Highly discriminatory single-nucleotide polymorphism interrogation of Escherichia coli by use of allele-specific real-time PCR and eBURST analysis

In total, 782 Escherichia coli strains originating from various host sources have been analyzed in this study by using a highly discriminatory single-nucleotide polymorphism (SNP) approach. A set of eight SNPs, with a discrimination value (Simpson's index of diversity [D]) of 0.96, was determined using the Minimum SNPs software, based on sequences of housekeeping genes from the E. coli multilocus sequence typing (MLST) database. Allele-specific real-time PCR was used to screen 114 E. coli isolates from various fecal sources in Southeast Queensland (SEQ). The combined analysis of both the MLST database and SEQ E. coli isolates using eight high-D SNPs resolved the isolates into 74 SNP profiles. The data obtained suggest that SNP typing is a promising approach for the discrimination of host-specific groups and allows for the identification of human-specific E. coli in environmental samples. However, a more diverse E. coli collection is required to determine animal- and environment-specific E. coli SNP profiles due to the abundance of human E. coli strains (56%) in the MLST database.

Genome-wide identification and expression analysis of the NF-Y family of transcription factors in Triticum aestivum

Nuclear Factor Y (NF-Y) is a trimeric complex that binds to the CCAAT box, a ubiquitous eukaryotic promoter element. The three subunits NF-YA, NF-YB and NF-YC are represented by single genes in yeast and mammals. However, in model plant species (Arabidopsis and rice) multiple genes encode each subunit providing the impetus for the investigation of the NF-Y transcription factor family in wheat. A total of 37 NF-Y and Dr1 genes (10 NF-YA, 11 NF-YB, 14 NF-YC and 2 Dr1) in Triticum aestivum were identified in the global DNA databases by computational analysis in this study. Each of the wheat NF-Y subunit families could be further divided into 4-5 clades based on their conserved core region sequences. Several conserved motifs outside of the NF-Y core regions were also identified by comparison of NF-Y members from wheat, rice and Arabidopsis. Quantitative RT-PCR analysis revealed that some of the wheat NF-Y genes were expressed ubiquitously, while others were expressed in an organ-specific manner. In particular, each TaNF-Y subunit family had members that were expressed predominantly in the endosperm. The expression of nine NF-Y and two Dr1 genes in wheat leaves appeared to be responsive to drought stress. Three of these genes were up-regulated under drought conditions, indicating that these members of the NF-Y and Dr1 families are potentially involved in plant drought adaptation. The combined expression and phylogenetic analyses revealed that members within the same phylogenetic clade generally shared a similar expression profile. Organ-specific expression and differential response to drought indicate a plant-specific biological role for various members of this transcription factor family.

Multiscale analysis of protein functions and stochastic modelling of gene transcriptional regulatory networks

Genomic and proteomic analyses have attracted a great deal of interests in biological research in recent years. Many methods have been applied to discover useful information contained in the enormous databases of genomic sequences and amino acid sequences. The results of these investigations inspire further research in biological fields in return. These biological sequences, which may be considered as multiscale sequences, have some specific features which need further efforts to characterise using more refined methods. This project aims to study some of these biological challenges with multiscale analysis methods and stochastic modelling approach. The first part of the thesis aims to cluster some unknown proteins, and classify their families as well as their structural classes. A development in proteomic analysis is concerned with the determination of protein functions. The first step in this development is to classify proteins and predict their families. This motives us to study some unknown proteins from specific families, and to cluster them into families and structural classes. We select a large number of proteins from the same families or superfamilies, and link them to simulate some unknown large proteins from these families. We use multifractal analysis and the wavelet method to capture the characteristics of these linked proteins. The simulation results show that the method is valid for the classification of large proteins. The second part of the thesis aims to explore the relationship of proteins based on a layered comparison with their components. Many methods are based on homology of proteins because the resemblance at the protein sequence level normally indicates the similarity of functions and structures. However, some proteins may have similar functions with low sequential identity. We consider protein sequences at detail level to investigate the problem of comparison of proteins. The comparison is based on the empirical mode decomposition (EMD), and protein sequences are detected with the intrinsic mode functions. A measure of similarity is introduced with a new cross-correlation formula. The similarity results show that the EMD is useful for detection of functional relationships of proteins. The third part of the thesis aims to investigate the transcriptional regulatory network of yeast cell cycle via stochastic differential equations. As the investigation of genome-wide gene expressions has become a focus in genomic analysis, researchers have tried to understand the mechanisms of the yeast genome for many years. How cells control gene expressions still needs further investigation. We use a stochastic differential equation to model the expression profile of a target gene. We modify the model with a Gaussian membership function. For each target gene, a transcriptional rate is obtained, and the estimated transcriptional rate is also calculated with the information from five possible transcriptional regulators. Some regulators of these target genes are verified with the related references. With these results, we construct a transcriptional regulatory network for the genes from the yeast Saccharomyces cerevisiae. The construction of transcriptional regulatory network is useful for detecting more mechanisms of the yeast cell cycle.

