The Biological Connection Markup Language: a SBGN-compliant format for visualization, filtering and analysis of biological pathways

Next-Generation Sequencing Markup Language (NGSML): A Medium for the Representation and Exchange of NGS Data

With the increasing demand for low-cost high-throughput sequencing of large genomes, next-generation sequencing (NGS) technology has developed rapidly. NGS can not only be used in basic scientific research but also in clinical diagnostics and healthcare. Numerous software systems and tools have been developed to analyze NGS data, and various data formats have been produced to accommodate different sequencing equipment providers or analytical software. However, the data interoperability between these tools brings great challenges to researchers. A generic format that could be shared by most of the software and tools in the NGS field would make data interoperability and sharing easier. In this paper, we defined a general XML-based NGS markup language (NGSML) format for the representation and exchange of NGS data. We also developed a user-friendly GUI tool, NGSMLEditor, for presenting, creating, editing, and converting NGSML files. By using NGSML, various types of NGS data can be saved in one unified format. Compared with the unstructured plain text file, a structured data format based on XML technology solves the incompatibility of various NGS data formats. The NGSML specifications are freely available from http://www.sysbio.org.cn/NGSML. NGSMLEditor is open source under GNU GPL and can be downloaded from the website.

MecCog: A knowledge representation framework for genetic disease mechanism

MOTIVATION

Experimental findings on genetic disease mechanisms are scattered throughout the literature and represented in many ways, including unstructured text, cartoons, pathway diagrams, and network graphs. Integration and structuring of such mechanistic information greatly enhances its utility.

RESULTS

MecCog is a graphical framework for building integrated representations (mechanism schemas) of mechanisms by which a genetic variant causes a disease phenotype. A MecCog mechanism schema displays the propagation of system perturbations across stages of biological organization, using graphical notations to symbolize perturbed entities and activities, hyperlinked evidence tagging, a mechanism ontology, and depiction of knowledge gaps, ambiguities, and uncertainties. The web platform enables a user to construct, store, publish, browse, query, and comment on schemas. MecCog facilitates the identification of potential biomarkers, therapeutic intervention sites, and critical future experiments.

AVAILABILITY

The MecCog framework is freely available at http://www.meccog.org.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Network-Based Approaches for Pathway Level Analysis

Identification of impacted pathways is an important problem because it allows us to gain insights into the underlying biology beyond the detection of differentially expressed genes. In the past decade, a plethora of methods have been developed for this purpose. The last generation of pathway analysis methods are designed to take into account various aspects of pathway topology in order to increase the accuracy of the findings. Here, we cover 34 such topology-based pathway analysis methods published in the past 13 years. We compare these methods on categories related to implementation, availability, input format, graph models, and statistical approaches used to compute pathway level statistics and statistical significance. We also discuss a number of critical challenges that need to be addressed, arising both in methodology and pathway representation, including inconsistent terminology, data format, lack of meaningful benchmarks, and, more importantly, a systematic bias that is present in most existing methods. © 2018 by John Wiley & Sons, Inc.

A graphical and computational modeling platform for biological pathways

A major endeavor of systems biology is the construction of graphical and computational models of biological pathways as a means to better understand their structure and function. Here, we present a protocol for a biologist-friendly graphical modeling scheme that facilitates the construction of detailed network diagrams, summarizing the components of a biological pathway (such as proteins and biochemicals) and illustrating how they interact. These diagrams can then be used to simulate activity flow through a pathway, thereby modeling its dynamic behavior. The protocol is divided into four sections: (i) assembly of network diagrams using the modified Edinburgh Pathway Notation (mEPN) scheme and yEd network editing software with pathway information obtained from published literature and databases of molecular interaction data; (ii) parameterization of the pathway model within yEd through the placement of 'tokens' on the basis of the known or imputed amount or activity of a component; (iii) model testing through visualization and quantitative analysis of the movement of tokens through the pathway, using the network analysis tool Graphia Professional and (iv) optimization of model parameterization and experimentation. This is the first modeling approach that combines a sophisticated notation scheme for depicting biological events at the molecular level with a Petri net-based flow simulation algorithm and a powerful visualization engine with which to observe the dynamics of the system being modeled. Unlike many mathematical approaches to modeling pathways, it does not require the construction of a series of equations or rate constants for model parameterization. Depending on a model's complexity and the availability of information, its construction can take days to months, and, with refinement, possibly years. However, once assembled and parameterized, a simulation run, even on a large model, typically takes only seconds. Models constructed using this approach provide a means of knowledge management, information exchange and, through the computation simulation of their dynamic activity, generation and testing of hypotheses, as well as prediction of a system's behavior when perturbed.

