Using ontologies to do integrative systems biology
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Using ontologies to do integrative systems biology

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To really get ahead with complex health problems like cancer and diabetes we need to become better at combining different types of studies, including large scale genomics and genetics studies and we need to learn to better combine such studies with biological knowledge we already. Typically that leads to questions like “I did this study with high-fat low fat diet comparison in mice and looked at the transcriptomics results in liver, fat and muscle. Did somebody else maybe do a study like that and publish the data, maybe for proteomics? Could I find that in one of these open data repositories?”. Or, “I did that, can I find which biological pathways are affected most and whether any of the proteins in that pathway is a known target for an existing drug?”. Or even “I did that study, could I find another study that yielded the same kind of biological results even if it was from a different research field with a completely different result?”.
To answer this kind of questions we need to describe studies and study results, structure knowledge allow mapping of “equal” things with different identifier schemes and essentially do a lot of mapping to and between ontologies. More and more of this is getting real and I will try to describe some of that.

Homepage for this webinar is here: http://www.bioontology.org/ontologies-in-integrative-systems-biology

It is part of this series: http://www.bioontology.org/webinar-series

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  • The home page for this webinar is http://www.bioontology.org/ontologies-in-integrative-systems-biology. There will be a recording of the webinar on that page.
  • The slides labeled TNO and the dbNP/dbXP screen shots curtousy of JildauBouman
  • A closer look at the same pathway.Note that this uses MIM notation from the MIM PathVisio plugin.In general the connections between different genes and metabolites describe the network underlying the pathway. Note that this is already quite complex since there are different ways to show what interacts with what.Graphical methods to capture this like MIM and SBGN definitely help. The result can be captures in descriptive relationships in BioPax,
  • As soon as you have entered one (and only one) identifier to describe what gene product or metabolite you really mean this information is linked to many other identifiers from other databases and links to these respective pages are shown in the so called “backpage” (actually one of the pages under the tabs at the righthand side of the pathway).
  • BridgeDB development lead by Martijn van Iersel.
  • BridgeDB (see www.bridgedb.org and the paper mentioned on the slide) provides the mechanism needed for that identifier mapping.
  • Note that BridgeDB now also is part of the Indentifier Mapping service of Open PHACTS.
  • Showing the concept. Integrating flux predictions from modelling (of course that could also be real fluxomics data)
  • Probably not an iPAD, those microarrays were at least 10 years old.
  • Introducing a problem
  • And a solution that isn’t really a solution. There are just too many things you could add.
  • There are just too many SNPs for any given gene.
  • And a solution that isn’t really a solution. There are just too many things you could add.
  • The PathVisio Regulatory Interaction plugin (author Stefan van Helden) has a new approach where information is not really added to a pathway, but shown in a separate page upon request.
  • Probably not an iPAD, those microarrays were at least 10 years old.
  • The approach takes into account all data use (pathways, interactions and experimentally determined weight). Check out the original paper for details.
  • Example result. Pathways with stronger interaction based on gene snot present in them.
  • And you can do the same for relatively large sets of pathways “driving” a process like apoptosis.
  • CyTargetLinker is a Cytoscape plugin that can be used to extend one network with information about things targeting entities in that network from databases that are created as a network. It already provides a number of target relation databases as mentioned on the slide.
  • Example of a target network. (You will normally see this, it contains the information that is used to extend your source network).
  • You can drive it from a gene set, that isn’t even a network at the start. But when miRNAs are found to target more than one gene in the ggroup the network is created on the fly.
  • Or you can bootstrap the approach from an existing network. Which can be a pathway based one imported with the GPML plugin like shown here.