Multifractal analysis of geomagnetic storm and solar flare indices and their class dependence

The multifractal properties of two indices of geomagnetic activity, D st (representative of low latitudes) and a p (representative of the global geomagnetic activity), with the solar X-ray brightness, X l , during the period from 1 March 1995 to 17 June 2003 are examined using multifractal detrended fluctuation analysis (MF-DFA). The h(q) curves of D st and a p in the MF-DFA are similar to each other, but they are different from that of X l , indicating that the scaling properties of X l are different from those of D st and a p . Hence, one should not predict the magnitude of magnetic storms directly from solar X-ray observations. However, a strong relationship exists between the classes of the solar X-ray irradiance (the classes being chosen to separate solar flares of class X-M, class C, and class B or less, including no flares) in hourly measurements and the geomagnetic disturbances (large to moderate, small, or quiet) seen in D st and a p during the active period. Each time series was converted into a symbolic sequence using three classes. The frequency, yielding the measure representations, of the substrings in the symbolic sequences then characterizes the pattern of space weather events. Using the MF-DFA method and traditional multifractal analysis, we calculate the h(q), D(q), and τ (q) curves of the measure representations. The τ (q) curves indicate that the measure representations of these three indices are multifractal. On the basis of this three-class clustering, we find that the h(q), D(q), and τ (q) curves of the measure representations of these three indices are similar to each other for positive values of q. Hence, a positive flare storm class dependence is reflected in the scaling exponents h(q) in the MF-DFA and the multifractal exponents D(q) and τ (q). This finding indicates that the use of the solar flare classes could improve the prediction of the D st classes.

Fuzzy methods for analysis of microarrays and networks

Bioinformatics involves analyses of biological data such as DNA sequences, microarrays and protein-protein interaction (PPI) networks. Its two main objectives are the identification of genes or proteins and the prediction of their functions. Biological data often contain uncertain and imprecise information. Fuzzy theory provides useful tools to deal with this type of information, hence has played an important role in analyses of biological data. In this thesis, we aim to develop some new fuzzy techniques and apply them on DNA microarrays and PPI networks. We will focus on three problems: (1) clustering of microarrays; (2) identification of disease-associated genes in microarrays; and (3) identification of protein complexes in PPI networks. The first part of the thesis aims to detect, by the fuzzy C-means (FCM) method, clustering structures in DNA microarrays corrupted by noise. Because of the presence of noise, some clustering structures found in random data may not have any biological significance. In this part, we propose to combine the FCM with the empirical mode decomposition (EMD) for clustering microarray data. The purpose of EMD is to reduce, preferably to remove, the effect of noise, resulting in what is known as denoised data. We call this method the fuzzy C-means method with empirical mode decomposition (FCM-EMD). We applied this method on yeast and serum microarrays, and the silhouette values are used for assessment of the quality of clustering. The results indicate that the clustering structures of denoised data are more reasonable, implying that genes have tighter association with their clusters. Furthermore we found that the estimation of the fuzzy parameter m, which is a difficult step, can be avoided to some extent by analysing denoised microarray data. The second part aims to identify disease-associated genes from DNA microarray data which are generated under different conditions, e.g., patients and normal people. We developed a type-2 fuzzy membership (FM) function for identification of diseaseassociated genes. This approach is applied to diabetes and lung cancer data, and a comparison with the original FM test was carried out. Among the ten best-ranked genes of diabetes identified by the type-2 FM test, seven genes have been confirmed as diabetes-associated genes according to gene description information in Gene Bank and the published literature. An additional gene is further identified. Among the ten best-ranked genes identified in lung cancer data, seven are confirmed that they are associated with lung cancer or its treatment. The type-2 FM-d values are significantly different, which makes the identifications more convincing than the original FM test. The third part of the thesis aims to identify protein complexes in large interaction networks. Identification of protein complexes is crucial to understand the principles of cellular organisation and to predict protein functions. In this part, we proposed a novel method which combines the fuzzy clustering method and interaction probability to identify the overlapping and non-overlapping community structures in PPI networks, then to detect protein complexes in these sub-networks. Our method is based on both the fuzzy relation model and the graph model. We applied the method on several PPI networks and compared with a popular protein complex identification method, the clique percolation method. For the same data, we detected more protein complexes. We also applied our method on two social networks. The results showed our method works well for detecting sub-networks and give a reasonable understanding of these communities.

