing proof-of-principle of the tool’s enhanced potential for knowledge extraction. Leishmania infection pathway is in its first version, and must be expanded to cover the current knowledge base of the Leishmania-host interaction. We strongly encourage contributions from domain experts for the completion of Leishmania infection pathways.
-
pathwaymatcher proteoform centric network construction enables fine granularity multiomics pathway mapping
2019Co-Authors: Antonio Fabregat, Henning Hermjakob, Luis Francisco Hernandez Sanchez, Bram Burger, Carlos Horro, Stefan Johansson, Pal R Njolstad, Harald BarsnesAbstract:Background Mapping 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. Findings Based 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. Conclusions PathwayMatcher 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.
-
pathwaymatcher proteoform centric network construction enables fine granularity multi omics pathway mapping
2018Co-Authors: Luis Francisco Hernandez Sanchez, Antonio Fabregat, Henning Hermjakob, Bram Burger, Carlos Horro, Stefan Johansson, Pal R Njolstad, Harald Barsnes, Marc VaudelAbstract:Background: Mapping biomedical data to functional knowledge is an essential task in bioinformatics and can be achieved by querying identifiers, e.g. gene sets, in pathway knowledgebases. However, the isoform and post-translational modification states of proteins are lost when converting input and pathways into gene-centric lists. Findings: Based on the Reactome knowledgebase, 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. Conclusions: PathwayMatcher enables refining the network-representation of pathways by including isoform and post-translational 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.
-
interleukins and their signaling pathways in the Reactome biological pathway database
2018Co-Authors: Steven Jupe, Lincoln Stein, Peter Deustachio, Henning Hermjakob, Thawfeek M Varusai, Veronica Shamovsky, Corina Duenas RocaAbstract:Background There is a wealth of biological pathway information available in the scientific literature, but it is spread across many thousands of publications. Alongside publications that contain definitive experimental discoveries are many others that have been dismissed as spurious, found to be irreproducible, or are contradicted by later results and consequently now considered controversial. Many descriptions and images of pathways are incomplete stylized representations that assume the reader is an expert and familiar with the established details of the process, which are consequently not fully explained. Pathway representations in publications frequently do not represent a complete, detailed, and unambiguous description of the molecules involved; their precise posttranslational state; or a full account of the molecular events they undergo while participating in a process. Although this might be sufficient to be interpreted by an expert reader, the lack of detail makes such pathways less useful and difficult to understand for anyone unfamiliar with the area and of limited use as the basis for computational models. Objective Reactome was established as a freely accessible knowledge base of human biological pathways. It is manually populated with interconnected molecular events that fully detail the molecular participants linked to published experimental data and background material by using a formal and open data structure that facilitates computational reuse. These data are accessible on a Web site in the form of pathway diagrams that have descriptive summaries and annotations and as downloadable data sets in several formats that can be reused with other computational tools. The entire database and all supporting software can be downloaded and reused under a Creative Commons license. Methods Pathways are authored by expert biologists who work with Reactome curators and editorial staff to represent the consensus in the field. Pathways are represented as interactive diagrams that include as much molecular detail as possible and are linked to literature citations that contain supporting experimental details. All newly created events undergo a peer-review process before they are added to the database and made available on the associated Web site. New content is added quarterly. Results The 63rd release of Reactome in December 2017 contains 10,996 human proteins participating in 11,426 events in 2,179 pathways. In addition, analytic tools allow data set submission for the identification and visualization of pathway enrichment and representation of expression profiles as an overlay on Reactome pathways. Protein-protein and compound-protein interactions from several sources, including custom user data sets, can be added to extend pathways. Pathway diagrams and analytic result displays can be downloaded as editable images, human-readable reports, and files in several standard formats that are suitable for computational reuse. Reactome content is available programmatically through a REpresentational State Transfer (REST)-based content service and as a Neo4J graph database. Signaling pathways for IL-1 to IL-38 are hierarchically classified within the pathway "signaling by interleukins." The classification used is largely derived from Akdis et al. Conclusion The addition to Reactome of a complete set of the known human interleukins, their receptors, and established signaling pathways linked to annotations of relevant aspects of immune function provides a significant computationally accessible resource of information about this important family. This information can be extended easily as new discoveries become accepted as the consensus in the field. A key aim for the future is to increase coverage of gene expression changes induced by interleukin signaling.
