PubMed · 3304141
The source did not provide an abstract. Follow the original record for more information.
Explore connections, maps & timelines
P Modrich. 1987. DNA mismatch correction.. https://doi.org/10.1146/annurev.bi.56.070187.002251
Cite the original work for its findings. Save a collection to share your selection of sources.
KEEP EXPLORING
The melting of the coding and non-coding classes of natural DNA sequences was investigated using a program, MELTSIM, which simulates DNA melting based upon an empirically parameterized nearest neighbor thermodynamic model. We calculated T(m) results of 8144 natural sequences from 28 eukaryotic organisms of varying F(GC) (mole fraction of G and C) and of 3775 coding and 3297 non-coding sequences derived from those natural sequences. These data demonstrated that the T(m) vs. F(GC) relationships in coding and non-coding DNAs are both linear but have a statistically significant difference (6.6%) in their slopes. These relationships are significantly different from the T(m) vs. F(GC) relationship embodied in the classical Marmur-Schildkraut-Doty (MSD) equation for the intact long natural sequences. By analyzing the simulation results from various base shufflings of the original DNAs and the average nearest neighbor frequencies of those natural sequences across the F(GC) range, we showed that these differences in the T(m) vs. F(GC) relationships are largely a direct result of systematic F(GC)-dependent biases in nearest neighbor frequencies for those two different DNA classes. Those differences in the T(m) vs. F(GC) relationships and biases in nearest neighbor frequencies also appear between the sequences from multicellular and unicellular organisms in the same coding or non-coding classes, albeit of smaller but significant magnitudes.
Using the complete genome of Thermoplasma volcanium, as an example, we have examined the distribution functions for the amount of C or G in consecutive, non-overlapping blocks of m bases in this system. We find that these distributions are very much broader (by many factors) than those expected for a random distribution of bases. If we plot the widths of the C-G distributions relative to the widths expected for random distributions, as a function of the block size used, we obtain a power law with a characteristic exponent. The broadening of the C-G distributions follows from the empirical finding that blocks containing a given C-G content tend to be followed by blocks of similar C-G content thus indicating a statistical persistence of composition. The exponent associated with the power law thus measures the strength of persistence in a given DNA. This behavior can be understood using Mandelbrot's model of a fractional Brownian walk. In this model there is a hierarchy of persistence (correlation between blocks) between all parts of the system. The model gives us a way to scale the C-G distributions such that all these functions are collapsed onto a master curve. For a fractional Brownian walk, the fractal dimension of the C-G distribution is simply related to the persistence exponent for the power law. The persistence exponent for T. volcanium is found to be gamma = 0.29 while for a 10 million base segment of the human genome we obtain gamma = 0.39, similar to but not identical with the value found for the microbe.
BACKGROUND: Codon usage bias has been widely reported to correlate with GC composition. However, the quantitative relationship between codon usage bias and GC composition across species has not been reported. RESULTS: Based on an informatics method (SCUO) we developed previously using Shannon informational theory and maximum entropy theory, we investigated the quantitative relationship between codon usage bias and GC composition. The regression based on 70 bacterial and 16 archaeal genomes showed that in bacteria, SCUO = -2.06 * GC3 + 2.05*(GC3)2 + 0.65, r = 0.91, and that in archaea, SCUO = -1.79 * GC3 + 1.85*(GC3)2 + 0.56, r = 0.89. We developed an analytical model to quantify synonymous codon usage bias by GC compositions based on SCUO. The parameters within this model were inferred by inspecting the relationship between codon usage bias and GC composition across 70 bacterial and 16 archaeal genomes. We further simplified this relationship using only GC3. This simple model was supported by computational simulation. CONCLUSIONS: The synonymous codon usage bias could be simply expressed as 1+ (p/2)log2(p/2) + ((1-p)/2)log2((l-p)/2), where p = GC3. The software we developed for measuring SCUO (codonO) is available at http://digbio.missouri.edu/~wanx/cu/codonO.