MEDLINE in Oracle XML-DB and Oracle Text

1. MEDLINE in Oracle XML-DB and Oracle Text Peter Stoehr Head of Database Operations European Bioinformatics Institute (EBI) www.ebi.ac.uk Oracle Life Sciences, OracleWorld, San Francisco, Sep 10 2003

2. European Molecular Biology Laboratory (EMBL) • International network of research institutes dedicated to research in molecular biology • Treaty organisation funded by 16 member states • Headquarters established in Heidelberg in 1974- research programmes in cell biology, developmental biology, instrumentation, gene expression etc • Outstations- Hamburg and Grenoble: structural biology- Monterotondo: mouse genetics- Hinxton, EBI: bioinformatics

3. European Bioinformatics Institute (EBI) • Mandate • to ensure the growing body of data and information from molecular biology and genome research is placed in the public domain and is accessible freely to all facets of the scientific community in ways that promote scientific progress and global competitiveness • to support academic research as well as biotech, agricultural, chemical and pharmaceutical industriesThe EBI builds, develops and publishes databases and information services relevant to molecular biology, as well as conducting research in bioinformatics.

4. About EBI • Located in Hinxton, Cambridge, England (since 1993) • Non-profit organisation - part of EMBL • Started as EMBL Data Library in 1980 • Centre for research and services in bioinformatics • Three branches: Services, Research, Industry • Funding mainly from EMBL and EU

5. EBI Resources • Personnel • - 220 people • - ~100 Database developers, software engineers • - DBA - 4 • - Systems - 6 • Environment • - OS: Tru64 ES45s, Solaris • - Storage: SAN storage, NetApp NFS • - 350-cpu linux compute farm

6. Major public databases at the EBI • DNA sequences - EMBL Nucleotide Sequence Database • protein sequences - SWISS-PROT, TrEMBL, Interpro, CluSTr • genome annotation - Ensembl • protein structures - MSD • microarrays - ArrayExpress • literature - MEDLINE, patents, fulltext • enzymes - IntEnz • protein interactions - IntAct • immunogenetics - IMGT, HLA • integration - Integr8, EnsMart

7. Oracle Production Instances

8. Statistics • 26 million nucleotide sequences (25b bases) • 1 million protein sequences • 200 complete genomes (+viruses, organelles) • 28,000 genes in human genome • 10,000 protein 3D structures • 2500 journals with sequences • 12 million MEDLINE citations, 4500 journals • 130,000 biotech patent documents • 550,000 web hits per day, www.ebi.ac.uk

9. EBI interest in text resources • provide links from factual databases to full-text literature (journals, patents…) • mine for information relevant to factual database annotation. • most scientific information buried in free text resources  • enable indexing and searching of full-text literature

11. Searches based on bibliographic data in patent documents Full text documents - PDF files ID AX067464 standard; DNA; PRO; 100848 BP. XX AC AX067464; XX SV AX067464.1 XX DT 24-JAN-2001 (Rel. 66, Created) DT 24-JAN-2001 (Rel. 66, Last updated, Version 1) XX DE Sequence 39 from Patent WO0078968. XX KW . XX OS Moraxella catarrhalis OC Bacteria; Proteobacteria; gamma subdivision; OC Moraxellaceae; Moraxella. XX RN [1] RA Lagace R.E., Patterson C., Berg K.L.; RT "Nucleotide sequences of moraxella catarrhalis genome"; RL Patent number WO0078968-A/39, 28-DEC-2000. RL Incyte Genomics, Inc. (US). XX FH Key Location/Qualifiers FH FT source 1..100848 FT /db_xref="taxon:480" FT /organism="Moraxella catarrhalis" espacenet

12. EBI interest in text resources • provide access from factual databases to full-text literature (journals, patents…) • mine for information relevant to factual database annotation. • most scientific information buried in free text resources  • enable indexing and intelligent searching of full-text literature

14. Areas of improvement for public text resources • improve text retrieval functionality • improve and add text corpora • use of thesauri and ontologies (UMLS, SNOMED,GO, GOBO) • interfaces