Lincoln Stein - One of the best experts on this subject based on the ideXlab platform.
-
using Reactome to build an autophagy mechanism knowledgebase
2021Co-Authors: Thawfeek M Varusai, Lincoln Stein, Peter Deustachio, Marc Gillespie, Lisa Matthews, Cristoffer Sevilla, S Jupe, Emmanouil Metzakopian, Henning HermjakobAbstract:The 21st century has revealed much about the fundamental cellular process of autophagy. Autophagy controls the catabolism and recycling of various cellular components both as a constitutive process...
-
Reactome diagram viewer data structures and strategies to boost performance
2018Co-Authors: Antonio Fabregat, Guilherme Viteri, Konstantinos Sidiropoulos, Pablo Maringarcia, Peipei Ping, Lincoln Stein, Peter Deustachio, Henning HermjakobAbstract:Motivation Reactome is a free, open-source, open-data, curated and peer-reviewed knowledgebase of biomolecular pathways. For web-based pathway visualization, Reactome uses a custom pathway diagram viewer that has been evolved over the past years. Here, we present comprehensive enhancements in usability and performance based on extensive usability testing sessions and technology developments, aiming to optimize the viewer towards the needs of the community. Results The pathway diagram viewer version 3 achieves consistently better performance, loading and rendering of 97% of the diagrams in Reactome in less than 1 s. Combining the multi-layer html5 canvas strategy with a space partitioning data structure minimizes CPU workload, enabling the introduction of new features that further enhance user experience. Through the use of highly optimized data structures and algorithms, Reactome has boosted the performance and usability of the new pathway diagram viewer, providing a robust, scalable and easy-to-integrate solution to pathway visualization. As graph-based visualization of complex data is a frequent challenge in bioinformatics, many of the individual strategies presented here are applicable to a wide range of web-based bioinformatics resources. Availability and implementation Reactome is available online at: https://Reactome.org. The diagram viewer is part of the Reactome pathway browser (https://Reactome.org/PathwayBrowser/) and also available as a stand-alone widget at: https://Reactome.org/dev/diagram/. The source code is freely available at: https://github.com/Reactome-pwp/diagram. Contact fabregat@ebi.ac.uk or hhe@ebi.ac.uk. Supplementary information Supplementary data are available at Bioinformatics online.
-
interleukins and their signaling pathways in the Reactome biological pathway database
2018Co-Authors: Steven Jupe, Lincoln Stein, Peter Deustachio, Henning Hermjakob, Thawfeek M Varusai, Veronica Shamovsky, Corina Duenas RocaAbstract:Background There is a wealth of biological pathway information available in the scientific literature, but it is spread across many thousands of publications. Alongside publications that contain definitive experimental discoveries are many others that have been dismissed as spurious, found to be irreproducible, or are contradicted by later results and consequently now considered controversial. Many descriptions and images of pathways are incomplete stylized representations that assume the reader is an expert and familiar with the established details of the process, which are consequently not fully explained. Pathway representations in publications frequently do not represent a complete, detailed, and unambiguous description of the molecules involved; their precise posttranslational state; or a full account of the molecular events they undergo while participating in a process. Although this might be sufficient to be interpreted by an expert reader, the lack of detail makes such pathways less useful and difficult to understand for anyone unfamiliar with the area and of limited use as the basis for computational models. Objective Reactome was established as a freely accessible knowledge base of human biological pathways. It is manually populated with interconnected molecular events that fully detail the molecular participants linked to published experimental data and background material by using a formal and open data structure that facilitates computational reuse. These data are accessible on a Web site in the form of pathway diagrams that have descriptive summaries and annotations and as downloadable data sets in several formats that can be reused with other computational tools. The entire database and all supporting software can be downloaded and reused under a Creative Commons license. Methods Pathways are authored by expert biologists who work with Reactome curators and editorial staff to represent the consensus in the field. Pathways are represented as interactive diagrams that include as much molecular detail as possible and are linked to literature citations that contain supporting experimental details. All newly created events undergo a peer-review process before they are added to the database and made available on the associated Web site. New content is added quarterly. Results The 63rd release of Reactome in December 2017 contains 10,996 human proteins participating in 11,426 events in 2,179 pathways. In addition, analytic