Census 2010 Demographic Profile: Madison County

This demographic profile describes characteristics of the local and state population based on results from the 2010 Census. The decennial census is an official enumeration, or count, of all residents on April 1st of the census year. The results of the census provide us with information about basic demographic characteristics of the population, including age, race, ethnicity, household composition, housing occupancy, and housing tenure

Characterization and Inference of Gene Gain/Loss Along Burkholderia Evolutionary History

A comparative analysis of 60 complete Burkholderia genomes was conducted to obtain insight in the evolutionary history behind the diversity and pathogenicity at species level. A concatenated multiprotein phyletic pattern and a dataset with Burkholderia clusters of orthologous genes (BuCOGs) were constructed. The extent of horizontal gene transfer (HGT) was assessed using a Markov based probabilistic method. A reconstruction of the gene gains and losses history shows that more than half of the Burkholderia genes families are inferred to have experienced HGT at least once during their evolution. Further analysis revealed that the number of gene gain and loss was correlated with the branch length. Genomic islands (GEIs) analysis based on evolutionary history reconstruction not only revealed that most genes in ancient GEIs were gained but also suggested that the fraction of the genome located in GEIs in the small chromosomes is higher than in the large chromosomes in Burkholderia. The mapping of coexpressed genes onto biological pathway schemes revealed that pathogenicity of Burkholderia strains is probably mainly determined by the gained genes in its ancestor. Taken together, our results strongly support that gene gain and loss especially in ancient evolutionary history play an important role in strain divergence, pathogenicity determinants of Burkholderia and GEIs formation

Interkingdom Gene Transfer May Contribute to the Evolution of Phytopathogenicity in Botrytis Cinerea

The ascomycete Botrytis cinerea is a phytopathogenic fungus infecting and causing significant yield losses in a number of crops. The genome of B. cinerea has been fully sequenced while the importance of horizontal gene transfer (HGT) to extend the host range in plant pathogenic fungi has been recently appreciated. However, recent data confirm that the B. cinerea fungus shares conserved virulence factors with other fungal plant pathogens with narrow host range. Therefore, interkingdom HGT may contribute to the evolution of phytopathogenicity in B. cinerea. In this study, a stringent genome comparison pipeline was used to identify potential genes that have been obtained by B. cinerea but not by other fungi through interkingdom HGT. This search led to the identification of four genes: a UDP-glucosyltransferase (UGT), a lipoprotein and two alpha/beta hydrolase fold proteins. Phylogenetic analysis of the four genes suggests that B. cinerea acquired UGT from plants and the other 3 genes from bacteria. Based on the known gene functions and literature searching, a correlation between gene acquision and the evolution of pathogenicity in B. cinerea can be postulated

Genomic adaptation to drought in wild barley is driven by edaphic natural selection at the Tabigha Evolution Slope

Ecological divergence at a microsite suggests adaptive evolution, and this study examined two abutting wild barley populations, each 100 m across, differentially adapted to drought tolerance on two contrasting soil types, Terra Rossa and basalt at the Tabigha Evolution Slope, Israel. We resequenced the genomes of seven and six wild barley genotypes inhabiting the Terra Rossa and basalt soils, respectively, and identified a total of 69,192,653 single-nucleotide variants (SNVs) and insertions/deletions in comparison with a reference barley genome. Comparative genomic analysis between these abutting wild barley populations involved 19,615,087 high-quality SNVs. The results revealed dramatically different selection sweep regions relevant to drought tolerance driven by edaphic natural selection within 2,577 selected genes in these regions, including key drought-responsive genes associated with ABA synthesis and degradation (such as Cytochrome P450 protein) and ABA receptor complex (such as PYL2, SNF1-related kinase). The genetic diversity of the wild barley population inhabiting Terra Rossa soil is much higher than that from the basalt soil. Additionally, we identified different sets of genes for drought adaptation in the wild barley populations from Terra Rossa soil and from wild barley populations from Evolution Canyon I at Mount Carmel. These genes are associated with abscisic acid signaling, signaling and metabolism of reactive oxygen species, detoxification and antioxidative systems, rapid osmotic adjustment, and deep root morphology. The unique mechanisms for drought adaptation of the wild barley from the Tabigha Evolution Slope may be useful for crop improvement, particularly for breeding of barley cultivars with high drought tolerance

Identification of novel microRNAs for cold deacclimation in barley

Cold acclimation is crucial for the overwintering process of plants. Cold deacclimation is also important for plant survival in winter, which results in loss of freezing tolerance and initiation of growth. MicroRNAs (miRNAs) play crucial roles in regulating various physiological activities including cold response in plants. However, there is no study on miRNAs and their target genes in response to cold deacclimation in a cold-tolerant crop – barley (Hordeum vulgare L.). Here, we performed high-throughput sequencing of miRNAs of leaves during the cold deacclimation process using two barley cultivars with contrasting cold tolerance (Nure, tolerant and Tremois, sensitive). We found a total of 36 known and 267 novel miRNAs, including 12 known and 112 novel ones that are differentially expressed during cold deacclimation. The number of detected differentially expressed miRNAs was larger in Nure than that in Tremois, and the expression profile of miRNAs was dramatically different between Nure and Tremois. Moreover, we identified 13 known and 97 novel miRNAs, which have putative target genes during cold deacclimation. The putative targets of the novel miRNAs included genes encoding C-repeat binding factor (CBF) transcription factors, phytohormones, antioxidant, osmopretectant and flower development. Our results suggest that barley miRNAs respond quickly to cold deacclimation, and the larger number of miRNAs differentially expressed in the cold tolerant cv. Nure indicating that miRNAs might play an important role in the process of deacclimation. It sets a solid foundation for future studies and breeding programs on low temperature tolerance in barley

FastGCN: A GPU Accelerated Tool for Fast Gene Co-Expression Networks

<div><p>Gene co-expression networks comprise one type of valuable biological networks. Many methods and tools have been published to construct gene co-expression networks; however, most of these tools and methods are inconvenient and time consuming for large datasets. We have developed a user-friendly, accelerated and optimized tool for constructing gene co-expression networks that can fully harness the parallel nature of GPU (Graphic Processing Unit) architectures. Genetic entropies were exploited to filter out genes with no or small expression changes in the raw data preprocessing step. Pearson correlation coefficients were then calculated. After that, we normalized these coefficients and employed the False Discovery Rate to control the multiple tests. At last, modules identification was conducted to construct the co-expression networks. All of these calculations were implemented on a GPU. We also compressed the coefficient matrix to save space. We compared the performance of the GPU implementation with those of multi-core CPU implementations with 16 CPU threads, single-thread C/C++ implementation and single-thread R implementation. Our results show that GPU implementation largely outperforms single-thread C/C++ implementation and single-thread R implementation, and GPU implementation outperforms multi-core CPU implementation when the number of genes increases. With the test dataset containing 16,000 genes and 590 individuals, we can achieve greater than 63 times the speed using a GPU implementation compared with a single-thread R implementation when 50 percent of genes were filtered out and about 80 times the speed when no genes were filtered out.</p></div

Curves of speedups against Single-thread R CPU implementation.

<p>(a) Speedup curves when 50% genes were filtered out at data preprocessing stage. (b) Speedup curves when no gene was filtered out at data preprocessing stage.</p