International Journal of Bioinformatics Research and Applications最新文献

DNA microarray technology can simultaneously screen thousands of gene expression profiles, transforming how genetics is applied in medicine. However, the lack of normality in microarray data renders common statistical methods ineffective. We propose a novel statistical method which does not require stringent assumptions but is still more powerful than some of its competitors. Using both simulation studies and clinical data, we show that our novel method outperforms previous methods. The limiting distribution for the proposed test is obtained for under null and alternative hypotheses. The proposed test will help make cancer treatment and gene therapy more successful, and it may facilitate research regarding cancer vaccinations. The proposed test may also help in the development of a prediction model in genetic profiling studies built on a subset of differentially expressed genes and the clinical data to assess the accuracy of the clinical prediction.

An interactome is defined as a network of protein-protein interactions built from experimentally verified interactions. Basic science as well as application-based research of potential new drugs can be promoted by including proteins that are only predicted into interactomes. The disadvantage of doing so is the risk of devaluing the definition of interactomes. By adding proteins that have only been predicted, an interactome can no longer be classified as experimentally verified and the integrity of the interactome will be endured. Therefore, we propose the term 'hypothome' (collection of hypothetical interactions of predicted proteins). The purpose of such a term is to provide a denotation to the interactome concept allowing the interaction of predicted proteins without devaluing the integrity of the interactome. We define a rule-set for a hypothome and have integrated the predicted protein interaction partners to the hypothetical protein. EAW74251 is an example for the usage of a hypothome.

Recently, the utility of trait-based approaches for microbial communities has been identified. Increasing availability of whole genome sequences provide the opportunity to explore the genetic foundations of a variety of functional traits. We proposed a machine learning framework to quantitatively link the genomic features with functional traits. Genes from bacteria genomes belonging to different functional traits were grouped to Cluster of Orthologs (COGs), and were used as features. Then, TF-IDF technique from the text mining domain was applied to transform the data to accommodate the abundance and importance of each COG. After TF-IDF processing, COGs were ranked using feature selection methods to identify their relevance to the functional trait of interest. Extensive experimental results demonstrated that functional trait related genes can be detected using our method. Further, the method has the potential to provide novel biological insights.

Stochastic models are increasingly used to study the behaviour of biochemical systems. While the structure of such models is often readily available from first principles, unknown quantitative features of the model are incorporated into the model as parameters. Algorithmic discovery of parameter values from experimentally observed facts remains a challenge for the computational systems biology community. We present a new parameter discovery algorithm that uses simulated annealing, sequential hypothesis testing, and statistical model checking to learn the parameters in a stochastic model. We apply our technique to a model of glucose and insulin metabolism used for in-silico validation of artificial pancreata and demonstrate its effectiveness by developing parallel CUDA-based implementation for parameter synthesis in this model.

A key challenge in upper extremity neuroprosthetics is variable levels of skill and inconsistent functional recovery. We examine the feasibility and benefits of using natural neuromotor strategies through the design and development of a proof-of-concept model for a feed-forward upper extremity neuroprosthetic controller. Developed using Artificial Neural Networks, the model is able to extract and classify neural correlates of movement intention from multiple brain regions that correspond to functional movements. This is unique compared to contemporary controllers that record from limited physiological sources or require learning of new strategies. Functional MRI (fMRI) data from healthy subjects (N = 13) were used to develop the model, and a separate group (N = 4) of subjects were used for validation. Results indicate that the model is able to accurately (81%) predict hand movement strictly from the neural correlates of movement intention. Information from this study is applicable to the development of upper extremity technology aided interventions.

Transposable Elements (TEs) play important roles in the evolution of eukaryotic organisms. TEs widely distribute depending on their properties present in the genome. This study elucidated the molecular characteristics of TEs in land plants and animals using bioinformatics and in silico mutational approach. We discovered that the GC-rich class I TEs is the predominant class of TEs in animal, but the AT-rich class II TEs is prevalent in plants. The GC-rich class I TEs appears to be evolved within the animals. On contrary, the preserved in AT-rich in class II TEs is believed to be contributed in host defence systems.

We develop novel single-GPU parallelisations of the Smith-Waterman algorithm for pairwise sequence alignment. Our algorithms, which are suitable for the alignment of a single pair of very long sequences, can be used to determine the alignment score as well as the actual alignment. Experimental results demonstrate an order of magnitude reduction in run time relative to competing GPU algorithms.

Next generation sequencing technologies have enabled sequencing many genomes. Because of the overall increasing demand and the inherent parallelism available in many required analyses, these bioinformatics applications should ideally run on clusters, clouds and/or grids. We present a modified annotation framework that achieves a speed-up of 45x using 50 workers using a Caenorhabditis japonica test case. We also evaluate these modifications within the Amazon EC2 cloud framework. The underlying genome annotation (MAKER) is parallelised as an MPI application. Our framework enables it to now run without MPI while utilising a wide variety of distributed computing resources. This parallel framework also allows easy explicit data transfer, which helps overcome a major limitation of bioinformatics tools that often rely on shared file systems. Combined, our proposed framework can be used, even during early stages of development, to easily run sequence analysis tools on clusters, grids and clouds.

The Non-Coding RNA (ncRNA) elements in the 3' Untranslated Regions (3'-UTRs) are known to participate in the genes' post-transcriptional regulations. Inferring co-expression patterns of the genes through clustering these 3'-UTR ncRNA elements will provide invaluable insights for studying their biological functions. In this paper, we propose an improved RNA structural clustering pipeline. Benchmark of the new pipeline on Rfam data demonstrates over 10% performance improvements compared to the traditional hierarchical clustering pipeline. By applying the new clustering pipeline to 3'-UTRs of Drosophila melanogaster's genome, we have successfully identified 184 ncRNA clusters with 91.3% accuracy. One of these clusters corresponds to genes that are preferentially expressed in male Drosophila. Another cluster contains genes that are responsible for the functions of septate junction in epithelial cells. These discoveries encourage more studies on novel post-transcriptional regulation mechanisms.