tools allow data set submission for the identification and visualization of pathway enrichment and representation of expression profiles as an overlay on Reactome pathways. Protein-protein and compound-protein interactions from several sources, including custom user data sets, can be added to extend pathways. Pathway diagrams and analytic result displays can be downloaded as editable images, human-readable reports, and files in several standard formats that are suitable for computational reuse. Reactome content is available programmatically through a REpresentational State Transfer (REST)-based content service and as a Neo4J graph database. Signaling pathways for IL-1 to IL-38 are hierarchically classified within the pathway "signaling by interleukins." The classification used is largely derived from Akdis et al. Conclusion The addition to Reactome of a complete set of the known human interleukins, their receptors, and established signaling pathways linked to annotations of relevant aspects of immune function provides a significant computationally accessible resource of information about this important family. This information can be extended easily as new discoveries become accepted as the consensus in the field. A key aim for the future is to increase coverage of gene expression changes induced by interleukin signaling.
-
Reactome graph database efficient access to complex pathway data
2018Co-Authors: Antonio Fabregat, Florian Korninger, Guilherme Viteri, Konstantinos Sidiropoulos, Pablo Maringarcia, Peipei Ping, Guanming Wu, Lincoln Stein, Peter DeustachioAbstract:Reactome is a free, open-source, open-data, curated and peer-reviewed knowledgebase of biomolecular pathways. One of its main priorities is to provide easy and efficient access to its high quality curated data. At present, biological pathway databases typically store their contents in relational databases. This limits access efficiency because there are performance issues associated with queries traversing highly interconnected data. The same data in a graph database can be queried more efficiently. Here we present the rationale behind the adoption of a graph database (Neo4j) as well as the new ContentService (REST API) that provides access to these data. The Neo4j graph database and its query language, Cypher, provide efficient access to the complex Reactome data model, facilitating easy traversal and knowledge discovery. The adoption of this technology greatly improved query efficiency, reducing the average query time by 93%. The web service built on top of the graph database provides programmatic access to Reactome data by object oriented queries, but also supports more complex queries that take advantage of the new underlying graph-based data storage. By adopting graph database technology we are providing a high performance pathway data resource to the community. The Reactome graph database use case shows the power of NoSQL database engines for complex biological data types.
-
Reactome pathway analysis a high performance in memory approach
2017Co-Authors: Antonio Fabregat, Guilherme Viteri, Konstantinos Sidiropoulos, Pablo Maringarcia, Lincoln Stein, Peter Deustachio, Oscar Forner, Vicente Arnau, Henning HermjakobAbstract:Abstract Background Reactome aims to provide bioinformatics tools for visualisation, interpretation and analysis of pathway knowledge to support basic research, genome analysis, modelling, systems biology and education. Pathway analysis methods have a broad range of applications in physiological and biomedical research; one of the main problems, from the analysis methods performance point of view, is the constantly increasing size of the data samples. Results Here, we present a new high-performance in-memory implementation of the well-established over-representation analysis method. To achieve the target, the over-representation analysis method is divided in four different steps and, for each of them, specific data structures are used to improve performance and minimise the memory footprint. The first step, finding out whether an identifier in the user’s sample corresponds to an entity in Reactome, is addressed using a radix tree as a lookup table. The second step, modelling the proteins, chemicals, their orthologous in other species and their composition in complexes and sets, is addressed with a graph. The third and fourth steps, that aggregate the results and calculate the statistics, are solved with a double-linked tree. Conclusion Through the use of highly optimised, in-memory data structures and algorithms, Reactome has achieved a stable, high performance pathway analysis service, enabling the analysis of genome-wide datasets within seconds, allowing interactive exploration and analysis of high throughput data. The proposed pathway analysis approach is available in the Reactome production web site either via the AnalysisService for programmatic access or the user submission interface integrated into the PathwayBrowser. Reactome is an open data and open source project and all of its source code, including the one described here, is available in the AnalysisTools repository in the Reactome GitHub ( https://github.com/Reactome/ ).