Data Sharing: Convert Challenges into Opportunities

Initiatives for sharing research data are opportunities to increase the pace of knowledge discovery and scientific progress. The reuse of research data has the potential to avoid the duplication of data sets and to bring new views from multiple analysis of the same data set. For example, the study of genomic variations associated with cancer profits from the universal collection of such data and helps in selecting the most appropriate therapy for a specific patient. However, data sharing poses challenges to the scientific community. These challenges are of ethical, cultural, legal, financial, or technical nature. This article reviews the impact that data sharing has in science and society and presents guidelines to improve the efficient sharing of research data.

Pathway analysis of complex diseases for GWAS, extending to consider rare variants, multi-omics and interactions

BACKGROUND

Genome-wide association studies (GWAS) is a major method for studying the genetics of complex diseases. Finding all sequence variants to explain fully the aetiology of a disease is difficult because of their small effect sizes. To better explain disease mechanisms, pathway analysis is used to consolidate the effects of multiple variants, and hence increase the power of the study. While pathway analysis has previously been performed within GWAS only, it can now be extended to examining rare variants, other "-omics" and interaction data.

SCOPE OF REVIEW

1. Factors to consider in the choice of software for GWAS pathway analysis. 2. Examples of how pathway analysis is used to analyse rare variants, other "-omics" and interaction data.

MAJOR CONCLUSIONS

To choose appropriate software tools, factors for consideration include covariate compatibility, null hypothesis, one- or two-step analysis required, curation method of gene sets, size of pathways, and size of flanking regions to define gene boundaries. For rare variants, analysis performance depends on consistency between assumed and actual effect distribution of variants. Integration of other "-omics" data and interaction can better explain gene functions.

GENERAL SIGNIFICANCE

Pathway analysis methods will be more readily used for integration of multiple sources of data, and enable more accurate prediction of phenotypes.

Modelling the Structure and Dynamics of Biological Pathways

There is a need for formalised diagrams that both summarise current biological pathway knowledge and support modelling approaches that explain and predict their behaviour. Here, we present a new, freely available modelling framework that includes a biologist-friendly pathway modelling language (mEPN), a simple but sophisticated method to support model parameterisation using available biological information; a stochastic flow algorithm that simulates the dynamics of pathway activity; and a 3-D visualisation engine that aids understanding of the complexities of a system’s dynamics. We present example pathway models that illustrate of the power of approach to depict a diverse range of systems.

This Community Page presents a biologist-friendly method for compiling detailed graphical models of biological pathways from the literature, including their subsequent parameterization for use in dynamic simulations of their activity.

Comparison of human cell signaling pathway databases--evolution, drawbacks and challenges

Elucidating the complexities of cell signaling pathways is of immense importance to gain understanding about various biological phenomenon, such as dynamics of gene/protein expression regulation, cell fate determination, embryogenesis and disease progression. The successful completion of human genome project has also helped experimental and theoretical biologists to analyze various important pathways. To advance this study, during the past two decades, systematic collections of pathway data from experimental studies have been compiled and distributed freely by several databases, which also integrate various computational tools for further analysis. Despite significant advancements, there exist several drawbacks and challenges, such as pathway data heterogeneity, annotation, regular update and automated image reconstructions, which motivated us to perform a thorough review on popular and actively functioning 24 cell signaling databases. Based on two major characteristics, pathway information and technical details, freely accessible data from commercial and academic databases are examined to understand their evolution and enrichment. This review not only helps to identify some novel and useful features, which are not yet included in any of the databases but also highlights their current limitations and subsequently propose the reasonable solutions for future database development, which could be useful to the whole scientific community.

Recent advances in modeling languages for pathway maps and computable biological networks

As our theories of systems biology grow more sophisticated, the models we use to represent them become larger and more complex. Languages necessarily have the expressivity and flexibility required to represent these models in ways that support high-resolution annotation, and provide for simulation and analysis that are sophisticated enough to allow researchers to master their data in the proper context. These languages also need to facilitate model sharing and collaboration, which is currently best done by using uniform data structures (such as graphs) and language standards. In this brief review, we discuss three of the most recent systems biology modeling languages to appear: BEL, PySB and BCML, and examine how they meet these needs.

