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.

Motivation: FastTree-2 is one of the most successful tools for inferring large phylogenies. With speed at the core of its design, there are still important issues in the FastTree-2 implementation that harm its performance and scalability. To deal with these limitations, we introduce VeryFastTree, a highly tuned implementation of the FastTree-2 tool that takes advantage of parallelization and vectorization strategies to boost performance. Results: VeryFastTree is able to construct a tree on a standard server using double-precision arithmetic from an ultra-large 330k alignment in only 4.5 h, which is 7.8× and 3.5× faster than the sequential and best parallel FastTree-2 times, respectively. Availability and implementation: VeryFastTree is available at the GitHub repository: https://github.com/citiususc/veryfasttree. Supplementary information: Supplementary data are available at Bioinformatics online.

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.

In this paper we propose a scalable platform for real-time processing of Social Media data. The platform ingests huge amounts of contents, such as Social Media posts or comments, and can support Public Health surveillance tasks. The processing and analytical needs of multiple screening tasks can easily be handled by incorporating user-defined execution graphs. The design is modular and supports different processing elements, such as crawlers to extract relevant contents or classifiers to categorise Social Media. We describe here an implementation of a use case built on the platform that monitors Social Media users and detects early signs of depression.