The family Adenoviridae consists of five genera, including the genus Aviadenovirus, which infects avian species. The genus Aviadenovirus currently comprises five fowl (Fowl adenovirus A-E), one falcon (Falcon adenovirus A), and one goose (Goose adenovirus) adenovirus species. Fowl adenoviruses (FAdVs) have a worldwide distribution. Some are associated with diseases such as inclusion body hepatitis (IBH), while FAdV species C serotype 4 (FAdV-4) has been associated with hydropericardium-hepatitis syndrome (HHS). In this study, the complete nucleotide sequence of fowl adenovirus serotype 8 (FAdV-8) was determined. The full genome was 44,055 nucleotides (nt) in length, with an organization similar to that of the FAdV-1 and FAdV-9 genomes. No regions homologous to early regions E1, E3, and E4 of mastadenoviruses were recognized Pathogenicity of FAdV-8 and FAdV-4 were studied in specific-pathogen-free chickens following oral and intramuscular inoculations. Pathogenicity was determined on the basis of clinical signs and gross and histological lesions. Additionally, virus shedding and viral genome copy numbers in liver, cecal tonsil, and bursa of Fabricius were determined. The role of interleukins (IL) in the pathogenicity of and immune response to FAdVs is unknown. Therefore, in a chicken experiment, interferon-γ, IL-10, IL-18, and IL-8 gene expression was evaluated following FAdV-8 and FAdV-4 infection. Cytokine gene expression was examined in the liver, spleen, and cecal tonsils. This study explored the ability of fowl adenoviruses to subvert the host cell’s secretion of cytokines in response to infection as an important viral mechanism for immune evasion during infection. Variations in virulence of FAdVs are likely to be determined by the fiber alone as shown by Pallister et al. (1996). Therefore, we compared and analyzed the nt and amino acid (aa) sequences of the fiber gene of pathogenic and non-pathogenic FAdVs representing species groups D (FAdV-11) and E (FAdV-8). According to our data, virulence might not be associated only with sequence of the fiber gene. This work is a continuation of our efforts towards better understanding of the molecular biology of FAdVs and the pathogenesis of the disease, with an emphasis on the role of interleukins, an unknown area.

During HIV-1 assembly, the Gag structural protein specifically encapsidates two copies of viral genomic RNA in the form of a dimer. An RNA stem-loop structure (SL1) in the 5' untranslated region, known as the dimerization initiation site (DIS), is important for dimerization and packaging of HIV-1 genomic RNA; however, the mechanisms involved are not fully understood. The major goal of this PhD study was to further understand HIV-1 RNA dimerization, and to study the role of the Gag protein in the dimerization and packaging processes. Despite the known involvement of the DIS in RNA dimerization, DIS-mutated viruses still contain significant levels of dimerized RNA, and electron microscopy studies suggest that the RNA molecules are linked at the extreme 5' end. We show here that RNA sequences on both sides of the DIS are also required for HIVA genome dimerization, suggesting that multiple RNA elements are involved. We have also examined the contribution of specific amino acids within Gag to the dimerization and packaging processes. Previous work showed that partial deletion of the DIS impacted on viral replication capacity, but could largely be corrected by compensatory point mutations within Gag. To further elucidate the mechanism(s) of these compensatory mutations, we generated DIS mutants lacking the entire SL1, or only the SL1 loop sequences, and combined these deletions with various combinations of compensatory mutations. Analysis of virion-derived RNA showed that the relevant mutant viruses contained increased levels of spliced viral RNA compared to wild type, indicating that a defect in genome packaging specificity was present. However, this defect was corrected by our compensatory mutations, and a T121 substitution in p2 was shown to be solely responsible for this activity. These results suggest that the p2 spacer peptide plays a critical role in the specific packaging of viral genomic RNA. In summary, these findings provide new insig

