Search

Making the knowledge contained in scientific papers machine-readable and formally computable would allow researchers to take full advantage of this information by enabling integration with other knowledge sources to support data analysis and interpretation. Here we describe Biofactoid, a web-based platform that allows scientists to specify networks of interactions between genes, their products, and chemical compounds, and then translates this information into a representation suitable for computational analysis, search and discovery. We also report the results of a pilot study to encourage the wide adoption of Biofactoid by the scientific community.

Technological advances in computing provide major opportunities to accelerate scientific discovery. The wide availability of structured knowledge would allow us to take full advantage of these by enabling efficient human-computer interaction. Traditionally, biological knowledge is captured in publications and knowledge bases, however, the information in articles is not directly accessible to computers, and knowledge bases are constrained by finite resources available for manual curation. To accelerate knowledge capture and communication and to keep pace with the rapid growth of scientific reports, we developed the Biofactoid (biofactoid.org) software suite, which crowdsources structured knowledge in articles from authors. Biofactoid is a web-based system that lets scientists draw a network of interactions between genes, their products, and chemical compounds and employs smart-automation to translate user input into a structured language using the expressive power of a formal ontology. The resulting data is shared via public information resources, enabling author-curated knowledge to be appreciated in the context of all existing computable knowledge. Authors of recently published papers across a range of journals have already contributed their pathway information, much of which is novel and extends existing pathway databases into new biological areas. We envision the adoption of Biofactoid for crowdsourced curation by scientists and publishers as part of an ecosystem of tools that accelerate scientific communication and discovery.AvailabilityBiofactoid server at https://biofactoid.org

We describe a large-scale community effort to build an open-access, interoperable, and computable repository of COVID-19 molecular mechanisms - the COVID-19 Disease Map. We discuss the tools, platforms, and guidelines necessary for the distributed development of its contents by a multi-faceted community of biocurators, domain experts, bioinformaticians, and computational biologists. We highlight the role of relevant databases and text mining approaches in the enrichment and validation of the curated mechanisms. We describe the contents of the Map and their relevance to the molecular pathophysiology of COVID-19 and the analytical and computational modelling approaches that can be applied for mechanistic data interpretation and predictions. We conclude by demonstrating concrete applications of our work through several use cases and highlight new testable hypotheses.; We describe a large-scale community effort to build an open-access, interoperable, and computable repository of COVID-19 molecular mechanisms - the COVID-19 Disease Map. We discuss the tools, platforms, and guidelines necessary for the distributed development of its contents by a multi-faceted community of biocurators, domain experts, bioinformaticians, and computational biologists. We highlight the role of relevant databases and text mining approaches in the enrichment and validation of the curated mechanisms. We describe the contents of the Map and their relevance to the molecular pathophysiology of COVID-19 and the analytical and computational modelling approaches that can be applied for mechanistic data interpretation and predictions. We conclude by demonstrating concrete applications of our work through several use cases and highlight new testable hypotheses.

Summary We present a computational method to infer causal mechanisms in cell biology by analyzing changes in high-throughput proteomic profiles on the background of prior knowledge captured in biochemical reaction knowledge bases. The method mimics a biologist's traditional approach of explaining changes in data using prior knowledge but does this at the scale of hundreds of thousands of reactions. This is a specific example of how to automate scientific reasoning processes and illustrates the power of mapping from experimental data to prior knowledge via logic programming. The identified mechanisms can explain how experimental and physiological perturbations, propagating in a network of reactions, affect cellular responses and their phenotypic consequences. Causal pathway analysis is a powerful and flexible discovery tool for a wide range of cellular profiling data types and biological questions. The automated causation inference tool, as well as the source code, are freely available at http://causalpath.org., Graphical abstract, Highlights • CausalPath builds mechanistic models from proteomic profiles • It integrates biological pathway models with molecular measurements • It supports logical reasoning with post-translational modifications • A web server, free software, and a source code are available, The bigger picture Molecular profiling of biological organisms provides us with a great amount of information on cellular differences, but converting it to mechanistic insights is still a very challenging task. A prominent approach is to integrate new measurements with the mechanistic knowledge described in the scientific literature and build a model that is supported by both. Although this can be done in many ways, an adept approach will use the literature knowledge in detail and follow high standards of logical reasoning while integrating the known and the new. This article describes an approach that utilizes the details in human biological pathways to identify pairs of changes with a likely cause-effect relation within. The approach automatically converts comparative proteomic and other molecular profiles into hypotheses of differentially active mechanistic relations that explain how the profiles came to be., CausalPath integrates detailed biological pathways with proteomic and other molecular profiles to generate mechanistic models explaining how the observed changes are related. It is applicable to a wide range of contexts and a variety of experiment types. The method can be accessed at causalpath.org.

