SLIDE 1
Annotation Analytics for Gene and Protein functions Nigam Shah, - - PowerPoint PPT Presentation
Annotation Analytics for Gene and Protein functions Nigam Shah, - - PowerPoint PPT Presentation
Annotation Analytics for Gene and Protein functions Nigam Shah, MBBS, PhD nigam@stanford.edu Annotation service Process textual metadata to automatically tag text with as many ontology terms as possible. 107 million calls, ~1000 GB data
SLIDE 2
SLIDE 3
Resource index
Pubmed Abstracts Adverse Events (AERS) GEO : Clinical Trials Drug Bank
Won 1st prize at the 2010 Semantic Web Challenge @ ISWC
SLIDE 4
Understanding the genome
- Units of study range in
length from ‘whole chromosome’ to ‘singe nucleotide’
- E.g. three copies of Chr.
21 Down’s syndrome
- The focus in on finding
the functional associations of strings in the genome
SLIDE 5
Genome
Generic GO based analysis routine
Reference set Study Set
- Get annotations for each
gene in a set
- Count the occurrence of
each annotation term in the study set
- Count the occurrence of
that term in some reference set (whole genome?)
- P-value for how surprising
their overlap is.
SLIDE 6
Genes2MSH GOPubMed
Annotation Analytics Landscape
SNOMED-CT Gene Ontology Gene Sets NCIT ICD-9 Human Disease Cell Type MeSH Drugs, Chemicals Grant Sets Paper Sets Patient Sets Drug Sets :
?
Health Indicator Warehouse datasets
SLIDE 7
Mutation enrichment
SLIDE 8
Profiling a set of Aging genes
Disease Ontology
~ 30% of genome 261 Age-related genes
Genome
SLIDE 9
Genes2MSH GOPubMed
Annotation Analytics Landscape
SNOMED-CT Gene Ontology Gene Sets NCIT ICD-9 Human Disease Cell Type MeSH Drugs, Chemicals Grant Sets Paper Sets Patient Sets Drug Sets : Health Indicator Warehouse datasets
Aging
Mutations
What else can we do?
1. Units of study range in length from ‘whole chromosome’ to ‘singe nucleotide’ 2. The focus in on finding the functional associations of strings in the genome 3. For each type of “string”, there will be some textual descriptions that you can process computationally.
SLIDE 10
10