Analysis of genome survey sequences and SSR marker development for Siamese Mud Carp, Henicorhynchus siamensis, using 454 Pyrosequencing

Siamese mud carp (Henichorynchus siamensis) is a freshwater teleost of high economic importance in the Mekong River Basin. However, genetic data relevant for delineating wild stocks for management purposes currently are limited for this species. Here, we used 454 pyrosequencing to generate a partial genome survey sequence (GSS) dataset to develop simple sequence repeat (SSR) markers from H. siamensis genomic DNA. Data generated included a total of 65,954 sequence reads with average length of 264 nucleotides, of which 2.79% contain SSR motifs. Based on GSS-BLASTx results, 10.5% of contigs and 8.1% singletons possessed significant similarity (E value < 10–5) with the majority matching well to reported fish sequences. KEGG analysis identified several metabolic pathways that provide insights into specific potential roles and functions of sequences involved in molecular processes in H. siamensis. Top protein domains detected included reverse transcriptase and the top putative functional transcript identified was an ORF2-encoded protein. One thousand eight hundred and thirty seven sequences containing SSR motifs were identified, of which 422 qualified for primer design and eight polymorphic loci have been tested with average observed and expected heterozygosity estimated at 0.75 and 0.83, respectively. Regardless of their relative levels of polymorphism and heterozygosity, microsatellite loci developed here are suitable for further population genetic studies in H. siamensis and may also be applicable to other related taxa.

Dimensions of the venture creation process : amount, dynamics, and sequences of action in nascent entrepreneurship