Motivation Visualization of cellular processes and pathways is a key recurring requirement for effective biological data analysis. There is a considerable need for sophisticated web-based pathway viewers and editors operating with widely accepted standard formats, using the latest visualization techniques and libraries. Results We developed a web-based tool named Newt for viewing, constructing and analyzing biological maps in standard formats such as SBGN, SBML and SIF. Availability and implementation Newt’s source code is publicly available on GitHub and freely distributed under the GNU LGPL. Ample documentation on Newt can be found on http://newteditor.org and on YouTube.

We present a computational method to infer causal mechanisms in cell biology by analyzing changes in high-throughput proteomic profiles on the background of prior knowledge captured in biochemical reaction knowledge bases. The method mimics a biologist's approach of explaining changes in data using prior knowledge in their memory and in the literature, but at the scale of hundreds of thousands of reactions. The identified mechanisms can explain how experimental and physiological perturbations, propagating in a network of reactions, affect cellular responses and their phenotypic consequences. We first demonstrate the validity of the method on cell line perturbation experiments. In discovery mode we then identify 1) differential signaling within breast cancer and ovarian cancer, 2) subtype-specific signaling in these cancers, and 3) recurrent signaling relations across 32 cancer types. Causal pathway analysis is a powerful and flexible discovery tool for a wide range of cellular profiling data types and biological questions. The automated causation inference tool, as well as the source code, are freely available at causalpath.org.

Pathway Commons (https://www.pathwaycommons.org) is an integrated resource of publicly available information about biological pathways including biochemical reactions, assembly of biomolecular complexes, transport and catalysis events and physical interactions involving proteins, DNA, RNA, and small molecules (e.g., metabolites and drug compounds). Data is collected from multiple providers in standard formats, including the Biological Pathway Exchange (BioPAX) language and the Proteomics Standards Initiative Molecular Interactions format, and then integrated. Pathway Commons provides biologists with (1) tools to search this comprehensive resource, (2) a download site offering integrated bulk sets of pathway data (e.g., tables of interactions and gene sets), (3) reusable software libraries for working with pathway information in several programming languages (Java, R, Python, and Javascript), and (4) a web service for programmatically querying the entire dataset. Visualization of pathways is supported using the Systems Biological Graphical Notation (SBGN). Pathway Commons currently contains data from 22 databases with 4,794 detailed human biochemical processes (i.e., pathways) and ∼2.3 million interactions. To enhance the usability of this large resource for end-users, we develop and maintain interactive web applications and training materials that enable pathway exploration and advanced analysis.

PubMed, a repository and search engine for biomedical literature, now indexes >1 million articles each year. This exceeds the processing capacity of human domain experts, limiting our ability to truly understand many diseases. We present Reach, a system for automated, large-scale machine reading of biomedical papers that can extract mechanistic descriptions of biological processes with relatively high precision at high throughput. We demonstrate that combining the extracted pathway fragments with existing biological data analysis algorithms that rely on curated models helps identify and explain a large number of previously unidentified mutually exclusive altered signaling pathways in seven different cancer types. This work shows that combining human-curated ‘big mechanisms’ with extracted ‘big data’ can lead to a causal, predictive understanding of cellular processes and unlock important downstream applications.

Motivation While existing network visualization tools enable the exploration of cancer genomics data, most biologists prefer simplified, curated pathway diagrams, such as those featured in many manuscripts from The Cancer Genome Atlas (TCGA). These pathway diagrams typically summarize how a pathway is altered in individual cancer types, including alteration frequencies for each gene. Results To address this need, we developed the web-based tool PathwayMapper, which runs in most common web browsers. It can be used for viewing pre-curated cancer pathways, or as a graphical editor for creating new pathways, with the ability to overlay genomic alteration data from cBioPortal. In addition, a collaborative mode is available that allows scientists to co-operate interactively on constructing pathways, with support for concurrent modifications and built-in conflict resolution. Availability and Implementation The PathwayMapper tool is accessible at http://pathwaymapper.org and the code is available on Github (https://github.com/iVis-at-Bilkent/pathway-mapper). Supplementary information Supplementary data are available at Bioinformatics online.

