MOTIVATION: Alternative polyadenylation (APA) has been widely recognized as a widespread mechanism modulated dynamically. Studies based on 3' end sequencing and/or RNA-seq have profiled poly(A) sites in various species with diverse pipelines, yet no unified and easy-to-use toolkit is available for comprehensive APA analyses. RESULTS: We developed an R package called movAPA for modeling and visualization of dynamics of alternative polyadenylation across biological samples. movAPA incorporates rich functions for preprocessing, annotation and statistical analyses of poly(A) sites, identification of poly(A) signals, profiling of APA dynamics and visualization. Particularly, seven metrics are provided for measuring the tissue-specificity or usages of APA sites across samples. Three methods are used for identifying 3' UTR shortening/lengthening events between conditions. APA site switching involving non-3' UTR polyadenylation can also be explored. Using poly(A) site data from rice and mouse sperm cells, we demonstrated the high scalability and flexibility of movAPA in profiling APA dynamics across tissues and single cells. AVAILABILITY AND IMPLEMENTATION: https://github.com/BMILAB/movAPA. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Alternative polyadenylation (APA) has been implicated to play an important role in post-transcriptional regulation by regulating mRNA abundance, stability, localization and translation, which contributes considerably to transcriptome diversity and gene expression regulation. RNA-seq has become a routine approach for transcriptome profiling, generating unprecedented data that could be used to identify and quantify APA site usage. A number of computational approaches for identifying APA sites and/or dynamic APA events from RNA-seq data have emerged in the literature, which provide valuable yet preliminary results that should be refined to yield credible guidelines for the scientific community. In this review, we provided a comprehensive overview of the status of currently available computational approaches. We also conducted objective benchmarking analysis using RNA-seq data sets from different species (human, mouse and Arabidopsis) and simulated data sets to present a systematic evaluation of 11 representative methods. Our benchmarking study showed that the overall performance of all tools investigated is moderate, reflecting that there is still lot of scope to improve the prediction of APA site or dynamic APA events from RNA-seq data. Particularly, prediction results from individual tools differ considerably, and only a limited number of predicted APA sites or genes are common among different tools. Accordingly, we attempted to give some advice on how to assess the reliability of the obtained results. We also proposed practical recommendations on the appropriate method applicable to diverse scenarios and discussed implications and future directions relevant to profiling APA from RNA-seq data.

Network intrusion detection based on SVM is the hot topic of network security research, and the existing researches have low detection rate, high false positive rate and other issues. Optimizing particle swarm optimization parameters of SVM is an effective solution, but the PSO algorithm is easy to fall into local optimum and results premature convergence.We propose an improved particle swarm optimization algorithm ICPSO, which use chaos operator ergodicity, randomness, sensitivity to initial conditions and other characteristics and the ICPSO is used to make the chaos into the inertia weight factor parameters and The chaos is applied to the optimization of the RBF kernel function parameter g and the penalty factor C, and to improve the convergence speed and precision of the particle swarm optimization. The experimental results show that: relative to the PSO-SVM algorithm and GA-SVM algorithm, ICPSO-SVM improves the efficiency of intrusion detection, and is an effective intrusion detection model.