The contributions of this paper are both of theoretical and of experimental nature. From the experimental point of view, we conduct an empirical study on some dynamic connectivity algorithms which where developed recently. In particular, the following implementations were tested and compared with simple algorithms: simple sparsification by Eppstein et al. and the recent randomized algorithm by Henzinger and King. In our experiments, we considered both random and non-random inputs. Moreover, we present a simplified variant of the algorithm by Henzinger and King, which for random inputs was always faster than the original implementation. For non-random inputs, simple sparsification was the fastest algorithm for small sequences of updates; for medium and large sequences of updates, the original algorithm by Henzinger and King was faster. From the theoretical point of view, we analyze the average case running time of simple sparsification and prove that for dynamic random graphs its logarithmic overhead vanishes.

No abstract

No abstract

Background Distributed approaches based on the MapReduce programming paradigm have started to be proposed in the Bioinformatics domain, due to the large amount of data produced by the next-generation sequencing techniques. However, the use of MapReduce and related Big Data technologies and frameworks (e.g., Apache Hadoop and Spark) does not necessarily produce satisfactory results, in terms of both efficiency and effectiveness. We discuss how the development of distributed and Big Data management technologies has affected the analysis of large datasets of biological sequences. Moreover, we show how the choice of different parameter configurations and the careful engineering of the software with respect to the specific framework under consideration may be crucial in order to achieve good performance, especially on very large amounts of data. We choose k -mers counting as a case study for our analysis, and Spark as the framework to implement FastKmer, a novel approach for the extraction of k -mer statistics from large collection of biological sequences, with arbitrary values of k . Results One of the most relevant contributions of FastKmer is the introduction of a module for balancing the statistics aggregation workload over the nodes of a computing cluster, in order to overcome data skew while allowing for a full exploitation of the underlying distributed architecture. We also present the results of a comparative experimental analysis showing that our approach is currently the fastest among the ones based on Big Data technologies, while exhibiting a very good scalability. Conclusions We provide evidence that the usage of technologies such as Hadoop or Spark for the analysis of big datasets of biological sequences is productive only if the architectural details and the peculiar aspects of the considered framework are carefully taken into account for the algorithm design and implementation.

Supplementary data are available at Bioinformatics online.

In this paper is presented an extended experimental evaluation of one of the most effective source camera identification techniques proposed so far, by Lukáš et al. (IEEE Trans Inf Forensics Security 1(2):205-214, 2006). This method uses the characteristic noise left by the sensor on a digital picture as a fingerprint in order to identify the source camera used to take the picture. The aim of the experiments is to assess the effectiveness of this technique when used with pictures that were previously modified using several common image-processing functions coming with photo-editing tools. Moreover, the technique is applied to photos passed through Online Social Networks or Online Photo Sharing websites, without any "human" explicit modification but only elaborated by such Web 2. 0 tools. The results confirm that, in several cases, the method by Lukáš et al. (IEEE Trans Inf Forensics Security 1(2):205-214, 2006) is resilient to the modifications introduced by the considered image-processing functions. However, in the experiments it has been possible to identify several cases where the quality of the identification process deteriorated because of the noise introduced by the image-processing. In addition, when dealing with Online Social Networks and Online Photo Sharing services, it has been noted that some of them process and modify the uploaded pictures. These modifications make ineffective, in many cases, the method by Lukáš et al. (IEEE Trans Inf Forensics Security 1(2):205-214, 2006) © 2011 Springer-Verlag

Alignment-free methods are one of the mainstays of biological sequence comparison, i.e., the assessment of how similar two biological sequences are to each other, a fundamental and routine task in computational biology and bioinformatics. They have gained popularity since, even on standard desktop machines, they are faster than methods based on alignments. However, with the advent of Next-Generation Sequencing Technologies, datasets whose size, i.e., number of sequences and their total length, is a challenge to the execution of alignment-free methods on those standard machines are quite common. Here, we propose the first paradigm for the computation of k-mer-based alignment-free methods for Apache Hadoop that extends the problem sizes that can be processed with respect to a standard sequential machine while also granting a good time performance. Technically, as opposed to a standard Hadoop implementation, its effectiveness is achieved thanks to the incremental management of a persistent hash table during the map phase, a task not contemplated by the basic Hadoop functions and that can be useful also in other contexts

No abstract