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Advanced PGDB Editing: Regulation GO Terms

Advanced PGDB Editing: Regulation GO Terms. Ingrid M. Keseler Bioinformatics Research Group SRI International keseler@ai.sri.com. Motivation: Why Regulation?. For example: Genome and regulatory overview Global perspective Omics data Data sets for promoter prediction etc.

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Advanced PGDB Editing: Regulation GO Terms

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  1. Advanced PGDB Editing:RegulationGO Terms Ingrid M. Keseler Bioinformatics Research Group SRI International keseler@ai.sri.com

  2. Motivation: Why Regulation? For example: • Genome and regulatory overview • Global perspective • Omics data • Data sets for promoter prediction etc.

  3. Omics Viewer: Regulatory Overview Data from J Bacteriol. 2010 Feb;192(3):870-82. A comprehensive proteomics and transcriptomics analysis of Bacillus subtilis salt stress adaptation.

  4. Defining a New Transcription Unit • Gene > New Operon • Key elements – gene names in order • PTools will prompt you for a citation for the TU • Specify promoter • Can use absolute or relative position of transcription start site • PTools will calculate the other value for you • PTools will prompt you for a citation for the TSS • Specify sigma factor (if appropriate) • It may be necessary to first classify sigma factors under |Sigma-Factors|

  5. Adding Transcription Factor Binding Sites • Click on TU name – Edit > Create Regulatory Interaction • Select type of regulatory interaction • Can put in a protein name, or select a defined TF • Indicate whether it activates, represses or both • Define relative distance from transcription start site • Draws DNA footprint from feature defined in TF • Can edit TF binding sites by clicking on site name • Edit > Regulatory Interaction Editor • Can add summaries and citations • This builds the transcriptional regulatory network

  6. More Regulatory Interactions • Attenuation • Regulation of translation • RNA-mediated • Protein-mediated • Small molecule-mediated • Regulated protein or mRNA degradation (planned) • If you have suggestions for types of regulation you would like to represent, or for improvements on what is there, please let us know. • Tools for genome-scale datasets?

  7. Gene Ontology (GO) Terms

  8. Motivation: Why GO Terms? For example: • Standardization of annotation • Data mining across genomes • Genome annotation by similarity (e.g. via InterPro, Pfam, TIGRFAM, COG mappings) • Microarray data clustering • Etc.

  9. A Word (Or Two) About GO • Learn what you can about using GO • Surf the geneontology.org web site • Attend a GO Annotation Camp • Ask questions on the GO mailing lists • Request new GO terms if appropriate • Useful for everybody • Have input when it counts • A new GO database can be incorporated into Pathway Tools; request help with setting up the process • Computational GO term assignments may be available for your genome via UniProt

  10. GO Classification Editor • Accessible via the Protein Editor • Expand/contract, select/deselect by clicking on +/- and the actual terms • Selected items move to “Selections” section • Search feature: can search by name/substring or GO id (in the full format only, e.g. “GO:0007165”, not just the number) • For example, search for “arginine” yields many options • Click an option to highlight in hierarchy • Hovering over a term in the hierarchy brings up its definition in the middle panel • Must still click on entry in hierarchy to select the term for annotating the protein

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