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. 2003 Sep;73(3):688-92.
doi: 10.1086/378134. Epub 2003 Jul 24.

Dependence of mutational asymmetry on gene-expression levels in the human genome

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Dependence of mutational asymmetry on gene-expression levels in the human genome

Jacek Majewski. Am J Hum Genet. 2003 Sep.

Abstract

A great deal of effort has been devoted to measuring the rates of different types of nucleotide substitutions. Mutation rates are known to depend on factors such as methylation status and nearest-neighbor nucleotide effects. However, until recently, in eukaryotes, the rates have not been considered to be strand specific. In a recent analysis of mammalian lineages, Green et al. (2003) uncovered an asymmetry in the frequencies of substitutions on the coding and noncoding strands of genes and showed that this resulted in a nucleotide-content asymmetry within most genes. The authors argue that this bias may be caused by the mammalian transcription-coupled repair in germ cells, but they did not demonstrate an association with germ-cell gene expression. In this work, I analyze nucleotide contents in genes with known expression patterns and levels and provide evidence that the observed asymmetry in mutation rates is, in fact, caused by transcription. The results also imply that germline transcription may occur in a large percentage, 71%-91%, of all human genes.

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Figures

Figure  1
Figure  1
Pearson correlation between mean expression levels of housekeeping genes (Hsiao et al. 2001) and intronic mutational bias. The sample consists of 374 genes that are common to both the RefSeq and the HuGE databases and have appreciable intronic sequences (>100 total bp). In case of alternatively spliced genes, only the longest transcript was used. A 95% concentration ellipse is shown. The histogram plots illustrate that the logarithmic transformation provides a good approximation of a normal distribution of expression levels.

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References

Electronic-Database Information

    1. HuGE Index, http://www.hugeindex.org/
    1. UCSC Genome Bioinformatics, http://genome.cse.ucsc.edu/

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