MarDRe: efficient MapReduce-based removal of duplicate DNA reads in the cloud

This is a pre-copyedited, author-produced version of an article accepted for publication in Bioinformatics following peer review. The version of record Roberto R. Expósito, Jorge Veiga, Jorge González-Domínguez, Juan Touriño; MarDRe: efficient MapReduce-based removal of duplicate DNA reads in the cloud, Bioinformatics, Volume 33, Issue 17, 1 September 2017, Pages 2762–2764 is available online at: https://doi.org/10.1093/bioinformatics/btx307[Abstract] This article presents MarDRe, a de novo cloud-ready duplicate and near-duplicate removal tool that can process single- and paired-end reads from FASTQ/FASTA datasets. MarDRe takes advantage of the widely adopted MapReduce programming model to fully exploit Big Data technologies on cloud-based infrastructures. Written in Java to maximize cross-platform compatibility, MarDRe is built upon the open-source Apache Hadoop project, the most popular distributed computing framework for scalable Big Data processing. On a 16-node cluster deployed on the Amazon EC2 cloud platform, MarDRe is up to 8.52 times faster than a representative state-of-the-art tool.Ministerio de Economia y Competitividad; TIN2016-75845-PMinisterio de Educación; FPU014/0280

Performance Evaluation of Distributed Computing Environments with Hadoop and Spark Frameworks

Recently, due to rapid development of information and communication technologies, the data are created and consumed in the avalanche way. Distributed computing create preconditions for analyzing and processing such Big Data by distributing the computations among a number of compute nodes. In this work, performance of distributed computing environments on the basis of Hadoop and Spark frameworks is estimated for real and virtual versions of clusters. As a test task, we chose the classic use case of word counting in texts of various sizes. It was found that the running times grow very fast with the dataset size and faster than a power function even. As to the real and virtual versions of cluster implementations, this tendency is the similar for both Hadoop and Spark frameworks. Moreover, speedup values decrease significantly with the growth of dataset size, especially for virtual version of cluster configuration. The problem of growing data generated by IoT and multimodal (visual, sound, tactile, neuro and brain-computing, muscle and eye tracking, etc.) interaction channels is presented. In the context of this problem, the current observations as to the running times and speedup on Hadoop and Spark frameworks in real and virtual cluster configurations can be very useful for the proper scaling-up and efficient job management, especially for machine learning and Deep Learning applications, where Big Data are widely present.Comment: 5 pages, 1 table, 2017 IEEE International Young Scientists Forum on Applied Physics and Engineering (YSF-2017) (Lviv, Ukraine