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Griebel T.,Functional Bioinformatics Group | Zacher B.,Functional Bioinformatics Group | Zacher B.,Computational Biology and Regulatory Networks Group | Ribeca P.,Algorithm | And 3 more authors.
Nucleic Acids Research | Year: 2012

High-throughput sequencing of cDNA libraries constructed from cellular RNA complements (RNA-Seq) naturally provides a digital quantitative measurement for every expressed RNA molecule. Nature, impact and mutual interference of biases in different experimental setups are, however, still poorly understood - mostly due to the lack of data from intermediate protocol steps. We analysed multiple RNA-Seq experiments, involving different sample preparation protocols and sequencing platforms: we broke them down into their common - and currently indispensable - technical components (reverse transcription, fragmentation, adapter ligation, PCR amplification, gel segregation and sequencing), investigating how such different steps influence abundance and distribution of the sequenced reads. For each of those steps, we developed universally applicable models, which can be parameterised by empirical attributes of any experimental protocol. Our models are implemented in a computer simulation pipeline called the Flux Simulator, and we show that read distributions generated by different combinations of these models reproduce well corresponding evidence obtained from the corresponding experimental setups. We further demonstrate that our in silico RNA-Seq provides insights about hidden precursors that determine the final configuration of reads along gene bodies; enhancing or compensatory effects that explain apparently controversial observations can be observed. Moreover, our simulations identify hitherto unreported sources of systematic bias from RNA hydrolysis, a fragmentation technique currently employed by most RNA-Seq protocols. © 2012 The Author(s). Source


Azuma H.,Institute of Computational Fluid Dynamics | Azuma H.,Algorithm
Progress of Theoretical Physics | Year: 2011

In this paper, we propose a method for building a two-qubit gate with the Jaynes- Cummings model (JCM). In our scheme, we construct a qubit from a pair of optical paths where a photon is running. Generating Knill, Laflamme and Milburn's nonlinear sign-shift gate by the JCM, we construct the conditional sign-flip gate, which works with small error probability in principle. We also discuss two experimental setups for realizing our scheme. In the first experimental setup, we make use of coherent lights to examine whether or not our scheme works. In the second experimental setup, an optical loop circuit made out of the polarizing beam splitter and the Pockels cell takes an important role in the cavity. Source


Djuric P.M.,State University of New York at Stony Brook | Khan M.,Algorithm | Johnston D.E.,Quantalysis LLC
IEEE Journal on Selected Topics in Signal Processing | Year: 2012

In this paper, we address univariate stochastic volatility models that allow for correlation of the perturbations in the state and observation equations, i.e., models with leverage. We propose a particle filtering method for estimating the posterior distributions of the log-volatility, where we employ Rao-Blackwellization of the unknown static parameters of the model. We also propose a scheme for choosing the best model from a set of considered models and a test for assessing the validity of the selected model. We demonstrate the performance of the proposed method on simulated and S&P 500 data. © 2011 IEEE. Source


Ribeca P.,Algorithm | Valiente G.,University of Barcelona
Briefings in Bioinformatics | Year: 2011

Next-generation sequencing technologies have opened up an unprecedented opportunity for microbiology by enabling the culture-independent genetic study of complex microbial communities, which were so far largely unknown. The analysis of metagenomic data is challenging: potentially, one is faced with a sample containing a mixture of many different bacterial species, whose genome has not necessarily been sequenced beforehand. In the simpler case of the analysis of 16S ribosomal RNA metagenomic data, for which databases of reference sequences are known, we survey the computational challenges to be solved in order to be able to characterize and quantify a sample. In particular, we examine two aspects: how the necessary adoption of new tools geared towards high-throughput analysis impacts the quality of the results, and how good is the performance of various established methods to assign sequence reads to microbial species, with and without taking taxonomic information into account. © The Author 2011. Published by Oxford University Press. Source


Luo W.,Guangxi University for Nationalities | Luo W.,Southwest Jiaotong University | Fang X.,Guangxi University for Nationalities | Cheng M.,Guangxi University for Nationalities | Zhao Y.,Algorithm
IEEE Transactions on Vehicular Technology | Year: 2013

The capacity of a multiple-input-multiple-output (MIMO) channel with $N$ transmit and receive antennas for high-speed railways (HSRs) is analyzed based on the 3-D modeling of the line of sight (LOS). The MIMO system utilizes a uniform linear antenna array. Instead of increasing the number of antennas or simply changing the parameters of the antenna array, such as separation and geometry, the capacity gain can be obtained by adjusting the weights of multiantenna array groups, because there are few scatterers in strong LOS environments. On the other hand, it is hard to obtain the array gain of MIMO beamforming for HSRs because of drastic changes in the receiving angle when the train travels across E-UTRAN Node B. Without changing the antenna design of Long-Term Evolution systems, this paper proposes a multiple-group multiple-antenna (MGMA) scheme that makes the columns of such a MIMO channel orthogonal by adjusting the weights among MGMA arrays, and the stable capacity gain can be obtained. The value of weights depends on the practical network topologies of the railway wireless communication system. However, the reasonable scope of group number $N$ is less than 6. In selecting $N$, one important consideration is the tradeoff between practical benefit and cost of implementation. © 1967-2012 IEEE. Source

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