  • Adapted by Nadia and Martijn from General Bioinformatics
  • 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
  • Many people involved in this work. (Really many if you count associated groups like the plugin developers, pathway curators etc).Most importantSF group (Kristina Hanspers, Bruce Conklin and Alex Pico) collaborating on many things but primarily WikiPatwhaysMartijn van Iersel top left (PathVisio, BridgeDB). Thomas Kelder (top middle) (WikiPathways including webservices, pathway integration networks for nutrigenomics), Martina Kutmon (top right) (CyTargetLinker, PathVisio further development), Andra Waagmeester (second row, right) (WikiPathways RDF), Anwesha Dutta (bottom, 2nd from the left) (flux visualization), Stefan van Helden (not on the picture) for the RI PathVisio plugin
  • Many people involved in this work. (Really many if you count associated groups like the plugin developers, pathway curators etc).Most importantSF group (Kristina Hanspers, Bruce Conklin and Alex Pico) collaborating on many things but primarily WikiPatwhaysMartijn van Iersel top left (PathVisio, BridgeDB). Thomas Kelder (top middle) (WikiPathways including webservices, pathway integration networks for nutrigenomics), Martina Kutmon (top right) (CyTargetLinker, PathVisio further development), Andra Waagmeester (second row, right) (WikiPathways RDF), Anwesha Dutta (bottom, 2nd from the left) (flux visualization), Stefan van Helden (not on the picture) for the RI PathVisio plugin
  • These last slides were not presented during the webinar. They are the result of a masters student project by Christ Leemans supervised by Martina Kutmon

Using ontologies to do integrative systems biology Presentation Transcript

  • 1. Using ontologies to do integrativesystems biologyChris EveloDepartment of Bioinformatics - BiGCaTMaastricht University @Chris_Evelochris.evelo@maastrichtuniversity.nl
  • 2. Typically we want to:• Find studies.• Process data.• Integrate.• Evaluate.• Combine with yet other data.Faculty of Health, Medicine and Life Sciences
  • 3. Systems Biology Issues:• Environment• Multi-compartment• Different levels of gene expression cascade (multi-omics)Needs:• Link information from different analysis techniques• Combine many studies (store study design)Faculty of Health, Medicine and Life Sciences
  • 4. Using ISA tobe able tofind studieshttp://dx.doi.org/10.1038/ng.1054Faculty of Health, Medicine and Life Sciences
  • 5. Why a study capturing application? New studies can be performed based on old data Translational comparisons (mouse, human, rat etc) Structured storage Facilitate collaborations between groups - Data sharing on joined project - Start a collaboration
  • 6. What do we need to accomplish this Acceptance - Using standards (e.g. ISA-TAB & MAGE-TAB) - User friendly (interface via web browser) - Open source - Examples Collaboration - Ontologies - Security of data (log-in and store data locally) - Open source (make own module)
  • 7. dbXP: a total study capturing solution Simple assay module Metabolomics moduleWeb input Study capturing module Web output Feature layer Transcriptomics module Any new module
  • 8. dbNP Architecture GSCF Simple Assay module Query module Body weight, BMI, etc. Pathways, GO, metabolite profiles Templates Templates Templates Transcriptomics module Full-text querying Clean data Result data Raw dataSubjects Groups gene p-values cell files Structured expression z-values queryingEvents Protocols Profile-based analysis Epigenetics module Raw data Clean ResultingSamples Assays Nimblegen CPG island Genome Study comparison Illumina data Feature data Web user interfaceFaculty of Health, Medicine and Life Sciences
  • 9. Generic Study Capture FrameworkData input / output GSCF Templates Templates Templates Subjects Groups xls, cvs, text Data import NCBO web Events Protocols Ontologies interface Samples Assays custom custom custom custom custom Molgenis programs programs EBI custom programs dbs dbs repository dbs
  • 10. Used in European Projects Food4me (Dublin) NU-AGE (UNIBO, Bologna) Bioclaims (UIB, Palma) Nutritech (TNO, Zeist) EuroDish (WUR, Wageningen) ITFoM (proposed for metabolic syndrome studies)
  • 11. Process the data…Faculty of Health, Medicine and Life Sciences
