No abstract

BackgroundAlternative Splicing (AS) as a post-transcription regulation mechanism is an important application of RNA-seq studies in eukaryotes. A number of software and computational methods have been developed for detecting AS. Most of the methods, however, are designed and tested on animal data, such as human and mouse. Plants genes differ from those of animals in many ways, e.g., the average intron size and preferred AS types. These differences may require different computational approaches and raise questions about their effectiveness on plant data. The goal of this paper is to benchmark existing computational differential splicing (or transcription) detection methods so that biologists can choose the most suitable tools to accomplish their goals.ResultsThis study compares the eight popular public available software packages for differential splicing analysis using both simulated and real Arabidopsis thaliana RNA-seq data. All software are freely available. The study examines the effect of varying AS ratio, read depth, dispersion pattern, AS types, sample sizes and the influence of annotation. Using a real data, the study looks at the consistences between the packages and verifies a subset of the detected AS events using PCR studies.ConclusionsNo single method performs the best in all situations. The accuracy of annotation has a major impact on which method should be chosen for AS analysis. DEXSeq performs well in the simulated data when the AS signal is relative strong and annotation is accurate. Cufflinks achieve a better tradeoff between precision and recall and turns out to be the best one when incomplete annotation is provided. Some methods perform inconsistently for different AS types. Complex AS events that combine several simple AS events impose problems for most methods, especially for MATS. MATS stands out in the analysis of real RNA-seq data when all the AS events being evaluated are simple AS events.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-014-0364-4) contains supplementary material, which is available to authorized users.

Accurate detection of somatic mutations is still a challenge in cancer analysis. Here we present NeuSomatic, the first convolutional neural network approach for somatic mutation detection, which significantly outperforms previous methods on different sequencing platforms, sequencing strategies, and tumor purities. NeuSomatic summarizes sequence alignments into small matrices and incorporates more than a hundred features to capture mutation signals effectively. It can be used universally as a stand-alone somatic mutation detection method or with an ensemble of existing methods to achieve the highest accuracy.

Wearable sensors have recently attracted extensive interest not only in the field of healthcare monitoring but also for convenient and intelligent human–machine interactions. However, challenges such as wearable comfort, multiple applicable conditions, and differentiation of mechanical stimuli are yet to be fully addressed. Herein, we developed a breathable and waterproof electronic skin (E-skin) that can perceive pressure/strain with nonoverlapping signals. The synergistic effect from magnetic attraction and nanoscaled aggregation renders the E-skin with microscaled pores for breathability and three-dimensional microcilia for superhydrophobicity. Upon applied pressure, the bending of conductive microcilia enables sufficient contacts for resistance decrease, while the stretching causes increased resistance due to the separation of conductive materials. The optimized E-skin exhibits a high gauge factor of 7.747 for small strain (0–80%) and a detection limit down to 0.04%. The three-dimensional microcilia also exhibit a sensitivity of −0.0198 kPa–1 (0–3 kPa) and a broad detection range up to 200 kPa with robustness. The E-skin can reliably and precisely distinguish kinds of the human joint motions, covering a broad spectrum including bending, stretching, and pressure. With the nonoverlapping readouts, ternary inputs “1”, “0”, and “–1” could be produced with different stimuli, which expands the command capacity for logic outputs such as effective Morse code and intuitive robotic control. Owing to the rapid response, long-term stability (10 000 cycles), breathability, and superhydrophobicity, we believe that the E-skin can be widely applied as wearable devices from body motion monitoring to human–machine interactions toward a more convenient and intelligent future.

We present NeuSomatic, the first convolutional neural network approach for somatic mutation detection, which significantly outperforms previous methods on different sequencing platforms, sequencing strategies, and tumor purities. NeuSomatic summarizes sequence alignments into small matrices and incorporates more than a hundred features to capture mutation signals effectively. It can be used universally as a stand-alone somatic mutation detection method or with an ensemble of existing methods to achieve the highest accuracy.