Inferring causal molecular networks: empirical assessment through a community-based effort

Inferring molecular networks is a central challenge in computational biology. However, it has remained unclear whether causal, rather than merely correlational, relationships can be effectively inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge that focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results constitute the most comprehensive assessment of causal network inference in a mammalian setting carried out to date and suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess the causal validity of inferred molecular networks

Inferring causal molecular networks: empirical assessment through a community-based effort

It remains unclear whether causal, rather than merely correlational, relationships in molecular networks can be inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge, which focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective, and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess inferred molecular networks in a causal sense

Adding automated Statistical Analysis and Biological Evaluation modules to www.arrayanalysis.org

<p>Genomics refers to the comprehensive study of genes and their function. Recent advances in<br>bioinformatics and high-throughput technologies such as microarray analysis are bringing about<br>a revolution in our understanding of the molecular mechanisms underlying normal and<br>dysfunctional biological processes. In accordance with the word genome, used for the entire<br>DNA in the cell, the collection of all RNAs present in the cell is called the transcriptome. To<br>detect expression levels of tens of thousands of RNA molecules or even the whole known<br>transcriptome of the organism at hand, gene expression arrays have been developed. Gene<br>expression microarrays provide a snapshot of all the transcriptional activity in a biological<br>sample at a particular time.<br>The task of analysing microarray data is often at least as much an art as a science and it typically<br>consumes considerably more time than the laboratory protocols required to generate the data.<br>Part of the challenge is assessing the quality of the data and ensuring t hat all samples are<br>comparable for further analysis. Normalisation of the raw data, which controls for technical<br>variation between arrays within a study, is essential. The challenge of normalisation is to remove<br>as much of the technical variation as possible while leaving the biological variation untouched.<br>The fundamental goal of most microarray experiments is to identify biological processes or<br>pathways that consistently display differential expression between groups of samples. Statistical<br>analysis of genes can show statistically significant differences in expression between samples<br>with different phenotypes or characteristics. The set of genes thus identified is then examined for<br>over-representation on specific pathways.<br>Affymetrix microarrays of different technology versions are very often used in transcriptomic<br>analyses. Quality control and normalisation approaches do exist, especially as packages in<br>Bioconductor/R. However they are not always easy to access as they run through command lines<br>and it is often not clear what the meaning of the specific settings and results are. To tackle this,<br>the BiGCaT department of bioinformatics at Maastricht University, proposed an automated,<br>well-documented and user-friendly pipeline the affyanalysisQC workflow which is called by the<br>61<br>www.arrayanalysis.org server. The existing workflow allows for the quality control (QC) and<br>pre-processing (normalisation) of Affymetrix microarray gene expression data sets.<br>However, the functionality offered by the www.arrayanalysis.org needs to be extended for a<br>more complete analysis of microarray data. This can achieved by adding statistical analysis and<br>biological evaluation modules in the pipeline essentially creating a custom data analysis pipeline<br>for Affymetrix GeneChips.<br>Thus main goal of this thesis is as follows:<br>Goal: Develop user friendly packages for statistical analysis and pathway visualisation of gene<br>expression microarray data<br>Statistical analysis usually involves parametric or nonparametric statistics. It provides statistical<br>significance to the discovered gene expression values. This type of analysis is used for research<br>designs involving hypothesis formulation and is suited for finding differentially expressed genes.<br>The statistical analysis module developed uses the functionality of the R/Bioconductor package<br>LIMMA as it allows great flexibility, customised analysis, and access to many specialised<br>packages designed for analysing gene expression data. Not only is R freely available, but it also<br>allows the use of BioConductor, a collection of R tools including many powerful current gene<br>expression analysis methods written and tested by experts from the growing microarray<br>community.<br>To investigate the biological effect of the genes of interest obtained from the stat istical<br>comparisons performed, the genes can be visualised on pathways of interest. The Biological<br>Evaluation module developed in this project uses the basic functions provided by<br>www.pathvisio.org extended with a new pathvisioRPC package developed using XMLRPC to<br>allow automated calls from the www.arrayanalysis.org server for direct pathway over representation analysis and visualization of the genes analysed.</p> <p> </p

Inferring causal molecular networks: empirical assessment through a community-based effort

It remains unclear whether causal, rather than merely correlational, relationships in molecular networks can be inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge, which focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective, and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess inferred molecular networks in a causal sense

Inferring causal molecular networks: empirical assessment through a community-based effort

It remains unclear whether causal, rather than merely correlational, relationships in molecular networks can be inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge, which focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective, and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess inferred molecular networks in a causal sense

Understanding signaling and metabolic paths using semantified and harmonized information about biological interactions

