コンピュータサイエンスとシステム生物学のジャーナル

Short interfering RNA (siRNA) has been widely used for studying gene functions in mammalian cells but varies markedly in its gene silencing efficacy. Although many design rules/guidelines for effective siRNAs based on various criteria have been reported recently, there are only a few consistencies among them. This makes it difficult to select effective siRNA sequences in mammalian genes. This paper first clarifies problems of the recently reported siRNA design guidelines and then proposes a new method for selecting effective siRNA sequences from many possible candidates by using the average silencing probability on the basis of large number of known effective siRNAs. It is different from the previous score-based siRNA design techniques and can predict the probability that a candidate siRNA sequence will be effective. The results of evaluating it by applying it to recently reported effective and ineffective siRNA sequences for various genes indicate that it would be useful for many other genes. The evaluation results indicate that the proposed method would be useful for many other genes. It should therefore be useful for selecting siRNA sequences effective for mammalian genes. The paper also describes another method using a Hidden Markov Model (HMM) to select the optimal functional siRNAs.

The neuron is a basic element of brain networks. Changes of nerve cell excitability, the conduction of excitation, synaptic memory-forming in the case of the temporal coincidence of synaptic events are the obvious functions of the neuron - element of brain networks. The implementation of the neuron function depends on actions of its numerous molecular systems. The generalization of the complex processes of emergence of synaptic memory, that occur even in separate neuronal compartments, without special tools is a difficult, if at all possible, task. A technology that combines the creation of databases (elements and their relationships) with a visual representation in the form of networks facilitates this process. The developed protein-protein interaction network in dendritic spines of hippocampal pyramidal neurons facilitates the synthesis of numerous experimental data in conceptual knowledge about the principles and molecular mechanisms of neurons functioning.

Purpose: To investigate how activity of natural pupils under binocular and monocular conditions affect wave front aberrations. Materials and Methods: Eighteen eyes from 18 subjects (mean age 22.3 ± 0.8 years) were included in the study. The undilated pupil diameters under photopic conditions were measured using the FP-10000 (TMI, Japan) infrared pupillometer. Aberrometry measurements were performed using the KR-9000PW (Topcon, Japan) Hartmann- Shack wavefront sensor. Zernike coefficients were recalculated for the diameters of each pupil under binocular and monocular conditions using Schwiegerling’s algorithm. Multiple regression analysis was performed to analyze independent predictors of the change of higher-order aberration for 6.0 mm from the binocular to the monocular condition. The independent variables were the change of pupil diameter from binocular to monocular condition; binocular pupil diameter; total higher-order aberration for 6.0 mm, sphere, and cylinder. Results: Pupil diameter, total, total higher-order, coma-like, and spherical-like aberrations under monocular conditions were significantly greater than the binocular condition (all P<0.01). The multiple regression of analysis of variables showed that the change of total higher order aberration from the binocular to the monocular condition was related to the change of pupil diameter, and the amount of higher-order aberrations for 6.0 mm (P<0.05). Conclusion: The outcomes suggest that increased pupil diameter under monocular conditions produces higher wavefront aberrations than under binocular conditions, resulting in a degradation of retinal image quality. This effect is enhanced in eyes with greater higher order aberrations and pupil diameter

Clinicians have unique insight into the diseases and medical conditions they treat, and may develop their own hypotheses they wish to explore by examining existing cases in a data warehouse. To facilitate manual data mining by clinicians and scientists, we have developed an interface for our clinical data warehouse, the Aggregated Biomedical-information Browser (ABB), based on OLAP (On-Line Analytical Processing) technology. The ABB enables clinicians, researchers, and other domain experts to quickly and intuitively explore data in our data warehouse, the Data Warehouse for Translational Research (DW4TR), without needing to involve informatics staff for data extraction. The ABB is capable of handling “on the fly” queries of any data element within the DW4TR. This functionality enables researchers to use their domain knowledge to connect disparate data points as one discovery leads to another. Hypotheses generated through manual data mining combined with domain knowledge, can then be tested using more advanced statistical methods. To illustrate this process a manual data mining example comparing breast cancer pathology in African American and Caucasian American women is performed using the ABB. Analysis of several breast cancer pathology markers suggest African American women will have a worse clinical outcome than Caucasian American women, a clinically meaningful outcome well documented in scientific literature. This report demonstrates the simple yet powerful use of the ABB for manual data exploration in the initial hypothesis generation stage.

Next-generation sequencing technologies provide a revolutionary tool for generating gene expression data. Starting with a fixed RNA sample, they construct a library of millions of differentially abundant short sequence tags or “reads”, which constitute a fundamentally discrete measure of the level of gene expression. A common limitation in experiments using these technologies is the low number or even absence of biological replicates, which complicates the statistical analysis of digital gene expression data. Analysis of this type of data has often been based on modified tests originally devised for analysing microarrays; both these and even de novo methods for the analysis of RNA-seq data are plagued by the common problem of low replication. We propose a novel, non-parametric Bayesian approach for the analysis of digital gene expression data. We begin with a hierarchical model for modelling over-dispersed count data and a blocked Gibbs sampling algorithm for inferring the posterior distribution of model parameters conditional on these counts. The algorithm compensates for the problem of low numbers of biological replicates by clustering together genes with tag counts that are likely sampled from a common distribution and using this augmented sample for estimating the parameters of this distribution. The number of clusters is not decided a priori, but it is inferred along with the remaining model parameters. We demonstrate the ability of this approach to model biological data with high fidelity by applying the algorithm on a public dataset obtained from cancerous and non-cancerous neural tissues. Source code implementing the methodology presented in this paper takes the form of the Python Package DGEclust, which is freely available at the following link: https://bitbucket.org/DimitrisVavoulis/dgeclust.

The various genome sequencing projects have led to the accumulation of entire set of gene sequences of many organisms. Among the sequenced genomes are numerous genes which code for proteins of unknown function. These genes are termed as hypothetical genes and their corresponding gene products are known as Hypothetical Proteins (HPs). Analyzing and annotating the functions of these HPs is important in pathogenic organisms such as Chlamydia trachomatis that causes various sequelae of diseases by infecting different sites in humans. Functional annotations of these HPs provides insights into their exact molecular function and may help in identification of novel drug or vaccine candidates for the control of infections caused by C. trachomatis. In the present study, entire set of 336 HPs of C. trachomatis were retrieved from NCBI and analyzed for their function using bioinformatics tools such as CDD-BLAST, PFAM, TIGRFAM and SCANPROSITE. The analysis revealed that some of the HPs possessed functionally important domains like protease, ligase, synthase, translocase and zinc finger domain. Some of the hypothetical proteins were found to be similar to transcriptional regulators while others were homologous to chaperonins. A few of the HPs corresponded to the bacterial secretory pathway proteins. The structural prediction of the annotated proteins has been performed which further substantiate the functional characterization results. Bioinformatics approach used in this study, including sophisticated sequence analysis, domain characterization and structural prediction studies, can provide a useful lead to experimentally annotate and corroborate these studies. Data generated by this study might facilitate swift identification of potential therapeutic targets and thereby enabling the search for new inhibitors or vaccines.