Summary Mobile element insertion (MEI) is a major category of structure variations (SVs). The rapid development of long read sequencing technologies provides the opportunity to detect MEIs sensitively. However, the signals of MEI implied by noisy long reads are highly complex due to the repetitiveness of mobile elements as well as the high sequencing error rates. Herein, we propose the Realignment-based Mobile Element insertion detection Tool for Long read (rMETL). Benchmarking results of simulated and real datasets demonstrate that rMETL enables to handle the complex signals to discover MEIs sensitively. It is suited to produce high-quality MEI callsets in many genomics studies. Availability and implementation rMETL is available from https://github.com/hitbc/rMETL. Supplementary information Supplementary data are available at Bioinformatics online.
Background: Searching the drug molecules from the medicinal plants become more and more popular given that herbal components have been widely considered to be safe. In medical virtual plant studies, development rules are difficult to be extracted, the construction of plant organs is highly dependent on equipment and the process is complicated. Aim: To establish three-dimensional structural virtual plant growth model. Methods: The quasi-binary tree structure and its properties were obtained through the research of theory on binary tree, then the relationship between quasi-binary tree structure and plant three-dimensional branching structure model was analyzed, and the three-dimensional morphology of plants was described. Results: A three-dimensional plant branch structure pattern extracting algorithm based on quasi-binary tree structure. By using 3-D L-system method, the extracted rules were systematized, and standardized. Further more, we built a comprehensive L-model system. With the aid of graphics and PlantVR, we implemented the plant shape and 3-D structure's reconstruction. Conclusion: Three-dimensional structure virtual plant growth model based on time-controlled L-system has been successfully established.
The advent of SOA and Grid technology has brought new challenges to workflow operation and performance evaluation. In this paper, the characteristics of service-oriented workflow are presented, based on which the service-oriented workflow performance evaluation model is described and the performance analysis methods are depicted. Finally the design and implementation of our prototype system are introduced briefly.
The aim of this paper is to solve the problem of a large amount of redundant data in the process of large-scale acquisition of Internet of Things sensors. This paper proposes a frequency adaptive data sensing method based on revolving gate algorithm for STM32 power safety data acquisition system. The method realizes the intelligent control of current protection terminal through three-stage current protection algorithm and frequency adaptive data acquisition algorithm. First, the three-stage current protection algorithm is used to protect the circuit, which can trip quickly in case of overload, and reduce the peak current caused by some equipment when starting up, so as to avoid damage to the equipment. Second, the arithmetic mean of each statistic is compared with the last value reported to the server. If the absolute value of the difference between the two exceeds the specified threshold range, the reported value is updated. Otherwise, it is filtered out, and the data smoothness is calculated according to the rules. The data collection interval is dynamically adjusted according to the data smoothness, which can greatly reduce the collection of redundant data and the traffic consumption of Internet of Things devices. On the server side, the inverse algorithm is used for interpolation and reconstruction to recover the collected data. This method is applied to the developed electricity safety equipment, and the electricity consumption is monitored and collected continuously for 24 h. After filtering by the frequency adaptive algorithm, only 108 pieces of data records are reported for 28,713 pieces of original data, and the compression ratio reaches 99.49[Formula: see text]. Compared with other data collection strategies, this method significantly reduces the amount of redundant data collection and the energy consumption of terminal nodes. Furthermore, this method realizes the real-time perception of line data and environmental data through current, residual current, temperature sensor and electric energy metering chip, and controls the opening and closing of wire controlled micro circuit breaker through PC817 optocoupler to protect the safety of the circuit.
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