Understanding the mechanisms responsible for a cellular behaviour often begins with observations of genes and gene products. Depending on the type of experiment, the number of resulting genes can be small, but increasingly, researchers are faced with many thousands of measurements, as in the case of transcriptomic or protein-DNA binding observations. Here, we describe ways to pair experimental results consisting of one or more genes with analysis tools with the overall aim being to make results more biologically interpretable. In certain cases, experimental approaches such as screens for essential genes can generate one or a few ‘genes of interest' and there is a desire to understand their relationship to one another as well as discover links to additional, interesting genes. To this end, ‘GeneMANIA' is a web tool that accepts gene names and returns a network visualization of related genes based on similarity in expression, localization, protein domains and those involved in physical interactions. Likewise, ‘PCViz' is a web tool that displays a network of interactions drawn from Pathway Commons, a web resource for pathway and interaction knowledge. In cases where experiments generate a lengthy list of genes, for instance, transcriptomic measurements, there is a desire to understand their relevance to a phenotype of interest. Pathway enrichment analysis methods aim to summarize gene lists as pathways, which have a closer link to cell function. An online ‘Guide' by Pathway Commons includes workflows that illustrate how to chain together software tools to identify pathways from the corresponding gene-level data then organize and summarize the pathway-level results in an interactive visualization known as an Enrichment Map. For those wishing to drill-down to individual pathways, Pathway Commons offers a set of web apps, including ‘Search' that enables users to query by keyword and visualize ranked search results. Ongoing development of web apps aims to enhance the accessibility to pathways and integrate support for analysis and visualization of experimental data. The full complement of data, tools and resources offered by Pathway Commons in support of pathway analysis are described. Citation Format: Jeffrey V. Wong, Augustin Luna, Emek Demir, Igor Rodchenkov, Özgün Babur, Chris Sander, Gary D. Bader. How can you interpret gene lists from -omics experiments [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 1284.

Purpose: PaxtoolsR package enables access to pathway data represented in the BioPAX format and made available through the Pathway Commons webservice for users of the R language to aid in advanced pathway analyses. Features include the extraction, merging and validation of pathway data represented in the BioPAX format. This package also provides novel pathway datasets and advanced querying features for R users through the Pathway Commons webservice allowing users to query, extract and retrieve data and integrate these data with local BioPAX datasets. Availability and implementation: The PaxtoolsR package is compatible with versions of R 3.1.1 (and higher) on Windows, Mac OS X and Linux using Bioconductor 3.0 and is available through the Bioconductor R package repository along with source code and a tutorial vignette describing common tasks, such as data visualization and gene set enrichment analysis. Source code and documentation are at http://www.bioconductor.org/packages/paxtoolsr. This plugin is free, open-source and licensed under the LGPL-3. Contact: paxtools@cbio.mskcc.org or lunaa@cbio.mskcc.org

Motivation: As the scientific curiosity in genome studies shifts toward identification of functions of the genomes in large scale, data produced about cellular processes at molecular level has been accumulating with an accelerating rate. In this regard, it is essential to be able to store, integrate, access and analyze this data effectively with the help of software tools. Clearly this requires a strong ontology that is intuitive, comprehensive and uncomplicated. Results: We define an ontology for an intuitive, comprehensive and uncomplicated representation of cellular events. The ontology presented here enables integration of fragmented or incomplete pathway information via collaboration, and supports manipulation of the stored data. In addition, it facilitates concurrent modifications to the data while maintaining its validity and consistency. Furthermore, novel structures for representation of multiple levels of abstraction for pathways and homologies is provided. Lastly, our ontology supports efficient querying of large amounts of data. We have also developed a software tool named pathway analysis tool for integration and knowledge acquisition (PATIKA) providing an integrated, multi-user environment for visualizing and manipulating network of cellular events. PATIKA implements the basics of our ontology. Availability: PATIKA version 1.0 beta is available upon request at http://www.patika.org

Background Information about cellular processes and pathways is becoming increasingly available in detailed, computable standard formats such as BioPAX and SBGN. Effective visualization of this information is a key recurring requirement for biological data analysis, especially for -omic data. Biological data analysis is rapidly migrating to web based platforms; thus there is a substantial need for sophisticated web based pathway viewers that support these platforms and other use cases. Results Towards this goal, we developed a web based viewer named SBGNViz for process description maps in SBGN (SBGN-PD). SBGNViz can visualize both BioPAX and SBGN formats. Unique features of SBGNViz include the ability to nest nodes to arbitrary depths to represent molecular complexes and cellular locations, automatic pathway layout, editing and highlighting facilities to enable focus on sub-maps, and the ability to inspect pathway members for detailed information from EntrezGene. SBGNViz can be used within a web browser without any installation and can be readily embedded into web pages. SBGNViz has two editions built with ActionScript and JavaScript. The JavaScript edition, which also works on touch enabled devices, introduces novel methods for managing and reducing complexity of large SBGN-PD maps for more effective analysis. Conclusion SBGNViz fills an important gap by making the large and fast-growing corpus of rich pathway information accessible to web based platforms. SBGNViz can be used in a variety of contexts and in multiple scenarios ranging from visualization of the results of a single study in a web page to building data analysis platforms. © 2015 Sari et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Motivation: Availability of the sequences of entire genomes shifts the scientific curiosity towards the identification of function of the genomes in large scale as in genome studies. In the near future, data produced about cellular processes at molecular level will accumulate with an accelerating rate as a result of proteomics studies. In this regard, it is essential to develop tools for storing, integrating, accessing, and analyzing this data effectively. Results: We define an ontology for a comprehensive representation of cellular events. The ontology presented here enables integration of fragmented or incomplete pathway information and supports manipulation and incorporation of the stored data, as well as multiple levels of abstraction. Based on this ontology, we present the architecture of an integrated environment named Patika (Pathway Analysis Tool for Integration and Knowledge Acquisition). Patika is composed of a server-side, scalable, object-oriented database and client-side editors to provide an integrated, multi-user environment for visualizing and manipulating network of cellular events. This tool features automated pathway layout, functional computation support, advanced querying and a user-friendly graphical interface. We expect that Patika will be a valuable tool for rapid knowledge acquisition, microarray generated large-scale data interpretation, disease gene identification, and drug development. Availability: A prototype of Patika is available upon request from the authors. Contact: patika@cs.bilkent.edu.tr * To whom correspondence should be addressed.

