An ever-growing number of resources on model organisms have emerged with the continued development of sequencing technologies. In this paper, we review 13 databases of model organisms, most of which are reported by th...An ever-growing number of resources on model organisms have emerged with the continued development of sequencing technologies. In this paper, we review 13 databases of model organisms, most of which are reported by the National Institutes of Health of the United States（NIH; http：//www.nih.gov/science/models/）. We provide a brief description for each database, as well as detail its data source and types, functions, tools, and availability of access. In addition,we also provide a quality assessment about these databases. Significantly, the organism databases instituted in the early 1990s––such as the Mouse Genome Database（MGD）, Saccharomyces Genome Database（SGD）, and Fly Base––have developed into what are now comprehensive, core authority resources. Furthermore, all of the databases mentioned here update continually according to user feedback and with advancing technologies.展开更多
In the past decade, the remarkable development of high-throughput sequencing technology accelerates the generation of large amount of multiple dimensional data such as genomic, epigenomic, transcriptomic and proteomic...In the past decade, the remarkable development of high-throughput sequencing technology accelerates the generation of large amount of multiple dimensional data such as genomic, epigenomic, transcriptomic and proteomic data. The comprehensive data make it possible to understand the underlying mechanisms of biology and disease such as cancer systematically. It also provides great challenges for computa- tional cancer genomics due to the complexity, scale and noise of data. In this article, we aim to review the recent develop- ments and progresses of computational models, algorithms and analysis of complex data in cancer genomics. These topics of this paper include the identification of driver mutations, the genetic heterogeneity analysis, genomic markers discovery of drug response, pan-cancer scale analysis and so on.展开更多
基金supported by the Strategic Priority Research Program of the Chinese Academy of Sciences of China(Grant No.XDB13040500)
文摘An ever-growing number of resources on model organisms have emerged with the continued development of sequencing technologies. In this paper, we review 13 databases of model organisms, most of which are reported by the National Institutes of Health of the United States（NIH; http：//www.nih.gov/science/models/）. We provide a brief description for each database, as well as detail its data source and types, functions, tools, and availability of access. In addition,we also provide a quality assessment about these databases. Significantly, the organism databases instituted in the early 1990s––such as the Mouse Genome Database（MGD）, Saccharomyces Genome Database（SGD）, and Fly Base––have developed into what are now comprehensive, core authority resources. Furthermore, all of the databases mentioned here update continually according to user feedback and with advancing technologies.
文摘In the past decade, the remarkable development of high-throughput sequencing technology accelerates the generation of large amount of multiple dimensional data such as genomic, epigenomic, transcriptomic and proteomic data. The comprehensive data make it possible to understand the underlying mechanisms of biology and disease such as cancer systematically. It also provides great challenges for computa- tional cancer genomics due to the complexity, scale and noise of data. In this article, we aim to review the recent develop- ments and progresses of computational models, algorithms and analysis of complex data in cancer genomics. These topics of this paper include the identification of driver mutations, the genetic heterogeneity analysis, genomic markers discovery of drug response, pan-cancer scale analysis and so on.
