The Systems Biology Graphical Notation (SBGN) is an international community effort that aims to standardise the visualisation of pathways and networks for readers with diverse scientific backgrounds ...as well as to support an efficient and accurate exchange of biological knowledge between disparate research communities, industry, and other players in systems biology. SBGN comprises the three languages Entity Relationship, Activity Flow, and Process Description (PD) to cover biological and biochemical systems at distinct levels of detail. PD is closest to metabolic and regulatory pathways found in biological literature and textbooks. Its well-defined semantics offer a superior precision in expressing biological knowledge. PD represents mechanistic and temporal dependencies of biological interactions and transformations as a graph. Its different types of nodes include entity pools (e.g. metabolites, proteins, genes and complexes) and processes (e.g. reactions, associations and influences). The edges describe relationships between the nodes (e.g. consumption, production, stimulation and inhibition). This document details Level 1 Version 2.0 of the PD specification, including several improvements, in particular: 1) the addition of the equivalence operator, subunit, and annotation glyphs, 2) modification to the usage of submaps, and 3) updates to clarify the use of various glyphs (i.e. multimer, empty set, and state variable).
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.
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•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
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.
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This document defines Version 0.3 Markup Language (ML) support for the Systems Biology Graphical Notation (SBGN), a set of three complementary visual languages developed for biochemists, modelers, ...and computer scientists. SBGN aims at representing networks of biochemical interactions in a standard, unambiguous way to foster efficient and accurate representation, visualization, storage, exchange, and reuse of information on all kinds of biological knowledge, from gene regulation, to metabolism, to cellular signaling. SBGN is defined neutrally to programming languages and software encoding; however, it is oriented primarily towards allowing models to be encoded using XML, the eXtensible Markup Language. The notable changes from the previous version include the addition of attributes for better specify metadata about maps, as well as support for multiple maps, sub-maps, colors, and annotations. These changes enable a more efficient exchange of data to other commonly used systems biology formats (e. g., BioPAX and SBML) and between tools supporting SBGN (e. g., CellDesigner, Newt, Krayon, SBGN-ED, STON, cd2sbgnml, and MINERVA). More details on SBGN and related software are available at
. With this effort, we hope to increase the adoption of SBGN in bioinformatics tools, ultimately enabling more researchers to visualize biological knowledge in a precise and unambiguous manner.
As a conceptual model of disease mechanisms, a disease map integrates available knowledge and is applied for data interpretation, predictions and hypothesis generation. It is possible to model ...disease mechanisms on different levels of granularity and adjust the approach to the goals of a particular project. This rich environment together with requirements for high-quality network reconstruction makes it challenging for new curators and groups to be quickly introduced to the development methods. In this review, we offer a step-by-step guide for developing a disease map within its mainstream pipeline that involves using the CellDesigner tool for creating and editing diagrams and the MINERVA Platform for online visualisation and exploration. We also describe how the Neo4j graph database environment can be used for managing and querying efficiently such a resource. For assessing the interoperability and reproducibility we apply FAIR principles.
CausalPath (causalpath.org) evaluates proteomic measurements against prior knowledge of biological pathways and infers causality between changes in measured features, such as global protein and ...phospho-protein levels. It uses pathway resources to determine potential causality between observable omic features, which are called prior relations. The subset of the prior relations that are supported by the proteomic profiles are reported and evaluated for statistical significance. The end result is a network model of signaling that explains the patterns observed in the experimental dataset.
For complete details on the use and execution of this protocol, please refer to Babur et al. (2021).
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•A free open-source tool for exploration of proteomic data•Analysis focuses on causal relationships revealed by proteomic changes•Network visualization and publication-quality figures of CausalPath results
CausalPath (causalpath.org) evaluates proteomic measurements against prior knowledge of biological pathways and infers causality between changes in measured features, such as global protein and phospho-protein levels. It uses pathway resources to determine potential causality between observable omic features, which are called prior relations. The subset of the prior relations that are supported by the proteomic profiles are reported and evaluated for statistical significance. The end result is a network model of signaling that explains the patterns observed in the experimental dataset.
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Evolving technology has increased the focus on genomics. The combination of today's advanced techniques with decades of molecular biology research has yielded huge amounts of pathway data. A ...standard, named the Systems Biology Graphical Notation (SBGN), was recently introduced to allow scientists to represent biological pathways in an unambiguous, easy-to-understand and efficient manner. Although there are a number of automated layout algorithms for various types of biological networks, currently none specialize on process description (PD) maps as defined by SBGN.