Summary: Representing models of cellular processes or pathways in a graphically rich form facilitates interpretation of biological observations and generation of new hypotheses. Solving biological problems using large pathway datasets requires software that can combine data mapping, querying and visualization as well as providing access to diverse data resources on the Internet. ChiBE is an open source software application that features user-friendly multi-view display, navigation and manipulation of pathway models in BioPAX format. Pathway views are rendered in a feature-rich format, and may be laid out and edited with state-of-the-art visualization methods, including compound or nested structures for visualizing cellular compartments and molecular complexes. Users can easily query and visualize pathways through an integrated Pathway Commons query tool and analyze molecular profiles in pathway context. Availability: http://www.bilkent.edu.tr/%7Ebcbi/chibe.html Contact: ugur@cs.bilkent.edu.tr Supplementary information: Supplementary data are available at Bioinformatics online.

Motivation: BioPAX is a standard language for representing complex cellular processes, including metabolic networks, signal transduction and gene regulation. Owing to the inherent complexity of a BioPAX model, searching for a specific type of subnetwork can be non-trivial and difficult. Results: We developed an open source and extensible framework for defining and searching graph patterns in BioPAX models. We demonstrate its use with a sample pattern that captures directed signaling relations between proteins. We provide search results for the pattern obtained from the Pathway Commons database and compare these results with the current data in signaling databases SPIKE and SignaLink. Results show that a pattern search in public pathway data can identify a substantial amount of signaling relations that do not exist in signaling databases. Availability: BioPAX-pattern software was developed in Java. Source code and documentation is freely available at http://code.google.com/p/biopax-pattern under Lesser GNU Public License. Contact: patternsearch@cbio.mskcc.org Supplementary information: Supplementary data are available at Bioinformatics online.

A rapidly growing corpus of formal, computable pathway information can be used to answer important biological questions including finding non-trivial connections between cellular processes, identifying significantly altered portions of the cellular network in a disease state and building predictive models that can be used for precision medicine. Due to its complexity and fragmented nature, however, working with pathway data is still difficult. We present Paxtools, a Java library that contains algorithms, software components and converters for biological pathways represented in the standard BioPAX language. Paxtools allows scientists to focus on their scientific problem by removing technical barriers to access and analyse pathway information. Paxtools can run on any platform that has a Java Runtime Environment and was tested on most modern operating systems. Paxtools is open source and is available under the Lesser GNU public license (LGPL), which allows users to freely use the code in their software systems with a requirement for attribution. Source code for the current release (4.2.0) can be found in Software S1. A detailed manual for obtaining and using Paxtools can be found in Protocol S1. The latest sources and release bundles can be obtained from biopax.org/paxtools.

BioPAX (Biological Pathway Exchange) is a standard language to represent biological pathways at the molecular and cellular level. Its major use is to facilitate the exchange of pathway data (http://www.biopax.org). Pathway data captures our understanding of biological processes, but its rapid growth necessitates development of databases and computational tools to aid interpretation. However, the current fragmentation of pathway information across many databases with incompatible formats presents barriers to its effective use. BioPAX solves this problem by making pathway data substantially easier to collect, index, interpret and share. BioPAX can represent metabolic and signaling pathways, molecular and genetic interactions and gene regulation networks. BioPAX was created through a community process. Through BioPAX, millions of interactions organized into thousands of pathways across many organisms, from a growing number of sources, are available. Thus, large amounts of pathway data are available in a computable form to support visualization, analysis and biological discovery.