Peter Deustachio - One of the best experts on this subject based on the ideXlab platform.
-
using Reactome to build an autophagy mechanism knowledgebase
2021Co-Authors: Thawfeek M Varusai, Lincoln Stein, Peter Deustachio, Marc Gillespie, Lisa Matthews, Cristoffer Sevilla, S Jupe, Emmanouil Metzakopian, Henning HermjakobAbstract:The 21st century has revealed much about the fundamental cellular process of autophagy. Autophagy controls the catabolism and recycling of various cellular components both as a constitutive process...
-
Reactome and the gene ontology digital convergence of data resources
2021Co-Authors: Benjamin M Good, Kimberly Van Auken, David P Hill, Seth Carbon, James P Balhoff, Laurentphilippe Albou, Paul D Thomas, Christopher J Mungall, Judith A Blake, Peter DeustachioAbstract:MOTIVATION GO Causal Activity Models (GO-CAMs) assemble individual associations of gene products with cellular components, molecular functions, and biological processes into causally linked activity flow models. Pathway databases such as the Reactome Knowledgebase create detailed molecular process descriptions of reactions and assemble them, based on sharing of entities between individual reactions into pathway descriptions. RESULTS To convert the rich content of Reactome into GO-CAMs, we have developed a software tool, Pathways2GO, to convert the entire set of normal human Reactome pathways into GO-CAMs. This conversion yields standard GO annotations from Reactome content and supports enhanced quality control for both Reactome and GO, yielding a nearly seamless conversion between these two resources for the bioinformatics community. SUPPLEMENTARY INFORMATION Supplementary material is available at Bioinformatics online.
-
plant Reactome a knowledgebase and resource for comparative pathway analysis
2019Co-Authors: Sushma Naithani, Peter Deustachio, Justin Cook, Parul Gupta, Justin Preece, Justin Elser, Priyanka Garg, Daemon Dikeman, Jason Kiff, Andrew OlsonAbstract: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).
-
Reactome diagram viewer data structures and strategies to boost performance
2018Co-Authors: Antonio Fabregat, Guilherme Viteri, Konstantinos Sidiropoulos, Pablo Maringarcia, Peipei Ping, Lincoln Stein, Peter Deustachio, Henning HermjakobAbstract:Motivation Reactome is a free, open-source, open-data, curated and peer-reviewed knowledgebase of biomolecular pathways. For web-based pathway visualization, Reactome uses a custom pathway diagram viewer that has been evolved over the past years. Here, we present comprehensive enhancements in usability and performance based on extensive usability testing sessions and technology developments, aiming to optimize the viewer towards the needs of the community. Results The pathway diagram viewer version 3 achieves consistently better performance, loading and rendering of 97% of the diagrams in Reactome in less than 1 s. Combining the multi-layer html5 canvas strategy with a space partitioning data structure minimizes CPU workload, enabling the introduction of new features that further enhance user experience. Through the use of highly optimized data structures and algorithms, Reactome has boosted the performance and usability of the new pathway diagram viewer, providing a robust, scalable and easy-to-integrate solution to pathway visualization. As graph-based visualization of complex data is a frequent challenge in bioinformatics, many of the individual strategies presented here are applicable to a wide range of web-based bioinformatics resources. Availability and implementation Reactome is available online at: https://Reactome.org. The diagram viewer is part of the Reactome pathway browser (https://Reactome.org/PathwayBrowser/) and also available as a stand-alone widget at: https://Reactome.org/dev/diagram/. The source code is freely available at: https://github.com/Reactome-pwp/diagram. Contact fabregat@ebi.ac.uk or hhe@ebi.ac.uk. Supplementary information Supplementary data are available at Bioinformatics online.
