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For these reasons, there is a need to integrate better models of sequencing error into a wide variety of sequence analysis methods, including alignment, assembly, gene finding, and phylogenetic modeling.
At this time, efforts were undertaken to measure the overall error rates in the growing sequence databases [4], to detect errors automatically in protein-coding sequences [5], and to incorporate explicit models of sequencing error into algorithms for alignment and assembly [6], [7].
Although ESTcalc and the underlying simulations do not currently incorporate explicit models of sequencing and assembly errors, the results provide a null hypothesis of predicted outcomes with theoretically perfect sequence data and no assembly error.
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Recurrent Network Models of Sequence Generation and Memory.
Title Recurrent Network Models of Sequence Generation and Memory.
We determined appropriate models of sequence evolution under the AICc4 criterion using Kakusan4106.
Rajan, K., Harvey, C. D. & Tank, D. W. Recurrent network models of sequence generation and memory.
Different models of sequence evolution also have a large impact on the outcome of phylogenetic analyses.
These pages will consider some of the fundamental models of mutation as well as models of sequence evolution commonly used with DNA sequence data.
He has worked on models of sequence evolution, and has applied these models to estimate the fraction of the genome under selection.
Prior to performing the ML analyses, the models of sequence evolution were determined in Modeltest 3.7 [62].
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