Browsing by Author "Yang, Kuan"
Now showing 1 - 5 of 5
Results Per Page
Sort Options
- Ancestral Genome Reconstruction in BacteriaYang, Kuan (Virginia Tech, 2012-06-06)The rapid accumulation of numerous sequenced genomes has provided a golden opportunity for ancestral state reconstruction studies, especially in the whole genome reconstruction area. However, most ancestral genome reconstruction methods developed so far only focus on gene or replicon sequences instead of whole genomes. They rely largely on either detailed modeling of evolutionary events or edit distance computation, both of which can be computationally prohibitive for large data sets. Hence, most of these methods can only be applied to a small number of features and species. In this dissertation, we describe the design, implementation, and evaluation of an ancestral genome reconstruction system (REGEN) for bacteria. It is the first bacterial genome reconstruction tool that focuses on ancestral state reconstruction at the genome scale instead of the gene scale. It not only reconstructs ancestral gene content and contiguous gene runs using either a maximum parsimony or a maximum likelihood criterion but also replicon structures of each ancestor. Based on the reconstructed genomes, it can infer all major events at both the gene scale, such as insertion, deletion, and translocation, and the replicon scale, such as replicon gain, loss, and merge. REGEN finishes by producing a visual representation of the entire evolutionary history of all genomes in the study. With a model-free reconstruction method at its core, the computational requirement for ancestral genome reconstruction is reduced sufficiently for the tool to be applied to large data sets with dozens of genomes and thousands of features. To achieve as accurate a reconstruction as possible, we also develop a homologous gene family prediction tool for preprocessing. Furthermore, we build our in-house Prokaryote Genome Evolution simulator (PEGsim) for evaluation purposes. The homologous gene family prediction refinement module can refine homologous gene family predictions generated by third party de novo prediction programs by combining phylogeny and local gene synteny. We show that such refinement can be accomplished for up to 80% of homologous gene family predictions with ambiguity (mixed families). The genome evolution simulator, PEGsim, is the first random events based high level bacteria genome evolution simulator with models for all common evolutionary events at the gene, replicon, and genome scales. The concepts of conserved gene runs and horizontal gene transfer (HGT) are also built in. We show the validation of PEGsim itself and the evaluation of the last reconstruction component with simulated data produced by it. REGEN, REconstruction of GENomes, is an ancestral genome reconstruction tool based on the concept of neighboring gene pairs (NGPs). Although it does not cover the reconstruction of actual nucleotide sequences, it is capable of reconstructing gene content, contiguous genes runs, and replicon structure of each ancestor using either a maximum parsimony or a maximum likelihood criterion. Based on the reconstructed genomes, it can infer all major events at both the gene scale, such as insertion, deletion, and translocation, and the replicon scale, such as replicon gain, loss, and merge. REGEN finishes by producing a visual representation of the entire evolutionary history of all genomes in the study.
- Mining and visualization of microarray and metabolomic data reveal extensive cell wall remodeling during winter hardening in Sitka spruce (Picea sitchensis)Grene, Ruth; Klumas, Curtis; Suren, Haktan; Yang, Kuan; Collakova, Eva; Myers, Elijah; Heath, Lenwood S.; Holliday, Jason A. (Frontiers, 2012)Microarray gene expression profiling is a powerful technique to understand complex developmental processes, but making biologically meaningful inferences from such studies has always been challenging. We previously reported a microarray study of the freezing acclimation period in Sitka spruce (Picea sitchensis) in which a large number of candidate genes for climatic adaptation were identified. In the current paper, we apply additional systems biology tools to these data to further probe changes in the levels of genes and metabolites and activities of associated pathways that regulate this complex developmental transition. One aspect of this adaptive process that is not well understood is the role of the cell wall. Our data suggest coordinated metabolic and signaling responses leading to cell wall remodeling. Co-expression of genes encoding proteins associated with biosynthesis of structural and non-structural cell wall carbohydrates was observed, which may be regulated by ethylene signaling components. At the same time, numerous genes, whose products are putatively localized to the endomembrane system and involved in both the synthesis and trafficking of cell wall carbohydrates, were up-regulated. Taken together, these results suggest a link between ethylene signaling and biosynthesis, and targeting of cell wall related gene products during the period of winter hardening. Automated Layout Pipeline for Inferred NEtworks (ALPINE), an in-house plugin for the Cytoscape visualization environment that utilizes the existing GeneMANIA and Mosaic plugins, together with the use of visualization tools, provided images of proposed signaling processes that became active over the time course of winter hardening, particularly at later time points in the process. The resulting visualizations have the potential to reveal novel, hypothesis generating, gene association patterns in the context of targeted subcellular location.