  • 12. Epigenetics DNA Methylation Pipeline Raw data R Nimblegen QC, processing Clean DNA Result Raw data R methylation data Illumina QC, processing data Statistical with (Genome analysis p-values Feature (GFF)Raw sequencing data Sequence Format) MeDIP, BIS-Seq QC, processing
  • 13. Connecting to Pathways: 1) Prepare data for pathway analysis 2) Connect processing pipelines PathVisioRPC used from arrayanalysis.org see: http://pathvisiorpc.wordpress.com 3) Store Pathway profiles as vectors, Using pathways themselves as a vocabulary C Evelo, K van Bochove & J Saito. Genes Nutr (2011) 6: 81-87Answering biological questions - querying a systems biology database for nutrigenomics 4) Allow queries for studies with same outcomeFaculty of Health, Medicine and Life Sciences
  • 14. Integrate Example WikiPathway Pathway Pathway on glycolysis. Using modern systems iology annotation. And genes and metabolites connected to major databases.Faculty of Health, Medicine and Life Sciences
  • 15. Find the pathways: Biological processes in duodenal mucosa affected by glutamine administration number of genesPathway Changed Up Down Measured Total Z ScoreHs_Mitochondrial_fatty_acid_betaoxidation 6 6 0 16 16 4.456Hs_Electron_Transport_Chain 17 17 0 85 105 4.278Hs_Fatty_Acid_Synthesis 5 5 0 21 22 2.757Hs_Fatty_Acid_Beta-Oxidation 6 6 0 31 32 2.424Hs_mRNA_processing_Reactome 16 6 10 118 127 2.402Hs_Unsaturated_Fatty_Acid_Beta_Oxidation 2 2 0 6 6 2.342Hs_HSP70_and_Apoptosis 4 4 0 18 18 2.299Hs_Oxidative_Stress 5 5 0 27 28 2.097Hs_Fatty_Acid_Omega_Oxidation 3 3 0 14 15 1.915Hs_Proteasome_Degradation 8 8 0 60 61 1.629Hs_RNA_transcription_Reactome 5 5 0 38 40 1.25Hs_Irinotecan_pathway_PharmGKB 2 1 1 12 12 1.154Hs_Synthesis_and_Degradation_of_Ketone_Bodies_KEGG 1 1 0 5 5 1.023
  • 16. Connecting toother dataWe both needStudy CapturingFaculty of Health, Medicine and Life Sciences
  • 17. If the mountain will not come to Mahomet, Mahomet must go to the mountain. Other repositories (like dbXP!) have better study descriptions. Integrate in Sage Synapse. Pathway visualisation missing: integrate PathVisio in Synapse (started).Faculty of Health, Medicine and Life Sciences
  • 18. PathVisio www.pathvisio.org• Data modeling and visualization on biological pathways• Uses gene expression, proteomics and metabolomics data• Can identify significantly changed processes Martijn P van Iersel, Thomas Kelder, Alexander R Pico, Kristina Hanspers, Susan Coort, Bruce R Conklin, Chris Evelo (2008) Presenting and exploring biological pathways with PathVisio. BMC Bioinformatics 9: 399
  • 19. Understanding genomics Example WikiPathways Pathway Pathway on glycolysis. Using modern systems biology (MIM) annotation. And genes and metabolites connected to major databases.Faculty of Health, Medicine and Life Sciences
  • 20. Faculty of Health, Medicine and Life Sciences
  • 21. adding data =adding colour Example PathVisio result Showing proteomics and transcriptomics results on the glycolysis pathway in mice liver after starvation. [Data from Kaatje Lenaerts and Milka Sokolovic, analysis by Martijn van Iersel]Faculty of Health, Medicine and Life Sciences
  • 22. Download Pathways Web services SPARQL endpoint
  • 23. How to dodata visualization?
  • 24. Connect to Genome Databases
  • 25. Backpages link to databasesFaculty of Health, Medicine and Life Sciences
  • 26. BridgeDbhttp://dx.doi.org/10.1186/1471-2105-11-5 Martijn van Iersel BiGCaT Maastricht
  • 27. Problem: Identifier Mapping Entrez Gene 3643 ? Agilent probeset A65_P12450
  • 28. Solution: Built-in Mapping • Generic bioinformatics platforms should have identifier mapping built-in. BioConductor PathVisio Cytoscape ... Batteries Included
  • 29. Problem: Which mapping service?• Ensembl Biomart• Synergizer• CRONOS• DAVID• AliasServer• MatchMiner• OntoTranslate or• Local database
  • 30. BridgeDB: Abstraction Layer class IDMapperRdb relational database interface IDMapper class IDMapperFile tab-delimited text class IDMapperBiomart web serviceThe BridgeDb Framework: Standardized Access to Gene, Protein and Metabolite IdentifierMapping Services. Martijn P van Iersel, Alexander R Pico, Thomas Kelder, Jianjiong Gao, Isaac Ho,Kristina Hanspers, Bruce R Conklin, Chris T Evelo. BMC Bioinformatics 2010, 11: 5.
