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Biological Pathway Analysis: Trends and Applications

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Biological Pathway Analysis: Trends and Applications Pathway resources, data access, and pathway standards BIME 591 2017.1.11 Lucy Lu Wang Uses for pathway resources • Functional analysis • General information retrieval • Biosimulation • Visualizing relationships • Disease modeling What information should be represented? • Entities and their relationships • Biological entities (chemical species, genes, proteins/enzymes, cofactors) • Cellular entities (locality, type of cell, compartments) • Organism • Function • Rates? • Reactions • Reference to a published resource • Synonyms/aliases • Related pathways • Analogous pathways from different species (ortholog?) • Curation status • Meta-information for how to draw the pathway diagram Strengths and weaknesses of pathway representation Strengths Weaknesses • Useful abstraction for • Pathways are not human interpretation independent • Representations may • Clarifies relationships disagree between between genes and resources molecules • Human curation is slow and • Connects genes to expensive function • Lack of consistency in naming, classification, search, and download • Pathway boundaries are arbitrary Today’s topics Pathway resources and available data Data access channels Relevant linked databases Pathway Resources Different ways to view pathways: Pathway ontologies Pathway diagrams Pathway representations Protein-protein interaction networks Gene sets Pathway Ontologies These describe the organizational hierarchy of pathways, i.e. pathway classes. Actual pathway representations are instances of those classes. Pathway ontologies: • BioPortal’s Pathway Ontology • INOH (Integrating Network Objects w/ Hierarchies) — defunct, data available through PathwayCommons Compare: http://purl.bioontology.org/ontology/PW (ontology only) http://reactome.org/PathwayBrowser/ (ontology w/ pathway instances) Pathway Diagrams Visual display of pathway information (entities and relationships) Some are backed by pathway representations e.g. Reactome “Glycolysis” pathway Pathway Representations <?xml version="1.0" encoding="UTF-8" standalone=“no"?> <rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:bp="http://www.biopax.org/release/biopax-level3.owl#" xmlns:owl="http://www.w3.org/2002/07/owl#" xmlns:rdfs="http://www.w3.org/2000/01/rdf-schema#" xmlns:xsd="http://www.w3.org/2001/XMLSchema#" xml:base=“http://www.reactome.org/biopax/59/71387#"> <owl:Ontology rdf:about=“"> <owl:imports rdf:resource=“http://www.biopax.org/release/biopax-level3.owl"/> <rdfs:comment rdf:datatype=“http://www.w3.org/2001/XMLSchema#string"> BioPAX pathway converted from "Metabolism of carbohydrates" in the Reactome database. </rdfs:comment> </owl:Ontology> <bp:Pathway rdf:ID=“Pathway1"> <bp:pathwayComponent rdf:resource=“#Pathway2"/> <bp:pathwayComponent rdf:resource=“#BiochemicalReaction12"/> <bp:pathwayComponent rdf:resource=“#BiochemicalReaction13"/> <bp:pathwayComponent rdf:resource=“#Pathway3"/> <bp:pathwayComponent rdf:resource=“#Pathway6"/> Protein-Protein Interaction Network An example network of proteins involved in glycolysis From Krishna et al. (2014) Systems genomics evaluation of the SH-SY5Y neuroblastoma cell line as a model for Parkinson’s disease Gene Set An example glycolysis gene set ALDOA HK3 ALDOB PFKL ALDOC PGAM2 BPGM PGK1 ENO1 PGK2 ENO2 PGM1 ENO3 PGM2 GALM PGM3 GCK PKLR GPI TPI1 HK2 Databases of pathway representations Pathguide: http://pathguide.org/ Pathway Data Standards BioPAX (Biological pathway exchange) SBML (Systems biology markup language) Other: PSI-MI (Proteomics Standards Institute’s molecular interactions) KGML (KEGG markup language) GPML (Graphical pathway markup language) SBGN (Systems biology graphical notation) BioPAX Triple store with many pathway specific keywords BioPAX2 and BioPAX3 specifications NOT interoperable! http://www.biopax.org/webprotege/ http://www.biopax.org/mediawiki/index.php/Specification SBML XML encoding designed for describing biological models (entities, interactions, rates etc) e.g. given a set of reactions and initial conditions, how does the system proceed? References: libSBML SBML parser python libAntimony http://sbml.org/Documents/Specifications Pathway resource reference database stack Most entities in pathway databases are cross-referenced to identifiers in the following linked databases Pathway/Process: Protein/Enzyme: Gene Ontology EC Number Cellular location Entrez-protein Cellular location: MOPED Pathway Gene Ontology UniProt Reaction: Small molecule: EC Number (Enzyme CAS Reaction Commission) ChEBI ChemSpider Gene: HMDB Protein Small DNA Ensembl KEGG or Enzyme molecule or RNA Entrez-gene PubChem GeneCards HUGO Gene OMIM And now, some popular pathway databases… Public databases Reactome Central curation Supports BioPAX and SBML Public databases Reactome BioCyc (HumanCyc) Focuses on metabolic pathways Supports BioPAX and SBML Public databases Reactome BioCyc (HumanCyc) WikiPathways Community-based curation Uses GPML Public databases — other • NCI Pathway Interaction Database — data available at NDex or through PathwayCommons • PANTHER pathways — primarily signaling pathways • NetPath — focuses on signaling transduction pathways • SMPDB (Small Molecule Pathway Database) — focuses on human small molecule pathways • SignaLink — signaling pathways, cross-talks Pathway Diagrams: • BioCarta — available as gene sets from MSigDB; formed the basis of NCI PID • KEGG pathway diagrams — available at PathwayCommons, converted by BioModels • PharmGKB — available in BioPAX and GPML KEGG A history of pathway resources 1995 available subscription only BioCarta diagrams 2000 HumanCyc Reactome 2005 PANTHER NCI PID WikiPathways NetPath SMPDB 2010 2015 Subscription databases KEGG (Kyoto Encyclopedia of Genes and Genomes) Last public version: 2011 IPA (Ingenuity Pathway Analysis) MetaCore TRANSFAC Professional Last public version: 2005 Size of resources? Canonical versus species-specific pathways Pathway uniqueness Pathway overlap Hard to judge… Data access APIs/web services SPARQL endpoints Raw data APIs Quick questions might be best answered through API calls: Documentation: BioCyc Reactome Pathway Commons KEGG WikiPathways Libraries available in various programming languages, e.g. python bioservices SPARQL endpoints SPARQL is a query language for RDF Some resources have online SPARQL endpoints: Reactome WikiPathways Pathway Commons Or you can host your own: Stardog Virtuoso Raw data Download directly from sites with open data: Reactome HumanCyc Pathway Commons Interact with data through any programming language RDF libraries: rdflib (python), rrdf (R), Jena (Java) Paxtools (BioPAX — Java and R) libSBML (SBML — C/C++, Matlab, Java, Python) Redland (RDF library): C Let’s see some examples Assignment: • Find a partner NOTE: At least one person per pair should have taken KR • Pick a disease with a complex genetic component • Before next class, spend some time on Google Scholar or PubMed and find some of the genes that show association with your disease • We will use this next time Next class: 2017.1.18 Experimenting with APIs, SPARQL, and RDF libraries Think about: • When might you want to access data through APIs versus SPARQL versus directly? Read: http://www.pathwaycommons.org/pc2/ http://www.dataversity.net/introduction-to-sparql/ .
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