title: AnnotCompute: annotation-based exploration and meta-analysis of genomics experiments.
abstract: The ever-increasing scale of biological data sets, particularly those arising in the context of high-throughput technologies, requires the development of rich data exploration tools. In this article, we present AnnotCompute, an information discovery platform for repositories of functional genomics experiments such as ArrayExpress. Our system leverages semantic annotations of functional genomics experiments with controlled vocabulary and ontology terms, such as those from the MGED Ontology, to compute conceptual dissimilarities between pairs of experiments. These dissimilarities are then used to support two types of exploratory analysis-clustering and query-by-example. We show that our proposed dissimilarity measures correspond to a user's intuition about conceptual dissimilarity, and can be used to support effective query-by-example. We also evaluate the quality of clustering based on these measures. While AnnotCompute can support a richer data exploration experience, its effectiveness is limited in some cases, due to the quality of available annotations. Nonetheless, tools such as AnnotCompute may provide an incentive for richer annotations of experiments. Database URL: http://www.cbil.upenn.edu/annotCompute/
authors.affiliation: Department of Genetics, Center for Bioinformatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA.
authors.forename: Jie
authors.initials: J
authors.lastname: Zheng
authors.forename: Julia
authors.initials: J
authors.lastname: Stoyanovich
authors.forename: Elisabetta
authors.initials: E
authors.lastname: Manduchi
authors.forename: Junmin
authors.initials: J
authors.lastname: Liu
authors.forename: Christian J
authors.initials: CJ
authors.lastname: Stoeckert
mesh_terms: D000818:Animals; D023281:Genomics; D006801:Humans; D015201:Meta-Analysis as Topic; D058977:Molecular Sequence Annotation; D020411:Oligonucleotide Array Sequence Analysis