To grasp the complexity of biological processes, the biological knowledge is often translated into schematic diagrams of, for example, signalling and metabolic pathways. These pathway diagrams describe relevant connections between biological entities and incorporate domain knowledge in a visual format making it easier for humans to interpret. Still, these diagrams can be represented in machine readable formats, as done in the KEGG, Reactome, and WikiPathways databases. However, while humans are good at interpreting the message of the creators of diagrams, algorithms struggle when the diversity in drawing approaches increases. WikiPathways supports multiple drawing styles which need harmonizing to offer semantically enriched access. Particularly challenging, here, are the interactions between the biological entities that underlie the biological causality. These interactions provide information about the biological process (metabolic conversion, inhibition, etc.), the direction, and the participating entities. Availability of the interactions in a semantic and harmonized format is essential for searching the full network of biological interactions. We here study how the graphically-modelled biological knowledge in diagrams can be semantified and harmonized, and exemplify how the resulting data is used to programmatically answer biological questions. We find that we can translate graphically modelled knowledge to a sufficient degree into a semantic model and discuss some of the current limitations. We then use this to show that reproducible notebooks can be used to explore up- and downstream targets of MECP2 and to analyse the sphingolipid metabolism. Our results demonstrate that most of the graphical biological knowledge from WikiPathways is modelled into the semantic layer with the semantic information intact and connectivity information preserved. Being able to evaluate how biological elements affect each other is useful and allows, for example, the identification of up or downstream targets that will have a similar effect when modified

PathVisio 3: An Extendable Pathway Analysis Toolbox

PathVisio is a commonly used pathway editor, visualization and analysis software. Biological pathways have been used by biologists for many years to describe the detailed steps in biological processes. Those powerful, visual representations help researchers to better understand, share and discuss knowledge. Since the first publication of PathVisio in 2008, the original paper was cited more than 170 times and PathVisio was used in many different biological studies. As an online editor PathVisio is also integrated in the community curated pathway database WikiPathways. Here we present the third version of PathVisio with the newest additions and improvements of the application. The core features of PathVisio are pathway drawing, advanced data visualization and pathway statistics. Additionally, PathVisio 3 introduces a new powerful extension systems that allows other developers to contribute additional functionality in form of plugins without changing the core application. PathVisio can be downloaded from http://www.pathvisio.org and in 2014 PathVisio 3 has been downloaded over 5,500 times. There are already more than 15 plugins available in the central plugin repository. PathVisio is a freely available, open-source tool published under the Apache 2.0 license (http://www.apache.org/licenses/LICENSE-2.0). It is implemented in Java and thus runs on all major operating systems. The code repository is available at http://svn.bigcat.unimaas.nl/pathvisio. The support mailing list for users is available on https://groups.google.com/forum/#!forum/wikipathways-discuss and for developers on https://groups.google.com/forum/#!forum/wikipathways-devel

