SUMMARY: With the development of new high-throughput DNA sequencing technologies and decreasing costs, large gene expression datasets are being generated at an accelerating rate, but can be complex to visualize. New, more interactive and intuitive tools are needed to visualize the spatiotemporal context of expression data and help elucidate gene function. Using tomato fruit as a model, we have developed the Tomato Expression Atlas to facilitate effective data analysis, allowing the simultaneous visualization of groups of genes at a cell/tissue level of resolution within an organ, enhancing hypothesis development and testing in addition to candidate gene identification. This atlas can be adapted to different types of expression data from diverse multicellular species. AVAILABILITY AND IMPLEMENTATION: The Tomato Expression Atlas is available at http://tea.solgenomics.net/ . Source code is available at https://github.com/solgenomics/Tea . CONTACT: [email protected] or [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

This study is concerned with the classification of transient signals that can be represented by piecewise stationary processes. Within each stationary segment, the time series is modeled by a Gaussian autoregressive moving-average (ARMA) process. An algorithm for global classification, based on the entire transient, is presented. A global likelihood function, defined as the product of the generalized likelihood functions associated with each individual segment, performs the classification. For two arbitrary classes of transients, Monte Carlo simulations demonstrate that the method performs better than a classical classification scheme based on a single stationary segment.>

Deconvolution methods are applied to underwater propagation, and the received signal is modeled as the convolution between the wavelet and the medium impulse response. The aim of the method is to extract information on backscattering, travel time delays, boundary reflection and refraction from the received signal on a point receiver on an array. Since experimental data are generally mixed-phase, due in part to the multiple reflections (bottom and surface), conventional linear filtering, which assumes the minimum-phase property, loses in efficacy. To handle the mixed-phase characteristic of the data, the author first applies a homomorphic filter to separate the signal into its minimum and maximum phase components and to deconvolve the wavelet. He then estimates the medium impulse response by means of a Wiener filter. The efficacy of the method is shown on both simulated and real data.>

The authors present an adaptive method to detect transient signals of unknown waveform in colored Gaussian noise. The signal is modeled as the impulse response of a rational transfer function. After a prewhitening of the observed signal, the order of the rational transfer function is estimated by a method based on the covariance matrix perturbation and the coefficients by a maximum-likelihood estimator with constraint. The detection of the signal is performed by means of the generalized likelihood ratio test. The method is compared to an adaptive detection scheme using an autoregressive (AR) model of the signal and to the conventional energy detector. Theoretical detection probabilities are compared to the experimental ones obtained by Monte Carlo simulations.>