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  • 1
    Electronic Resource
    Electronic Resource
    Springer
    Journal of molecular evolution 33 (1991), S. 442-449 
    ISSN: 1432-1432
    Keywords: Humans ; Mouse ; Rat ; Codon usage ; Mutation bias ; Selection
    Source: Springer Online Journal Archives 1860-2000
    Topics: Biology
    Notes: Summary A new statistical test has been developed to detect selection on silent sites. This test compares the codon usage within a gene and thus does not require knowledge of which genes are under the greatest selection, that there exist common trends in codon usage across genes, or that genes have the same mutation pattern. It also controls for mutational biases that might be introduced by the adjacent bases. The test was applied to 62 mammalian sequences, the significant codon usage biases were detected in all three species examined (humans, rats, and mice). However, these biases appear not to be the consequence of selection, but of the first base pair in the codon influencing the mutation pattern at the third position.
    Type of Medium: Electronic Resource
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  • 2
    Publication Date: 2016-09-20
    Description: Across independent cancer genomes it has been observed that some sites have been recurrently hit by single nucleotide variants (SNVs). Such recurrently hit sites might be either (i) drivers of cancer that are postively selected during oncogenesis, (ii) due to mutation rate variation, or (iii) due to sequencing and assembly errors. We have investigated the cause of recurrently hit sites in a dataset of 〉3 million SNVs from 507 complete cancer genome sequences. We find evidence that many sites have been hit significantly more often than one would expect by chance, even taking into account the effect of the adjacent nucleotides on the rate of mutation. We find that the density of these recurrently hit sites is higher in non-coding than coding DNA and hence conclude that most of them are unlikely to be drivers. We also find that most of them are found in parts of the genome that are not uniquely mappable and hence are likely to be due to mapping errors. In support of the error hypothesis, we find that recurently hit sites are not randomly distributed across sequences from different laboratories. We fit a model to the data in which the rate of mutation is constant across sites but the rate of error varies. This model suggests that ∼4% of all SNVs are errors in this dataset, but that the rate of error varies by thousands-of-fold between sites.
    Electronic ISSN: 2167-8359
    Topics: Biology , Medicine
    Published by PeerJ
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