We propose a new automated layout algorithm for PD maps drawn in SBGN. Our algorithm is based on a force-directed automated layout algorithm called Compound Spring Embedder (CoSE). On top of the existing force scheme, additional heuristics employing new types of forces and movement rules are defined to address SBGN-specific rules. Our algorithm is the only automatic layout algorithm that properly addresses all SBGN rules for drawing PD maps, including placement of substrates and products of process nodes on opposite sides, compact tiling of members of molecular complexes and extensively making use of nested structures (compound nodes) to properly draw cellular locations and molecular complex structures. As demonstrated experimentally, the algorithm results in significant improvements over use of a generic layout algorithm such as CoSE in addressing SBGN rules on top of commonly accepted graph drawing criteria.
An implementation of our algorithm in Java is available within ChiLay library (https://github.com/iVis-at-Bilkent/chilay).
ugur@cs.bilkent.edu.tr or dogrusoz@cbio.mskcc.org
Supplementary data are available at Bioinformatics online.
Abstract
The cBioPortal for Cancer Genomics provides intuitive visualization and analysis of complex cancer genomics data. The public site (http://cbioportal.org/) is accessed by more than 1,500 ...researchers per day, and there are now dozens of local instances of the software that host private data sets at cancer centers around the globe.
We have recently released the software under an open source license, making it free to use and modify by anybody. The software and detailed documentation are available at https://github.com/cBioPortal/cbioportal.
We are now establishing a multi-institutional software development network, which will coordinate and drive the future development of the software and associated data pipelines. This group will focus on four main areas:
1. New analysis and visualization features, including:
a. Improved support for cross-cancer queries and cohort comparisons.
b. Enhanced clinical decision support for precision oncology, including an improved patient view with knowledge base integration, patient timelines and improved tools for visualizing tumor evolution.
2. New data pipelines, including support for new genomic data types and streamlined pipelines for TCGA and the International Cancer Genome Consortium (ICGC).
3. Software architecture and performance improvements.
4. Community engagement: Documentation, user support, and training.
This coordinated effort will help to further establish the cBioPortal as the software of choice in cancer genomics research, both in academia and the pharmaceutical industry. Furthermore, as the sequencing of tumor samples has entered clinical practice, we are expanding the features of the software so that it can be used for precision medicine at cancer centers. In particular, clean, web-accessible, interactive clinical reports integrating multiple sources of genome variation and clinical annotation over time has potential to improve clinical action beyond current text-based molecular reports. By making complex genomic data easily interpretable and linking it to information about drugs and clinical trials, the cBioPortal software has the potential to facilitate the use of genomic data in clinical decision making.
Citation Format: Jianjiong Gao, James Lindsay, Stuart Watt, Istemi Bahceci, Pieter Lukasse, Adam Abeshouse, Hsiao-Wei Chen, Ino de Bruijn, Benjamin Gross, Dong Li, Ritika Kundra, Zachary Heins, Jorge Reis-Filho, Onur Sumer, Yichao Sun, Jiaojiao Wang, Qingguo Wang, Hongxin Zhang, Priti Kumari, M. Furkan Sahin, Sander de Ridder, Fedde Schaeffer, Kees van Bochove, Ugur Dogrusoz, Trevor Pugh, Chris Sander, Ethan Cerami, Nikolaus Schultz. The cBioPortal for cancer genomics and its application in precision oncology. abstract. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 5277.
Various types of artifacts (requirements, source code, test cases, documents, etc.) are produced throughout the lifecycle of a software. These artifacts are often related with each other via ...traceability links that are stored in modern application lifecycle management repositories. Throughout the lifecycle of a software, various types of changes can arise in any one of these artifacts. It is important to review such changes to minimize their potential negative impacts. To maximize benefits of the review process, the reviewer(s) should be chosen appropriately.
In this study, we reformulate the reviewer suggestion problem using software artifact traceability graphs. We introduce a novel approach, named RSTrace, to automatically recommend reviewers that are best suited based on their familiarity with a given artifact. The proposed approach, in theory, could be applied to all types of artifacts. For the purpose of this study, we focused on the source code artifact and conducted an experiment on finding the appropriate code reviewer(s). We initially tested RSTrace on an open source project and achieved top-3 recall of 0.85 with an MRR (mean reciprocal ranking) of 0.73. In a further empirical evaluation of 37 open source projects, we confirmed that the proposed reviewer recommendation approach yields promising top-k and MRR scores on the average compared to the existing reviewer recommendation approaches.
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.
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.
The PathwayMapper tool is accessible at http://pathwaymapper.org and the code is available on Github ( https://github.com/iVis-at-Bilkent/pathway-mapper ).
ivis@cs.bilkent.edu.tr.
Supplementary data are available at Bioinformatics online.
Abstract
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.
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