Genome informatics. International Conference on Genome Informatics最新文献

Several technologies are currently used for gene expression profiling, such as Real Time RT-PCR, microarray and CAGE (Cap Analysis of Gene Expression). CAGE is a recently developed method for constructing transcriptome maps and it has been successfully applied to analyzing gene expressions in diverse biological studies. The principle of CAGE has been developed to address specific issues such as determination of transcriptional starting sites, the study of promoter regions and identification of new transcripts. Here, we present both quantitative and qualitative comparisons among three major gene expression quantification techniques, namely: CAGE, illumina microarray and Real Time RT-PCR, by showing that the quantitative values of each method are not interchangeable, however, each of them has unique characteristics which render all of them essential and complementary. Understanding the advantages and disadvantages of each technology will be useful in selecting the most appropriate technique for a determined purpose.

Annotating genes is a fundamental issue in the post-genomic era. A typical procedure for this issue is first clustering genes by their features and then assigning functions of unknown genes by using known genes in the same cluster. A lot of genomic information are available for this issue, but two major types of data which can be measured for any gene are microarray expressions and sequences, both of which however have their own flaws. Thus a natural and promising approach for gene annotation is to integrate these two data sources, especially in terms of their costs to be optimized in clustering. We develop an efficient gene annotation method with three steps containing spectral clustering over the integrated cost, based on the idea of network modularity. We rigorously examined the performance of our proposed method from three different viewpoints. All experimental results indicate the performance advantage of our method over possible clustering/classification-based approaches of gene function annotation, using expressions and/or sequences.

Phenotype prediction from genotype data is one of the most important issues in computational genetics. In this work, we propose a new kernel (i.e., an SVM: Support Vector Machine) method for phenotype prediction from genotype data. In our method, we first infer multiple suboptimal haplotype candidates from each genotype by using the HMM (Hidden Markov Model), and the kernel matrix is computed based on the predicted haplotype candidates and their emission probabilities from the HMM. We validated the performance of our method through experiments on several datasets: One is an artificially constructed dataset via a program GeneArtisan, others are a real dataset of the NAT2 gene from the international HapMap project, and a real dataset of genotypes of diseased individuals. The experiments show that our method is superior to ordinary naive kernel methods (i.e., not based on haplotype prediction), especially in cases of strong LD (linkage disequilibrium).

We propose a state space representation of vector autoregressive model and its sparse learning based on L1 regularization to achieve efficient estimation of dynamic gene networks based on time course microarray data. The proposed method can overcome drawbacks of the vector autoregressive model and state space model; the assumption of equal time interval and lack of separation ability of observation and systems noises in the former method and the assumption of modularity of network structure in the latter method. However, in a simple implementation the proposed model requires the calculation of large inverse matrices in a large number of times during parameter estimation process based on EM algorithm. This limits the applicability of the proposed method to a relatively small gene set. We thus introduce a new calculation technique for EM algorithm that does not require the calculation of inverse matrices. The proposed method is applied to time course microarray data of lung cells treated by stimulating EGF receptors and dosing an anticancer drug, Gefitinib. By comparing the estimated network with the control network estimated using non-treated lung cells, perturbed genes by the anticancer drug could be found, whose up- and down-stream genes in the estimated networks may be related to side effects of the anticancer drug.

We calculated intramolecular interaction energies of DNA by threading DNA sequences around crystal structures of nucleosomes. The strength of the intramolecular energy oscillations at frequency approximately 10 bps for dinucleotides was in agreement with previous nucleosome models. The intramolecular energy calculated along yeast genome positively correlated with nucleosome positioning experimentally measured.

Activities of prokaryotes are pivotal in shaping the environment, and are also greatly influenced by the environment. With the substantial progress in genome and metagenome sequencing and the about-to-be-standardized ecological context information, environment-centric comparative genomics will complement species-centric comparative genomics, illuminating how environments have shaped and maintained prokaryotic diversities. In this paper we report our preliminary studies on the association analysis of a particular duo of genomic and ecological traits of prokaryotes--gene-gene functional association patterns vs. oxygen requirement conditions. We first establish a stochastic model to describe gene arrangements on chromosomes, based on which the functional association between genes are quantified. The gene-gene functional association measures are validated using biological process ontology and KEGG pathway annotations. Student's t-tests are then performed on the aerobic and anaerobic organisms to identify those gene pairs that exhibit different functional association patterns in the two different oxygen requirement conditions. As it is difficult to design and conduct biological experiments to validate those genome-environment association relationships that have resulted from long-term accumulative genome-environment interactions, we finally conduct computational validations to determine whether the oxygen requirement condition of an organism is predictable based on gene-gene functional association patterns. The reported study demonstrates the existence and significance of the association relationships between certain gene-gene functional association patterns and oxygen requirement conditions of prokaryotes, as well as the effectiveness of the adopted methodology for such association analysis.