  • 31. CyThe- Network saurus Merge Wiki Tools PathVisio Pathways Cytoscape Plugins BridgeDb Internet webservices Local Tab-Mapping BridgeDb Databas delimitedServices BioMart PICR - e text files REST
  • 32. BridgeDb interface1: JAVA interface 2: REST interface
  • 33. API Overview BridgeDb.connect(...) IDMapper.mapID(...) Xref.getUrl() DataSource.getUrl()
  • 34. Easy & Flexible Code
  • 35. REST APIhttp://webservice.bridgedb.org/Human/xrefs/L/1234ILMN_1713029 Illumina3255967 AffyNP_001025186 RefSeqIPI00005930 IPIGO:0042752 GeneOntologyNM_033282 RefSeq3255968 Affy94233 Entrez GeneENSG00000122375Ensembl Human234226_at AffyA6NEB4 Uniprot/TrEMBL0001780601 IlluminaGO:0008020 GeneOntology606665 OMIMA_23_P24234 Agilent14449 HUGO
  • 36. REST APIhttp://<Base URL>/<Species>/<function> [ /<argument> ... ]http://webservice.bridgedb.org/Human/xrefs/L/1234http://webservice.bridgedb.org/Human/search/ENSG00000122375http://webservice.bridgedb.org/Human/attributeSethttp://webservice.bridgedb.org/Human/propertieshttp://webservice.bridgedb.org/Human/targetDataSourceshttp://webservice.bridgedb.org/Human/attributes/L/3643http://localhost:8183/Human/xrefs/L/3643
  • 37. R Example
  • 38. Problem: Custom Microarrays ? Custom probe #QXZCY!34
  • 39. Solution: Stacking EnsMart Custom table
  • 40. CyThesaurus
  • 41. MIRIAM and Identifiers.org Regular expression for autodetection Pattern for generating URLs Link to documentation
  • 42. Availibility BMC Bioinformatics. 2010 Jan 4;11(1):5.www.bridgedb.orgwww.helixsoft.nl/blog bridgedb-discuss@googlegroups.com
  • 43. Innovate using BridgeDBDataMetabolite FluxVisualizing fluxes on metabolic pathways 46
  • 44. Integrating it allVisualizing fluxes, data and annotation
  • 45. Extending pathways, how to do it?Faculty of Health, Medicine and Life Sciences
  • 46. Network approaches to extend pathwaysE.g. most pathways don’t have miRNA’s
  • 47. Adding miRNA’s
  • 48. Pathway Loom, weaving pathwaysFaculty of Health, Medicine and Life Sciences
  • 49. Faculty of Health, Medicine and Life Sciences
  • 50. Adding miRNA’s clutters
  • 51. PathVisio RI plugin provides backpage info microRNAs in pathway analysis. The Regulatory Interaction plugin offers a suitable middle-ground between not including any miRNAs in pathways, which misses this regulatory information, and including all validated miRNA-target interactions, which clutters the pathway. After loading interaction file(s), selecting a pathway element shows the interaction partners of this element and their expressions in a side panel. This allows for the detection of potential active regulatory mechanisms in the study at hand. http://www.bigcat.unimaas.nl/wiki/images/f/f6/VanHelden-poster-nbic2012.pdf
  • 52. Or consider pathway as a networkFaculty of Health, Medicine and Life Sciences
  • 53. GPML Cytoscape Pluginhttp://www.pathvisio.org/wiki/Cytoscape_plugin
  • 54. Cytoscape visualization used to groupPPS1LiverAll pathwaysPathways with high z-scoregrouped together.Explains why there arerelatively few significantgenes, but many pathwayswith high z-score. Robert Caesar et al (2010) A combined transcriptomics and lipidomics analysis of subcutaneous, epididymal and mesenteric adipose tissue reveals marked functional differences. PLoS One 5: 7. e11525 http://dx.doi.org/doi:10.1371/journal.pone.0011525
  • 55. Explore pathway interactionsThomas Kelder, Lars Eijssen, Robert Kleemann, Marjan van Erk, Teake Kooistra, Chris Evelo(2011) Exploring pathway interactions in insulin resistant mouse liver BMC Systems Biology 5: 127Aug. http://dx.doi.org/doi:10.1186/1752-0509-5-127
  • 56. What we usedNon-redundant shortest paths in a weightedgraph.1. A set of pathways2. An interaction network3. Weight value for all edges = experimental expression of connected genes.
