Biomedical subjects

Joshua M Stuart

Publications and source records attributed to Joshua M Stuart.

4 recordsLinked to original sources

PubMed2003

A gene-coexpression network for global discovery of conserved genetic modules.

To elucidate gene function on a global scale, we identified pairs of genes that are coexpressed over 3182 DNA microarrays from humans, flies, worms, and yeast. We found 22,163 such coexpression relationships, each of which has been conserved across evolution. This conservation implies that the coexpression of these gene pairs confers a selective advantage and therefore that these genes are functionally related. Many of these relationships provide strong evidence for the involvement of new genes in core biological functions such as the cell cycle, secretion, and protein expression. We experimentally confirmed the predictions implied by some of these links and identified cell proliferation functions for several genes. By assembling these links into a gene-coexpression network, we found several components that were animal-specific as well as interrelationships between newly evolved and ancient modules.

Animals↗

PubMed2002

Chromosomal clustering of muscle-expressed genes in Caenorhabditis elegans.

Chromosomes are divided into domains of open chromatin, where genes have the potential to be expressed, and domains of closed chromatin, where genes are not expressed. Classic examples of open chromatin domains include 'puffs' on polytene chromosomes in Drosophila and extended loops from lampbrush chromosomes. If multiple genes were typically expressed together from a single open chromatin domain, the position of co-expressed genes along the chromosomes would appear clustered. To investigate whether co-expressed genes are clustered, we examined the chromosomal positions of the genes expressed in the muscle of Caenorhabditis elegans at the first larval stage. Here we show that co-expressed genes in C. elegans are clustered in groups of 2-5 along the chromosomes, suggesting that expression from a chromatin domain can extend over several genes. These observations reveal a higher-order organization of the structure of the genome, in which the order of the genes along the chromosome id correlated with their expression in specific tissues.

Animals↗

PubMed2002

PharmGKB: the Pharmacogenetics Knowledge Base.

The Pharmacogenetics Knowledge Base (PharmGKB; http://www.pharmgkb.org/) contains genomic, phenotype and clinical information collected from ongoing pharmacogenetic studies. Tools to browse, query, download, submit, edit and process the information are available to registered research network members. A subset of the tools is publicly available. PharmGKB currently contains over 150 genes under study, 14 Coriell populations and a large ontology of pharmacogenetics concepts. The pharmacogenetic concepts and the experimental data are interconnected by a set of relations to form a knowledge base of information for pharmacogenetic researchers. The information in PharmGKB, and its associated tools for processing that information, are tailored for leading-edge pharmacogenetics research. The PharmGKB project was initiated in April 2000 and the first version of the knowledge base went online in February 2001.

Biotransformation↗

PubMed2002

Ontology development for a pharmacogenetics knowledge base.

Research directed toward discovering how genetic factors influence a patient's response to drugs requires coordination of data produced from laboratory experiments, computational methods, and clinical studies. A public repository of pharmacogenetic data should accelerate progress in the field of pharmacogenetics by organizing and disseminating public datasets. We are developing a pharmacogenetics knowledge base (PharmGKB) to support the storage and retrieval of both experimental data and conceptual knowledge. PharmGKB is an Internet-based resource that integrates complex biological, pharmacological, and clinical data in such a way that researchers can submit their data and users can retrieve information to investigate genotype-phenotype correlations. Successful management of the names, meaning, and organization of concepts used within the system is crucial. We have selected a frame-based knowledge-representation system for development of an ontology of concepts and relationships that represent the domain and that permit storage of experimental data. Preliminary experience shows that the ontology we have developed for gene-sequence data allows us to accept, store, and query data submissions.

Antineoplastic Agents↗