北京大学生物信息学(转录组)

博客内容涉及转录组测序中的特征选择策略,包括启发式选择、前向、后向和双向选择。强调测序深度对精度的影响,介绍了read归一化的RPKM、TMM、deseq和TPM方法。还讨论了基因链特异性、基因组mapping工具Tophat及其参数设置,以及如何处理不同测序平台和建库方式。Cufflinks用于有参和无参比对,Cuffdiff进行差异表达分析,最终通过可视化展示结果。

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特征选择
启发式选择

前向选择
后向选择
双向选择

转录组测序 精度严重依赖于测序深度,因此需要对测序深度进行read 归一化。常用的方法是RPKM ,除了RPKM 外还有TMM,deseq,以及TPM。
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除了测序深度外,还有练的特异性也会影响基因的表达,需要考虑基因的链的特异性。

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常见的基因组mapping 的工具
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Tophat
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参数
-r 内部的插入片段
-G 是否需要参考基因组
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文库的类型:

Model predictive control (MPC) has a long history in the field of control en- gineering. It is one of the few areas that has received on-going interest from researchers in both the industrial and academic communities. Four major as- pects of model predictive control make the design methodology attractive to both practitioners and academics. The first aspect is the design formulation, which uses a completely multivariable system framework where the perfor- mance parameters of the multivariable control system are related to the engi- neering aspects of the system; hence, they can be understood and ‘tuned’ by engineers. The second aspect is the ability of the method to handle both ‘soft’ constraints and hard constraints in a multivariable control framework. This is particularly attractive to industry where tight profit margins and limits on the process operation are inevitably present. The third aspect is the ability to perform on-line process optimization. The fourth aspect is the simplicity of the design framework in handling all these complex issues. This book gives an introduction to model predictive control, and recent developments in design and implementation. Beginning with an overview of the field, the book will systematically cover topics in receding horizon con- trol, MPC design formulations, constrained control, Laguerre-function-based predictive control, predictive control using exponential data weighting, refor- mulation of classical predictive control, tuning of predictive control, as well as simulation and implementation using MATLAB and SIMULINK as a platform. Both continuous-time and discrete-time model predictive control is presented in a similar framework.
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