Class assignment of sequence-unrelated members of enzyme superfamilies by activity-based protein profiling.
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Amino Acid Sequence↗
Biomedical subjects
Jason A Young
Publications and source records attributed to Jason A Young.
The Plasmodium falciparum sexual development transcriptome: a microarray analysis using ontology-based pattern identification.
The sexual stages of malarial parasites are essential for the mosquito transmission of the disease and therefore are the focus of transmission-blocking drug and vaccine development. In order to better understand genes important to the sexual development process, the transcriptomes of high-purity stage I-V Plasmodium falciparum gametocytes were comprehensively profiled using a full-genome high-density oligonucleotide microarray. The interpretation of this transcriptional data was aided by applying a novel knowledge-based data-mining algorithm termed ontology-based pattern identification (OPI) using current information regarding known sexual stage genes as a guide. This analysis resulted in the identification of a sexual development cluster containing 246 genes, of which approximately 75% were hypothetical, exhibiting highly-correlated, gametocyte-specific expression patterns. Inspection of the upstream promoter regions of these 246 genes revealed putative cis-regulatory elements for sexual development transcriptional control mechanisms. Furthermore, OPI analysis was extended using current annotations provided by the Gene Ontology Consortium to identify 380 statistically significant clusters containing genes with expression patterns characteristic of various biological processes, cellular components, and molecular functions. Collectively, these results, available as part of a web-accessible OPI database (http://carrier.gnf.org/publications/Gametocyte), shed light on the components of molecular mechanisms underlying parasite sexual development and other areas of malarial parasite biology.
Animals↗
Using expression information to discover new drug and vaccine targets in the malaria parasite Plasmodium falciparum.
The recent completion of the malaria parasite Plasmodium falciparum genome has opened the door for applying a variety of genomic-based systems biology approaches that complement existing gene-by-gene methods of investigation. Transcriptomic analyses of P. falciparum using DNA microarrays has allowed for the rapid elucidation of gene function, parasite drug response, and in vivo expression profiles, as well as general mechanisms guiding the parasite life cycle that are vital to disease pathogenesis. The results of these studies have identified promising novel gene targets for the development of new drug and vaccine therapies.
Animals↗
In silico gene function prediction using ontology-based pattern identification.
MOTIVATION: With the emergence of genome-wide expression profiling data sets, the guilt by association (GBA) principle has been a cornerstone for deriving gene functional interpretations in silico. Given the limited success of traditional methods for producing clusters of genes with great amounts of functional similarity, new data-mining algorithms are required to fully exploit the potential of high-throughput genomic approaches. RESULTS: Ontology-based pattern identification (OPI) is a novel data-mining algorithm that systematically identifies expression patterns that best represent existing knowledge of gene function. Instead of relying on a universal threshold of expression similarity to define functionally related groups of genes, OPI finds the optimal analysis settings that yield gene expression patterns and gene lists that best predict gene function using the principle of GBA. We applied OPI to a publicly available gene expression data set on the life cycle of the malarial parasite Plasmodium falciparum and systematically annotated genes for 320 functional categories based on current Gene Ontology annotations. An ontology-based hierarchical tree of the 320 categories provided a systems-wide biological view of this important malarial parasite.
Algorithms↗