Selecting effective siRNA sequences by using radial basis function network and decision tree learning

BACKGROUND: Although short interfering RNA (siRNA) has been widely used for studying gene functions in mammalian cells, its gene silencing efficacy varies markedly and there are only a few consistencies among the recently reported design rules/guidelines for selecting siRNA sequences effective for mammalian genes. Another shortcoming of the previously reported methods is that they cannot estimate the probability that a candidate sequence will silence the target gene. RESULTS: We propose two prediction methods for selecting effective siRNA target sequences from many possible candidate sequences, one based on the supervised learning of a radial basis function (RBF) network and other based on decision tree learning. They are quite different from the previous score-based siRNA design techniques and can predict the probability that a candidate siRNA sequence will be effective. The proposed methods were evaluated by applying them to recently reported effective and ineffective siRNA sequences for various genes (15 genes, 196 siRNA sequences). We also propose the combined prediction method of the RBF network and decision tree learning. As the average prediction probabilities of gene silencing for the effective and ineffective siRNA sequences of the reported genes by the proposed three methods were respectively 65% and 32%, 56.6% and 38.1%, and 68.5% and 28.1%, the methods imply high estimation accuracy for selecting candidate siRNA sequences. CONCLUSION: New prediction methods were presented for selecting effective siRNA sequences. As the proposed methods indicated high estimation accuracy for selecting candidate siRNA sequences, they would be useful for many other genes

Drug interaction prediction using ontology-driven hypothetical assertion framework for pathway generation followed by numerical simulation

<p>Abstract</p> <p>Background</p> <p>In accordance with the increasing amount of information concerning individual differences in drug response and molecular interaction, the role of <it>in silico </it>prediction of drug interaction on the pathway level is becoming more and more important. However, in view of the interferences for the identification of new drug interactions, most conventional information models of a biological pathway would have limitations. As a reflection of real world biological events triggered by a stimulus, it is important to facilitate the incorporation of known molecular events for inferring (unknown) possible pathways and hypothetic drug interactions. Here, we propose a new Ontology-Driven Hypothetic Assertion (OHA) framework including pathway generation, drug interaction detection, simulation model generation, numerical simulation, and hypothetic assertion. Potential drug interactions are detected from drug metabolic pathways dynamically generated by molecular events triggered after the administration of certain drugs. Numerical simulation enables to estimate the degree of side effects caused by the predicted drug interactions. New hypothetic assertions of the potential drug interactions and simulation are deduced from the Drug Interaction Ontology (DIO) written in Web Ontology Language (OWL).</p> <p>Results</p> <p>The concept of the Ontology-Driven Hypothetic Assertion (OHA) framework was demonstrated with known interactions between irinotecan (CPT-11) and ketoconazole. Four drug interactions that involved cytochrome p450 (CYP3A4) and albumin as potential drug interaction proteins were automatically detected from Drug Interaction Ontology (DIO). The effect of the two interactions involving CYP3A4 were quantitatively evaluated with numerical simulation. The co-administration of ketoconazole may increase AUC and Cmax of SN-38(active metabolite of irinotecan) to 108% and 105%, respectively. We also estimates the potential effects of genetic variations: the AUC and Cmax of SN-38 may increase to 208% and 165% respectively with the genetic variation UGT1A1*28/*28 which reduces the expression of UGT1A1 down to 30%.</p> <p>Conclusion</p> <p>These results demonstrate that the Ontology-Driven Hypothetic Assertion framework is a promising approach for <it>in silico </it>prediction of drug interactions. The following future researches for the <it>in silico </it>prediction of individual differences in the response to the drug and drug interactions after the administration of multiple drugs: expansion of the Drug Interaction Ontology for other drugs, and incorporation of virtual population model for genetic variation analysis, as well as refinement of the pathway generation rules, the drug interaction detection rules, and the numerical simulation models.</p

Improving the Research Environment of High Performance Computing for Non-Cluster Experts Based on Knoppix Instant Computing Technology