- Next-generation phage display: integrating and comparing available molecular tools to enable cost-effective high-throughput analysisDias-Neto, Emmanue; Nunes, Diana N.; Giordano, Ricardo J.; Sun, Jessica; Botz, Gregory H.; Yang, Kuan; Setubal, João C.; Pasqualini, Renata; Arap, Wadih (Public Library of Science, 2009-12-17)Background: Combinatorial phage display has been used in the last 20 years in the identification of protein-ligands and protein-protein interactions, uncovering relevant molecular recognition events. Rate-limiting steps of combinatorial phage display library selection are (i) the counting of transducing units and (ii) the sequencing of the encoded displayed ligands. Here, we adapted emerging genomic technologies to minimize such challenges. Methodology/Principal Findings: We gained efficiency by applying in tandem real-time PCR for rapid quantification to enable bacteria-free phage display library screening, and added phage DNA next-generation sequencing for large-scale ligand analysis, reporting a fully integrated set of high-throughput quantitative and analytical tools. The approach is far less labor-intensive and allows rigorous quantification; for medical applications, including selections in patients, it also represents an advance for quantitative distribution analysis and ligand identification of hundreds of thousands of targeted particles from patient-derived biopsy or autopsy in a longer timeframe post library administration. Additional advantages over current methods include increased sensitivity, less variability, enhanced linearity, scalability, and accuracy at much lower cost. Sequences obtained by qPhage plus pyrosequencing were similar to a dataset produced from conventional Sanger-sequenced transducing-units (TU), with no biases due to GC content, codon usage, and amino acid or peptide frequency. These tools allow phage display selection and ligand analysis at .1,000-fold faster rate, and reduce costs ,250-fold for generating 106 ligand sequences. Conclusions/Significance: Our analyses demonstrates that whereas this approach correlates with the traditional colonycounting, it is also capable of a much larger sampling, allowing a faster, less expensive, more accurate and consistent analysis of phage enrichment. Overall, qPhage plus pyrosequencing is superior to TU-counting plus Sanger sequencing and is proposed as the method of choice over a broad range of phage display applications in vitro, in cells, and in vivo.
- Performance comparison between k-tuple distance and four model-based distances in phylogenetic tree reconstructionYang, Kuan; Zhang, Liqing (2008-03)Phylogenetic tree reconstruction requires construction of a multiple sequence alignment (MSA) from sequences. Computationally, it is difficult to achieve an optimal MSA for many sequences. Moreover, even if an optimal MSA is obtained, it may not be the true MSA that reflects the evolutionary history of the underlying sequences. Therefore, errors can be introduced during MSA construction which in turn affects the subsequent phylogenetic tree construction. In order to circumvent this issue, we extend the application of the k-tuple distance to phylogenetic tree reconstruction. The k-tuple distance between two sequences is the sum of the differences in frequency, over all possible tuples of length k, between the sequences and can be estimated without MSAs. It has been traditionally used to build a fast guide tree to assist the construction of MSAs. Using the 1470 simulated sets of sequences generated under different evolutionary scenarios, the neighbor-joining trees and BioNJ trees, we compared the performance of the k-tuple distance with four commonly used distance estimators including JukesCantor, Kimura, F84 and TamuraNei. These four distance estimators fall into the category of model-based distance estimators, as each of them takes account of a specific substitution model in order to compute the distance between a pair of already aligned sequences. Results show that trees constructed from the k-tuple distance are more accurate than those from other distances most time; when the divergence between underlying sequences is high, the tree accuracy could be twice or higher using the k-tuple distance than other estimators. Furthermore, as the k-tuple distance voids the need for constructing an MSA, it can save tremendous amount of time for phylogenetic tree reconstructions when the data include a large number of sequences.
- REGEN: Ancestral Genome Reconstruction for BacteriaYang, Kuan; Heath, Lenwood S.; Setubal, João C. (MDPI, 2012-07-18)Ancestral genome reconstruction can be understood as a phylogenetic study with more details than a traditional phylogenetic tree reconstruction. We present a new computational system called REGEN for ancestral bacterial genome reconstruction at both the gene and replicon levels. REGEN reconstructs gene content, contiguous gene runs, and replicon structure for each ancestral genome. Along each branch of the phylogenetic tree, REGEN infers evolutionary events, including gene creation and deletion and replicon fission and fusion. The reconstruction can be performed by either a maximum parsimony or a maximum likelihood method. Gene content reconstruction is based on the concept of neighboring gene pairs. REGEN was designed to be used with any set of genomes that are sufficiently related, which will usually be the case for bacteria within the same taxonomic order. We evaluated REGEN using simulated genomes and genomes in the Rhizobiales order.