Ha Thomas, Swanson Douglas, Larouche Matt, Glenn Randy, Weeden Dave, Zhang Peter, Hamre Kristin, Langston Michael, Phillips Charles, Song Mingzhou, Ouyang Zhengyu, Chesler Elissa, Duvvurru Suman, Yordanova Roumyana, Cui Yan, Campbell Kate, Ricker Greg, Phillips Carey, Homayouni Ramin, Goldowitz Dan
Centre for Molecular Medicine and Therapeutics, Child and Family Research Institute, Department of Medical Genetics, University of British Columbia, 950 West 28th Avenue, Vancouver, BC, Canada V5Z 4H4.
Department of Anatomy and Neurobiology, University of Tennessee Health Science Center, Memphis, TN, USA.
Dev Biol. 2015 Jan 1;397(1):18-30. doi: 10.1016/j.ydbio.2014.09.032. Epub 2014 Oct 23.
The mammalian CNS is one of the most complex biological systems to understand at the molecular level. The temporal information from time series transcriptome analysis can serve as a potent source of associative information between developmental processes and regulatory genes. Here, we introduce a new transcriptome database called, Cerebellar Gene Regulation in Time and Space (CbGRiTS). This dataset is populated with transcriptome data across embryonic and postnatal development from two standard mouse strains, C57BL/6J and DBA/2J, several recombinant inbred lines and cerebellar mutant strains. Users can evaluate expression profiles across cerebellar development in a deep time series with graphical interfaces for data exploration and link-out to anatomical expression databases. We present three analytical approaches that take advantage of specific aspects of the time series for transcriptome analysis. We demonstrate the use of CbGRiTS dataset as a community resource to explore patterns of gene expression and develop hypotheses concerning gene regulatory networks in brain development.
哺乳动物的中枢神经系统是在分子水平上最难理解的生物系统之一。来自时间序列转录组分析的时间信息可以作为发育过程与调控基因之间关联信息的有力来源。在此,我们引入了一个名为“时空小脑基因调控(CbGRiTS)”的新转录组数据库。该数据集包含了来自两种标准小鼠品系C57BL/6J和DBA/2J、多个重组近交系以及小脑突变品系在胚胎期和出生后发育阶段的转录组数据。用户可以通过图形界面在深度时间序列中评估小脑发育过程中的表达谱,以进行数据探索并链接到解剖学表达数据库。我们提出了三种利用时间序列的特定方面进行转录组分析的方法。我们展示了如何将CbGRiTS数据集作为一种社区资源,用于探索基因表达模式并提出有关大脑发育中基因调控网络的假设。