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Huai-Chun Wang

Publications and source records attributed to Huai-Chun Wang.

4 recordsLinked to original sources

Testing for covarion-like evolution in protein sequences.

The covarion hypothesis of molecular evolution proposes that selective pressures on an amino acid or nucleotide site change through time, thus causing changes of evolutionary rate along the edges of a phylogenetic tree. Several kinds of Markov models for the covarion process have been proposed. One model, proposed by Huelsenbeck (2002), has 2 substitution rate classes: the substitution process at a site can switch between a single variable rate, drawn from a discrete gamma distribution, and a zero invariable rate. A second model, suggested by Galtier (2001), assumes rate switches among an arbitrary number of rate classes but switching to and from the invariable rate class is not allowed. The latter model allows for some sites that do not participate in the rate-switching process. Here we propose a general covarion model that combines features of both models, allowing evolutionary rates not only to switch between variable and invariable classes but also to switch among different rates when they are in a variable state. We have implemented all 3 covarion models in a maximum likelihood framework for amino acid sequences and tested them on 23 protein data sets. We found significant likelihood increases for all data sets for the 3 models, compared with a model that does not allow site-specific rate switches along the tree. Furthermore, we found that the general model fit the data better than the simpler covarion models in the majority of the cases, highlighting the complexity in modeling the covarion process. The general covarion model can be used for comparing tree topologies, molecular dating studies, and the investigation of protein adaptation.

Algorithms↗

Thermal adaptation of the small subunit ribosomal RNA gene: a comparative study.

We carried out a comprehensive survey of small subunit ribosomal RNA sequences from archaeal, bacterial, and eukaryotic lineages in order to understand the general patterns of thermal adaptation in the rRNA genes. Within each lineage, we compared sequences from mesophilic, moderately thermophilic, and hyperthermophilic species. We carried out a more detailed study of the archaea, because of the wide range of growth temperatures within this group. Our results confirmed that there is a clear correlation between the GC content of the paired stem regions of the 16S rRNA genes and the optimal growth temperature, and we show that this correlation cannot be explained simply by phylogenetic relatedness among the thermophilic archaeal species. In addition, we found a significant, positive relationship between rRNA stem length and growth temperature. These correlations are found in both bacterial and archaeal rRNA genes. Finally, we compared rRNA sequences from warm-blooded and cold-blooded vertebrates. We found that, while rRNA sequences from the warm-blooded vertebrates have a higher overall GC content than those from the cold-blooded vertebrates, this difference is not concentrated in the paired regions of the molecule, suggesting that thermal adaptation is not the cause of the nucleotide differences between the vertebrate lineages.

Acclimatization↗

On the correlation between genomic G+C content and optimal growth temperature in prokaryotes: data quality and confounding factors.

The correlation between genomic G+C content and optimal growth temperature in prokaryotes has gained renewed interest after Musto et al. [H. Musto, H. Naya, A. Zavala, H. Romero, F. Alvarex-Valin, G. Bernardi, Correlations between genomic GC levels and optimal growth temperatures in prokaryotes, FEBS Lett. 573 (2004) 73-77], reported that positive correlations exist in 15 families studied. We have reanalyzed their data and found that when genome size and data quality were adjusted for, there was no significant evidence of relationship between optimal temperature and GC content for two of the families that had previously shown strongly significant correlations. Using updated temperature optima for Halobacteriaceae species we found the correlation is insignificant in this family. For the family Enterobacteriaceae when genome size and optimal temperature are included in a multiple linear regression, only genome size is significant as a predictor of GC content. We showed that more profound statistical methods than simple two factor correlation analysis should be used for analyzing complex intrinsic and extrinsic factors that affect genomic GC content. We further found that a positive correlation between temperature and genomic GC is only evident in free-living species of low optimal growth temperatures.

Base Composition↗

The Rapid Identification of Protein Through Its Amino Acid Composition.

This paper described a method for the identification of protein through its composition. The basic idea is as follows: A database for lengths, compositions and molecular weights of all known proteins is easily derived from a protein sequence database and by comparing the composition, length and molecular weight of the protein in question with each array of data in the composition database, proteins with similar length, composition and molecular weight may be found; as such a tentative identification for the said protein may be made. In some cases, it can be perfectly accurate to predict the right protein(s) from the composition database. A computer program applying this method was developed and used to search for the compositions of human insulin precursor and other proteins. The results are good, which proves that it is a fast and economic way to identify a protein based on its amino acid composition.

Journal Article↗