This research examines the entrepreneurship phenomenon, and the question: Why are some venture attempts more successful than others? This question is not a new one. Prior research has answered this by describing those that engage in nascent entrepreneurship. Yet, this approach yielded little consensus and offers little comfort for those newly considering venture creation (Gartner, 1988). Rather, this research considers the process of venture creation, by focusing on the actions of nascent entrepreneurs. However, the venture creation process is complex (Liao, Welsch, & Tan, 2005), and multi-dimensional (Davidsson, 2004). The process can vary in the amount of action engaged by the entrepreneur; the temporal dynamics of how action is enacted (Lichtenstein, Carter, Dooley, and Gartner 2007); or the sequence in which actions are undertaken. And little is known about whether any, or all three, of these dimensions matter. Further, there exists scant general knowledge about how the venture creation process influences venture creation outcomes (Gartner & Shaver, 2011). Therefore, this research conducts a systematic study of what entrepreneurs do as they create a new venture. The primary goal is to develop general principles so that advice may be offered on how to ‘proceed’, rather than how to ‘be’. Three integrated empirical studies were conducted that separately focus on each of the interrelated dimensions. The basis for this was a randomly sampled, longitudinal panel, of nascent ventures. Upon recruitment these ventures were in the process of being created, but yet to be established as new businesses. The ventures were tracked one year latter to follow up on outcomes. Accordingly, this research makes the following original contributions to knowledge. First, the findings suggest that all three of the dimensions play an important role: action, dynamics, and sequence. This implies that future research should take a multi-dimensional view of the venture creation process. Failing to do so can only result in a limited understanding of a complex phenomenon. Second, action is the fundamental means through which venture creation is achieved. Simply put, more active venture creation efforts are more likely more successful. Further, action is the medium which allows resource endowments their effect upon venture outcomes. Third, the dynamics of how venture creation plays out over time is also influential. Here, a process with a high rate of action which increases in intensity will more likely achieve positive outcomes. Forth, sequence analysis, suggests that the order in which actions are taken will also drive outcomes. Although venture creation generally flows in sequence from discovery toward exploitation (Shane & Venkataraman, 2000; Eckhardt & Shane, 2003; Shane, 2003), processes that actually proceed in this way are less likely to be realized. Instead, processes which specifically intertwine discovery and exploitation action together in symbiosis more likely achieve better outcomes (Sarasvathy, 2001; Baker, Miner, & Eesley, 2003). Further, an optimal venture creation order exists somewhere between these sequential and symbiotic process archetypes. A process which starts out as symbiotic discovery and exploitation, but switches to focus exclusively on exploitation later on is most likely to achieve venture creation. These sequence findings are unique, and suggest future integration between opposing theories for order in venture creation.

Health informatics domain knowledge analysis : an information technology perspective

Health Informatics is an intersection of information technology, several disciplines of medicine and health care. It sits at the common frontiers of health care services including patient centric, processes driven and procedural centric care. From the information technology perspective it can be viewed as computer application in medical and/or health processes for delivering better health care solutions. In spite of the exaggerated hype, this field is having a major impact in health care solutions, in particular health care deliveries, decision making, medical devices and allied health care industries. It also affords enormous research opportunities for new methodological development. Despite the obvious connections between Medical Informatics, Nursing Informatics and Health Informatics, most of the methodologies and approaches used in Health Informatics have so far originated from health system management, care aspects and medical diagnostic. This paper explores reasoning for domain knowledge analysis that would establish Health Informatics as a domain and recognised as an intellectual discipline in its own right.

The use of artificial MicroRNA technology to control gene expression in Arabidopsis thaliana

In plants, double-stranded RNA (dsRNA) is an effective trigger of RNA silencing, and several classes of endogenous small RNA (sRNA), processed from dsRNA substrates by DICER-like (DCL) endonucleases, are essential in controlling gene expression. One such sRNA class, the microRNAs (miRNAs) control the expression of closely related genes to regulate all aspects of plant development, including the determination of leaf shape, leaf polarity, flowering time, and floral identity. A single miRNA sRNA silencing signal is processed from a long precursor transcript of nonprotein-coding RNA, termed the primary miRNA (pri-miRNA). A region of the pri-miRNA is partially self-complementary allowing the transcript to fold back onto itself to form a stem-loop structure of imperfectly dsRNA. Artificial miRNA (amiRNA) technology uses endogenous pri-miRNAs, in which the miRNA and miRNA*(passenger strand of the miRNA duplex) sequences have been replaced with corresponding amiRNA/ amiRNA*sequences that direct highly efficient RNA silencing of the targeted gene. Here, we describe the rules for amiRNA design, as well as outline the PCR and bacterial cloning procedures involved in the construction of an amiRNA plant expression vector to control target gene expression in Arabidopsis thaliana. © 2014 Springer Science+Business Media New York.

A copia-like element in Pisum demonstrates the uses of dispersed repeated sequences in genetic analysis

A DNA sequence between two legumin genes in Pisum is a member of the copia-like class of retrotransposons and represents one member of a polymorphic and heterogeneous dispersed repeated sequence family in Pisum. This sequence can be exploited in genetic studies either by RFLP analysis where several markers can be scored together, or the segregation of individual elements can be followed after PCR amplification of specific members.

