A Natural Human-Drone Embodied Interface: Empirical Comparison With a Traditional Interface.

Despite the importance of usability in human-machine interaction (HMI), most commonly used devices are not usable by all potential users. In particular, users with low or null technological experience, or with special needs, require carefully designed systems and easy-to-use interfaces supporting recognition over recall. To this purpose, Natural User Interfaces (NUIs) represent an effective strategy as the user's learning is facilitated by features of the interface that mimic the human "natural" sensorimotor embodied interactions with the environment. This paper compares the usability of a new NUI (based on an eye-tracker and hand gesture recognition) with a traditional interface (keyboard) for the distal control of a simulated drone flying in a virtual environment. The whole interface relies on "dAIsy", a new software allowing the flexible use of different input devices and the control of different robotic platforms. The 59 users involved in the study were required to complete two tasks with each interface, while their performance was recorded: (a) exploration: detecting trees embedded in an urban environment; (b) accuracy: guiding the drone as accurately and fast as possible along a predefined track. Then they were administered questionnaires regarding the user's background, the perceived embodiment of the device, and the perceived quality of the virtual experience while either using the NUI or the traditional interface. The results appear controversial and call for further investigation: (a) contrary to our hypothesis, the specific NUI used led to lower performance than the traditional interface; (b) however, the NUI was evaluated as more natural and embodied. The final part of the paper discusses the possible causes underlying these results that suggest possible future improvements of the NUI.

The power of word-frequency-based alignment-free functions: a comprehensive large-scale experimental analysis.

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.

FASTA/Q data compressors for MapReduce-Hadoop genomics: space and time savings made easy.

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.

Alignment-free Genomic Analysis via a Big Data Spark Platform.

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.

Rank-Similarity Measures for Comparing Gene Prioritizations: A Case Study in Autism.

We discuss the challenge of comparing three gene prioritization methods: network propagation, integer linear programming rank aggregation (RA), and statistical RA. These methods are based on different biological categories and estimate disease-gene association. Previously proposed comparison schemes are based on three measures of performance: receiver operating curve, area under the curve, and median rank ratio. Although they may capture important aspects of gene prioritization performance, they may fail to capture important differences in the rankings of individual genes. We suggest that comparison schemes could be improved by also considering recently proposed measures of similarity between gene rankings. We tested this suggestion on comparison schemes for prioritizations of genes associated with autism that were obtained using brain- and tissue-specific data. Our results show the effectiveness of our measures of similarity in clustering brain regions based on their relevance to autism.