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Cerebellar development transcriptome database (CDT-DB): Profiling of spatio-temporal gene expression during the postnatal development of mouse cerebellum

Authors :
Noriyuki Morita
Akira Sato
Hirozumi Nishibe
Yo Shinoda
Toshio Kojima
Yukiko Sekine
Chihiro Saruta
Yumi Sato
Tetsushi Sadakata
Teiichi Furuichi
Source :
Neural Networks. 21:1056-1069
Publication Year :
2008
Publisher :
Elsevier BV, 2008.

Abstract

A large amount of genetic information is devoted to brain development and functioning. The neural circuit of the mouse cerebellum develops through a series of cellular and morphological events (including neuronal proliferation and migration, axogenesis, dendritogenesis, synaptogenesis and myelination) all within three weeks of birth. All of these events are controlled by specific gene groups, whose temporal and spatial expression profiles must be encoded in the genome. To understand the genetic basis underlying cerebellar circuit development, we analyzed gene expression (transcriptome) during the developmental stages on a genome-wide basis. Spatio-temporal gene expression data were collected using in situ hybridization for spatial (cellular and regional) resolution and fluorescence differential display, GeneChip, microarray and RT-PCR for temporal (developmental time series) resolution, and were annotated using Gene Ontology (controlled terminology for genes and gene products) and anatomical context (cerebellar cell types and circuit structures). The annotated experimental data were integrated into a knowledge resource database, the Cerebellar Development Transcriptome Database (CDT-DB http://www.cdtdb.brain.riken.jp), with seamless links to the relevant information at various bioinformatics database websites. The CDT-DB not only provides a unique informatics tool for mining both spatial and temporal pattern information on gene expression in developing mouse brains, but also opens up opportunities to elucidate the transcriptome for cerebellar development.

Details

ISSN :
08936080
Volume :
21
Database :
OpenAIRE
Journal :
Neural Networks
Accession number :
edsair.doi.dedup.....80606a121926e122a0d54ba2e61c962c