Summary: We have implemented a high-performance computing (HPC) version of ProtTest that can be executed in parallel in multicore desktops and clusters. This version, called ProtTest 3, includes new features and extended capabilities. Availability: ProtTest 3 source code and binaries are freely available under GNU license for download from http://darwin.uvigo.es/software/prottest3, linked to a Mercurial repository at Bitbucket (https://bitbucket.org/). Contact:dposada@uvigo.es Supplementary information: Supplementary data are available at Bioinformatics online.

Summary: The selection of models of nucleotide substitution is one of the major steps of modern phylogenetic analysis. Different tools exist to accomplish this task, among which jModelTest 2 (jMT2) is one of the most popular. Still, to deal with large DNA alignments with hundreds or thousands of loci, users of jMT2 need to have access to High Performance Computing clusters, including installation and configuration capabilities, conditions not always met. Here we present jmodeltest.org, a novel web server for the transparent execution of jMT2 across different platforms and for a wide range of users. Its main benefit is straightforward execution, avoiding any configuration/execution issues, and reducing significantly in most cases the time required to complete the analysis. Availability and implementation:jmodeltest.org is accessible using modern browsers, such as Firefox, Chrome, Opera, Safari and IE from http://jmodeltest.org. User registration is not mandatory, but users wanting to have additional functionalities, like access to previous analyses, have the possibility of opening a user account. Contact:info@jmodeltest.org

The increasing adoption of Big Data analytics has led to a high demand for efficient technologies in order to manage and process large datasets. Popular MapReduce frameworks such as Hadoop are being replaced by emerging ones like Spark or Flink, which improve both the programming APIs and performance. However, few works have focused on comparing these frameworks. This paper addresses this issue by performing a comparative evaluation of Hadoop, Spark and Flink using representative Big Data workloads and considering factors like performance and scalability. Moreover, the behavior of these frameworks has been characterized by modifying some of the main parameters of the workloads such as HDFS block size, input data size, interconnect network or thread configuration. The analysis of the results has shown that replacing Hadoop with Spark or Flink can lead to a reduction in execution times by 77% and 70% on average, respectively, for non-sort benchmarks.