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ABSTRACT: DIVERGE is a software system for phylogeny-based analyses of protein family evolution and functional divergence. It provides a suite of statistical tools for selection and prioritization of the amino acid sites that are responsible for the functional divergence of a gene family. The synergistic efforts of DIVERGE and other methods have convincingly demonstrated that the pattern of rate change at a particular amino acid site may contain insightful information about the underlying functional divergence following gene duplication. These predicted sites may be used as candidates for further experiments. We are now releasing an updated version of DIVERGE with the following improvements: (i) a feasible approach to examining functional divergence in nearly complete sequences by including deletions and insertions (indels); (ii) the calculation of the false discovery rate (FDR) of functionally diverging sites; (iii) estimation of the effective number of functional divergence-related sites that is reliable and insensitive to cutoffs; (iv) a statistical test for asymmetric functional divergence; and (v) a new method to infer functional divergence specific to a given duplicate cluster. In addition, we have made efforts to improve software design and produce a well-written software manual for the general user.
Molecular Biology and Evolution 04/2013; · 5.55 Impact Factor
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ABSTRACT: The debate of genomic correlations between sequence conservation, protein connectivity, gene essentiality and gene expression, has generated a number of new hypotheses that are challenging the classical framework of molecular evolution. For instance, the translational selection hypothesis claims that the determination of the rate of protein evolution is the protein stability to avoid the misfolding toxicity. In this short article, we propose that gene pleiotropy, the capacity for affecting multiple phenotypes, may play a vital role in molecular evolution. We discuss several approaches to testing this hypothesis.
Biology Direct 01/2010; 5:37. · 4.02 Impact Factor
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ABSTRACT: Biologists have long recognized the importance of gene pleiotropy, that is, single genes affect multiple traits, which is one of the most commonly observed attributes of genes. Yet the extent of gene pleiotropy has been seriously under-explored. Theoretically, Fisher's model assumed a universal pleiotropy, that is, a mutation can potentially affect all phenotypic traits. On the other hand, experimental assays of a gene usually showed a few distinct phenotypes. Our recent work provides a new approach by estimating the degree of pleiotropy effectively from the phylogenetic sequence analysis. In this article, we estimated the effective gene pleiotropy for 321 vertebrate genes, and found that a gene typically affects 6-7 molecular phenotypes that correspond to the components of organismal fitness, respectively. The positive correlation of gene pleiotropy with the number of Gene Ontology biological processes, as well as the expression broadness provides a biological basis for the sequence-based estimation of gene pleiotropy. On the other hand, the degree of gene pleiotropy has been restricted to a digital number of molecular phenotypes, indicating that some cautions are needed for theoretical analysis of gene pleiotropy based on the assumption of universal pleiotropy.
Journal of Experimental Zoology Part B Molecular and Developmental Evolution 08/2009; 314(2):115-22. · 2.42 Impact Factor
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ABSTRACT: Genetical genomics, a novel approach combining microarray technology and quantitative genetic analysis, aims to identify the expression quantitative trait loci (eQTLs), which may regulate the genome-wide expression pattern. In this article, we have studied yeast genomic eQTL data to investigate how the genetic eQTL regulation of ancestral gene has diverged since gene duplication. Our findings are as follows: (i) Duplicate genes have higher heritability for gene expression than single-copy genes, but little difference in their epistasis and directional effect. (ii) The divergence of trans-acting eQTLs between duplicate pairs increases with the evolutionary time since gene duplication. (iii) Trans-acting eQTL divergence can explain about 21% of the variation in expression divergence between duplicate pairs with K(S)<2.0, which increases to 27% when the transcription factor (TF)-target interaction divergence is combined. Moreover, under the partial correlation analysis, trans-acting eQTL divergence seems make a bigger contribution to expression divergence than does TF divergence. (iv) Trans-acting eQTL divergence between duplicate pairs is correlated with gene ontology categories "Biological processes" and "Cellular components," but not with "Molecular functions," and is related to fitness defect under treatment conditions, but not with fitness under normal condition. We conclude that eQTL analysis provides a novel approach to explore the effect of gene duplications on the genetic regulatory network.
Journal of Experimental Zoology Part B Molecular and Developmental Evolution 04/2009; 312(7):722-33. · 2.42 Impact Factor
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ABSTRACT: A total of 28941 ESTs were sequenced from five 5'-directed non-normalized cDNA libraries, which were assembled into 2212 contigs and 5642 singlets using CAP3. These sequences were annotated and clustered into 6857 unique genes, 2072 of which having no functional annotations were considered as novel genes. These genes were further classified into Gene Ontology categories. By comparing the expression profiles, we identified some breed- and developmental-stage-specific gene groups. These genes may be relative to reproductive performance or play important roles in milk synthesis, secretion and mammary involution. The unknown EST sequences and expression profiles at different developmental stages and breeds are very important resources for further research.
Science in China Series C Life Sciences 03/2006; 49(1):26-36. · 1.61 Impact Factor
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ABSTRACT: Since pig is an important livestock species worldwide, its gene expression has been investigated intensively, but rarely in brain. In order to study gene expression profiles in the pig central nervous system, we sequenced and analyzed 43,122 high-quality 5' end expressed sequence tags (ESTs) from porcine cerebellum, cortex cerebrum, and brain stem cDNA libraries, involving several different prenatal and postnatal developmental stages. The initial ESTs were assembled into 16,101 clusters and compared to protein and nucleic acid databases in GenBank. Of these sequences, 30.6% clusters matched protein databases and represented function known sequences; 75.1% had significant hits to nucleic acid databases and partial represented known function; 73.3% matched known porcine ESTs; and 21.5% had no matches to any known sequences in GenBank. We used the categories defined by the Gene Ontology to survey gene expression in the porcine brain.
Genomics Proteomics & Bioinformatics 12/2004; 2(4):237-44.
