Elucidation of the mechanisms of stem cell differentiation is of great scientific interest. Increasing evidence suggests that stem cell differentiation involves changes at multiple levels of biological regulation, which together orchestrate the complex differentiation process; many related studies have been performed to investigate the various levels of regulation. The resulting valuable data, however, remain scattered. Most of the current stem cell-relevant databases focus on a single level of regulation (mRNA expression) from limited stem cell types; thus, a unifying resource would be of great value to compile the multiple levels of research data available. Here we present a database for this purpose, SyStemCell, deposited with multi-level experimental data from stem cell research. The database currently covers seven levels of stem cell differentiation-associated regulatory mechanisms, including DNA CpG 5-hydroxymethylcytosine/methylation, histone modification, transcript products, microRNA-based regulation, protein products, phosphorylation proteins and transcription factor regulation, all of which have been curated from 285 peer-reviewed publications selected from PubMed. The database contains 43,434 genes, recorded as 942,221 gene entries, for four organisms (Homo sapiens, Mus musculus, Rattus norvegicus, and Macaca mulatta) and various stem cell sources (e.g., embryonic stem cells, neural stem cells and induced pluripotent stem cells). Data in SyStemCell can be queried by Entrez gene ID, symbol, alias, or browsed by specific stem cell type at each level of genetic regulation. An online analysis tool is integrated to assist researchers to mine potential relationships among different regulations, and the potential usage of the database is demonstrated by three case studies. SyStemCell is the first database to bridge multi-level experimental information of stem cell studies, which can become an important reference resource for stem cell researchers. The database is available at http://lifecenter.sgst.cn/SyStemCell/.
Abstract Identifying functional regions in the human genome is a major goal in human genetics. Great efforts have been made to functionally annotate the human genome either through computational predictions, such as genomic conservation, or high-throughput experiments, such as the ENCODE project. These efforts have resulted in a rich collection of functional annotation data of diverse types that need to be jointly analyzed for integrated interpretation and annotation. Here we present GenoCanyon, a whole-genome annotation method that performs unsupervised statistical learning using 22 computational and experimental annotations thereby inferring the functional potential of each position in the human genome. With GenoCanyon, we are able to predict many of the known functional regions. The ability of predicting functional regions as well as its generalizable statistical framework makes GenoCanyon a unique and powerful tool for whole-genome annotation. The GenoCanyon web server is available at http://genocanyon.med.yale.edu
'/%3e%3cuse xlink:href='%23a' transform='rotate(23.036 .093 25.536)'/%3e%3cuse xlink:href='%23a' transform='rotate(45.87 1.273 16.18)'/%3e%3cuse xlink:href='%23a' transform='rotate(69.945 .996 12.078)'/%3e%3cuse xlink:href='%23a' transform='rotate(20.66 -19.689 31.932)'/%3e%3c/svg%3e)
