Data mining beta cells
The Beta Cell Biology Consortium has collected a significant number of curated datasets from microarray and high throughput sequencing analysis of gene expression, transcription factor binding, and epigenetic states. Potential projects include meta-analysis of datasets for specific associations related to beta cell growth, function, or development. These datasets can also be used for exploring and expanding networks of interacting genes. May involve use of or expanding the Cell Ontology, Gene Ontology and others to capture detailed context.
* Generate new relationships between genes based on analysis of datasets in Beta Cell Genomics or from the literature. Some relationships are predicted (from transcription factor binding sites) or inferred (altered expression in knockout experiments). Can supportive evidence be found from other datasets or the literature? Can they lead to hypotheses about new relationships? Can network motifs (feed forward loops) be found?
* Explore genes expressed in beta cells and their specificity. RNA-seq provides an opportunity to comprehensively assess transcriptomes. However, alignment to gene models is complicated by the ability to distinguish and quantify alternative transcripts. As the number of available RNA-seq datasets grows so does the opportunity to reexamine and refine the gene models (including non-coding genes) and what genes/ transcripts are expressed in islets and beta cells.
Ontology for Parasite LifeCycle (OPL) development
The Ontology for Parasite LifeCycle (OPL) models the life cycle stage details of various parasites, including Trypanosoma sp., Leishmania major, and Plasmodium sp., etc. In addition to life cycle stages, the ontology also models necessary contextual details, such as host information, vector information, and anatomical location. OPL is based on the Basic Formal Ontology (BFO) and follows the rules set by the OBO Foundry consortium.
Current development goals are to increase representation of Plasmodium species using WebProtege. We expect to use OPL for describing phenotypes and finding gene associations. For example, OPL can be used to annotate datasets in PlasmoDB for specific life cycle stages to characterize genes.
* Learn about ontologies and knowledge representation
* Learn about parasite biology
