Biomedical Databases & Tools

Rolando Garcia-MilianRolando.milian@ufl.edu

Biomedical & Health Information Services DepartmentHealth Sciences Center Library

October 3, 2013

Problem – Rapid Growth of Biomedical data

GenBank Statistics http://www.ncbi.nlm.nih.gov/genbank/genbankstats-2008/

0.00

500,000.00

1,000,000.00

1,500,000.00

2,000,000.00

2,500,000.00

3,000,000.00

3,500,000.00

Samples Submitted to Gene Ex-pression Omnibus Database

Compiled from GEO historic data http://www.ncbi.nlm.nih.gov/geo/summary/?type=history

Compiled by from PubMed http://www.ncbi.nlm.nih.gov/pubmed

1940 1950 1960 1970 1980 1990 2000 2010 20200.00

5,000,000.00

10,000,000.00

15,000,000.00

20,000,000.00

25,000,000.00

Number of Records in 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)

Querying the biomedical literature becomes more difficult

Medical Subject HeadingsFiltersBoolean operators

Problem – Querying the Biomedical Literature

Alternative Mining Tools for the Biomedical Literature

Main gene queryProtein/gene associated

SynonymMedical terminology

• 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

Alternative Mining Tools for the Biomedical Literature

Linked to Entrez Gene database

Workshop- Novel Online Tools for Mining the Biomedical Literature

Case 1 – Few Results in the Biomedical Literature

• Searching for novel genes

Case 2 – Few Results in the Biomedical Literature

• Searching for side effects of drugs: Cerebyx – respiratory failure

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

Freely Available Up-To-Date Discovery Tools

National Center for Biotechnology Information, USA

European Bioinformatics Institute, UK

Proprietary Tools

Annotation/ Visualization Tools – Genome Browsers

Workshop- Introduction to Genome Browsers

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

Islamaj Dogan R, Murray GC, Névéol A, Lu Z. (2009) Understanding PubMed user search behavior. Database (Oxford) http://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

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/

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

Data ResourcesData Management Resources, UF Libraries: http://library.ufl.edu/datamgmt

Surveys on Your Needs• UF Research Data Needs Assessment:

http://bit.ly/UFdatasurvey

• Big Data, Little Data Workshop Evaluation:  http://bit.ly/dataeval