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Bioinformatics & LIS

Bioinformatics & LIS. A brief talk for librarians, information scientists, and computer scientists about resources and collaborative opportunities with biology. April 18, 2006 G. Benoit. Outline of the talk. Bioinformatics defined Generation of data Tools and databases

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Bioinformatics & LIS

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  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) • 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 • Alpha list 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/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.shtml • RasMol http://www.umass.edu/microbio/rasmol/ • Protein Explorer http://www.umass.edu/microbio/rasmol/rotating.htm • OpenRasMol http://www.openrasmol.org/ • MolviZ.org 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 … woo-hoo!

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