Abstract In this paper, we introduce MARVEL, a system designed to simplify the teaching of MapReduce, a popular distributed programming paradigm, through software visualization. At its core, it allows a teacher to describe and recreate a MapReduce application by interactively requesting, through a graphical interface, the execution of a sequence of MapReduce transformations that target an input dataset. Then, the execution of each operation is illustrated on the screen by playing an appropriate graphical animation stage, highlighting aspects related to its distributed nature. The sequence of all animation stages, played back one after the other in a sequential order, results in a visualization of the whole algorithm. The content of the resulting visualization is not simulated or fictitious, but reflects the real behavior of the requested operations, thanks to the adoption of an architecture based on a real instance of a distributed system running on Apache Spark. On the teacher’s side, it is expected that by using MARVEL he/she will spend less time preparing materials and will be able to design a more interactive lesson than with electronic slides or a whiteboard. To test the effectiveness of the proposed approach on the learner side, we also conducted a small scientific experiment with a class of volunteer students who formed a control group. The results are encouraging, showing that the use of software visualization guarantees students a learning experience at least equivalent to that of conventional approaches.

MOTIVATION: Alignment-free (AF) distance/similarity functions are a key tool for sequence analysis. Experimental studies on real datasets abound and, to some extent, there are also studies regarding their control of false positive rate (Type I error). However, assessment of their power, i.e. their ability to identify true similarity, has been limited to some members of the D2 family. The corresponding experimental studies have concentrated on short sequences, a scenario no longer adequate for current applications, where sequence lengths may vary considerably. Such a State of the Art is methodologically problematic, since information regarding a key feature such as power is either missing or limited. RESULTS: By concentrating on a representative set of word-frequency-based AF functions, we perform the first coherent and uniform evaluation of the power, involving also Type I error for completeness. Two alternative models of important genomic features (CIS Regulatory Modules and Horizontal Gene Transfer), a wide range of sequence lengths from a few thousand to millions, and different values of k have been used. As a result, we provide a characterization of those AF functions that is novel and informative. Indeed, we identify weak and strong points of each function considered, which may be used as a guide to choose one for analysis tasks. Remarkably, of the 15 functions that we have considered, only four stand out, with small differences between small and short sequence length scenarios. Finally, to encourage the use of our methodology for validation of future AF functions, the Big Data platform supporting it is public. AVAILABILITY AND IMPLEMENTATION: The software is available at: https://github.com/pipp8/power_statistics. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Following publication of the original article [1], the authors identified that the affiliations of Giuseppe Cattaneo and Raffaele Giancarlo were interchanged. The correct affiliations are given below. The correct affiliation of Giuseppe Cattaneo is: 2Dipartimento di Informatica, Università di Salerno, Fisciano, Italy. The correct affiliation of Raffaele Giancarlo is: 3Dipartimento di Matematica ed Informatica, Università di Palermo, Palermo, Italy. The original article [1] has been corrected.

MOTIVATION: Alignment-free distance and similarity functions (AF functions, for short) are a well-established alternative to pairwise and multiple sequence alignments for many genomic, metagenomic and epigenomic tasks. Due to data-intensive applications, the computation of AF functions is a Big Data problem, with the recent literature indicating that the development of fast and scalable algorithms computing AF functions is a high-priority task. Somewhat surprisingly, despite the increasing popularity of Big Data technologies in computational biology, the development of a Big Data platform for those tasks has not been pursued, possibly due to its complexity. RESULTS: We fill this important gap by introducing FADE, the first extensible, efficient and scalable Spark platform for alignment-free genomic analysis. It supports natively eighteen of the best performing AF functions coming out of a recent hallmark benchmarking study. FADE development and potential impact comprises novel aspects of interest. Namely, (i) a considerable effort of distributed algorithms, the most tangible result being a much faster execution time of reference methods like MASH and FSWM; (ii) a software design that makes FADE user-friendly and easily extendable by Spark non-specialists; (iii) its ability to support data- and compute-intensive tasks. About this, we provide a novel and much needed analysis of how informative and robust AF functions are, in terms of the statistical significance of their output. Our findings naturally extend the ones of the highly regarded benchmarking study, since the functions that can really be used are reduced to a handful of the eighteen included in FADE. AVAILABILITYAND IMPLEMENTATION: The software and the datasets are available at https://github.com/fpalini/fade. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: Storage of genomic data is a major cost for the Life Sciences, effectively addressed via specialized data compression methods. For the same reasons of abundance in data production, the use of Big Data technologies is seen as the future for genomic data storage and processing, with MapReduce-Hadoop as leaders. Somewhat surprisingly, none of the specialized FASTA/Q compressors is available within Hadoop. Indeed, their deployment there is not exactly immediate. Such a State of the Art is problematic. RESULTS: We provide major advances in two different directions. Methodologically, we propose two general methods, with the corresponding software, that make very easy to deploy a specialized FASTA/Q compressor within MapReduce-Hadoop for processing files stored on the distributed Hadoop File System, with very little knowledge of Hadoop. Practically, we provide evidence that the deployment of those specialized compressors within Hadoop, not available so far, results in better space savings, and even in better execution times over compressed data, with respect to the use of generic compressors available in Hadoop, in particular for FASTQ files. Finally, we observe that these results hold also for the Apache Spark framework, when used to process FASTA/Q files stored on the Hadoop File System. CONCLUSIONS: Our Methods and the corresponding software substantially contribute to achieve space and time savings for the storage and processing of FASTA/Q files in Hadoop and Spark. Being our approach general, it is very likely that it can be applied also to FASTA/Q compression methods that will appear in the future. AVAILABILITY: The software and the datasets are available at https://github.com/fpalini/fastdoopc.