There are many programs available for generating simulated whole-genome shotgun sequence reads. The data generated by many of these programs follow predefined models, which limits their use to the authors' original intentions. For example, many models assume that read lengths follow a uniform or normal distribution. Other programs generate models from actual sequencing data, but are limited to reads from single-genome studies. To our knowledge, there are no programs that allow a user to generate simulated data for metagenomics applications following empirical read-length distributions and quality profiles based on empirically-derived information from actual sequencing data. We present BEAR (Better Emulation for Artificial Reads), a program that uses a machine-learning approach to generate reads with lengths and quality values that closely match empirically-derived distributions. BEAR can emulate reads from various sequencing platforms, including Illumina, 454, and Ion Torrent. BEAR requires minimal user input, as it automatically determines appropriate parameter settings from user-supplied data. BEAR also uses a unique method for deriving run-specific error rates, and extracts useful statistics from the metagenomic data itself, such as quality-error models. Many existing simulators are specific to a particular sequencing technology; however, BEAR is not restricted in this way. Because of its flexibility, BEAR is particularly useful for emulating the behaviour of technologies like Ion Torrent, for which no dedicated sequencing simulators are currently available. BEAR is also the first metagenomic sequencing simulator program that automates the process of generating abundances, which can be an arduous task. BEAR is useful for evaluating data processing tools in genomics. It has many advantages over existing comparable software, such as generating more realistic reads and being independent of sequencing technology, and has features particularly useful for metagenomics work.

Determining protein structure and function experimentally is both costly and time consuming. Transferring function-related protein annotations based on homology-based methods is relatively straightforward for proteins that have sequence identity of more than 40%. However, there are many proteins in the "twilight zone" where sequence similarity with any other protein is very weak, while being structurally similar to several. Such cases require methods that are capable of using and exploiting both sequence and structural similarity. To understand ways of how such methods can and should be designed is the focus of this study. In this thesis, models that use both sequence and structure features are applied on two protein prediction problems that are particularly challenging when relying on sequence alone. Enzyme classification benefits from both kinds of features because on one hand, enzymes can have identical function with limited sequence similarity while on the other hand, proteins with similar fold may have disparate enzyme class annotation. This thesis shows that the full integration of protein sequence and structure-related features (via the use of kernels) automatically places proteins with similar biological properties closer together, leading to superior classification accuracy using Support Vector Machines. Disulfide-bonds link residues in a protein structure, but may appear distant in sequence. Sequence similarity reflecting such structural properties is thus very hard to detect. It is sufficient for the structure to be similar for accurate prediction of disulfide-bonds, but such information is very scarce and predictors that rely on protein structure are not nearly as useful as those operating on sequence alone. This thesis proposes a novel approach based on Kernel Canonical Correlation Analysis that uses structural features during training only. It does so by finding sequence representations that correlate with structural features that are essential for a disulfide bond. The resulting representations enable high prediction accuracy for a range of disulfide-bond problems. The proposed model thus taps the advantage of structural features without requiring protein structure to be available in the prediction process. The merits of this approach should apply to a number of open protein structure prediction problems.

Thesis (Ph. D.)--University of Washington, 2006. / Vita. Includes bibliographical references (p. 103-108).

