1. Bioinformatics & LIS A brief talk for librarians, information scientists, and computer scientists about resources and collaborative opportunities with biology. April 18, 2006 G. Benoit

2. Outline of the talk Bioinformatics defined Generation of data Tools and databases Activities for Librarianship, Computer and Information Science Examples: –Entrez, NCBI, Visualization Collaborations

3. Bioinformatics defined Over 70 defintions Differences arise from the work Nat’l Center for Biotechnical Information (NCBI) The development of new algorithms and statistics with which to assess relationships among members of large data sets; The analysis and interpretation of various types of data including nucleotide and amino acid sequences, protein domains, and protein structures; and The development and implementation of tools that enable efficient access and management of different types of information.

4. Without getting into the science… How the data started … Four chemical bases (purines [adenine (A), guanin (G)] and pyrimidines [cytosine (C) and thymine (T)] ) Their precise order and linking (attached to a sugar molecule and to a phosphate molecule to create a nucleotide) …

5. DNA

6. A pairs with T; G with C to make unique and very long strings, called sequences E.g., AATGACCAT codes for a different gene than GGGCCATAG would Replication: RNA consists of A, G, C, and Uracil and has ribose instead of deoxyribose Point is one can predict missing data, sometimes…

7. In short… the nucleotides are linked in a certain order or sequence through the phosphate group; their precise order and linking within the DNA determines what proteins the gene produces and the phenotype of the organism

8. Generation of Data Raw data from sequencing Expression data Data generated by linking other raw data in very large, multidimensional databases (e.g., OMIM) Research literature (full-text journals) Data models to describe the literature for retrieval, linking to other data, and linking to the raw data New data models to support greater flexibility in describing & manipulating data …

9. Generation of Data To support integrated search and retrieval To focus on single organisms or find similarities across them Feed other technology Visualization of natural phenomena and of abstract phenomena

10. Tools & Databases A host of tools for database searching… –BLAST (basic local alignment search tool) –FASTA (sequence strings) –ChopUp (protein analysis) –Integrated packages (Lasergene Sequence Analysis Software) –The many services offered through NCBI and NLM

11. Take a look at handout, Table 1, publically accessible databases

12. Data Categories Monographs, Journals, Announcements (text) Datasets: –Bibliographic (http://www.expasy.org/links.html)http://www.expasy.org/links.html –Taxonomic –Nucleic acid –Genomic (e.g., GDB, OMIM) –Protein DB (SwissProt, TrEMBL) –Protein families, domains, and functional sites –Proteomics initiative –Enzyme/metabolic pathways –Sequence Retrieval System (SRS) and NCBI Data Model

13. Take a look at handout, Table 2, publically- accessible databases defined and then Entrez sample, Table 3

14. Entrez example Notice the familiar access points (author, journal, title) as well as domain- specific ones (exon, gene, organism) Notice, too, the DNA …

15. NCBI Homepage http://www.ncbi.nih.gov/ Notice the variety of tools (left menu) Site map: http://www.ncbi.nih.gov/Sitemap/index.html http://www.ncbi.nih.gov/Sitemap/index.html Alpha list http://www.ncbi.nih.gov/Sitemap/AlphaList.html http://www.ncbi.nih.gov/Sitemap/AlphaList.html

16. Linking across resources http://www.ncbi.nlm.nih.gov/entrez/query/static/linking.html NCBI’s structure database is called Molecular Modeling Database (MMDB), and is a subset of non-theoretical models 3D structures obtained from the Protein Data Bank (PDB). Data are obtained from X-ray crystallography and NMR- spectroscopy. Goal is to make it easier to compare structures. Searching : variety of access points: author, title, text terms, or a PDB 4-character code or a numerical MMDB-id MMDB Data : PDB records are parsed (to extract sequences and citations from PDB records, and structural info). Converted to ASN.1. Taxonomy : is used to help end users see term relationships and databases, along with literature references: Example: http://www.ncbi.nlm.nih.gov/Taxonomy/tax.html/http://www.ncbi.nlm.nih.gov/Taxonomy/tax.html/ http://www.ncbi.nlm.nih.gov/Taxonomy/Browser/wwwtax.cgi?mode=Undef&name= Escherichia+coli&lvl=0&srchmode=1

17. Linking across resources XML - there are hundreds of XML schema used in biology Calls for mapping to ASN1 records [see NCBI example] Calls for mapping across schema Calls for exporting data for different devices…

18. Visualization Cn3D - uses MMDB-Entrez’s structure database –http://www.ncbi.nlm.nih.gov/Structure/CN3D/cn3d.shtmlhttp://www.ncbi.nlm.nih.gov/Structure/CN3D/cn3d.shtml RasMol http://www.umass.edu/microbio/rasmol/ Protein Explorer http://www.umass.edu/microbio/rasmol/rotating.htm http://www.umass.edu/microbio/rasmol/rotating.htm OpenRasMol http://www.openrasmol.org/ http://www.openrasmol.org/ MolviZ.org http://www.umass.edu/microbio/chime http://www.umass.edu/microbio/chime World Index of Molecular Visualization http://molvis.sdsc.edu/visres/index.html

19. Recap main points Very large data sets - “homogenized” thru ASN.1 Goal to integrate (text-text, visualization-text, text-vis) Raw data + research literature + visualization Biologists provide domain knowledge XML is a big player CS and IS provide technology Librarians provide maintenance and access to resources

20. Collaborative Opportunities For LIS and CS: –Domain analysis –information use, communication, theories of information; –systems analysis and design, –data modeling, –classification, –storage and retrieval, –HCI mapped onto a generalized model of a molecular biology experimental cycle [Denn & MacMullen, 2002, p. 556]

21. Collaborative Opportunities “Insertion Points” - development of new tools and methods for managing, integrating & visualization For local use: download selected data sets for local needs (Stapley & Benoit, 2000) XML Transformations XML - SVG - X3D Automated retrieval Clustering (data- and text-mining)

22. Collaborative Opportunities Biologists’ needs: –To go beyond mining of genomic data to investigate causal entailments in intra- and intracellular dynamics LIS’s response: –To aid understanding of the scientific processes thru visualization of literature, metadata and graphic representations in general and for disease-specific analysis

23. Back to you… Thanks …