-
interleukins and their signaling pathways in the Reactome biological pathway database
2018Co-Authors: Steven Jupe, Lincoln Stein, Peter Deustachio, Henning Hermjakob, Thawfeek M Varusai, Veronica Shamovsky, Corina Duenas RocaAbstract:Background There is a wealth of biological pathway information available in the scientific literature, but it is spread across many thousands of publications. Alongside publications that contain definitive experimental discoveries are many others that have been dismissed as spurious, found to be irreproducible, or are contradicted by later results and consequently now considered controversial. Many descriptions and images of pathways are incomplete stylized representations that assume the reader is an expert and familiar with the established details of the process, which are consequently not fully explained. Pathway representations in publications frequently do not represent a complete, detailed, and unambiguous description of the molecules involved; their precise posttranslational state; or a full account of the molecular events they undergo while participating in a process. Although this might be sufficient to be interpreted by an expert reader, the lack of detail makes such pathways less useful and difficult to understand for anyone unfamiliar with the area and of limited use as the basis for computational models. Objective Reactome was established as a freely accessible knowledge base of human biological pathways. It is manually populated with interconnected molecular events that fully detail the molecular participants linked to published experimental data and background material by using a formal and open data structure that facilitates computational reuse. These data are accessible on a Web site in the form of pathway diagrams that have descriptive summaries and annotations and as downloadable data sets in several formats that can be reused with other computational tools. The entire database and all supporting software can be downloaded and reused under a Creative Commons license. Methods Pathways are authored by expert biologists who work with Reactome curators and editorial staff to represent the consensus in the field. Pathways are represented as interactive diagrams that include as much molecular detail as possible and are linked to literature citations that contain supporting experimental details. All newly created events undergo a peer-review process before they are added to the database and made available on the associated Web site. New content is added quarterly. Results The 63rd release of Reactome in December 2017 contains 10,996 human proteins participating in 11,426 events in 2,179 pathways. In addition, analytic tools allow data set submission for the identification and visualization of pathway enrichment and representation of expression profiles as an overlay on Reactome pathways. Protein-protein and compound-protein interactions from several sources, including custom user data sets, can be added to extend pathways. Pathway diagrams and analytic result displays can be downloaded as editable images, human-readable reports, and files in several standard formats that are suitable for computational reuse. Reactome content is available programmatically through a REpresentational State Transfer (REST)-based content service and as a Neo4J graph database. Signaling pathways for IL-1 to IL-38 are hierarchically classified within the pathway "signaling by interleukins." The classification used is largely derived from Akdis et al. Conclusion The addition to Reactome of a complete set of the known human interleukins, their receptors, and established signaling pathways linked to annotations of relevant aspects of immune function provides a significant computationally accessible resource of information about this important family. This information can be extended easily as new discoveries become accepted as the consensus in the field. A key aim for the future is to increase coverage of gene expression changes induced by interleukin signaling.
Antonio Fabregat - One of the best experts on this subject based on the ideXlab platform.
-
pathwaymatcher proteoform centric network construction enables fine granularity multiomics pathway mapping
2019Co-Authors: Antonio Fabregat, Henning Hermjakob, Luis Francisco Hernandez Sanchez, Bram Burger, Carlos Horro, Stefan Johansson, Pal R Njolstad, Harald BarsnesAbstract:Background Mapping 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. Findings Based 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. Conclusions PathwayMatcher 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.
-
pathwaymatcher proteoform centric network construction enables fine granularity multi omics pathway mapping
2018Co-Authors: Luis Francisco Hernandez Sanchez, Antonio Fabregat, Henning Hermjakob, Bram Burger, Carlos Horro, Stefan Johansson, Pal R Njolstad, Harald Barsnes, Marc VaudelAbstract:Background: Mapping biomedical data to functional knowledge is an essential task in bioinformatics and can be achieved by querying identifiers, e.g. gene sets, in pathway knowledgebases. However, the isoform and post-translational modification states of proteins are lost when converting input and pathways into gene-centric lists. Findings: Based on the Reactome knowledgebase, 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. Conclusions: PathwayMatcher enables refining the network-representation of pathways by including isoform and post-translational 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.
