Exploring and Exploiting the Biological Maze

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Exploring and Exploiting the Biological Maze

• Zoé Lacroix
• Arizona State University

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Data collection queries

• Scientific protocol
• Must be able to reproduce the process
• Involve multiple resources
• Data sources
• Applications

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Expressing scientific protocols

• Scientific protocols mix design and
  implementation
• Design
• What the protocols does (tasks)
• Scientific objects involved
• Implementation
• How the protocol is executed
• Data sources and applications

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Expressing scientific protocols

• Scientific protocols are driven by their
  implementation
• Scientists use the resources they know
• data (quality)
• access to data
• format, limits, etc.
• Scientists may not exploit better resources
  because they do not know them
• Queries should be driven by the design, the
  implementation should meet the design needs

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Example - Pipeline for Analysis of Protein
Variation Due to Alternative Splicing and SNPs

• The alternative splicing pipeline will provide
  a complete characterization of variations in
  proteins due to splice variation or SNPs evident
  in repositiories of contiguous genome sequence
  data and expressed sequence tags (ESTs). The
  pipeline applies secondary structure, tertiary
  structure, domain motif detection and sequence
  comparison tools to proteins encoded by genes
  with alternatively splice forms or SNPs.
• Courtesy of Dr. Marta Janer, Institute for
  Systems Biology

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Step 2 - Pipeline for Analysis of Protein
Variation Due to Alternative Splicing and SNPs

• From GenBank, Dbest and the Riken Clone
  Collection, collect all EST and full-length cDNA
  sequences from the target organisms of interest
  (in this case, human and mouse) that match the
  query proteins (mouse DNA binding proteins) using
  tblastn. Map the query protein to the target DNA
  sequences, keeping track of which query amino
  acids correspond to which nucleotides.

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Step 2 - Pipeline for Analysis of Protein
Variation Due to Alternative Splicing and SNPs

• From GenBank, Dbest and the Riken Clone
  Collection, collect all EST and full-length cDNA
  sequences from the target organisms of interest
  (in this case, human and mouse) that match the
  query proteins (mouse DNA binding proteins) using
  tblastn. Map the query protein to the target DNA
  sequences, keeping track of which query amino
  acids correspond to which nucleotides.

Data sources
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Step 2 - Pipeline for Analysis of Protein
Variation Due to Alternative Splicing and SNPs

• From GenBank, Dbest and the Riken Clone
  Collection, collect all EST and full-length cDNA
  sequences from the target organisms of interest
  (in this case, human and mouse) that match the
  query proteins (mouse DNA binding proteins) using
  tblastn. Map the query protein to the target DNA
  sequences, keeping track of which query amino
  acids correspond to which nucleotides.

tools
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Step 2 - Pipeline for Analysis of Protein
Variation Due to Alternative Splicing and SNPs

• From GenBank, Dbest and the Riken Clone
  Collection, collect all EST and full-length cDNA
  sequences from the target organisms of interest
  (in this case, human and mouse) that match the
  query proteins (mouse DNA binding proteins) using
  tblastn. Map the query protein to the target DNA
  sequences, keeping track of which query amino
  acids correspond to which nucleotides.

tasks
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Step 2 - Pipeline for Analysis of Protein
Variation Due to Alternative Splicing and SNPs

• From GenBank, Dbest and the Riken Clone
  Collection, collect all EST and full-length cDNA
  sequences from the target organisms of interest
  (in this case, human and mouse) that match the
  query proteins (mouse DNA binding proteins) using
  tblastn. Map the query protein to the target DNA
  sequences, keeping track of which query amino
  acids correspond to which nucleotides.

Scientific objects
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Pipeline Selecting Target Proteins
Step 1 retrieve all proteins from SMART and
Swiss-Prot with textual search with the keyword
apoptosis Step 2 retrieve all proteins from
Swiss-Prot with a signal peptide feature and the
keyword apoptosis Step 3 retrieve their
binding partners from DIP, BIND and the C.elegans
dataset Step 4 run through a signal peptide
prediction program such as SigPep to check for
the presence of signal peptides in each of the
sequences Step 5 homology search using BLAST of
the retrieved sequences with proteins predicted
from the Drosophila melanogaster genome might
yield additional candidates Output final set of
signal peptide proteins involved in apoptosis
Courtesy of Dr. Terry Gaasterland, The
Rockefeller University
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Design and implementation
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Expressing scientific pipelines with BioNavigation

• Queries are expressed at a conceptual level
  (design)

Disease
Protein Seq.
Scientific classes
DNA Seq.
Gene
Citation
Conceptual level
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Conceptual graph

• Labeled edges
• Scientific meaningful edges

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Conceptual graph
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Mapping to physical resources
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Mapping to physical resources
Disease
Protein Seq.
Scientific classes
DNA Seq.
Gene
Citation
Conceptual level
Physical level
Gen- Bank
Pub- Med
HUGO
Data Sources
OMIM
NCBI Protein
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Exploring biological metadata

• Return all citations that are related to some
  disease or condition
• Diabetes 11 Aging 71 Cancer 391

• Link Entrez provides an index with the Links in
  the display option from each entry
• Parse Parsing each entry to retrieve its
  related entries
• All Entrez provides an index with the Links in
  the display option which allows to look at a set
  of entries at a time

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Selecting biological resources

• 3 resources that look the same
• Are they the same?
• 3 paths that will retrieve PubMed entries related
  to citations
• Do they have the same semantics?

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Results for the disease conditions diabetes,
aging and cancer
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Overlap results for the disease conditions
diabetes
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Evaluating resources

• Similar applications
• Different outputs
• Similar data sources
• Different output
• Number of resources
• Different output
• Order of resources
• Different output

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Exploiting semantics of resources

• Number of entries
• Characterization of entries (number of
  attributes)
• Time

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Exploiting the semantics of links
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BioNavigation (joint work with Louiqa Raschid and
Maria-Esther Vidal)

• Conceptual graph
• No labeled links
• Queries
• Regular expressions of concepts
• ESearch
• Path cardinality - number of instances of paths
  of the result. For a path of length 1 between two
  sources S1 and S2, it is the number of pairs (e1,
  e2) of entries e1 of S1 linked to an entry e2 of
  S2.
• Target Object Cardinality number of distinct
  objects retrieved from the final data source.
• Evaluation Cost cost of the evaluation plan,
  which involves both the local processing cost and
  remote network access delays.

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Work in progress

• Conceptual graph
• Labeled links
• Queries
• Complex dataflows
• Physical graph
• Access to a BioMetaDatabase
• Data sources
• Applications

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Representing the conceptual graph in Protégé
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Visualization Limitations in Protégé

• Using the GraphViz plugin
• Shows only IsA hierarchy

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• TgiViz plugin

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Conclusion

• Scientists need support to select resources to
  express their protocols
• Semantics of resources may be exploited to
  enhance the data collection process
• Need for a repository of biological metadata
  (BioMetaDatabase)