Transcript Editing-Regulation&GO - Bioinformatics Research Group at SRI

Advanced PGDB Editing:
Regulation
GO Terms
Ingrid M. Keseler
Bioinformatics Research Group
SRI International
[email protected]
1
SRI International Bioinformatics
Motivation: Why Regulation?
For example:
• Genome and regulatory overview
 Global perspective
 Omics data
• Data sets for promoter prediction etc.
2
SRI International Bioinformatics
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.
3
SRI International Bioinformatics
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|
4
SRI International Bioinformatics
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
5
SRI International Bioinformatics
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?
6
SRI International Bioinformatics
Gene Ontology (GO) Terms
7
SRI International Bioinformatics
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.
8
SRI International Bioinformatics
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

9
Computational GO term assignments may be available for
your genome via UniProt
SRI International Bioinformatics
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)




10
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
SRI International Bioinformatics