WikiPathways: how open source and open data can make omics technology more useful
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This document discusses WikiPathways, an open source pathway database. It began in 2007 with the goals of having an online platform by March 2007 and gaining a first unknown user by January 2008, both of which were successes. WikiPathways has grown significantly since, now containing over 400 human pathways and 6,200 unique human genes. It receives over 1 million pageviews annually. The document advocates for opening up data and code to make omics technology more useful. It describes WikiPathways' various features including its BioPAX format, REST services, and integration with Cytoscape. It also discusses professionalizing open source and collaborating with existing communities and tools rather than trying to change the world alone.
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WikiPathways: how open source and open data can make omics technology more useful
- 1. WikiPathways: how open source code and open data can make omics technology more useful @Chris_Evelo Department of Bioinformatics - BiGCaT Maastricht University
- 3. Recon2 a Google map of human metabolism Collaborating Systems Biology and Metabolomics groups: 2013
- 7. X
- 10. • XML format (BioPAX) • RESTful web services • HTML embed code • RDF and SPARQL endpoint http://www.wikipathways.org
- 13. Set Early Milestones • Online (Mar ‘07) Success! • Firstunknown user (Jan (Jan ’08) • First unknown user ’08)
- 14. 400 Number of human pathways Number of unique human genes 320 240 6,200 160 4,700 80 3,200 http://www.wikipathways.org
- 15. 2,800 Over 1 million pageviews by 280,000 unique visitors 1,400 0 ~22% http://www.wikipathways.org
- 16. Don’t Try to Change the World Work with (not against) established: • First unknown user (Jan ’08) • Models • Communities • Tools and pipelines • Publishing models
- 17. Go Ahead, Change the World • Tweak established models ••First unknown user (Jan ’08) Grow communities • Change perspectives • everyone is a curator • knowledge should be open
- 18. Go Ahead, Change the World • Tweak established models • Grow communities • Change perspectives • New attribution systems • redefine “publication” • redefine “productive”
- 19. Go Ahead, Change the World • Tweak established models • Grow communities • Change perspectives • New attribution systems • New analysis pipelines • connect with other communitycurated resources
- 21. Professional Open Source • Subversion source repository • License! • Development web site • Bug tracker • Mailing lists • Development and Release plans • Modular (plugins, OSGi)
- 22. The New Agilent Academic Government Research Chemical & Energy Forensic & Drug testing Pharmaceutical Cancer Diagnostics Cytogenetic research Environment Genomics for Molecular Analysis Food Food CORE TECHNOLOGY PLATFORMS Gas chromatography | Liquid Chromatography | Mass spectrometry | Spectroscopy | NMR Spectroscopy | Automation | Software | Chemistries | Immunohistochemistry | FISH probes | Microarrays | Target enrichment | Bioreagents | Services 26/11/13 22
- 23. Key Academic Innovation Partners About 100 active university collaborations annually U of Washington U of Calgary UC Davis UCSF UC Berkeley Stanford Technical U of Denmark Karolinska Inst., Sweden U of Michigan CU Boulder UC San Diego Baylor U Yale MIT Princeton U of Kent, UK Harvard U of Illinois Arizona State Karlsruhe U, Germany NIBRT, Ireland Johns Hopkins U of Maryland U of Twente, NL Maastricht U, NL U of Manchester, UK Tsinghua U, China Charles U, Czech Republic U of Oviedo, Spain Technion, Israel Seoul Nat’l U, Korea Chungnam Nat’l U, Korea Tohoku U, Japan U of Alicante, Spain Hamner Inst. U of Texas U of Houston Southeast U, China U Teknologi MARA, Malaysia NUS, Singapore NTU, Singapore U of São Paulo, Brazil U of Queensland UNICAMP, Brazil Electronics Chemical Analysis Life Sciences U of Pretoria, S. Africa Macquarie U Curtin U. U of W. Australia U of Sydney U Tech, Sydney RMIT U Monash U U of Tasmania 26/11/13 23
