Summary: BigBWA is a new tool that uses the Big Data technology Hadoop to boost the performance of the Burrows–Wheeler aligner (BWA). Important reductions in the execution times were observed when using this tool. In addition, BigBWA is fault tolerant and it does not require any modification of the original BWA source code. Availability and implementation: BigBWA is available at the project GitHub repository: https://github.com/citiususc/BigBWA Contact:josemanuel.abuin@usc.es Supplementary information: Supplementary data are available at Bioinformatics online.

Cloud deployment architectures have become a preferable computation model of Big Data (BD) operations. Their scalability, flexibility, and cost-effectiveness motivated this trend. In a such deployment model, the data are no longer physically maintained under the user’s direct control, which raises new security concerns. In this context, BD security plays a decisive role in the widespread adoption of cloud architectures. However, it is challenging to develop a comprehensive security plan unless it is based on a preliminary analysis that ensures a realistic secure assembly and addresses domain-specific vulnerabilities. This article presents a novel security-by-design framework for BD frameworks deployment over cloud computing (BigCloud). In particular, it relies on a systematic security analysis methodology and a completely automated security assessment framework. Our framework enables the mapping of BigCloud security domain knowledge to the best practices in the design phase. We validated the proposed framework by implementing an Apache Hadoop stack use case. The study findings demonstrate its effectiveness in improving awareness of security aspects and reducing security design time. It also evaluates the strengths and limitations of the proposed framework, from which it highlights the main existing and open challenges in the BigCloud-related area.

Next-generation sequencing (NGS) technologies have led to a huge amount of genomic data that need to be analyzed and interpreted. This fact has a huge impact on the DNA sequence alignment process, which nowadays requires the mapping of billions of small DNA sequences onto a reference genome. In this way, sequence alignment remains the most time-consuming stage in the sequence analysis workflow. To deal with this issue, state of the art aligners take advantage of parallelization strategies. However, the existent solutions show limited scalability and have a complex implementation. In this work we introduce SparkBWA, a new tool that exploits the capabilities of a big data technology as Spark to boost the performance of one of the most widely adopted aligner, the Burrows-Wheeler Aligner (BWA). The design of SparkBWA uses two independent software layers in such a way that no modifications to the original BWA source code are required, which assures its compatibility with any BWA version (future or legacy). SparkBWA is evaluated in different scenarios showing noticeable results in terms of performance and scalability. A comparison to other parallel BWA-based aligners validates the benefits of our approach. Finally, an intuitive and flexible API is provided to NGS professionals in order to facilitate the acceptance and adoption of the new tool. The source code of the software described in this paper is publicly available at https://github.com/citiususc/SparkBWA, with a GPL3 license.

A graph-based segmentation technique has been tailored to segment airborne LiDAR points which, unlike images, are irregularly distributed. In our method, every LiDAR point is labeled as a node and interconnected as a graph extended to its neighborhood, defined in a 4-D feature space: the spatial coordinates ($x,y,z$) and the reflection intensity. The interconnections between pairs of neighboring nodes are weighted based on the distance in the feature space. The segmentation consists of an iterative process of classification of nodes into homogeneous groups based on their similarity. This approach is intended to be part of a complete system for the classification of structures from LiDAR point clouds in applications needing fast response times. In this sense, a study of the performance/accuracy trade-off has been performed, extracting some conclusions about the benefits of the proposed solution. In addition, an interlaced graph-based approach is proposed to increase the reliability in general purpose segmentations.