Visualizing multi-omics data on models

<p>Biological pathways provide intuitive frameworks to integrate and co-analyze different kinds of biological data, such as system-wide transcriptomic, proteomic, and metabolomic measurements. While insightful, pathway analysis is generally limited to qualitative conclusions, and the analyses can only be as powerful as the curated annotations can enable. Using our open-source pathway analysis platform, PathVisio, we bridge pathway analysis to the wealth of quantitative approaches already in development for metabolic network modeling, such as flux balance analysis and dynamic simulation. Our focus is on the visualization of the modeling results, which will be critical for understanding how simulated models correlate with experimental measurements.<br>The same biological processes that are visualized in pathways are also described by quantitative models. For example, the arrows that connect entities within metabolic pathways actually represent metabolite fluxes. The integration of large scale data analysis with modeled or measured fluxomics data, will help to gain more insights into the mechanism of the biological process.<br>PathVisio[1] is an open source pathway editing and analysis tool. It can be used to draw biological pathways as graphical diagrams, nodes of which denoted the genes, proteins, and metabolites and the edges linking the nodes denote the relationship between them. The genes, proteins, and metabolites in the pathway can be annotated with database identifiers linking them to online resources. This also allows experimental data (e.g. microarray data) to be mapped and visualized on the pathway diagram . The PathVisio core software has been extended as part of this project to allow annotation of interactions in a biological pathway. This would allow linking interactions to online data sources as well as visualization of data on them.</p> <p>PathVisio uses BridgeDb[2], an identifier mapping framework for biological applications, to allow the translation of identifiers between different systems. We have created a BridgeDb interaction database using mappings between interaction identifiers obtained from Rhea [3]. Due to the transitivity feature in bridgedb, mapping databases used can be clubbed, we plan to develop a second interaction database using mappings from ConsensusPathdb [4], we have an ongoing collaboration with the ConsensusPathDb team. This will allow users to define interactions using most common interaction identifier systems.</p> <p>PathVisio uses GPML[5] to store and exchange pathways. While mathematical models are commonly described in SBML [6], a standard format widely accepted for storing and exchanging mathematical models. We developed an SBML importer plugin for PathVisio in order to facilitate model curation and extension by providing modelers with an up-to-date graphical (SBGN-PD) representation of their model. This representation can then be compared to existing pathways for the same process, which will facilitate both pathway improvement and critical assessment of model implementation aspects like lumping of reaction steps and reduction of parallel routes. The plugin also enables model validation and direct import of SBML models from Biomodels. Furthermore, imported models can then be shared with the community for distribution and further curation through WikiPathways (wikipathways.org), which also uses GPML as its native format.<br>We have also developed a PathVisio plugin for visualizing data on interactions, e.g flux data on biological pathways. This along with existing PathVisio functionality enables model outcomes to be graphically visualized on the model representation to ease comprehension.<br>Visualization of cutting-edge models, including their outputs, have traditionally lagged behind development of the models themselves. As such, the proposed integrated visualization will have several important impacts on the field:<br>makes modeling results immediately more accessible and interpretable to biologists who wish to learn from them<br>enables researchers who develop flux modeling approaches to better improve and distribute their models<br>facilitates new ways to explore effects on mechanism, such as genetic effects. In our group, we are planning to use pathways to link copy number variations and polymorphisms in genes to their biological consequences. These changes will often affect model kinetics, so by integrating flux modeling with pathways, we can use models to evaluate which of the observed genetic changes will significantly alter metabolism.</p> <p>References:<br>1) Presenting and exploring biological pathways with PathVisio. van Iersel MP, Kelder T, Pico AR, Hanspers K, Coort S, Conklin BR, Evelo C. BMC Bioinformatics 2008, 9:399 (25 Sep 2008)<br>2)The BridgeDb framework: standardized access to gene, protein and metabolite identifier mapping services. van Iersel MP, Pico AR, Kelder T, Gao J, Ho I, Hanspers K, Conklin BR, Evelo CT. BMC Bioinformatics. 2010 Jan 4;11(1):5.<br>3) Rhea - a manually curated resource of biochemical reactions. Rafael Alcántara, Kristian B. Axelsen, Anne Morgat, Eugeni Belda, Elisabeth Coudert, Alan Bridge, Hong Cao, Paula de Matos, Marcus Ennis, Steve Turner, Gareth Owen, Lydie Bougueleret, Ioannis Xenarios, Christoph Steinbeck, Nucleic Acids Research (2012) 40:D754-D760; doi: 10.1093/nar/gkr1126<br>4) The ConsensusPathDB interaction database: 2013 update. Kamburov, A. et al. Nucleic Acids Res. ( 2013)41 (D1): D793-D800.doi: 10.1093/nar/gks1055<br>5) http://developers.pathvisio.org/wiki/EverythingGpml<br>6)The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models. M. Hucka, A. Finney, H. M. Sauro, H. Bolouri, J. C. Doyle, H. Kitano, and the rest of the SBML Forum , Bioinformatics (2003) 19 (4): 524-531 doi:10.1093/bioinformatics/btg015<br>7) WikiPathways: building research communities on biological pathways. Kelder T, van Iersel MP, Hanspers K, Kutmon M, Conklin BR, Evelo C, Pico AR., Nucleic Acids Resl. 2012 Jan;40(1):D1301-7.</p

Curation, Visualization and Analysis of Biological Pathways

<p>Pathway diagrams are found everywhere: in textbooks, in review articles, on posters and on whiteboards. Their utility to biologists as conceptual models is obvious. They have also become immensely useful for computational analysis and interpretationof large-scale experimental data when properly modeled. We will highlight the latest developments and newest features of WikiPathways (www.wikipathways.org), a community curated pathway database that enables researchers to capture rich, intuitive models of pathways. WikiPathways and the associated tools PathVisio and pathvisio.js are developed as open source projects with a lot of community engagement.</p> <p>The new interactive JavaScript-based pathway viewer, pathvisio.js (https://github.com/wikipathways/pathvisiojs/), is integrated in the WikiPathways website and enables users to zoom in and click on pathway elements to show linkouts to other databases. In the future pathvisio.js will replace the Java applet editor and introduce a quick and simple way to curate and edit pathways.</p> <p>The standalone pathway editor and analysis and visualization tool, PathVisio (www.pathvisio.org), was refactored with the goal to achieve a better, modular system that can be easily extended with plugins. Plugins are accessible through the new plugin repository and can be installed through the plugin manager from within the application. This is an important aspect of usability that will allow users to build an application with all the necessary modules relevant for their work. The WikiPathways plugin of PathVisio allows searching and browsing WikiPathways from within PathVisio. Furthermore users can upload new pathways or update existing pathways.</p