A transcriptomic analysis of striped catfish (Pangasianodon hypophthalmus) in response to salinity adaptation: De novo assembly, gene annotation and marker discovery

The striped catfish (Pangasianodon hypophthalmus) culture industry in the Mekong Delta in Vietnam has developed rapidly over the past decade. The culture industry now however, faces some significant challenges, especially related to climate change impacts notably from predicted extensive saltwater intrusion into many low topographical coastal provinces across the Mekong Delta. This problem highlights a need for development of culture stocks that can tolerate more saline culture environments as a response to expansion of saline water-intruded land. While a traditional artificial selection program can potentially address this need, understanding the genomic basis of salinity tolerance can assist development of more productive culture lines. The current study applied a transcriptomic approach using Ion PGM technology to generate expressed sequence tag (EST) resources from the intestine and swim bladder from striped catfish reared at a salinity level of 9 ppt which showed best growth performance. Total sequence data generated was 467.8 Mbp, consisting of 4,116,424 reads with an average length of 112 bp. De novo assembly was employed that generated 51,188 contigs, and allowed identification of 16,116 putative genes based on the GenBank non-redundant database. GO annotation, KEGG pathway mapping, and functional annotation of the EST sequences recovered with a wide diversity of biological functions and processes. In addition, more than 11,600 simple sequence repeats were also detected. This is the first comprehensive analysis of a striped catfish transcriptome, and provides a valuable genomic resource for future selective breeding programs and functional or evolutionary studies of genes that influence salinity tolerance in this important culture species.

Computational analysis of epigenetic information in human DNA sequences

Over the last few years, investigations of human epigenetic profiles have identified key elements of change to be Histone Modifications, stable and heritable DNA methylation and Chromatin remodeling. These factors determine gene expression levels and characterise conditions leading to disease. In order to extract information embedded in long DNA sequences, data mining and pattern recognition tools are widely used, but efforts have been limited to date with respect to analyzing epigenetic changes, and their role as catalysts in disease onset. Useful insight, however, can be gained by investigation of associated dinucleotide distributions. The focus of this paper is to explore specific dinucleotides frequencies across defined regions within the human genome, and to identify new patterns between epigenetic mechanisms and DNA content. Signal processing methods, including Fourier and Wavelet Transformations, are employed and principal results are reported.

An archival analysis of green information technology: The current state and future directions

This thesis provides a review of 199 papers published on Green IT/IS between 2007−2014, in order to present taxonomy of segments in Green IT/IS publications, where the segments are later used for multiple analyses to facilitate future research and to provide a retrospective analysis of existing knowledge and gaps thereof. This research also attempts to make a unique contribution to our understanding of Green IT/IS, by consolidating papers it observes current patterns of literature through approach analysis and segmentation, as well as allocating studies to the technology, process, or outcome (TPO) stage. Highlighting the necessity of a consolidated approach, these classification systems have been combined into a TPO matrix so that the studies could be arranged according to which stage of the Green IT/IS cycle they were focused on. We believe that these analyses will provide a solid platform from which future Green IT/IS research can be launched.

A generalized estimating equations approach for analysis of the impact of new technology on a trawl fishery

The article describes a generalized estimating equations approach that was used to investigate the impact of technology on vessel performance in a trawl fishery during 1988-96, while accounting for spatial and temporal correlations in the catch-effort data. Robust estimation of parameters in the presence of several levels of clustering depended more on the choice of cluster definition than on the choice of correlation structure within the cluster. Models with smaller cluster sizes produced stable results, while models with larger cluster sizes, that may have had complex within-cluster correlation structures and that had within-cluster covariates, produced estimates sensitive to the correlation structure. The preferred model arising from this dataset assumed that catches from a vessel were correlated in the same years and the same areas, but independent in different years and areas. The model that assumed catches from a vessel were correlated in all years and areas, equivalent to a random effects term for vessel, produced spurious results. This was an unexpected finding that highlighted the need to adopt a systematic strategy for modelling. The article proposes a modelling strategy of selecting the best cluster definition first, and the working correlation structure (within clusters) second. The article discusses the selection and interpretation of the model in the light of background knowledge of the data and utility of the model, and the potential for this modelling approach to apply in similar statistical situations.