ArticleGenomics2020
CodSeqGen: A tool for generating synonymous coding sequences with desired GC-contents.
Article in Genomics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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3 authors at 1 institution in 2 countries.
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Abstract
Identification of regulatory elements is essential for understanding the mechanism behind regulating gene expression. These regulatory elements-located in or near gene-bind to proteins called transcription factors to initiate the transcription process. Their occurrences are influenced by the GC-content or nucleotide composition. For generating synthetic coding sequences with pre-specified amino acid sequence and desired GC-content, there exist two stochastic methods, multinomial and maximum entropy. Both methods rely on the probability of choosing the codon synonymous for usage in regard to a specific amino acid. In spite the latter exhibited unbiased manner, the produced sequences are not exactly obeying the GC-content constraint. In this paper, we present an algorithmic solution to produce coding sequences that follow exactly a primary amino acid sequence and a desired GC-content. The proposed tool, namely CodSeqGen, depends on random selection for smaller subsets to be traversed using the backtracking approach.
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