Next-generation sequencing description

Rismani-Yazdi, H., Haznedaroglu, B., Bibby, K., Peccia, J., 2011. Transcriptome sequencing and annotation of the microalgae Dunaliella tertiolecta. pathway description and gene discovery for production of next-generation biofuels. BMC Genomics 12, 148. 

A series of probable transitions between states can be described with the Markov chain. A Markovian stochastic process is memoryless, and this is illustrated subsequently. We generate a sequence of random variables, (yo, yi, yi, ), so that each time t > 0, the next state yt+i would be sampled from a distribution P(y,+ily,), which would depend only on the current state of the chain, y,. Thus, given y, the next state y,+i would not depend additionally on the history of the chain (yo, yi, yi,---, y i). The name Markov chain is used to describe this sequence, and the transition kernel of the chain is i (.l.) does not depend on t if we assume that the chain is time homogeneous. A detail description of the Markov model is provided in Chapter 26. 

Use the default parameters (except the number of alignments and descriptions) such as nr as the database (see Note 3), e-value 10 and the statistical significance threshold to include a sequence for generating the PSSM for the next iteration as 0.005. Change the maximum number of alignments and descriptions, 1000, from the respective pull down menus to retrieve possibly all statistically significant hits. 

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