GPKB: Genomic and Proteomic Knowledge Base
- 1. Group Meeting 2016-08-17, Tech “GPKB: Genomic and Proteomic Knowledge Base” by Davide Chicco davide.chicco@gmail.com
- 2. ● A data warehouse developed and mantained by my former colleagues at Politecnico di Milano university ● Integration of several data sources: ● KEGG (Kyoto Encyclopedia of Genes and Genomes) ● OMIM (Online Mendelian Inheritance in Man) ● Gene Ontology Annotations (GOA) ● Gene Ontology (GO) ● Expasy Enzyme ● Entrez Gene ● Reactome ● UniProt ● BioCyc ● IntAct Genomic and Proteomic Knowledge Base (GPKB) (c) Flickr Vitlava: database-integration
- 3. ● Large amounts of biological datasets are available all around the world ● Especially, biomolecular annotations (associations between genes or gene products and biological function features) can help scientists in the understanding of biology and life science ● The hierarchical structure of the ontology structure of these datasets are able to highlight semantic relationships beween data Motivation
- 4. ● Implemented in PostgreSQL ● It can be downloaded or used through a web interface ● Dataset quantitative characteristics: ~ 20 milions of genes ~ 20 milions of proteins ~ 17 milions of gene annotations ~ 31 milions of protein annotations ● Some tables are simply imported from data sources (GO, Reactome, etc) ● Other tables are INFERED from the available datasets Technical details and quantitative characteristics
- 5. ● Data tables available: Technical details and quantitative characteristics Image from M. Masseroli, et al. "Explorative search of distributed bio-data to answer complex biomedical questions." BMC Bioinformatics 15.1 (2014): 1. Green-gray boxes: data table available in the general data warehouse and publically available on the web interface Gray boxes: data table available in the general data warehouse (publically available in the future)
- 6. Two main execution modes: ● Basic search ● Easy search GPKB
- 7. ● The Basic search functionality is available for searches aimed at retrieving all information directly associated with a single feature instance, either imported from external sources or inferred based on the integrated data ● For example, all annotations and interactions of a specific gene or protein (e.g. the human insulin-like growth factor 2 (somatome-din A) (IGF2) gene, Entrez Gene ID 3481), or all genes and proteins annotated to a particular biomedical feature instance, such as a specific pathway or genetic disorder (e.g. the Alzheimer disease , OMIM ID 104300). Basic search
- 13. ● Authors also implemented an enhanced functionality and graphical interface for multi-feature search, named Easy search. ● It supports the simple graphical composition of complex queries on multiple features just by orderly selecting the required features, e.g. gene, pathway, enzyme, biological function feature, genetic disorder, clinical synopsis, etc.; if needed, display and filtering constrains can be defined for any attribute of each selected feature just by specifying them in the feature windo. Easy search
- 14. ● Query example: relationship between genes, biological function features of pathologies (e.g. in Muscular dystrophy, Duchenne type). ● Using the Easy search functionality, the user can orderly select the gene feature, then the gene associated biological function feature and genetic disorder features, and then the genetic disorder associated clinical synopsis feature; finally, before submitting the query, if the user wants to investigate only some related pathologies, he/she can specify them as value of the name attribute in the genetic disorder feature window. Easy search
- 20. Distinct: only distinct results
- 21. Exact count: it runs exact count of the query results, otherwise it estimates the result count
- 22. Conceptual query (C): the query includes the conceptually equivalent database items coming from other data sources
- 23. Semantic expansion: When a query is executed with semantic expansion for a feature then the result contains not only the items that satisfy the query but also semantically related more general items based on the feature ontologies
- 24. Expand query: After obtaining results for an initial query, to expand the query only for the user selected rows of the previous query result
- 25. Show all: shows all the query results Only matching: shows only the query results matching values between all the selected features
- 26. “Find all the genes that are involved both in breast cancer and in prostate cancer, and then retrieve all the proteins that are encoded by one of those genes” http://www.bioinformatics.deib.polimi.it/GPKB Demo
- 27. Main advantages of GPKB compared to other systems (such as BioWarehouse, Biozon, etc): 1) flexible data schema and software architecture, to facilitate data import 2) integration of datasets from different sources highlight semantic relationships between data elements 3) ability to answer multi-domain biomedical questions GPKB advantages
- 28. M. Masseroli, A. Canakoglu, and S. Ceri. "Integration and querying of genomic and proteomic semantic annotations for biomedical knowledge extraction" IEEE/ACM Transactions on Computational Biology and Bioinformatics 13.2 (2016): 209-219. http://www.bioinformatics.deib.polimi.it/GPKB Citation and web link