Methods and approaches in the topology-based analysis of biological pathways

The goal of pathway analysis is to identify the pathways significantly impacted in a given phenotype. Many current methods are based on algorithms that consider pathways as simple gene lists, dramatically under-utilizing the knowledge that such pathways are meant to capture. During the past few years, a plethora of methods claiming to incorporate various aspects of the pathway topology have been proposed. These topology-based methods, sometimes referred to as “third generation,” have the potential to better model the phenomena described by pathways. Although there is now a large variety of approaches used for this purpose, no review is currently available to offer guidance for potential users and developers. This review covers 22 such topology-based pathway analysis methods published in the last decade. We compare these methods based on: type of pathways analyzed (e.g., signaling or metabolic), input (subset of genes, all genes, fold changes, gene p-values, etc.), mathematical models, pathway scoring approaches, output (one or more pathway scores, p-values, etc.) and implementation (web-based, standalone, etc.). We identify and discuss challenges, arising both in methodology and in pathway representation, including inconsistent terminology, different data formats, lack of meaningful benchmarks, and the lack of tissue and condition specificity.

Systems biology of host-mycobiota interactions: dissecting Dectin-1 and Dectin-2 signalling in immune cells with DC-ATLAS

Modelling the networks sustaining the fruitful coexistence between fungi and their mammalian hosts is becoming increasingly important to control emerging fungal pathogens. The C-type lectins Dectin-1 and Dectin-2 are involved in host defense mechanisms against fungal infection driving inflammatory and adaptive immune responses and complement in containing fungal burdens. Recognizing carbohydrate structures in pathogens, their engagement induces maturation of dendritic cells (DCs) into potent immuno-stimulatory cells endowed with the capacity to efficiently prime T cells. Owing to these properties, Dectin-1 and Dectin-2 agonists are currently under investigation as promising adjuvants in vaccination procedures for the treatment of fungal infection. Thus, a detailed understanding of events' cascade specifically triggered in DCs upon engagement is of great interest in translational research. Here, we summarize the current knowledge on Dectin-1 and Dectin-2 signalling in DCs highlighting similarities and differences. Detailed maps are annotated, using the Biological Connection Markup Language (BCML) data model, and stored in DC-ATLAS, a versatile resource for the interpretation of high-throughput data generated perturbing the signalling network of DCs.

Bioinformatic approaches for functional annotation and pathway inference in metagenomics data

Metagenomic approaches are increasingly recognized as a baseline for understanding the ecology and evolution of microbial ecosystems. The development of methods for pathway inference from metagenomics data is of paramount importance to link a phenotype to a cascade of events stemming from a series of connected sets of genes or proteins. Biochemical and regulatory pathways have until recently been thought and modelled within one cell type, one organism, one species. This vision is being dramatically changed by the advent of whole microbiome sequencing studies, revealing the role of symbiotic microbial populations in fundamental biochemical functions. The new landscape we face requires a clear picture of the potentialities of existing tools and development of new tools to characterize, reconstruct and model biochemical and regulatory pathways as the result of integration of function in complex symbiotic interactions of ontologically and evolutionary distinct cell types.

Knowledge representation in metabolic pathway databases

The accurate representation of all aspects of a metabolic network in a structured format, such that it can be used for a wide variety of computational analyses, is a challenge faced by a growing number of researchers. Analysis of five major metabolic pathway databases reveals that each database has made widely different choices to address this challenge, including how to deal with knowledge that is uncertain or missing. In concise overviews, we show how concepts such as compartments, enzymatic complexes and the direction of reactions are represented in each database. Importantly, also concepts which a database does not represent are described. Which aspects of the metabolic network need to be available in a structured format and to what detail differs per application. For example, for in silico phenotype prediction, a detailed representation of gene-protein-reaction relations and the compartmentalization of the network is essential. Our analysis also shows that current databases are still limited in capturing all details of the biology of the metabolic network, further illustrated with a detailed analysis of three metabolic processes. Finally, we conclude that the conceptual differences between the databases, which make knowledge exchange and integration a challenge, have not been resolved, so far, by the exchange formats in which knowledge representation is standardized.

