Xun Gu

The size distribution of insertions and deletions in human and rodent pseudogenes suggests the logarithmic gap penalty for sequence alignment

The size distributions of deletions, insertions, and indels (i.e., insertions or deletions) were studied, using 78 human processed pseudogenes and other published data sets. The following results were obtained: (1) Deletions occur more frequently than do insertions in sequence evolution; none of the pseudogenes studied shows significantly more insertions than deletions. (2) Empirically, the size distributions of deletions, insertions, and indels can be described well by a power law, i.e., fk = Ck−b, where fk is the frequency of deletion, insertion, or indel with gap length k, b is the power parameter, and C is the normalization factor. (3) The estimates of b for deletions and insertions from the same data set are approximately equal to each other, indicating that the size distributions for deletions and insertions are approximately identical. (4) The variation in the estimates of b among various data sets is small, indicating that the effect of local structure exists but only plays a secondary role in the size distribution of deletions and insertions. (5) The linear gap penalty, which is most commonly used in sequence alignment, is not supported by our analysis; rather, the power law for the size distribution of indels suggests that an appropriate gap penalty is wk = a + b ln k, where a is the gap creation cost and blnk is the gap extension cost. (6) The higher frequency of deletion over insertion suggests that the gap creation cost of insertion (ai) should be larger than that of deletion (ad); that is, ai − ad = In R, where R is the frequency ratio of deletions to insertions.

Directional mutational pressure affects the amino acid composition and hydrophobicity of proteins in bacteria

The relationship between change in genomic GC content and protein evolution in bacteria was studied by simple correlational analysis (at the genus level) and by Felsensteins (1985) independent contrast test. We first used the dnaA gene in bacteria as an example to show (1) that the amino acid composition of a protein can be dramatically affected by mutational pressure (the genomic GC content), (2) that surprisingly, deleting relatively closely-related genera may increase rather than decrease the correlation between genomic GC content and amino acid composition, and (3) that most unexpectedly, as the genomic GC content increases, both strongly hydrophobic and strongly hydrophilic amino acids tend to change to ambivalent amino acids, suggesting that the majority of these amino acid substitutions are not caused by positive Darwinian selection. These patterns were then also shown to hold for the 14 other genes studied, indicating their generality for the evolution of bacterial proteins. As directional mutation pressure can affect the amino acid composition of proteins, it may mislead phylogenetic inference, even if protein instead of DNA sequences are used.

Higher rates of amino acid substitution in rodents than in humans

An analysis of 54 protein sequences from humans and rodents (mice or rats), with the chicken as an outgroup, indicates that, from the common ancestor of primates and rodents, 35 of the proteins have evolved faster in the lineage to mouse or rat (rodent lineage) whereas only 12 proteins have evolved faster in the lineage to humans (human lineage). The average rate of amino acid substitution is significantly faster in the rodent lineage than in the human lineage. In addition, the average rate of insertion/deletion is also faster in rodents than in humans and there is a positive correlation between the rate of amino acid substitution and the rate of insertion/deletion in a protein sequence.

Sex differences in mutation rate in higher primates estimated from AMG intron sequences.

Abstract. To study sex differences in mutation rate in primates, we sequenced the third introns of the AMGX and AMGY genes from humans, orangutans, and squirrel monkeys and estimated that the male-to-female ratio of mutation rate is α= 5.14 with the 95% confidence interval (2.42, 16.6). Combining this data set and the data sets from ZFX/ZFY and SMCX/SMCY introns, we obtained an estimate of α= 5.06 with the 95% confidence interval reduced to (3.24, 8.79). The α value is significantly higher in higher primates than in rodents.n

A model for the correlation of mutation rate with GC content and the origin of GC-rich isochores

Based on the biochemical kinetics of DNA replication and mutagenesis, including misincorporation and correction, a model has been developed for studying the relationships among the mutation rate (u), the G + C content of the sequence (f), and the G + C proportion in the nucleotide precursor pool (N). Also a measure for the next-nucleotide effect, called the maximum capacity of the next-nucleotide effect (MC), has been proposed. Under the normal physiological conditions of mammalian germ cells, our results indicate: (1) the equilibrium G + C content in a sequence is approximately equal to the G + C proportion in the nucleotide precursor pool, i.e., f ≈ N, which is independent of the next-nucleotide effect; (2) an inverted-V-shaped distribution of mutation rates with respect to G + C contents is predicted, when the next-nucleotide effect is week, i.e., MC ≈ 1; (3) the distribution becomes flatter (i.e., inverted-U-shaped) as MC increases, but the peak at 50% GC is still observed when MC < 2; and (4) the peak disappears when MC > 2.8, that is, when the next-nucleotide effect becomes strong. Our results suggest that changes in the relative concentrations of nucleotide precursors can cause variations among genes both in mutation rate and in G + C content and that compositional isochores (DNA segments with a homogeneous G + C content) can arise in a genome due to differences in replication times of DNA segments.

Statistical models for studying DNA sequence evolution

Statistical models for DNA sequence evolution and several methods for estimating the number of substitutions per site between two sequences are discussed. These methods are modified to include the effect of rate variation among sites.