-
Reactome diagram viewer data structures and strategies to boost performance
2018Co-Authors: Antonio Fabregat, Guilherme Viteri, Konstantinos Sidiropoulos, Pablo Maringarcia, Peipei Ping, Lincoln Stein, Peter Deustachio, Henning HermjakobAbstract:Motivation Reactome is a free, open-source, open-data, curated and peer-reviewed knowledgebase of biomolecular pathways. For web-based pathway visualization, Reactome uses a custom pathway diagram viewer that has been evolved over the past years. Here, we present comprehensive enhancements in usability and performance based on extensive usability testing sessions and technology developments, aiming to optimize the viewer towards the needs of the community. Results The pathway diagram viewer version 3 achieves consistently better performance, loading and rendering of 97% of the diagrams in Reactome in less than 1 s. Combining the multi-layer html5 canvas strategy with a space partitioning data structure minimizes CPU workload, enabling the introduction of new features that further enhance user experience. Through the use of highly optimized data structures and algorithms, Reactome has boosted the performance and usability of the new pathway diagram viewer, providing a robust, scalable and easy-to-integrate solution to pathway visualization. As graph-based visualization of complex data is a frequent challenge in bioinformatics, many of the individual strategies presented here are applicable to a wide range of web-based bioinformatics resources. Availability and implementation Reactome is available online at: https://Reactome.org. The diagram viewer is part of the Reactome pathway browser (https://Reactome.org/PathwayBrowser/) and also available as a stand-alone widget at: https://Reactome.org/dev/diagram/. The source code is freely available at: https://github.com/Reactome-pwp/diagram. Contact fabregat@ebi.ac.uk or hhe@ebi.ac.uk. Supplementary information Supplementary data are available at Bioinformatics online.
-
Reactome graph database efficient access to complex pathway data
2018Co-Authors: Antonio Fabregat, Florian Korninger, Guilherme Viteri, Konstantinos Sidiropoulos, Pablo Maringarcia, Peipei Ping, Guanming Wu, Lincoln Stein, Peter DeustachioAbstract:Reactome is a free, open-source, open-data, curated and peer-reviewed knowledgebase of biomolecular pathways. One of its main priorities is to provide easy and efficient access to its high quality curated data. At present, biological pathway databases typically store their contents in relational databases. This limits access efficiency because there are performance issues associated with queries traversing highly interconnected data. The same data in a graph database can be queried more efficiently. Here we present the rationale behind the adoption of a graph database (Neo4j) as well as the new ContentService (REST API) that provides access to these data. The Neo4j graph database and its query language, Cypher, provide efficient access to the complex Reactome data model, facilitating easy traversal and knowledge discovery. The adoption of this technology greatly improved query efficiency, reducing the average query time by 93%. The web service built on top of the graph database provides programmatic access to Reactome data by object oriented queries, but also supports more complex queries that take advantage of the new underlying graph-based data storage. By adopting graph database technology we are providing a high performance pathway data resource to the community. The Reactome graph database use case shows the power of NoSQL database engines for complex biological data types.
-
Reactome pathway analysis a high performance in memory approach
2017Co-Authors: Antonio Fabregat, Guilherme Viteri, Konstantinos Sidiropoulos, Pablo Maringarcia, Lincoln Stein, Peter Deustachio, Oscar Forner, Vicente Arnau, Henning HermjakobAbstract:Abstract Background Reactome aims to provide bioinformatics tools for visualisation, interpretation and analysis of pathway knowledge to support basic research, genome analysis, modelling, systems biology and education. Pathway analysis methods have a broad range of applications in physiological and biomedical research; one of the main problems, from the analysis methods performance point of view, is the constantly increasing size of the data samples. Results Here, we present a new high-performance in-memory implementation of the well-established over-representation analysis method. To achieve the target, the over-representation analysis method is divided in four different steps and, for each of them, specific data structures are used to improve performance and minimise the memory footprint. The first step, finding out whether an identifier in the user’s sample corresponds to an entity in Reactome, is addressed using a radix tree as a lookup table. The second step, modelling the proteins, chemicals, their orthologous in other species and their composition in complexes and sets, is addressed with a graph. The third and fourth steps, that aggregate the results and calculate the statistics, are solved with a double-linked tree. Conclusion Through the use of highly optimised, in-memory data structures and algorithms, Reactome has achieved a stable, high performance pathway analysis service, enabling the analysis of genome-wide datasets within seconds, allowing interactive exploration and analysis of high throughput data. The proposed pathway analysis approach is available in the Reactome production web site either via the AnalysisService for programmatic access or the user submission interface integrated into the PathwayBrowser. Reactome is an open data and open source project and all of its source code, including the one described here, is available in the AnalysisTools repository in the Reactome GitHub ( https://github.com/Reactome/ ).
