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Building a Foundation to Enable Semantic Technologies for Phylogenetically-Based Comparative Analyses  ,[object Object],[object Object],[object Object],[object Object],[object Object],Phyloinformatics 06/24/11
Phyloinformatics 06/24/11 Motivation “ Nothing in biology makes sense except in the light of evolution ”  (Theodosius Dobzhansky, 1973)…. and Nothing in evolution makes sense except in the light of phylogeny
06/24/11 For example - Prediction of gene and protein function Jonathan A. Eisen, 1998,Genome Research, 8:163-167 Phyloinformatics 1. Choose gene of interest 2. Identify homolog 3.Align sequences 4.Calculate gene tree 5.Overaly known functions onto tree 6. Hypothesize function for all genes 7. Reconcile gene and species trees  After Eisen 1998,Genome Research
06/24/11 Example 2 – Testing congruence among phylogeographic analyses  Knowles 2009 after Avis 1992  Phyloinformatics 1. Compile results of phylogeographic analyses for multiple species from the same geographic region 2.  Apply demographic models to account for variation in generation times and substitution rates After Knowles 2009, Annu. Rev. Ecol. Evol. Syst.
Applying Semantics to Bioinformatics Integrative bioinformatics experimentation cycle  06/24/11 1.Problem Definition 2. Experimental Design 3. Data Integration 4. Data Analysis 15. Interpretation Biological hypothesis Protocol Raw integration result Analysis result knowledge 1. Imported or create data and knowledge models 2.Use data models to transform raw data to RDF data 3. Link data models to knowledge models 4. Select common domain 5. Construct and run semantic query Raw integration result Lennart J.G. Post, Marco Roos, M. Scott Marshall, Roel van Driel and Timo M. Breit. A semantic web approach applied to integrative bioinformatics experimentation: a biological use case with genomics data, Vol. 23 no. 22 2007, pages 3080–3087 doi:10.1093/bioinformatics/btm461
Phyloinformatics 06/24/11 Requirements for data reuse in comparative analyses: ,[object Object],[object Object],[object Object]
Bioinformatics and phylogeny 06/24/11 Proposed components of a minimum reporting standard for phylogenetic analyses: Leebens-Mack et al. 2006 OMICS
Bioinformatics and phylogeny 06/24/11 Developing an ontology for describing phylogentic workflows: ,[object Object],[object Object],[object Object],[object Object],[object Object]
Phyloinformatics 06/24/11 PhyloWays entry: ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
BPhyloinformatics 06/24/11 Current components of PhylOnt, an ontology for describing phylogenetics workflows: ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Phyloinformatics 06/24/11 Tree estimation program ontology
Phyloinformatics 06/24/11 Data analysis ontology diagram
Phyloinformatics 06/24/11 Models for character state transitions (e.g. nucleotide substitution model)
Phyloinformatics 06/24/11 ,[object Object],[object Object],[object Object],[object Object],Next steps:

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iEvobIO

  • 1.
  • 2. Phyloinformatics 06/24/11 Motivation “ Nothing in biology makes sense except in the light of evolution ” (Theodosius Dobzhansky, 1973)…. and Nothing in evolution makes sense except in the light of phylogeny
  • 3. 06/24/11 For example - Prediction of gene and protein function Jonathan A. Eisen, 1998,Genome Research, 8:163-167 Phyloinformatics 1. Choose gene of interest 2. Identify homolog 3.Align sequences 4.Calculate gene tree 5.Overaly known functions onto tree 6. Hypothesize function for all genes 7. Reconcile gene and species trees After Eisen 1998,Genome Research
  • 4. 06/24/11 Example 2 – Testing congruence among phylogeographic analyses Knowles 2009 after Avis 1992 Phyloinformatics 1. Compile results of phylogeographic analyses for multiple species from the same geographic region 2. Apply demographic models to account for variation in generation times and substitution rates After Knowles 2009, Annu. Rev. Ecol. Evol. Syst.
  • 5. Applying Semantics to Bioinformatics Integrative bioinformatics experimentation cycle 06/24/11 1.Problem Definition 2. Experimental Design 3. Data Integration 4. Data Analysis 15. Interpretation Biological hypothesis Protocol Raw integration result Analysis result knowledge 1. Imported or create data and knowledge models 2.Use data models to transform raw data to RDF data 3. Link data models to knowledge models 4. Select common domain 5. Construct and run semantic query Raw integration result Lennart J.G. Post, Marco Roos, M. Scott Marshall, Roel van Driel and Timo M. Breit. A semantic web approach applied to integrative bioinformatics experimentation: a biological use case with genomics data, Vol. 23 no. 22 2007, pages 3080–3087 doi:10.1093/bioinformatics/btm461
  • 6.
  • 7. Bioinformatics and phylogeny 06/24/11 Proposed components of a minimum reporting standard for phylogenetic analyses: Leebens-Mack et al. 2006 OMICS
  • 8.
  • 9.
  • 10.
  • 11. Phyloinformatics 06/24/11 Tree estimation program ontology
  • 12. Phyloinformatics 06/24/11 Data analysis ontology diagram
  • 13. Phyloinformatics 06/24/11 Models for character state transitions (e.g. nucleotide substitution model)
  • 14.

Notas do Editor

  1. Integrative bioinformatics experimentation cycle with five dis- tinct phases. At the end of a phase, an outcome is generated that is input for the next phase. Our semantic data integration approach is applied to phase II, which can be further divided into five steps. Lennart J.G. Post, Marco Roos, M. Scott Marshall, Roel van Driel and Timo M. Breit. A semantic web approach applied to integrative bioinformatics experimentation: a biological use case with genomics data, Vol. 23 no. 22 2007, pages 3080–3087 doi:10.1093/bioinformatics/btm461
  2. I would drop this slide