Speciation with gene-flow in eastern North American red oaks (Quercus section Lobatae)

As foundational species, oaks (Quercus : Fagaceae) support the activities of both humans and wildlife. However, many oaks in North America are declining, a crisis exacerbated by the previous disappearance of other hard mast-producing trees. In addition, the economic demands placed on this drought-tolerant group may intensify if climate change extirpates other, relatively mesophytic species. Genetic tools can help address these management challenges. To this end, we developed a suite of 27 microsatellite markers, of which 22 are derived from expressed sequence tags (ESTs). Many of these markers bear significant homology to known genes and may be able to directly assay functional genetic variation. Markers obtained from enriched microsatellite libraries, on the other hand, are typically located in heterochromatic regions and should reflect demographic processes. Considered jointly, genic and genomic microsatellites can elucidate patterns of gene-flow and natural selection, which are fundamental to both an organism's evolutionary ecology and conservation biology. To this end, we employed the developed markers in an FST-based genome scan to detect the signature of divergent selection among the red oaks (Quercus section Lobatae). Three candidate genes with putative roles in stress responses demonstrated patterns of diversity consistent with adaptation to heterogeneous selective pressures. These genes may be important in both local genetic adaptation within species and divergence among them. Next, we used an isolation-with-migration model to quantify levels of gene-flow among four red oaks species during speciation. Both speciation in allopatry and speciation with gene-flow were found to be major drivers of red oak biodiversity. Loci playing a key role in speciation are also likely to be ecologically important within species

POPLAR GENE EXPRESSION DATA ANALYSIS PIPELINES

Analyzing large-scale gene expression data is a labor-intensive and time-consuming process. To make data analysis easier, we developed a set of pipelines for rapid processing and analysis poplar gene expression data for knowledge discovery. Of all pipelines developed, differentially expressed genes (DEGs) pipeline is the one designed to identify biologically important genes that are differentially expressed in one of multiple time points for conditions. Pathway analysis pipeline was designed to identify the differentially expression metabolic pathways. Protein domain enrichment pipeline can identify the enriched protein domains present in the DEGs. Finally, Gene Ontology (GO) enrichment analysis pipeline was developed to identify the enriched GO terms in the DEGs. Our pipeline tools can analyze both microarray gene data and high-throughput gene data. These two types of data are obtained by two different technologies. A microarray technology is to measure gene expression levels via microarray chips, a collection of microscopic DNA spots attached to a solid (glass) surface, whereas high throughput sequencing, also called as the next-generation sequencing, is a new technology to measure gene expression levels by directly sequencing mRNAs, and obtaining each mRNA’s copy numbers in cells or tissues. We also developed a web portal (http://sys.bio.mtu.edu/) to make all pipelines available to public to facilitate users to analyze their gene expression data. In addition to the analyses mentioned above, it can also perform GO hierarchy analysis, i.e. construct GO trees using a list of GO terms as an input.