<p>  </p> <p>Forward genetics discovery of the molecular basis of induced mutants has fundamentally contributed to our understanding of basic biological processes such as metabolism, cell dynamics, growth, and development. Advances in Next-Generation Sequencing (NGS) technologies enabled rapid genome sequencing but also come with limitations such as sequencing errors, dependence on reference genome accuracy, and alignment errors. By incorporating pedigree information to help correct for some errors I optimized variant calling and filtering strategies to respond to experimental design. This led to the identification of multiple causative alleles, the detection of pedigree errors, and an ability to explore the mutational spectrum of multiple mutagens in Arabidopsis. Similar to the problems in forward genetic discovery of mutant alleles, variation in genomes complicates the analysis of gene expression affected by natural variation. The plant hypersensitive response (HR) is a highly localized and rapid form of programmed cell death that plants use to contain biotrophic pathogens. Substantial natural variation exists in the mechanisms that trigger and control HR, yet a complete understanding of the molecular mechanisms modulating HR is lacking. I explored the gene expression consequences of the plant HR in maize using a semi-dominant mutant encoding a constitutively active HR-inducing Nucleotide Binding Site Leucine Rich Repeat protein, <em>Rp1-D21,</em> derived from the receptor responsible for perceiving certain strains of the common rust <em>Puccinia sorghi</em>. Differentially expressed genes (DEG) in response to <em>Rp1-D21</em> were identified in different genetic backgrounds and hybrids that exhibit divergent enhancing (NC350) or suppressing (H95, B73) effects on the visual manifestations of HR. To enable this analysis, I created anonymized reference genomes for each comparison, so that the reference genome induced less bias in the mapping steps. Comprehensive identification of DEG corroborated the visual phenotypes and provided the identities of genes influential in plant hypersensitive response for further studies. The locations of expression quantitative trait loci (eQTL) that determined the differential response of NC350 and B73 were identified using 198 F1 families generated by crossing B73 x NC350 RIL population and <em>Rp1-D21</em>/+ in H95. This identified 3514 eQTL controlling the variability in differential expression between mutant versus wild-type. <em>Trans-</em>eQTL were dramatically arranged in the genome and identified 17 hotspots with more than 200 genes influenced by each locus. A single locus significantly affected expression variation in 5700 genes, 5396 (94.7%) of which were DGE. An allele specific expression analysis of NC350 x H95 and B73 x H95 F1 hybrids with and without <em>Rp1-D21</em> identified <em>cis-</em>eQTL and ASE at a subset of these genes. Bias in the confirmation of eQTL by ASE was still present despite the anonymized reference genomes indicating that additional efforts to improve signal processing in these experiments is needed.</p>

Methods to detect patterns of variation associated with ongoing positive selection often focus on identifying regions of the genome with extended haplotype homozygosity - indicative of recently shared ancestry. Whilst these have been shown to be powerful they have two major challenges. First, these methods are constructed to detect variation associated with a classical selective sweep; a single haplotype background gets swept up to a higher than expected frequency given its age. Recently studies have shown that other forms of positive selection, e.g. selection on standing variation, may be more prevalent than previous thought. Under such evolution, a mutation that is already segregating in the population becomes beneficial, possibly as a result of an environmental change. The second challenge with these methods is that they base their inference on non-parametric tests of significance which can result in uncontrolled false positive rates. We tackle these problems using two approaches. First, by exploiting a widely used model in population genomics we construct a new approach to detect regions where a subset of the chromosomes are much more related than expected genome-wide. Using this metric we show that it is sensitive to both classical selective sweeps, and to soft selective sweeps, e.g. selection on standing variation. Second, building on existing methods, we construct a Bayesian test which bi-partitions chromosomes at every position based on their allelic type and tests for association between chromosomes carrying one allele and significantly reduced time to common ancestor. Using simulated data we show that this approach results in a powerful, fast, and robust approach to detect signals of positive selection in sequence data. Moreover by comparing our model to existing techniques we show that we have similar power to detect recent classical selective sweeps, and considerably greater power to detect soft selective sweeps. We apply our method, ABACUS, to three human populations using data from the 1000 Genome Project. Using existing and novel candidates of positive selection, we show that the results between ABACUS and existing methods are comparable in regions of classical selection, and are arguably superior in regions that show evidence for recent selection on standing variation.

