HPG Aligner applies suffix arrays for DNA read mapping. This implementation produces a highly sensitive and extremely fast mapping of DNA reads that scales up almost linearly with read length. The approach presented here is faster (over 20× for long reads) and more sensitive (over 98% in a wide range of read lengths) than the current state-of-the-art mappers. HPG Aligner is not only an optimal alternative for current sequencers but also the only solution available to cope with longer reads and growing throughputs produced by forthcoming sequencing technologies.Availability and implementation:
https://github.com/opencb/hpg-aligner.Contact:
jdopazo@cipf.es or imedina@ebi.ac.ukSupplementary information:
Supplementary data are available at Bioinformatics online.
In this paper, a formal analysis of security protocols in the field of wireless sensor networks is presented. Three complementary protocols, TinySec, LEAP and TinyPK, are modelled using the high-level formal language HLPSL, and verified using the model checking tool AVISPA, where two main security properties are checked: authenticity and confidentiality of messages. As a result of this analysis, two attacks have been found: a man-in-themiddle-attack and a type flaw attack. In both cases confidentiality is compromised and an intruder may obtain confidential data from a node in the network. Two solutions to these attacks are proposed in the paper.
Abstract. The main goal of this paper is to extend sPBC with the iteration operator, providing an operational semantics for the language, as well as a denotational semantics, which is based on stochastic Petri nets. With this new operator we can model some repetitive behaviours, and then we obtain a formal method that can be easily used for the design of communication protocols and distributed systems.
Similarity search in a large collection of stored objects in a metric database has become a most interesting problem. The Spaghettis is an efficient metric data structure to index metric spaces. However, for real applications processing large volumes of generated data, query response times can be high enough. In these cases, it is necessary to apply mechanisms in order to significantly reduce the average query time. In this sense, the parallelization of metric structures is an interesting field of research. The recent appearance of GPU s for general purpose computing platforms offers powerful parallel processing capabilities. In this paper we propose a GPU-based implementation for Spaghettis metric structure. Firstly, we have adapted Spaghettis structure to GPU-based platform. Afterwards, we have compared both sequential and GPU-based implementation to analyse the performance, showing significant improvements in terms of time reduction, obtaining values of speed-up close to 10.
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