SUMMARY: Eukaryotic gene expression requires coordination among hundreds of transcriptional regulators. To characterize a specific transcriptional regulator, identifying how it shares genomic-binding sites with other regulators can generate important insights into its action. As genomic data such as chromatin immunoprecipitation assays with sequencing (ChIP-Seq) are being continously generated from individual labs, there is a demand for timely integration and analysis of these new data. We have developed an R package, GPSmatch (Genomic-binding Profile Similarity match), for calculating the Jaccard index to compare the ChIP-Seq peaks from one experiment to other experiments stored in a user-supplied customizable database. GPSmatch also evaluates the statistical significance of the calculated Jaccard index using a nonparametric Monte Carlo procedure. We show that GPSmatch is suitable for identifying and ranking transcriptional regulators with shared genomic-binding profiles, which may unravel potential mechanistic actions of gene regulation. AVAILABILITY AND IMPLEMENTATION: The software is freely available at https://github.com/Bao-Lab/GPSmatch. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A scheme of feature analysis in synthetic aperture radar (SAR) imaging based on compressed sensing (CS) is presented. First, a conventional imaging algorithm, such as range-Doppler algorithm or chirp-scaling algorithm, is used to acquire the complex image of the target scene. Then, a greedy algorithm is applied to this complex image to locate the scattering coefficients of the target, where the research of the peak and the removal of the point spread effect are involved. Third, the point set of each target is determined by a clutter method and the range of the target is further fitted by a geometric shape. They are, finally, used to extract the feature parameter of the target. A series of experimental results using real field data demonstrates the effectiveness of our method.

A novel despeckling algorithm for synthetic aperture radar (SAR) image is presented. This algorithm focuses on despeckling a local region for each target pixel. Thus, the calculations of similarities and weights are located within a limited region rather than the entire image. Also, the similarity is measured by using the Euclidean distance of features between the target pixel and neighborhood pixel. In addition, a cosine integral image technique is applied to this scheme to improve the computation efficiency. The weight function is first decomposed into a linear combination of cosine functions, and each convolution with a cosine function requires a constant number of operations per pixel. Then, all the summation operations are carried out by the summed image method. A series of experiment results demonstrates the effectiveness of our method.