Conversion of one cell type into another cell type by forcibly expressing specific cocktails of transcription factors （TFs） has demonstrated that cell fates are not fixed and that cellular differentiation can be a t...Conversion of one cell type into another cell type by forcibly expressing specific cocktails of transcription factors （TFs） has demonstrated that cell fates are not fixed and that cellular differentiation can be a two-way street with many intersections. These experiments also illustrated the sweeping potential of TFs to ＂read＂ genetically hardwired regulatory information even in cells where they are not normally expressed and to access and open up tightly packed chromatin to execute gene expression programs. Cellular reprogramming enables the modeling of diseases in a dish, to test the efficacy and toxicity of drugs in patient-derived cells and ultimately, could enable cell-based therapies to cure degenerative diseases. Yet, producing terminally differentiated cells that fully resemble their in vivo counterparts in sufficient quantities is still an unmet clinical need. While efforts are being made to reprogram cells nongeneticaUy by using drug.like molecules, defined TF cocktails still dominate reprogramming protocols. Therefore; the optimization of TFs by protein engineering has emerged as a strategy to enhance reprogramming to produce functional, stable and safe cells for regenerative biomedicine. Engineering approaches focused on Oct4, MyoD, Sox17, Nanog and Mef2c and range from chimeric TFs with added transactivation domains, designer transcription activator-like effectors to activate endogenous TFs to reprogramming TFs with rationally engineered DNA recognition principles. Possibly, applying the complete toolkit of protein design to cellular reprogramming can help to remove the hurdles that, thus far, impeded the clinical use of cells derived from reprogramming technologies.展开更多
Genomic datasets and the tools to analyze them have proliferated at an astonishing rate.However,such tools are often poorly integrated with each other:each program typically produces its own custom output in a variety...Genomic datasets and the tools to analyze them have proliferated at an astonishing rate.However,such tools are often poorly integrated with each other:each program typically produces its own custom output in a variety of non-standard file formats.Here we present glbase,a framework that uses a flexible set of descriptors that can quickly parse non-binary data files.glbase includes many functions to intersect two lists of data,including operations on genomic interval data and support for the efficient random access to huge genomic data files.Many glbase functions can produce graphical outputs,including scatter plots,heatmaps,boxplots and other common analytical displays of high-throughput data such as RNA-seq,ChIP-seq and microarray expression data.glbase is designed to rapidly bring biological data into a Python-based analytical environment to facilitate analysis and data processing.In summary,glbase is a flexible and multifunctional toolkit that allows the combination and analysis of high-throughput data(especially next-generation sequencing and genome-wide data),and which has been instrumental in the analysis of complex data sets.glbase is freely available at http://bitbucket.org/oaxiom/glbase/.展开更多
文摘Conversion of one cell type into another cell type by forcibly expressing specific cocktails of transcription factors （TFs） has demonstrated that cell fates are not fixed and that cellular differentiation can be a two-way street with many intersections. These experiments also illustrated the sweeping potential of TFs to ＂read＂ genetically hardwired regulatory information even in cells where they are not normally expressed and to access and open up tightly packed chromatin to execute gene expression programs. Cellular reprogramming enables the modeling of diseases in a dish, to test the efficacy and toxicity of drugs in patient-derived cells and ultimately, could enable cell-based therapies to cure degenerative diseases. Yet, producing terminally differentiated cells that fully resemble their in vivo counterparts in sufficient quantities is still an unmet clinical need. While efforts are being made to reprogram cells nongeneticaUy by using drug.like molecules, defined TF cocktails still dominate reprogramming protocols. Therefore; the optimization of TFs by protein engineering has emerged as a strategy to enhance reprogramming to produce functional, stable and safe cells for regenerative biomedicine. Engineering approaches focused on Oct4, MyoD, Sox17, Nanog and Mef2c and range from chimeric TFs with added transactivation domains, designer transcription activator-like effectors to activate endogenous TFs to reprogramming TFs with rationally engineered DNA recognition principles. Possibly, applying the complete toolkit of protein design to cellular reprogramming can help to remove the hurdles that, thus far, impeded the clinical use of cells derived from reprogramming technologies.
文摘Genomic datasets and the tools to analyze them have proliferated at an astonishing rate.However,such tools are often poorly integrated with each other:each program typically produces its own custom output in a variety of non-standard file formats.Here we present glbase,a framework that uses a flexible set of descriptors that can quickly parse non-binary data files.glbase includes many functions to intersect two lists of data,including operations on genomic interval data and support for the efficient random access to huge genomic data files.Many glbase functions can produce graphical outputs,including scatter plots,heatmaps,boxplots and other common analytical displays of high-throughput data such as RNA-seq,ChIP-seq and microarray expression data.glbase is designed to rapidly bring biological data into a Python-based analytical environment to facilitate analysis and data processing.In summary,glbase is a flexible and multifunctional toolkit that allows the combination and analysis of high-throughput data(especially next-generation sequencing and genome-wide data),and which has been instrumental in the analysis of complex data sets.glbase is freely available at http://bitbucket.org/oaxiom/glbase/.