- 24. Agilent Thought Leader Program http://www.agilent.com/univ_relation/TLP/index.shtml TL Network Thought Leader Awards Promote fundamental advances in life science through contribution to research of thought leaders • Align societal trends, academic research and rapidly advancing Agilent measurement platforms: Synthetic Biology, Structural Biology, OMICS & Integrated Biology, in vitro Toxicology, and Environemntal & Food Safety • Candidates selected based on scientific leadership, productivity, project significance (invitational program) • High-level executive sponsorship and active support throughout Agilent enable breakthrough research Early Career Professor Award Establish strong collaborative relationships with highly promising early career professors 2013 focus: Contributions to cancer diagnostics 26/11/13 24
- 25. LC/MS GC/MS MassHunter Qual/Quant ChemStation AMDIS Microarrays Feature Extraction Alignment to Reference Genome NGS GeneSpring Platform Biological Pathways
- 26. Agilent’s Platform for Multi-Omics Data Analysis
- 27. mRNA, miRNA, Exon arrays GWAS, CNV via SNP arrays SureSelect Target Enrichment Whole Genome Sequencing Proteomics Metabolomics
- 28. mRNA microRNA QPCR Alternative Splicing GWAS & CNV via SNP arrays NGS (Next-Gen Sequencing) SureSelect Target Enrichment Whole Genome Sequencing MPP (Mass Profiler Pro) Proteomics Metabolomics PA (Pathway Analysis)
- 29. GX (Gene Expression) SureSelect Target Enrichment Whole Genome Sequencing DNA-Seq RNA-Seq Methyl-Seq ChIP-Seq small RNA-Seq SureSelect Target Enrichment Whole Genome Sequencing MPP (Mass Profiler Pro) Proteomics Metabolomics PA (Pathway Analysis) NGS Analysis Workflow: 1. Align data 2. Load BAM/SAM into GS NGS 3. Measure Gene Expression, Find variants, methyl calls 4. Biological Contextualization (Integrated Genomics, GO, Pathways)
- 30. GX (Gene Expression) • Import, store, and visualize Agilent Metabolomics & Proteomics data (LC/MS, GC/MS) • Generic file import • Statistical analysis • ID Browser for compound identification NGS (Next-Gen Sequencing) SureSelect Target Enrichment Whole Genome Sequencing Proteomics Metabolomics PA (Pathway Analysis)
- 31. Multi-Omic Analysis Canonical Pathways Network Discovery GX (Gene Expression) NGS (Next-Gen Sequencing) SureSelect Target Enrichment Whole Genome Sequencing MPP (Mass Profiler Pro) Proteomics Metabolomics
- 32. • Map and visualize data from one or two types of –omic data on pathways Metabolite Data Overlay • Search, browse and filter pathways Supports pathways from: •WikiPathways •BioCyc •Supported pathway formats • List of all pathway entities, dynamically linked to pathway selection BioPAX 3 – Pathway Commons, Reactome, NCI Nature Pathway • GPML – PathVisio –custom drawing Export compound list from pathways
- 35. Import of WikiPathways Select WikiPathways for analysis View overlaid data on WikiPathways
- 37. Propose new experiments based on pathway analysis • Re-examine acquired untargeted metabolomics data based on pathway analysis • Design new experiments (metabolite, protein or genes) based on pathway results interpretation Build custom metabolite database PCDL Custom microarray or NGS design eArray Targeted MS/MS Spectrum Mill
- 38. Typical workflow used for identification of a relevant pathway using GeneSpring Identification of differential expression Statistical analysis and filtering Curated pathway analysis using Wikipathways Network analysis using NLP to identify interaction of pathways
- 39. Identification of Candidate Genes Step 1) Identification of differentially expressed genes via hierarchical cluster analysis Step 2) Volcano plot of showing significantly differentially expressed between two conditions
- 40. From Differential Expression to Pathways Step 3) Significantly changed pathways in Müller cells identified using pathway analysis in GeneSpring Step 4) The Protein-Protein Interactions analysis was further performed to identify the direct interaction of these genes products in GeneSpring Pathway analysis showed significant changes in MAPK signaling at both conditions. Network analysis shows interaction of MAPK with other gene products. Compare network analysis/extension in Cytoscape.
