1. Biomedical Databases & Tools Rolando Garcia-Milian Rolando.milian@ufl.edu Biomedical & Health Information Services Department Health Sciences Center Library October 3, 2013

2. Problem – Rapid Growth of Biomedical data GenBank Statistics http://www.ncbi.nlm.nih.gov/genbank/genbankstats-2008/ http://www.ncbi.nlm.nih.gov/genbank/genbankstats-2008/ Compiled from GEO historic data http://www.ncbi.nlm.nih.gov/geo/summary/?type=history http://www.ncbi.nlm.nih.gov/geo/summary/?type=history

3. Compiled by from PubMed http://www.ncbi.nlm.nih.gov/pubmed http://www.ncbi.nlm.nih.gov/pubmed Biomedical Literature Problem – Growth of the Biomedical Literature Huge volume (PubMed 23132342 citations) High diversity High quality (peer review) Users overwhelmed by long list of search results 1/3 of Pubmed queries result in 100 or more citations (Islamaj, 2009)

4. Querying the biomedical literature becomes more difficult Medical Subject Headings Filters Boolean operators Problem – Querying the Biomedical Literature

5. Alternative Mining Tools for the Biomedical Literature Main gene query Protein/gene associated Synonym Medical terminology

6. Based on Universal Medical Language System Repository of semantic predications (subject- predicate-object triples) 57.6 million predications from all of PubMed citations (Rindflesch, 2011) Alternative Mining Tools for the Biomedical Literature

7. Linked to Entrez Gene database

8. Workshop- Novel Online Tools for Mining the Biomedical Literature

9. Case 1 – Few Results in the Biomedical Literature Searching for novel genes

10. Case 2 – Few Results in the Biomedical Literature Searching for side effects of drugs: Cerebyx – respiratory failure

11. Phenotypic information can be used to infer molecular interactions and hinting at new uses of marketed drugs (Campillos, 2008) Case 2 – Few Results in the Biomedical Literature

12. Freely Available Up-To-Date Discovery Tools National Center for Biotechnology Information, USA European Bioinformatics Institute, UK

13. Proprietary Tools

14. Annotation/ Visualization Tools – Genome Browsers

15. Workshop- Introduction to Genome Browsers

16. References Campillos M*, Kuhn M*, Gavin AC, Jensen LJ, Bork P. Drug target identification using side-effect similarity. Science. 2008 Jul 11;321(5886):263-6. http://www.ncbi.nlm.nih.gov/pubmed/18621671 http://www.ncbi.nlm.nih.gov/pubmed/18621671 Islamaj Dogan R, Murray GC, Névéol A, Lu Z. (2009) Understanding PubMed user search behavior. Database (Oxford) http://www.ncbi.nlm.nih.gov/pubmed/20157491http://www.ncbi.nlm.nih.gov/pubmed/20157491 Kent WJ, Sugnet CW, Furey TS, Roskin KM, Pringle TH, Zahler AM, Haussler D. The human genome browser at UCSC. Genome Res. 2002 Jun;12(6):996-1006. http://www.ncbi.nlm.nih.gov/pubmed/12045153 http://www.ncbi.nlm.nih.gov/pubmed/12045153 Kuhn M, Campillos M, Letunic I, Jensen LJ, Bork P. A side effect resource to capture phenotypic effects of drugs. Mol Syst Biol. 2010;6:343. Epub 2010 Jan 19. http://sideeffects.embl.de/drugs/56338/ http://sideeffects.embl.de/drugs/56338/ Rindflesch, T.C. et al. (2011) Semantic MEDLINE: An advanced information management application for biomedicine. Information Services & Use, 31, 15-21. http://lhncbc.nlm.nih.gov/system/files/pub-lhncbc-2011-109.pdf http://lhncbc.nlm.nih.gov/system/files/pub-lhncbc-2011-109.pdf

17. Data Resources Data Management Resources, UF Libraries: http://library.ufl.edu/datamgmt http://library.ufl.edu/datamgmt Surveys on Your Needs UF Research Data Needs Assessment: http://bit.ly/UFdatasurvey http://bit.ly/UFdatasurvey Big Data, Little Data Workshop Evaluation: http://bit.ly/dataevalhttp://bit.ly/dataeval