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Allan R Jones

Publications and source records attributed to Allan R Jones.

2 recordsLinked to original sources

Neurogenomics: at the intersection of neurobiology and genome sciences.

Neurogenomics is the study of how the genome as a whole contributes to the evolution, development, structure and function of the nervous system. It includes investigations of how genome products (transcriptomes and proteomes) vary in time and space. Neurogenomics differs markedly from the application of genome sciences to other systems, particularly in the spatial category, because anatomy and connectivity are paramount to our understanding of function in the nervous system. We focus here on some of the influences of genomics and its associated technologies on neuroscience. We discuss comparative genomics, gene expression atlases of the brain, network genetics and applications to behavioral phenotypes, and consider the culture, organization and funding of genome-scale projects.

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Microarray standard data set and figures of merit for comparing data processing methods and experiment designs.

MOTIVATION: There is a very large and growing level of effort toward improving the platforms, experiment designs, and data analysis methods for microarray expression profiling. Along with a growing richness in the approaches there is a growing confusion among most scientists as to how to make objective comparisons and choices between them for different applications. There is a need for a standard framework for the microarray community to compare and improve analytical and statistical methods. RESULTS: We report on a microarray data set comprising 204 in-situ synthesized oligonucleotide arrays, each hybridized with two-color cDNA samples derived from 20 different human tissues and cell lines. Design of the approximately 24 000 60mer oligonucleotides that report approximately 2500 known genes on the arrays, and design of the hybridization experiments, were carried out in a way that supports the performance assessment of alternative data processing approaches and of alternative experiment and array designs. We also propose standard figures of merit for success in detecting individual differential expression changes or expression levels, and for detecting similarities and differences in expression patterns across genes and experiments. We expect this data set and the proposed figures of merit will provide a standard framework for much of the microarray community to compare and improve many analytical and statistical methods relevant to microarray data analysis, including image processing, normalization, error modeling, combining of multiple reporters per gene, use of replicate experiments, and sample referencing schemes in measurements based on expression change. AVAILABILITY/SUPPLEMENTARY INFORMATION: Expression data and supplementary information are available at http://www.rii.com/publications/2003/HE_SDS.htm

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