Bioinformatics

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Bioinformatics LIS

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

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Outline of the talk

• Bioinformatics defined
• Generation of data
• Tools and databases
• Activities for Librarianship,
• Computer and
• Information Science
• Examples
• Entrez, NCBI, Visualization
• Collaborations

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Bioinformatics defined

• Over 70 defintions
• Differences arise from the work
• Natl 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.

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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)

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DNA
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• 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

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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
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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

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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

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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

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• Take a look at handout, Table 1, publically
  accessible databases

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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

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

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Entrez example

• Notice the familiar access points (author,
  journal, title) as well as domain-specific ones
  (exon, gene, organism)
• Notice, too, the DNA

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NCBI Homepage

• http//www.ncbi.nih.gov/
• Notice the variety of tools (left menu)
• Site map http//www.ncbi.nih.gov/Sitemap/index.ht
  ml
• Alpha list http//www.ncbi.nih.gov/Sitemap/AlphaLi
  st.html

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Linking across resources

• http//www.ncbi.nlm.nih.gov/entrez/query/static/li
  nking.html
• NCBIs 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/ta
  x.html/
• http//www.ncbi.nlm.nih.gov/Taxonomy/Browser/wwwta
  x.cgi?modeUndefnameEscherichiacolilvl0srchm
  ode1

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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

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Visualization

• Cn3D - uses MMDB-Entrezs structure database
• http//www.ncbi.nlm.nih.gov/Structure/CN3D/cn3d.sh
  tml
• RasMol http//www.umass.edu/microbio/rasmol/
• Protein Explorer http//www.umass.edu/microbio/ras
  mol/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

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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

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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

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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)

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Collaborative Opportunities

• Biologists needs
• To go beyond mining of genomic data to
  investigate causal entailments in intra- and
  intracellular dynamics
• LISs response
• To aid understanding of the scientific processes
  thru visualization of literature, metadata and
  graphic representations in general and for
  disease-specific analysis

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Back to you

• Thanks

woo-hoo!