TY - JOUR T1 - Systematic Planning of Genome-Scale Experiments in Poorly Studied Species A1 - Guan, Yuanfang A1 - Dunham, Maitreya A1 - Caudy, Amy A1 - Troyanskaya, Olga Y1 - 2010/03/05 N2 - Author Summary Microarray expression experiments allow fast functional profiling of an organism's entire genome and significant efforts are devoted to analyzing the resulting data. Available genome sequences are also increasing quickly. However, it is unexplored how to use available functional genomics data to direct large-scale experiments in newly sequenced but poorly studied species. In this paper, we propose a strategy to systematically plan experimental treatments in the poorly studied species based on their model organism relatives. We consider both the accuracy of the datasets in capturing different biological processes and the redundancy between datasets. Quantifying the above information allows us to recommend a list of experimental treatments. We demonstrate the efficacy of this approach by designing, performing and evaluating S. bayanus microarray experiments using an available S. cerevisiae data repository. We show that this systematic planning process could reduce the labor in doing microarray experiments by 10 fold and achieve similar functional coverage. JF - PLOS Computational Biology JA - PLOS Computational Biology VL - 6 IS - 3 UR - https://doi.org/10.1371/journal.pcbi.1000698 SP - e1000698 EP - PB - Public Library of Science M3 - doi:10.1371/journal.pcbi.1000698 ER -