Microbial communities are recognized as major drivers of global biogeochemical processes. However, the genetic diversity and composition, as well as processes leading to the origin and diversification of these communities in space and time, are poorly understood. Character- ization of microbial communities using high-throughput sequencing of 16S tags shows that Operational Taxonomic Unit (OTU) abundances can be approximated by a gamma distribu- tion, which suggests structuring around small numbers of highly abundant OTUs and a large proportion of rare OTUs. The current methods used to characterize how communities are structured rely on multivariate statistics, which operate on pair-wise distance matrices. My analyses demonstrate that use of these methods, by reducing a highly-dimensional data set (tens of samples, thousands of OTUs), results in a significant loss of information. I demon- strate that, in some cases, up to 80% of the least abundant OTUs may be removed while still recovering the same community relationships; this indicates these metrics are biased toward the highly abundant OTUs. I also demonstrate that the observed patterns of OTU abundance detected from microbial communities can be robustly modeled using techniques similar to those used to model the presence and absence of genes in genome evolution. Using simulation studies, I show that general Markov models in a Bayesian inference framework out- perform traditional, multivariate ecological methods in recovering true community structure. Applying this new methodology to Atlantic Ocean communities uncovered a distance-decay effect which was not revealed by the traditional methods; applying to communities discov- ered on Hog Island point toward mechanisms of thicket establishment. Although the ocean data set operated on a much larger, continental scale, characterization of the sequence data generated from the nutrient-poor soil on Hog Island, a barrier island off the Virginia coast, allows for a better characterization of the processes affecting these communities on a much smaller scale. Finally, using 16S data from the Human Vaginal Microbiome Project, gener- ated here at VCU under the umbrella of the overall NIH HMP initiative, I give examples of the quality control, analysis and visualization pipeline that I developed to support the efforts of this project. In conclusion, my analyses of the metagenomic sequence data from bacterial communities sampled from different environments demonstrate that the proper identification of the biological processes influencing these communities requires the development and im- plementation of new statistical and computational methodologies that take advantage of the extensive amount of information generated in next-generation, high-throughput sequencing projects.

by Nathan, Yiu-hung Yam. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1995. / Includes bibliographical references (leaves 139-157). / Acknowledgements --- p.i / Table of Contents --- p.ii / Abstract --- p.viii / List of Abbreviations --- p.x / List of Tables --- p.xii / List of Figures --- p.xiii / Chapter Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- General introduction --- p.1 / Chapter 1.2 --- Aims of the present study --- p.2 / Chapter 1.3 --- Sequencing of an adult human heart cDNA library --- p.3 / Chapter 1.4 --- Rat/mouse CRIP --- p.5 / Chapter 1.5 --- LIM proteins --- p.13 / Chapter 