15. Text corpora • MEDLINE • Full-text literature • AGRICOLA • Biotech patent abstracts • Biotech patent full-text • OMIM • The web • => public searchable services

16. Speed of searches Speed of indexing Ability to search multiple data sources and formats, MEDLINE in XML EMBL/SWISS-PROT-type structured files websites Word, PDF and text, email/jitterbug files RDBMS (ORACLE and MySQL, Postgres) Ability to handle large database/collections Text query functions Natural language Boolean operators Phrase searches Proximity searches Use of synonyms, ontologies, thesauri esp. UMLS/MeSH, GO Stemming and wild-carding Multiple language support (for patent literature) Document clustering functionality Search refinenement, set operators Ranking of results (relevance, date) Weighting of search Scaleability of indexing, searching Load balancing on multiple nodes Parallel processing Incremental and off-line indexing Ease of use of APIs, documentation API languages, C/Java/Perl Interoperabilty with SRS Market strength of vendor Availability on multiple unix platforms esp. Tru64, Linux and Solaris Technical support Licence costs and flexibility Search engine evaluation criteria

17. Altavista Verity Inktomi ASPseek Google Autonomy Thunderstone Excalibur Fulcrum SPSS/LexiQuest Stratify/”Purple Yogi” Dolphinsearch Quiver Oracle Text X-Mine - “Opus” Diogene incellico - “Cell Entity Browser” Collexis Alma PharmDM Linguamatics Inxight ClearForest APRSmartLogik Text search/extraction systems

18. Search engine implementation • Verity K2-Red Hat Linux, 200-cpu pc farm- MEDLINE XML parser built • Oracle Text- Oracle 9iR2 implementation- text indexing of titles and abstract- XML DB- Oracle Life Sciences Initiative- MEDLINE + weekly updates implemented- tuning, performance analysis- in use for internal sequence DB maintenance

19. MEDLINE • National Library of Medicine (NLM) Bethesda • 530 XML files, following NLM DTD • ~ 12 million citations published in over 4500 biomedical journals • Daily updates

20. MEDLINE record view in SRS

21. Patent abstract record view in SRS

22. Patent abstract in XML

23. <!DOCTYPE MedlineCitationSet PUBLIC "-//NLM//DTD NLM Medline, 1st November 2002//EN” "http://www.nlm.nih.gov/databases/dtd/nlmmedline_021101.dtd> <MedlineCitationSet> <MedlineCitation Owner="NLM” Status="Completed"> <MedlineID>94033980</MedlineID> <PMID>8219565</PMID> …... <Journal> <ISSN>1051-0443</ISSN> <JournalIssue> <Volume>4</Volume> <Issue>5</Issue> <PubDate> <MedlineDate>1993 Sep-Oct</MedlineDate> </PubDate> </JournalIssue> </Journal> … </MedlineCitation> ………….. <MedlineCitationSet> Medline in XML

24. Possible Approaches MEDLINE/Patent XML Normalised relational tables Oracle XML-DB Verity K2 Oracle Text SRS Fast, efficient, Domain standard Lack of text query functions Efficient text query, scaleable, industry standard Return data as XML, post-processing required Fast, efficient for simple query Pre-processing required, hard to maintain, lack of text query functions, requesting multiple joins for more info Efficient, support text query Return data as XML, post-processing required

25. Why Oracle XML DB for Medline ? • Oracle 9iR2 embedded XML features with DBMS • XMLType datatypeLOB storage - maintains original XML byte for byte - can use an Oracle text index, support Xpath queries - flexible when schemas change object-relational storage - better performance, index specific fields - access to SQL features (constraints, indices etc) • - DOM fidelity (ordering, namespaces, inhertitance…) - piecewise XML element update • XMLSchema support

26. Why Oracle Text for Medline ? • Oracle 9i embedded Text features with DBMS • Powerful and extensive text functions- wildcards, boolean, stemming, proximity searches, NLP linguistic features, pattern matching, ‘soundex’ • XML specific operators, HASPATH, INPATH etc, to supportXPATH like expressions • Relevance ranking • Multi-lingual features • Extensions to SQL*Plus • Management of thesauri • Classification (CTXRULE indextype) • Unsupervised document Clustering • Documentation pretty good

27. Oracle RDBMS, XML DB and Text • One product range • - Cost (already purchased and used RDBMS) • - lower complexity – common administration, training, backup, replication, RAC etc • - lower latency of development/deployment • - no incompatible product updates, gateways etc • - greater performance for mixed queries

28. Prepare XMLSchema from DTD • NLM MEDLINE DTD • XML-Spy • To use Oracle CLOB type:<xs:schema xmlns:xs=http://www.w3.org/2001/XMLSchemaxmlns:xdb=http://xmlns.oracle.com/xdb elementFormDefault="qualified" xdb:storeVarrayAsTable="true"><xs:element name="Abstract" xdb:SQLType="CLOB"/> • => XMLSchema

29. Register XMLSchema, create table • begin • dbms_xmlschema.registerschema( • ’http://www3.ebi.ac.uk/internal/Services/medline/medlinecitation_Types.xsd’,xdburitype(’/public/medlinecitation_Types.xsd’).getClob(), TRUE,TRUE,FALSE,TRUE); • end;

30. Load data • SQL*Loader • We use a Java application, JDBC- need to control updates, deletions. • synchronize context indexexec ctx_ddl.sync_index(‘title_ind’,’40M’); • Complete MEDLINE:- 1 day to load, 1 day for context indexing • Updates- 10 mins

31. Actual MEDLINE instances @EBI • 9iR2 in Production- MEDLINE + patent abstracts- updated twice per week- used for in-house reference- CLOB storage of XMLType field- partitioned (by date) context index of XMLType • 9iR2 in development- using structured object-relational storage- indexing fields, inc. context indextype for titles, abstracts- no partioning

32. MED production schema

33. MedlineCitation #PMID number MedlineID number PubYear number dummyclob MedlineCitation XMLType Main Table: MedlineCitation Table is partitioned into 8 XMLtype Column is registered with XMLSchema, locally context type indexed

34. select m.MedlineCitation.getClobVal() AS MedXML from Medlinecitation m where pmid=8219565; --return a full XML document: <MedlineCitation Owner="NLM” Status="Completed"> <MedlineID>94033980</MedlineID> <PMID>8219565</PMID> …... <Article> <Journal> <ISSN>1051-0443</ISSN> <JournalIssue> <Volume>4</Volume> <Issue>5</Issue> <PubDate> <MedlineDate>1993 Sep-Oct</MedlineDate> </PubDate> </JournalIssue> </Journal> <ArticleTitle>Transcatheter manipulation of asymmetrically opened titanium Green field filters.</ArticleTitle> <Pagination> <MedlinePgn>687-90</MedlinePgn> </Pagination>…… <Article> … </MedlineCitation>

35. Select a part of XML document • select extract (MedlineCitation, '/MedlineCitation/Article/Journal/JournalIssue').getStringVal() ”JournalIssue” from Medlinecitation where pmid= 11194419; <JournalIssue> <Volume>40</Volume> <Issue>4</Issue> <PubDate> <Year>2000</Year> <Month>Nov</Month> </PubDate> </JournalIssue>

36. Select just Abstract text • select extractValue (MedlineCitation, '/MedlineCitation/Article/Abstract/AbstractText').getStringVal() “AbstractText” from Medlinecitation where pmid=8219565; AbstractText -------------------------------------------------------------------------------- A case of amebic meningoencephalitis recognized in an adult Zambian is described. This is the first authenticated case from Africa. The morphologic features of the organism, its ability to form cysts in tissue, and the granulomatous tissue response denote that the ameba is an hartmannellid rather than a Naegleria. Free -living amebas of the family Hartmannellidae have not been incriminated before as a cause of primary amebic meningoencephalitis in man. To our knowledge this is the only case where such an ameba was responsible for fulminating meningoencephalitis. The presence of the amebas in a cellulocutaneous abdominal lesion sugges ts hematogenous dissemination.

