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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 , with a GPL3 license. [ABSTRACT FROM AUTHOR]

Abstract: In this paper a heuristic segmentation algorithm is presented based on the oversegmentation of an image. The method uses a set of different segmentations of the image produced previously by standard techniques. These segmentations are combined to create the oversegmented image. They can be performed using different techniques or even the same technique with different initial conditions. Based on this oversegmentation a new method of region merging is proposed. The merging process is guided using only information about the behavior of each pixel in the input segmentations. Therefore, the results are an adequate combination of the features of these segmentations, allowing to mask the negative particularities of individual segmentations. In this work, the quality of the proposal is analyzed with both artificial and real images using a evaluation function as case of study. The results show that our algorithm produces high quality global segmentations from a set of low quality segmentations with reduced execution times. [Copyright &y& Elsevier]