Continues Probability Distributions and Estimation

Reviews from last class 1. Basic concepts of Discrete Probability Distributions 2. Binomial/Poisson Distributions 3. Expected Value and Variance of Binomial/Poisson Distributions 4. Poisson Approximation to Binomial Distribution

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The Central Dogma DNA RNA Protein transcription translation Single stranded Flexible/unstable Localization Structure I was not trained as a biologist, and the central dogma was almost the only thing I knew in modern biology. RNA characteristics makes RNA study more challenging and thus more interesting. Instead of a liner structure, they all seems to work together for a particular function. Get a figure for X-inactivation. We noticed that other than the sequencing of the RNAs, one of a key factor that affects RNA in functioning in all these processes is its SHAPE

Timeline of discoveries of RNAs in biological regulation Our stories start from discoveries of RNAs in biological regulation. It has been a long time since RNAs were discovered, and the very well-know dogma, named central dogma was established, in which DNA was transcribed to RNAs which were translated to proteins. And proteins regulating the processes of transcription and translation. Also, it has been a while that people thought of a special kind of RNAs functioning like proteins. These RNAs were usually genetic conserved, not translated to proteins, but regulating gene expression, however until very recently, a number of spectaculars discovers were made, of course benefited from the great advances of new technologies. These discoveries, for example, A, B and C, significantly expended this field. Today, we will be mainly focus on a certain type of non-coding RNA, which is called LncRNAs. LncRNAs were defined noncoding RNAs that have RNA sequence longer than 200bp, mainly in order to remove ribo-RNA, pi-RNAs, t-RNAs, etc. Are they really non-coding? Still under debate, mention mass spectrum, ribo-profiling, Eric Landar’s explaination etc. Rinn & Chang, Annual Review of Biochemistry, 2012

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Questions: Difficulties: How to estimate a relatively true expression value for each gene? How to identify significantly differential expressed genes across samples? Difficulties: How to reduce type-I error? How to control gene expression over-dispersion? How to deal with low/0 number of replicates?

Summary of this class Estimation of Mean and Variance Central-Limit Theorem RNA-seq differential analysis