B Jassal - One of the best experts on this subject based on the ideXlab platform.
-
exploring leishmania host interaction with Reactome a database of biological pathways and processes
2021Co-Authors: Julieth Murillo, B Jassal, Maria Adelaida Gomez, Henning HermjakobAbstract:Abstract Leishmaniasis is a parasitic disease with a wide range of clinical manifestations. Multiple aspects of the Leishmania-host interaction, such as genetic factors and modulation of microbicidal functions in host cells, influence pathogenesis, disease severity and treatment outcome. How do scientists contend with this complexity? Here, we work towards representing detailed, contextual knowledge on Leishmania-host interactions in the Reactome pathway database to facilitate the extraction of novel mechanistic insights from existing datasets. The Reactome database uses a hierarchy of abstractions that allows for the incorporation of detailed contextual knowledge on biological processes matched to differentially expressed genes. It also includes tools for enhanced over-representation analysis that exploits this extra information. We conducted a systematic curation of published studies documenting different aspects of the Leishmania-host interaction. The “Leishmania infection pathway” included four sub-pathways: phagocytosis, killing mechanisms, cell recruitment, and Leishmania parasite growth and survival. As proof-of-principle of the usefulness of the released pathway, we used it to analyze two previously released transcriptomic datasets of human and murine macrophages infected with Leishmania. Our results provide insights on the participation of ADORA2B signaling pathway in the modulation of IL10 and IL6 in infected macrophages. This work opens the way for other researchers to contribute to, and make use of, the Reactome database. Importance Leishmaniasis is a neglected disease infectious disease which affects more than 1.5 million people annually. Many researchers in the field apply -omic technologies to dissect the basis of clinical and therapeutic outcomes and access drug targetable features in the host-parasite interaction, among others. However, getting mechanistic insights from -omics data to such end is not an easy task. The most common approach is to use the -omics data to inquire pathways databases. The retrieved list of pathways often contains vague names that lack the biological context. In this study, we worked to create the Leishmania infection pathway in the Reactome database. With two practical examples from transcriptomics and microarray data, we demonstrated how this pathway facilitates the analysis of such data. In both datasets, we found a common mechanism of IL10 and IL6 production that the authors did not advert in their previous analysis, providing proof-of-principle of the tool’s enhanced potential for knowledge extraction. Leishmania infection pathway is in its first version, and must be expanded to cover the current knowledge base of the Leishmania-host interaction. We strongly encourage contributions from domain experts for the completion of Leishmania infection pathways.
-
Reactome enhanced pathway visualization
2017Co-Authors: Konstantinos Sidiropoulos, Guilherme Viteri, B Jassal, Cristoffer Sevilla, S Jupe, Marissa Webber, M Orlicmilacic, Bruce May, Veronica Shamovsky, Corina DuenasAbstract:Motivation Reactome is a free, open-source, open-data, curated and peer-reviewed knowledge base of biomolecular pathways. Pathways are arranged in a hierarchical structure that largely corresponds to the GO biological process hierarchy, allowing the user to navigate from high level concepts like immune system to detailed pathway diagrams showing biomolecular events like membrane transport or phosphorylation. Here, we present new developments in the Reactome visualization system that facilitate navigation through the pathway hierarchy and enable efficient reuse of Reactome visualizations for users' own research presentations and publications. Results For the higher levels of the hierarchy, Reactome now provides scalable, interactive textbook-style diagrams in SVG format, which are also freely downloadable and editable. Repeated diagram elements like 'mitochondrion' or 'receptor' are available as a library of graphic elements. Detailed lower-level diagrams are now downloadable in editable PPTX format as sets of interconnected objects. Availability and implementation http://Reactome.org. Contact fabregat@ebi.ac.uk or hhe@ebi.ac.uk.