  • 57. Pathway interactions and what causes them
  • 58. An indirect interaction between the Axon Guidance and Insulin Signaling pathways in the network forthe comparison between HF and LF diet at t = 0. Left: Network representation of the identified pathbetween the two pathways, consisting of three proteins Gsk3b, Sgk3 and Tsc1. Right: The location of theseproteins in the KEGG pathway diagrams. The newly found indirect interactions have been added in red.
  • 59. Pathway interactions anddetailed network visualizationfor the interactions with threeapoptosis related pathways forthe comparison between HF andLF diet at t = 0. A: Subgraph of thepathway interaction network, basedon incoming interactions to threestress response and apoptosispathways with the highest in-degree. Pathway nodes with a thickborder are significantly enriched (p< 0.05) with differentially expressedgenes. B: The protein interactionsthat compose the interactionsbetween the three apoptosisrelated pathways and theirneighbors in the subgraph asshown in box A (see inset, includedinteractions are colored orange).Protein nodes have a thick borderwhen their encoding genes aresignificantly differentially expressed(q < 0.05).
  • 60. We tried to make it easier withThe CyTargetLinker Cytoscape PluginExtending pathways on the fly. Provided databases with the plugin: • miRNAs with targets • Transciption Factors with targets • Drug – Target Interactions • ENCODE derived databases Extend with your own.
  • 61. MiRNAs of InterestmiRNA target information from mirTarBase
  • 62. miRTarBase as a target interaction network Collection of miRNA-target gene interactions in the miRTarBase database with 1,715 genes, 286 miRNAs and 2,817 interactions.
  • 63. miRNAs associated with colorectal cancerextended with validated target genes
  • 64. human ErbB signaling pathway extendedwith validated microRNA regulation
  • 65. OPS Framework OPS GUI Architecture. Dec 2011 App Framework Web Service API Sparql Web Services OPS Data Model Identity & Vocabulary Management Semantic Data Workflow Engine RDF Data Cache ChemistryNormalisation & Registration Descriptor Descriptor Descriptor Descriptor Nanopub Nanopub Feed in WikiPathways RDF 1 relationships, use BioPAX RDF 2 RDF 3 RDF 4 to create the RDF Public Vocabularies Data 1 Data 2 Data 3 Data 4
  • 66. And then we have linked data?
  • 67. Well yes, for Open PHACTS we do… OPS Data Model Identity & Vocabulary Management Semantic Data Workflow Engine ChemistryNormalisation & Registration Descriptor Descriptor RDF 1 RDF 2 Public Vocabularies Data 1 Data 2
  • 68. But really…,what about federated SPARQL queries? Descriptor Descriptor RDF 1 RDF 2 Other Public Vocabularies Data 1 Data 2 Public Vocabularies
  • 69. Most often partly… If the vocabularies used are different linking just database IDs not good enough. We need full mappings of ontologies. Identification of overlapping modules. And maybe… Suggestions for ontologies to use in specific field. Identity Mapping Descriptor Descriptor RDF 1 RDF 2 Other Public Vocabularies Data 1 Data 2 Public Vocabularies
  • 70. Thanks! WikiPathways team: • Martijn van Iersel (PathVisio, BridgeDB) • Thomas Kelder (WikiPathways, networks) • Alex Pico (US team leader) • Brice Conklin (former US team leader) • Kristina Hanspers (US curation) • Martina Kutmon (CyTargetLinker) • Susan Coort (Regulatory plugins) • Lars Eijssen (Data pipelines) • Anwesha Dutta (Flux visualisation) • Andra Waagmeester (LOOM) • Egon Willighagen (Open Phacts) Funding. Dutch: IOP, NBIC, NuGO, NCSB. Regional: Transnational University. EU: NuGO and Microgennet, IMI: Open Phacts + Agilent thought leader grant and NIH.