Abstract. We have designed and implemented a new portable system that can rapidly construct a computer environment where highthroughput research applications can be performed instantly. One challenge in the instant computing area is constructing a cluster system instantly, and then readily restoring it to its former state. This paper presents an approach for instant computing using Knoppix technology that can allow even a non-computer specialist to easily construct and operate a Beowulf cluster . In the present bio-research field, there is now an urgent need to address the nagging problem posed by having highperformance computers. Therefore, we were assigned the task of proposing a way to build an environment where a cluster computer system can be instantly set up. Through such research, we believe that the technology can be expected to accelerate scientific research. However, when employing this technology in bio-research, a capacity barrier exists when selecting a clustered Knoppix system for a data-driven bioinformatics application. We have approached ways to overcome said barrier by using a virtual integrated RAM-DISK to adapt to a parallel file system. To show an actual example using a reference application, we have chosen InterProScan, which is an integrated application prepared by the European Bioinformatics Institute (EBI) that utilizes many database and scan methods. InterProScan is capable of scaling workload with local computational resources, though biology researchers and even bioinformatics researchers find such extensions difficult to set up. We have achieved the purpose of allowing even researchers who are non-cluster experts to easily build a system of &quot;Knoppix for the InterProScan4.1 High Throughput Computing Edition.&quot; The system we developed is capable of not only constructing a cluster computer environment composed of 32 computers in about ten minutes (as opposed to six hours when done manually), but also restoring the original environment by rebooting the pre-existing operating system. The goal of our instant cluster computing is to provide an environment in which any target application can be built instantly from anywhere

RARGE: a large-scale database of RIKEN Arabidopsis resources ranging from transcriptome to phenome

The RIKEN Arabidopsis Genome Encyclopedia (RARGE) database houses information on biological resources ranging from transcriptome to phenome, including RIKEN Arabidopsis full-length (RAFL) complementary DNAs (cDNAs), their promoter regions, Dissociation (Ds) transposon-tagged lines and expression data from microarray experiments. RARGE provides tools for searching by resource code, sequence homology or keyword, and rapid access to detailed information on the resources. We have isolated 245 946 RAFL cDNA clones and collected 11 933 transposon-tagged lines, which are available from the RIKEN Bioresource Center and are stored in RARGE. The RARGE web interface can be accessed at http://rarge.gsc.riken.jp/. Additionally, we report 90 000 new RAFL cDNA clones here

A flexible representation of omic knowledge for thorough analysis of microarray data

BACKGROUND: In order to understand microarray data reasonably in the context of other existing biological knowledge, it is necessary to conduct a thorough examination of the data utilizing every aspect of available omic knowledge libraries. So far, a number of bioinformatics tools have been developed. However, each of them is restricted to deal with one type of omic knowledge, e.g., pathways, interactions or gene ontology. Now that the varieties of omic knowledge are expanding, analysis tools need a way to deal with any type of omic knowledge. Hence, we have designed the Omic Space Markup Language (OSML) that can represent a wide range of omic knowledge, and also, we have developed a tool named GSCope3, which can statistically analyze microarray data in comparison with the OSML-formatted omic knowledge data. RESULTS: In order to test the applicability of OSML to represent a variety of omic knowledge specifically useful for analysis of Arabidopsis thaliana microarray data, we have constructed a Biological Knowledge Library (BiKLi) by converting eight different types of omic knowledge into OSML-formatted datasets. We applied GSCope3 and BiKLi to previously reported A. thaliana microarray data, so as to extract any additional insights from the data. As a result, we have discovered a new insight that lignin formation resists drought stress and activates transcription of many water channel genes to oppose drought stress; and most of the 20S proteasome subunit genes show similar expression profiles under drought stress. In addition to this novel discovery, similar findings previously reported were also quickly confirmed using GSCope3 and BiKLi. CONCLUSION: GSCope3 can statistically analyze microarray data in the context of any OSML-represented omic knowledge. OSML is not restricted to a specific data type structure, but it can represent a wide range of omic knowledge. It allows us to convert new types of omic knowledge into datasets that can be used for microarray data analysis with GSCope3. In addition to BiKLi, by collecting various types of omic knowledge as OSML libraries, it becomes possible for us to conduct detailed thorough analysis from various biological viewpoints. GSCope3 and BiKLi are available for academic users at our web site