-
the Reactome pathway knowledgebase
2014Co-Authors: Antonio Fabregat, Konstantinos Sidiropoulos, Robin Haw, Marc Gillespie, Phani Garapati, B Jassal, S Jupe, Kerstin Hausmann, Florian KorningerAbstract:The Reactome Knowledgebase (www.Reactome.org) provides molecular details of signal transduction, transport, DNA replication, metabolism and other cellular processes as an ordered network of molecular transformations-an extended version of a classic metabolic map, in a single consistent data model. Reactome functions both as an archive of biological processes and as a tool for discovering unexpected functional relationships in data such as gene expression pattern surveys or somatic mutation catalogues from tumour cells. Over the last two years we redeveloped major components of the Reactome web interface to improve usability, responsiveness and data visualization. A new pathway diagram viewer provides a faster, clearer interface and smooth zooming from the entire reaction network to the details of individual reactions. Tool performance for analysis of user datasets has been substantially improved, now generating detailed results for genome-wide expression datasets within seconds. The analysis module can now be accessed through a RESTFul interface, facilitating its inclusion in third party applications. A new overview module allows the visualization of analysis results on a genome-wide Reactome pathway hierarchy using a single screen page. The search interface now provides auto-completion as well as a faceted search to narrow result lists efficiently.
-
the annotation of the asparagine n linked glycosylation pathway in the Reactome database
2011Co-Authors: Giovanni Marco Dallolio, B Jassal, Ludovica Montanucci, Pascal Gagneux, Jaume Bertranpetit, Hafid LaayouniAbstract:Asparagine N-linked glycosylation is one of the most important forms of protein post-translational modification in eukaryotes and is one of the first metabolic pathways described at a biochemical level. Here, we report a new annotation of this pathway for the Human species, published after passing a peer-review process in Reactome. The new annotation presented here offers a high level of detail and provides references and descriptions for each reaction, along with integration with GeneOntology and other databases. The open-source approach of Reactome toward annotation encourages feedback from its users, making it easier to keep the annotation of this pathway updated with future knowledge. Reactome's web interface allows easy navigation between steps involved in the pathway to compare it with other pathways and resources in other scientific databases and to export it to BioPax and SBML formats, making it accessible for computational studies. This new entry in Reactome expands and complements the annotations already published in databases for biological pathways and provides a common reference to researchers interested in studying this important pathway in the human species. Finally, we discuss the status of the annotation of this pathway and point out which steps are worth further investigation or need better experimental validation.
-
Reactome a database of reactions pathways and biological processes
2011Co-Authors: David Croft, Gopal R Gopinath, Guanming Wu, Gavin Okelly, Robin Haw, Marc Gillespie, Lisa Matthews, Michael Caudy, Phani Garapati, B JassalAbstract:Reactome (http://www.Reactome.org) is a collaboration among groups at the Ontario Institute for Cancer Research, Cold Spring Harbor Laboratory, New York University School of Medicine and The European Bioinformatics Institute, to develop an open source curated bioinformatics database of human pathways and reactions. Recently, we developed a new web site with improved tools for pathway browsing and data analysis. The Pathway Browser is an Systems Biology Graphical Notation (SBGN)-based visualization system that supports zooming, scrolling and event highlighting. It exploits PSIQUIC web services to overlay our curated pathways with molecular interaction data from the Reactome Functional Interaction Network and external interaction databases such as IntAct, BioGRID, ChEMBL, iRefIndex, MINT and STRING. Our Pathway and Expression Analysis tools enable ID mapping, pathway assignment and overrepresentation analysis of user-supplied data sets. To support pathway annotation and analysis in other species, we continue to make orthology-based inferences of pathways in non-human species, applying Ensembl Compara to identify orthologs of curated human proteins in each of 20 other species. The resulting inferred pathway sets can be browsed and analyzed with our Species Comparison tool. Collaborations are also underway to create manually curated data sets on the Reactome framework for chicken, Drosophila and rice.
