MOTIVATION AND RESULTS: A relational schema is described for capturing highly parallel gene expression experiments using different technologies. This schema grew out of efforts to build a database for collaborators working on different biological systems and using different types of platforms in their gene expression experiments as well as different types of image quantification software. The tables are conceptually organized into three categories of information: Platform, Experiment (which includes image scanning and quantification), and Data. The strengths of the schema are: (i) integrating information on array elements using a gene index; (ii) describing samples using ontologies; (iii) reducing an experiment to a single RNA source for precise descriptions yet not losing the relationships between experiments done at the same time or for the same project; and (iv) maintaining both raw and processed (e.g. cleansed and normalized) data and recording how the data is processed. The result is a novel schema, which can hold both array and non-array data, is extensible for detailed experimental descriptions that are precise and consistent, and allows for meaningful comparisons of genes between experiments.
MOTIVATION AND RESULTS: A relational schema is described for capturing highly parallel gene expression experiments using different technologies. This schema grew out of efforts to build a database for collaborators working on different biological systems and using different types of platforms in their gene expression experiments as well as different types of image quantification software. The tables are conceptually organized into three categories of information: Platform, Experiment (which includes image scanning and quantification), and Data. The strengths of the schema are: (i) integrating information on array elements using a gene index; (ii) describing samples using ontologies; (iii) reducing an experiment to a single RNA source for precise descriptions yet not losing the relationships between experiments done at the same time or for the same project; and (iv) maintaining both raw and processed (e.g. cleansed and normalized) data and recording how the data is processed. The result is a novel schema, which can hold both array and non-array data, is extensible for detailed experimental descriptions that are precise and consistent, and allows for meaningful comparisons of genes between experiments.
Authors: Kei-Hoi Cheung; Kevin White; Janet Hager; Mark Gerstein; Valerie Reinke; Kenneth Nelson; Peter Masiar; Ranjana Srivastava; Yuli Li; Ju Li; Hongyu Zhao; Jinming Li; David B Allison; Michael Snyder; Perry Miller; Kenneth Williams Journal: Proc AMIA Symp Date: 2002
Authors: Amit Bahl; Brian Brunk; Ross L Coppel; Jonathan Crabtree; Sharon J Diskin; Martin J Fraunholz; Gregory R Grant; Dinesh Gupta; Robert L Huestis; Jessica C Kissinger; Philip Labo; Li Li; Shannon K McWeeney; Arthur J Milgram; David S Roos; Jonathan Schug; Christian J Stoeckert Journal: Nucleic Acids Res Date: 2002-01-01 Impact factor: 16.971
Authors: William B Mattes; Syril D Pettit; Susanna-Assunta Sansone; Pierre R Bushel; Michael D Waters Journal: Environ Health Perspect Date: 2004-03 Impact factor: 9.031