  • 71. Thanks! Funding. Dutch: IOP, NBIC, NuGO, NCSB. Regional: Transnational University. EU: NuGO and Microgennet, IMI: Open Phacts + Agilent thought leader grant.
  • 72. Analyzing GO representation inpathways using an independent library for ontology analysisCombining efforts and information to increase biological understanding
  • 73. Structuring biological data• Gene Ontology (GO) – Protein function or localization – Hierarchically structured terms – 3 topics (namespaces) • Biological process • Molecular function • Cellular component – Disadvantage • No information on interactions
  • 74. Structuring biological data• Pathways – Network of interactions – Structural overview of elements in the pathway – Disadvantages: • Missing structure of interacting pathways • Overlap and abundance in pathways
  • 75. Analysis based on structures• Uses: – Better overview of the data – Increased biological understanding• Challenges in the field: – Difficulty comparing algorithms – Good work may be overlooked – Redundant efforts – Out-of-date algorithms used – Comparison extremely difficult
  • 76. Goals:• Develop an independent library for ontology analysis in which efforts can be combined• Increase biological understanding by combining knowledge on pathways and gene ontology.
  • 77. Independent library for ontology analysis• Open source: – Collaboration – Clear view of the algorithm – Free use – Minimalizing redundant efforts• Usable for multiple ontologys and identifiers
  • 78. Combining Pathways and GO• Display information on the function of the pathway• Make a comparison between pathways• Quality control – Single pathway – List of pathways
  • 79. Materials• PathVisio – Open source Tool for visualizing and analyzing pathway data• BridgeDb – id mapping framework for bioinformatics• WikiPathways – Community curated pathway data source
  • 80. Independent Library• Manager input: 1. Ontology Terms (File) 2. Map of term with identifier 3. Method Selection
  • 81. Methods Id’s linked Genes not to GO linked to GO Id’s in pathway a b a+b Id’s not in pathway c d c+d a+c b+d n
  • 82. Plug-in• Panel for the analysis of a single pathway – Display GO terms in a table with score – Highlight matches – Save results• Menu Item for analyzing a list of pathways – Select a folder containing pathway files – Individual result files – File containing all results with extra info
  • 83. Single Pathway analysis
  • 84. Single Pathway analysis• Regulation of blood pressure• Angiogenesis• Others: – G-protein coupled receptor – proteolysis Homo sapiens: Mus musculus: name score name score G-protein coupled receptor signaling kidney development 50% pathway 35% G-protein coupled receptor signaling regulation of cell proliferation 29% pathway 50% proteolysis 29% response to drug 37% regulation of blood pressure 29% negative regulation of cell proliferation 37% response to drug 29% positive regulation of apoptotic process 37% regulation of vasoconstriction 29% regulation of blood pressure 37% positive regulation of apoptotic process 29% response to salt stress 25% negative regulation of cell growth 23% regulation of systemic arterial blood kidney development 23% pressure by circulatory renin-angiotensin 25% elevation of cytosolic calcium ion arachidonic acid secretion 25% concentration 23% blood vessel development 25%
  • 85. Multiple Pathway analysis
  • 86. Multiple Pathway analysis 0 2 4 6 8 10 12 14 16 18Biological Process12 of 105 terms signal transduction xenobiotic metabolic process oxidation-reduction process metabolic process G-protein coupled receptor signaling pathway gene expression nerve growth factor receptor signaling pathway apoptotic process synaptic transmission DNA repair mitotic cell cycle innate immune response 0 10 20 30 40 50 60 70 80Cellular Compontent cytoplasm12 of 26 terms cytosol nucleus plasma membrane membrane integral to membrane mitochondrion nucleoplasm endoplasmic reticulum membrane extracellular region endoplasmic reticulum integral to plasma membrane microsome extracellular space
  • 87. Goals:• Develop an independent library for ontology analysis in which efforts can be combined• Increase biological understanding by combining knowledge on pathways and gene ontology.
  • 88. Independent library• Reads GO terms from file• Mapping from term to identifier• Analysis on sample data• Framework enables more methods to be added
  • 89. Combining Pathways and GO• Single Pathway: – More information on pathway – Quality control possible• Pathway List: – Separate results for every pathway – Enables structuring possibility’s – Quality control possible