37. Citation PMID number primary key, MedlineID number, ArticleTitle VARCHAR2(1500), Volume VARCHAR2(55), Issue VARCHAR2(55), StartPage VARCHAR2(55), EndPage VARCHAR2(55), MedlinePgn VARCHAR2(100), PubYear number, ISSN VARCHAR2(9), NlmUniqueId VARCHAR2(25), AuthorListCompleteYN VARCHAR2(1) Author PMID number foreign key, LastName VARCHAR2(255), Initials VARCHAR2(255), Suffix VARCHAR2(25), CollectiveName VARCHAR2(1000), Affiliation VARCHAR2(1000), Rank number Improvements -can create additional relational tables

38. Improvements – use O-R storage • Can ‘context’ index whole XML table … • create index MEDLINE_CITATION_INDEX on MEDLINE_CITATION_TABLE x • (value(x)) indextype is ctxsys.context • parameters(’storage med_storage lexer med_lexstoplist med_STOPLIST section group autogroup • memory 400M’); • or just text fields: • Create index Journal_Abstract_Index on MEDLINE_CITATION_TABLE c(extractValue(val(m),’/MedlineCitation/Article/Abstract/AbstractText’))indextype is ctxsys.context;

39. Improvements S-R storage - index specific fields • create unique index MEDLINE_PMID_INDEX on MEDLINE_CITATION_TABLE m • (extractValue(value(m),'/MedlineCitation/PMID')) • create index AUTHOR_LASTNAME_INDEX on ARTICLE_AUTHOR_TABLE a • (a."LastName") • create index JOURNAL_ISSN_INDEX on MEDLINE_CITATION_TABLE c • ( extractValue(value(c),'/MedlineCitation/Article/Journal/ISSN') ) • create index JOURNAL_VOLUME_INDEX on MEDLINE_CITATION_TABLE c • (extractValue(value(c),'/MedlineCitation/Article/Journal/JournalIssue/Volume'), • extractValue(value(c),'/MedlineCitation/Article/Journal/JournalIssue/Issue')) • create index PAGINATION_INDEX on MEDLINE_CITATION_TABLE c • ( extractValue(value(c),'/MedlineCitation/Article/Pagination/MedlinePgn'))

40. Improvements - create a view • create or replace view Citation • as select • to_Number(extractValue(value(m),'/MedlineCitation/PMID')) PMID, • to_Number(extractValue(value(m),'/MedlineCitation/MedlineID')) MedlineId, • extractValue(value(m),'/MedlineCitation/Article/Journal/ISSN') ISSN, • extractValue(value(m),'/MedlineCitation/Article/Journal/JournalIssue/Volume') Volume, • extractValue(value(m),'/MedlineCitation/Article/Journal/JournalIssue/Issue') Issue, • extractValue(value(m),'/MedlineCitation/Article/Pagination/MedlinePgn') MedlinePgn • from MEDLINE_CITATION_TABLE m;

41. Typical query becomes simple & fast • select PMID, MedlineId from Citation where Volume='12' and ISSN='1040-4651' and MedlinePgn like '1-%'; • PMID MEDLINEID • ---------- ---------- • 10634903 20102627 • Elapsed: 00:00:00.35

42. Text query examples • SELECT score, pmid, title FROM citationWHERE CONTAINS(abstract,’gene NEAR expression’,1) >0ORDER BY score(1) DESC; • SELECT pmid, title FROM citationWHERE CONTAINS(abstract,’DrosophilaAND ABOUT(adh)’)>0;

43. Next steps • lexical functionality of Oracle Text • thesauri (UMLS, GO, SNOMED) • scaleability (linux RAC ?) • concept extraction, classification, clustering • application to database curation • interfaces: web services, GUI

44. Acknowledgements • Leader:Peter Stoehr, Weimin Zhu • DBA:Muruli Rao, Olalekan Oyewole • Developer:Lichun Wang • Oracle Support:Mark Drake (XML) • Roger Ford (Text)