Metagenomics is the study of the totality of the complete genetic elements discovered from a defined environment. Different from traditional microbiology study, which only analyzes a small percent of microbes that could survive in laboratory, metagenomics allows researchers to get entire genetic information from all the samples in the communities. So metagenomics enables understanding of the target environments and the hidden relationships between bacteria and diseases. In order to efficiently analyze the metagenomics data, cutting-edge technologies for analyzing the relationships among microbes and communities are required. To overcome the challenges brought by rapid growth in metagenomics datasets, advances in novel methodologies for interpreting metagenomics data are clearly needed. The first two chapters of this dissertation summarize and compare the widely-used methods in metagenomics and integrate these methods into pipelines. Properly analyzing metagenomics data requires a variety of bioinformatcis and statistical approaches to deal with different situations. The raw reads from sequencing centers need to be processed and denoised by several steps and then be further interpreted by ecological and statistical analysis. So understanding these algorithms and combining different approaches could potentially reduce the influence of noises and biases at different steps. And an efficient and accurate pipeline is important to …

Plant architecture is an important agronomic trait driven by meristematic activities. Indeterminate meristems set repeating phytomers while determinate meristems produce terminal structures. The centroradialis/terminal flower1/self pruning (CETS) gene family modulates architecture by controlling determinate and indeterminate growth. Cotton (G. hirsutum) is naturally a photoperiodic perennial cultivated as a day-neutral annual. Management of this fiber crop is complicated by continued vegetative growth and asynchronous fruit set. Here, cotton CETS genes are phylogenetically and functionally characterized. We identified eight CETS genes in diploid cotton (G. raimondii and G. arboreum) and sixteen in tetraploid G. hirsutum that grouped within the three generally accepted CETS clades: flowering locus T (FT)-like, terminal flower1/self pruning (TFL1/SP)-like, and mother of FT and TFL1 (MFT)-like. Over-expression of single flower truss (GhSFT), the ortholog to Arabidopsis FT, accelerates the onset of flowering in Arabidopsis Col-0. In mutant rescue analysis, this gene driven by its native promoter rescues the ft-10 late flowering phenotype. GhSFT upstream sequence was used to drive expression of the uidA reporter gene. As anticipated, GUS accumulated in the vasculature of Arabidopsis leaves. Cotton has five TFL1-like genes, all of which delay flowering when ectopically expressed in Arabidopsis; the strongest phenotypes fail to produce functional flowers. Three …