1.6 --- Zinc-binding proteins --- p.17 / Chapter 1.7 --- Bacterial expression system using the pAED4 vector --- p.24 / Chapter Chapter 2 --- Identification and sequence analysis ofhCRHP --- p.26 / Chapter 2.1 --- Introduction --- p.26 / Chapter 2.2 --- Materials and methods --- p.29 / Chapter 2.2.1 --- Bacterial strains and vectors --- p.29 / Chapter 2.2.2 --- "Mediums, buffers and solutions" --- p.31 / Chapter 2.2.3 --- Bacteriophage clones preparation --- p.34 / Chapter 2.2.4 --- Amplification of clones by PCR --- p.35 / Chapter 2.2.5 --- Cycle sequencing of PCR products --- p.36 / Chapter 2.2.6 --- DNA sequences analysis --- p.38 / Chapter 2.3 --- Results --- p.39 / Chapter 2.3.1 --- Sequence analysis of hCRHP --- p.39 / Chapter 2.3.2 --- Comparison of hCRHP with CRIP --- p.52 / Chapter 2.3.3 --- Comparison of hCRHP with some LIM proteins --- p.56 / Chapter 2.4 --- Discussions --- p.61 / Chapter Chapter 3 --- Study of hCRHP at the nucleic acid level --- p.65 / Chapter 3.1 --- Introduction --- p.65 / Chapter 3.2 --- Materials and methods --- p.66 / Chapter 3.2.1 --- Animals --- p.66 / Chapter 3.2.2 --- "Mediums, buffers, enzymes and solutions" --- p.66 / Chapter 3.2.3 --- Preparation of total RNA --- p.70 / Chapter 3.2.3.1 --- Preparation of RNA by the CsCl method --- p.70 / Chapter 3.2.3.2 --- Preparation of RNA by the AGPC method --- p.71 / Chapter 3.2.4 --- Northern hybridization of hCRHP --- p.72 / Chapter 3.2.4.1 --- Formaldehyde agarose gel electrophoresis --- p.72 / Chapter 3.2.4.2 --- Preparation of radioactive probe --- p.73 / Chapter 3.2.4.3 --- RNA transfer and Northern hybridization --- p.74 / Chapter 3.2.5 --- Preparation of human genomic DNA --- p.77 / Chapter 3.2.6 --- Southern hybridization of hCRHP --- p.78 / Chapter 3.2.6.1 --- Restriction cutting and agarose gel electrophoresis of genomic DNA --- p.78 / Chapter 3.2.6.2 --- DNA transfer and Southern hybridization --- p.79 / Chapter 3.3 --- Results --- p.80 / Chapter 3.3.1 --- Southern hybridization of hCRHP --- p.80 / Chapter 3.3.2 --- Identification of hCRHP in neonatal human heart --- p.83 / Chapter 3.3.3 --- Tissue distribution of CRIP mRNA in rat tissues --- p.85 / Chapter 3.3.4 --- Time course of CRIP expression in rat heart --- p.85 / Chapter 3.4 --- Discussions --- p.87 / Chapter Chapter 4 --- Subcloning and expression of hCRHP --- p.89 / Chapter 4.1 --- Introduction --- p.89 / Chapter 4.2 --- Materials and methods --- p.90 / Chapter 4.2.1 --- Bacterial strains and vectors --- p.90 / Chapter 4.2.2 --- "Mediums, buffers, enzymes and solutions" --- p.92 / Chapter 4.2.3 --- Subcloning of hCRHP into pAED4 --- p.98 / Chapter 4.2.3.1 --- Primers design and PCR --- p.98 / Chapter 4.2.3.2 --- Purification of PCR products by Geneclean II´ёØ (BIO 101 Inc) --- p.99 / Chapter 4.2.3.3 --- Restriction digestion of purified PCR product and pAED4 --- p.100 / Chapter 4.2.3.4 --- Ligation and transformation of hCRHP --- p.101 / Chapter 4.2.3.5 --- Amplification and purification of pAED4-hCRHP --- p.103 / Chapter 4.2.4 --- Expression of hCRHP --- p.105 / Chapter 4.2.4.1 --- Induction of hCRHP expression --- p.105 / Chapter 4.2.4.2 --- SDS-PAGE and protein detection --- p.106 / Chapter 4.3 --- Results --- p.108 / Chapter 4.3.1 --- Subcloning of hCRHP into pAED4 --- p.108 / Chapter 4.3.2 --- Induction and optimization of hCRHP expression --- p.110 / Chapter 4.4 --- Discussions --- p.117 / Chapter Chapter 5 --- Partial purification and isoelectric focusing of hCRHP --- p.120 / Chapter 5.1 --- Introduction --- p.120 / Chapter 5.2 --- Materials and methods --- p.121 / Chapter 5.2.1 --- "Mediums, buffers and mediums" --- p.121 / Chapter 5.2.2 --- Purification of hCRHP by ammonium sulphate precipitation --- p.121 / Chapter 5.2.3 --- Purification of hCRHP by hydrochloric acid extraction --- p.122 / Chapter 5.2.4 --- Purification of hCRHP by ultrafiltration --- p.123 / Chapter 5.2.5 --- Isoelectric focusing of hCRHP --- p.127 / Chapter 5.3 --- Results --- p.128 / Chapter 5.3.1 --- Partial purification of hCRHP by ammonium sulphate precipitation --- p.128 / Chapter 5.3.2 --- Partial purification of hCRHP by hydrochloric acid extraction --- p.128 / Chapter 5.3.3 --- Partial purification of hCRHP by ultrafiltration --- p.131 / Chapter 5.3.4 --- Isoelectric focusing of hCRHP --- p.133 / Chapter 5.4 --- Discussions --- p.133 / Chapter Chapter 6 --- Discussions --- p.136 / Chapter 6.1 --- The possible role(s) of hCRHP/CRIP --- p.136 / Chapter 6.2 --- Future prospects --- p.137 / References --- p.139 / Appendix 1 --- p.158

by Lam Wai Yip. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1995. / Includes bibliographical references (leaves 184-195). / Contents --- p.I - IV / Abstract --- p.V / Abbreviations --- p.VI / List of Tables and Figures --- p.VII - XV / Chapter Chapter One: --- Introduction / Part I / Chapter 1.1 --- Human genome project --- p.1 / Chapter 1.2 --- Progress of human genome project --- p.2 / Chapter 1.3 --- Human heart cDNA sequencing --- p.3 / Chapter 1.4 --- Significance of the human heart cDNA library project --- p.5 / Chapter 1.5 --- Homology search tools for cDNA sequences alignment --- p.5 / Part II / Chapter 1.6 --- Investigation of a human heart cDNA clone A076 --- p.7 / Chapter 1.7 --- General introduction of Heat Shock Proteins (HSPs) --- p.7 / Chapter 1.7.1 --- Definition of HSP --- p.8 / Chapter 1.7.2 --- Discovery of HSP --- p.10 / Chapter 1.7.3 --- Transcriptional regulation of heat shock genes --- p.11 / Chapter 1.7.4 --- Nomenclature of HSPs --- p.13 / Chapter 1.7.5 --- HSP110 --- p.13 / Chapter 1.7.6 --- HSP90 --- p.14 / Chapter 1.7.7 --- HSP70 --- p.15 / Chapter 1.7.8 --- HSP60 --- p.17 / Chapter 1.7.9 --- Ubiquitin - HSP8 --- p.19 / Chapter 1.7.10 --- HSP27 --- p.20 / Chapter 1.8 --- The theme of this thesis --- p.28 / Chapter Chapter Two: --- Method and Materials / Chapter 2.1 --- The human heart cDNA library --- p.29 / Chapter 2.2 --- Plating out the cDNA library --- p.29 / Chapter 2.3 --- DNA amplification --- p.31 / Chapter 2.4 --- DNA sequencing reaction - Cycle sequencing reaction --- p.32 / Chapter 2.5 --- Operation of the A.L.F. DNA sequencer --- p.33 / Chapter 2.5.1 --- Preparation of the gel cassette --- p.33 / Chapter 2.5.2 --- Preparation of the acrylamide gel --- p.34 / Chapter 2.5.3 --- Fitting the gel cassette into the electrophoresis unit --- p.35 / Chapter 2.5.4 --- Settings of electrophoresis --- p.36 / Chapter 2.6 --- Comparison of DNA sequences to databases --- p.37 / Chapter 2.7 --- Programming for sending cDNA sequences to NCBI --- p.38 / Chapter 2.8 --- Storage of sequence data --- p.39 / Chapter 2.9 --- Synthesis and purification of primers --- p.40 / Chapter 2.10 --- Connection of cDNA clones using Polymerase Chain Reaction (PCR) --- p.41 / Chapter 2.11 --- Purification of DNA fragment from agarose gels by GENECLEAN´ёØ --- p.42 / Chapter 2.12 --- "Preparation of competent Escherichia coli for transformation (Hanahan, 1986)" --- p.43 / Chapter 2.13 --- Transformation of Plasmid into Competent Escherichia coli --- p.44 / Chapter 2.14 --- "Small scale preparation of plasmid DNA (Sambrook et al.,1989" --- p.45 / Chapter 2.15 --- Large scale plasmid preparation by QIAGEN´ёØ --- p.46 / Chapter 2.16 --- DNA sequencing reaction - Unicycle sequencing reaction --- p.48 / Chapter 2.17 --- Synthesis of Radiolabeled DNA probe --- p.49 / Chapter 2.18 --- "Isolation of genomic DNA from human blood cells (Thomas A. Ciulla, 1988)" --- p.51 / Chapter 2.19 --- Southern blotting --- p.52 / Chapter 2.20 --- Prehybridization and hybridization procedure for Southern blot analysis --- p.54 / Chapter 2.21 --- "AGPC-RNA extraction method (Chomczynski and Sacchi 1987, modifed)" --- p.56 / Chapter 2.22 --- Electrophoresis of RNA through gels containing formaldehyde --- p.58 / Chapter 2.23 --- First-Strand cDNA synthesis --- p.59 / Chapter 2.24 --- Use of T7 RNA polymerase to direct expression of the cloned hsp27b gene (A076&B490) --- p.60 / Chapter 2.25 --- "Sodium Dodecyl Sulphate Polyacrylamide Gel Electrophoresis (Laemmli, 1970)" --- p.61 / Chapter 2.26 --- Staining of the Gel by the Commassie Blue Method --- p.63 / Chapter Chapter Three: --- Results / Part I / Chapter 3.1 --- Sample results of Sequencing a few clones --- p.64 / Chapter 3.2 --- A Catalogue of 497 cDNA clones obtained from human heart cDNA sequencing --- p.71 / Chapter 3.3 --- Submission of novel sequences to genbank --- p.81 / Chapter 3.4 --- A Catalogues of genes that are expressed in the adult human heart --- p.83 / Chapter 3.5 --- The use of the programmes to assist the sending and receiving of sequence data E-mail message --- p.90 / Chapter 3.5.1 --- The use of the SENDMAIL.EXE programme --- p.91 / Chapter 3.5.2 --- "The use of the EDITBLN.EXE, ALLFILE.EXE and DATABASE.EXE" --- p.95 / Part II / Chapter 3.6 --- DNA sequence profiles of cDNA clones A076 and B490 --- p.105 / Chapter 3.7 --- Ligation of cDNA clones using Polymerase Chain Reaction (PCR) --- p.112 / Chapter 3.8 --- Cloning of the PCR product A076&B490 into the pAED4 expression vector --- p.117 / Chapter 3.9 --- Unicycle sequencing of the subcloned insert A076&B490 --- p.121 / Chapter 3.10 --- Southern hybridization of hsp27b (A076&B490) --- p.125 / Chapter 3.11 --- Results of RT-PCR and PCR --- p.127 / Chapter 3.12 --- Expression pAED4-A076&B490 in E.coli --- p.133 / Chapter Chapter Four: --- Discussion / Part I / Chapter 4.1 --- EST characterization --- p.138 / Chapter 4.2 --- Further investigation --- p.140 / Chapter 4.3 --- Disadvantage of randomly picked cDNA sequencing --- p.141 / Chapter 4.4 --- Problem of GenBank database searching --- p.141 / Part II / Chapter 4.5 --- The DNA sequence of A076 and B490 --- p.143 / Chapter 4.6 --- Ligation of HSP27B by using PCR --- p.144 / Chapter 4.7 --- Analysis of the DNA and protein sequence ofhsp27b (A076&B490) --- p.145 / Chapter 4.8 --- Southern hybridization of human hsp27b --- p.153 / Chapter 4.9 --- "RT-PCR and PCR of first strand cDNA with primers A076-ATG, A076-mid and oligo dT" --- p.153 / Chapter 4.10 --- Expression of human hsp27b --- p.154 / Chapter 4.11 --- The possible roles of human hsp27b --- p.156 / Chapter 4.12 --- Further analysis --- p.160 / Appendix I --- p.161-182 / Appendix II --- p.183 / References --- p.184-195