- 41. Combine further with Open Knowledge e.g. IMI semantic web project Open PHACTS Pathway content and extension Open Data e.g. ISAtab based study capturing in phenotype database (dbNP) pathway analysis and profiling
- 42. OPS Framework Architecture. Dec 2011 OPS GUI App Framework Web Service API Identity & Vocabulary Management Sparql OPS Data Model Semantic Data Workflow Engine RDF Data Cache Chemistry Normalisation & Registration Descriptor Feed in WikiPathways RDF 1 relationships, use BioPAX to create the RDF Public Data 1 Vocabularies Descriptor Descriptor Descriptor Nanopub Nanopub RDF 2 RDF 3 RDF 4 Data 2 Data 3 Data 4 Web Services
- 43. Data capturing Using ISA to connect to the rest of world Faculty of Health, Medicine and Life Sciences
- 44. Generic Study Capture Framework Data input / output GSCF Templates Templates Templates Events Molgenis custom custom custom programs programs programs Protocols Samples NCBO Ontologies Groups Assays web interface EBI repository Data import xls, cvs, text Subjects custom custom custom dbs dbs dbs
- 45. dbNP Architecture GSCF Subjects Groups Events Transcriptomics module Raw data cell files Clean data gene expression Result data p-values z-values Protocols Epigenetics module Samples Assays Raw data Nimblegen Illumina Clean CPG island data Resulting Genome Feature data Pathways, GO, metabolite profiles Body weight, BMI, etc. Templates Templates Templates Query module Simple Assay module Full-text querying Structured querying Profile-based analysis Study comparison Use PathVisioRPC to use WikiPathways content Web user interface Faculty of Health, Medicine and Life Sciences
- 46. Thomas Kelder Martijn van Iersel Kristina Hanspers Martina Kutmon Andra Waagmeester Chris Evelo Bruce Conklin nrnb.org wikipathways.org Acknowledgements
Notas del editor
- Here, for example, is a typical pathway from WikiPathways. Like most textbook pathways, it depicts proteins and metabolites, reactions and complexes, and their localization into subcellular compartments. But each one of these rectangles is also a data object connected to a database of standard identifiers that can be mapped to a variety of databsets.
- Here, for example, we’re seeing differential expression data with up- and down-regulated genes in yellow and blue. When a biologist looks at this, something very special happens. A little movie is triggered in their mind……the room goes quiet and their focus is drawn to a particular area of interest; they think about kinetics, rate-limiting factors, conditions and timing; they consider a number of "what if" scenarios: "what if this cascade of events……could be blocked by increasing this factor". This is in fact exactly what happens when we take statin durgs to lower our cholesterol. What I’m trying to illustrate here with ppt tricks is the act of visualization…
- The data-mapped image is really just sitting there and all of this is just going on in the mind of the researcher, right? …the researcher takes in this visual data, which allows it to mix with all the other associations up there from prior observations they’ve made (the majority of which have not been published or put into textbooks) and from conversations they’ve had with colleagues (at conferences like this).This wouldn’t be necessary if we could parameterize all these subtle associations……and model them all in a supercomputer. Then we could just mix in all known interactions, molecular concentrations and kinetic rates, and just read out the answers to our questions in concrete units of information. But alas, we can’t do this (at least not yet),… …so even though this is conference on intelligent systems, I’d argue that there are a number of situations where humans are actually still really important in data analysis.
- Returning now to this basic unit of pathway visualization, the pathway diagram. How exactly are these models constructed? They do not come from direct measurement. It was assembled from a wide variety of data types and assays, a curated set of observations left intentionally sparse. This pathway, for example, is showing the mechanism of a common cancer drug called 5-FU: it's metabolized in the liver, it's byproducts enter the blood stream and are taken up by cancer cells where they disrupt key pathways for cell survival. But this pathway is not representing all that we know about this process and new data about these components and their interactions continues to pour in with no end in sight. So, all we know for sure is that this model will change over time as we fill-in details and learn what’s most relevant.
- This is exactly what we had in mind when we started the WikiPathways project. It's a wiki, like wikipedia, but what we did is we ripped out the text editor and replace it with our own pathway drawing tool. So anyone can find a pathway, click 'edit', and then add new information [[like a new byproduct of 5-FU that also goes to cancer cells and triggers apoptosis]]. You then click ‘save’ and your changes are immediately available to the rest of the world. You can provide literature references to cite evidence for your changes. And the entire research community is your peer review group: they can approve or undo your changes. In this way, we can keep up with the flood of new data relating to biological processes.
- When you’re editing a pathway, you are not only editing the diagram, you’re also editing a standard XML file with BioPAX elements that can be exchanged and accessed programatically. For, the software developers in the audience, in addition to this XML formats, there is also web service access to the pathway content. You can programmtically return pathway images with highlighted nodes, for example. There is embed code to insert interactive pathway widgets into your own web sites, and we are starting to represent our pathway content as linked data to support semantic queries. The most common workflow today is import the XML into tools like PathVisio and Cytoscape.