The modular nature of dendritic cell responses to commensal and pathogenic fungi

The type of adaptive immune response following host-fungi interaction is largely determined at the level of the antigen-presenting cells, and in particular by dendritic cells (DCs). The extent to which transcriptional regulatory events determine the decision making process in DCs is still an open question. By applying the highly structured DC-ATLAS pathways to analyze DC responses, we classified the various stimuli by revealing the modular nature of the different transcriptional programs governing the recognition of either pathogenic or commensal fungi. Through comparison of the network parts affected by DC stimulation with fungal cells and purified single agonists, we could determine the contribution of each receptor during the recognition process. We observed that initial recognition of a fungus creates a temporal window during which the simultaneous recruitment of cell surface receptors can intensify, complement and sustain the DC activation process. The breakdown of the response to whole live cells, through the purified components, showed how the response to invading fungi uses a set of specific modules. We find that at the start of fungal recognition, DCs rapidly initiate the activation process. Ligand recognition is further enhanced by over-expression of the receptor genes, with a significant correspondence between gene expression and protein levels and function. Then a marked decrease in the receptor levels follows, suggesting that at this moment the DC commits to a specific fate. Overall our pathway based studies show that the temporal window of the fungal recognition process depends on the availability of ligands and is different for pathogens and commensals. Modular analysis of receptor and signalling-adaptor expression changes, in the early phase of pathogen recognition, is a valuable tool for rapid and efficient dissection of the pathogen derived components that determine the phenotype of the DC and thereby the type of immune response initiated.

Systems biology of host-fungus interactions: turning complexity into simplicity

Modeling interactions between fungi and their hosts at the systems level requires a molecular understanding both of how the host orchestrates immune surveillance and tolerance, and how this activation, in turn, affects fungal adaptation and survival. The transition from the commensal to pathogenic state, and the co-evolution of fungal strains within their hosts, necessitates the molecular dissection of fungal traits responsible for these interactions. There has been a dramatic increase in publically available genome-wide resources addressing fungal pathophysiology and host–fungal immunology. The integration of these existing data and emerging large-scale technologies addressing host–pathogen interactions requires novel tools to connect genome-wide data sets and theoretical approaches with experimental validation so as to identify inherent and emerging properties of host–pathogen relationships and to obtain a holistic view of infectious processes. If successful, a better understanding of the immune response in health and microbial diseases will eventually emerge and pave the way for improved therapies.

Ten years of pathway analysis: current approaches and outstanding challenges

Pathway analysis has become the first choice for gaining insight into the underlying biology of differentially expressed genes and proteins, as it reduces complexity and has increased explanatory power. We discuss the evolution of knowledge base–driven pathway analysis over its first decade, distinctly divided into three generations. We also discuss the limitations that are specific to each generation, and how they are addressed by successive generations of methods. We identify a number of annotation challenges that must be addressed to enable development of the next generation of pathway analysis methods. Furthermore, we identify a number of methodological challenges that the next generation of methods must tackle to take advantage of the technological advances in genomics and proteomics in order to improve specificity, sensitivity, and relevance of pathway analysis.

graphite - a Bioconductor package to convert pathway topology to gene network

BACKGROUND

Gene set analysis is moving towards considering pathway topology as a crucial feature. Pathway elements are complex entities such as protein complexes, gene family members and chemical compounds. The conversion of pathway topology to a gene/protein networks (where nodes are a simple element like a gene/protein) is a critical and challenging task that enables topology-based gene set analyses.Unfortunately, currently available R/Bioconductor packages provide pathway networks only from single databases. They do not propagate signals through chemical compounds and do not differentiate between complexes and gene families.

RESULTS

Here we present graphite, a Bioconductor package addressing these issues. Pathway information from four different databases is interpreted following specific biologically-driven rules that allow the reconstruction of gene-gene networks taking into account protein complexes, gene families and sensibly removing chemical compounds from the final graphs. The resulting networks represent a uniform resource for pathway analyses. Indeed, graphite provides easy access to three recently proposed topological methods. The graphite package is available as part of the Bioconductor software suite.

CONCLUSIONS

graphite is an innovative package able to gather and make easily available the contents of the four major pathway databases. In the field of topological analysis graphite acts as a provider of biological information by reducing the pathway complexity considering the biological meaning of the pathway elements.

PathVisio-Validator: a rule-based validation plugin for graphical pathway notations

PURPOSE

The PathVisio-Validator plugin aims to simplify the task of producing biological pathway diagrams that follow graphical standardized notations, such as Molecular Interaction Maps or the Systems Biology Graphical Notation. This plugin assists in the creation of pathway diagrams by ensuring correct usage of a notation, and thereby reducing ambiguity when diagrams are shared among biologists. Rulesets, needed in the validation process, can be generated for any graphical notation that a developer desires, using either Schematron or Groovy. The plugin also provides support for filtering validation results, validating on a subset of rules, and distinguishing errors and warnings.