کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
2820730 1160886 2013 8 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Universal tight correlation of codon bias and pool of RNA codons (codonome): The genome is optimized to allow any distribution of gene expression values in the transcriptome from bacteria to humans
موضوعات مرتبط
علوم زیستی و بیوفناوری بیوشیمی، ژنتیک و زیست شناسی مولکولی ژنتیک
پیش نمایش صفحه اول مقاله
Universal tight correlation of codon bias and pool of RNA codons (codonome): The genome is optimized to allow any distribution of gene expression values in the transcriptome from bacteria to humans
چکیده انگلیسی


• We define the “codonome value” as the total number of codons present at mRNA level.
• “CODONOME” software calculates the codon bias and the codonome bias of a tissue.
• A tight correlation exists between the codon bias and the codonome bias.
• Down Syndrome-related AMKL condition does not appear to alter this relationship.
• The correlation between codon bias and pool of RNA codons is a general property.

Codon bias is the phenomenon in which distinct synonymous codons are used with different frequencies. We define here the “codonome value” as the total number of codons present across all the expressed mRNAs in a given biological condition. We have developed the “CODONOME” software, which calculates the codon bias and, following integration with a gene expression profile, estimates the actual frequency of each codon at the transcriptome level (codonome bias) of a given tissue. Systematic analysis across different human tissues and multiple species shows a surprisingly tight correlation between the codon bias and the codonome bias. An aneuploidy and cancer condition such as that of Down Syndrome-related acute megakaryoblastic leukemia (DS-AMKL), does not appear to alter this relationship. The law of correlation between codon bias and codonome emerges as a property of the distribution and range of the number, sequence and expression level of the genes in a genome.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Genomics - Volume 101, Issue 5, May 2013, Pages 282–289
نویسندگان
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