- After loading a pathway into Cytoscape, for example, you can then import your own dataset from an excel spreadsheet and define how you want your data to map in terms of color gradients. This process is dynamic and interactive, so you can explore your dataset in the context of these pathways. And, of course, in Cytoscape you can make use of all the other apps that are available to calculate shortest path, perform clustering or over-representation analysis.
- Putting it all together, you can begin to see how we are feeding into this virtuous cycle. Data is synthesized into pathway diagrams. And orthogonal data can be mapped onto these pathway models. Computational analysis, together with the act of visualization can lead to new explanations and new ideas.And finally, these new ideas can be tested to generate new data, bringing us back to synthesis. And I have to say, the wiki model really working well here…
- We’ve been collecting and curating pathways since 2001. In the years just prior to launching WikiPathways we really struggled relying on our internal curation team alone. [This was our growth curve for number of pathways in blue and number of unique genes on those pathways in green]. In the years following the launch of WikiPathways we experienced a whole new level of growth. And this last year things are really starting to take off. I might have to start using logarithmic plots for the number of pathways. It’s difficult to quantity the effect on quality, but our curation team has been thrilled by the quality and overall improvements we’re seeing in the content. Basically, no internal team can curate all of biology; this task can only be done by a distributed system.
- And in terms of participation, not only has the number users increased since our launch in 2008, but so has the number of contributors, averaging at around 22%. Putting pathway editing and curation tools into the hands of researchers is the best (and only) way to keep up with the flood of new data coming in; and they are actually using them! You only need to register if you want to edit, so we also have lots of folks viewing and downloading pathways: over1 million pageviews by over a quarter million unique visitors. And these numbers don’t include access through Cytoscape, embed code and web services. I know these numbers don’t compare to wikipedia, but come on, we’re talking about biological pathways here: a niche market within the niche market of systems biology.
- Agilent has a unique combination of scientists from multiple disciplines – exciting to be in a place (esp Santa Clara) where on a daily basis you meet experts in biology, engineering, chemistry, data analysis, etc)In many ways, GS embodies that collaborative spirit. GeneSpring is not new but there are many facets to integrative data analysis and we are just at the beginning.Show/Mention easy and open import of various file formats; analysis tools, visualization, biological contextualization to provide insights, new hypothesis and make it easier to get to the next experiments
- DNA methylation is an important and common method for regulation of gene expression.DNA is methylated by methyl transferase enzymes which target cytosines.Methylation of DNA serves as a signal to attract proteins that lead to compact and silent chromatinMethylation of DNA serves as a mechanism for silencing genes and inactivating the X chromosome
- Metabolomics is the study of the metabolome, the collection of small molecule compounds that are essential for life. They are messengers both within cells and between cells and regulators of epigenetic events. Those produced by bacteria may represent the link between the microbiome and chronic diseases in the human host such as cancer, diabetes and obesity.
- DNA methylation is an important and common method for regulation of gene expression.DNA is methylated by methyl transferase enzymes which target cytosines.Methylation of DNA serves as a signal to attract proteins that lead to compact and silent chromatinMethylation of DNA serves as a mechanism for silencing genes and inactivating the X chromosome
- slide showing your experiences/assessment of how well the whole WikiPathways/open source approach works.
- The expression profiles were analyzed using GeneSpring software, and gene ontology analysis and the Kyoto Encyclopedia of Genes and Genomes (KEGG) were used to select, annotate, and visualize genes by function and pathway. The selected genes of interest were further validated by Quantitative Real-time PCR (qPCR).
- The author states that “The expression profiles were analyzed using GeneSpring software, and gene ontology analysis and the Kyoto Encyclopedia of Genes and Genomes (KEGG) were used to select, annotate, and visualize genes by function and pathway. The selected genes of interest were further validated by Quantitative Real-time PCR (qPCR)”
- An overview of the Open Phacts project that pulls in lots of information in a semantic web triple store (including information from WikiPathways RDF) and then provides that for use in other tools. In WikiPathways we use that to suggest possible pathway extensions to curators
- I'd like to acknowledge the teams of developers I work with on WikiPathways. I'm also affiliated with NRNB, the National Resource for Network Biology. Part of our mission is to promote the development and use of network biology tools and resources. If you are interested in developing WikiPathways or Cytoscape, for example, let us know. One way we coordinate this is through the annual Google Summer of Code program, where Google pays students from around the world to write open source code for our projects. You can find out more at nrnb.org. Thank you.