Highly accurate genotyping is essential for genomic projects aimed at understanding the etiology of diseases as well as for routinary screening of patients. For this reason, genotyping software packages are subject to a strict validation process that requires a large amount of sequencing data endowed with accurate genotype information. In-vitro assessment of genotyping is a long, complex and expensive activity that also depends on the specific variation and locus, and thus it cannot really be used for validation of in-silico genotyping algorithms. In this scenario, sequencing simulation has emerged as a practical alternative. Simulators must be able to keep up with the continuous improvement of different sequencing technologies producing datasets as much indistinguishable from real ones as possible. Moreover, they must be able to mimic as many types of genomic variant as possible. In this paper we describe OmniSim a simulator whose ultimate goal is that of being suitable in all the possible applicative scenarios. In order to fulfill this goal, OmniSim uses an abstract model where variations are read from a .vcf file and mapped into edit operations (insertion, deletion, substitution) on the reference genome. Technological parameters (e.g. error distributions, read length and per-base quality) are learned from real data. As a result of the combination of our abstract model and parameter learning module, OmniSim is able to output data in all aspects similar to that produced in a real sequencing experiment. The source code of OmniSim is freely available at the URL: https://gitlab.com/geraci/omnisim.

MOTIVATION: Large-scale sequencing projects have confirmed the hypothesis that eukaryotic DNA is rich in repetitions whose functional role needs to be elucidated. In particular, tandem repeats (TRs) (i.e. short, almost identical sequences that lie adjacent to each other) have been associated to many cellular processes and, indeed, are also involved in several genetic disorders. The need of comprehensive lists of TRs for association studies and the absence of a computational model able to capture their variability have revived research on discovery algorithms. RESULTS: Building upon the idea that sequence similarities can be easily displayed using graphical methods, we formalized the structure that TRs induce in dot-plot matrices where a sequence is compared with itself. Leveraging on the observation that a compact representation of these matrices can be built and searched in linear time, we developed Dot2dot: an accurate algorithm fast enough to be suitable for whole-genome discovery of TRs. Experiments on five manually curated collections of TRs have shown that Dot2dot is more accurate than other established methods, and completes the analysis of the biggest known reference genome in about one day on a standard PC. AVAILABILITY AND IMPLEMENTATION: Source code and datasets are freely available upon paper acceptance at the URL: https://github.com/Gege7177/Dot2dot. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: Biological networks play an increasingly important role in the exploration of functional modularity and cellular organization at a systemic level. Quite often the first tools used to analyze these networks are clustering algorithms. We concentrate here on the specific task of predicting protein complexes (PC) in large protein-protein interaction networks (PPIN). Currently, many state-of-the-art algorithms work well for networks of small or moderate size. However, their performance on much larger networks, which are becoming increasingly common in modern proteome-wise studies, needs to be re-assessed. RESULTS AND DISCUSSION: We present a new fast algorithm for clustering large sparse networks: Core&Peel, which runs essentially in time and storage O(a(G)m+n) for a network G of n nodes and m arcs, where a(G) is the arboricity of G (which is roughly proportional to the maximum average degree of any induced subgraph in G). We evaluated Core&Peel on five PPI networks of large size and one of medium size from both yeast and homo sapiens, comparing its performance against those of ten state-of-the-art methods. We demonstrate that Core&Peel consistently outperforms the ten competitors in its ability to identify known protein complexes and in the functional coherence of its predictions. Our method is remarkably robust, being quite insensible to the injection of random interactions. Core&Peel is also empirically efficient attaining the second best running time over large networks among the tested algorithms. CONCLUSIONS: Our algorithm Core&Peel pushes forward the state-of the-art in PPIN clustering providing an algorithmic solution with polynomial running time that attains experimentally demonstrable good output quality and speed on challenging large real networks.

Social networks have demonstrated in the last few years to be a powerful and flexible concept useful to represent and analyze data emerging form social interactions and social activities. The study of these networks can thus provide a deeper understanding of many emergent global phenomena. The amount of data available in the form of social networks data is growing by the day, and this poses many computational challenging problems for their analysis. In fact many analysis tools suitable to analyze small to medium sized networks are inefficient for large social networks. The computation of the betweenness centrality index is a well established method for network data analysis and it is also important as subroutine in more advanced algorithms, such as the Girvan-Newman method for graph partitioning. In this paper we present a new approach for the computation of the betweenness centrality, which speeds up considerably Brandes' algorithm (the current state of the art) in the context of social networks. Our approach exploits the natural sparsity of the data to algebraically (and efficiently) determine the betweenness of those nodes forming trees (tree-nodes) in the social network. Moreover, for the residual network, which is often of much smaller size, we modify directly the Brandes' algorithm so that we can remove the nodes already processed and perform the computation of the shortest paths only for the residual nodes. Tests conducted on a sample of publicly available large networks from the Stanford repository show that improvements of a factor ranging between 2 and 5 are possible on several such graphs, when the sparsity, measured by the ratio of tree-nodes to the total number of nodes, is in a medium range (30% to 50%). For some large networks from the Stanford repository and for a sample of social networks provided by Sistemi Territoriali with high sparsity (80% and above) tests show that our algorithm consistently runs between one and two orders of magnitude faster than the current state of the art exact algorithm.

MOTIVATION: Single nucleotide polymorphisms are the most common form of variation in human DNA, and are involved in many research fields, from molecular biology to medical therapy. The technological opportunity to deal with long DNA sequences using shotgun sequencing has raised the problem of fragment recombination. In this regard, Single Individual Haplotyping (SIH) problem has received considerable attention over the past few years. RESULTS: In this article, we survey seven recent approaches to the SIH problem and evaluate them extensively using real human haplotype data from the HapMap project. We also implemented a data generator tailored to the current shotgun sequencing technology that uses haplotypes from the HapMap project. AVAILABILITY: The data we used to compare the algorithms are available on demand, since we think they represent an important benchmark that can be used to easily compare novel algorithmic ideas with the state of the art. Moreover, we had to re-implement six of the algorithms surveyed because the original code was not available to us. Five of these algorithms and the data generator used in this article endowed with a Web interface are available at http://bioalgo.iit.cnr.it/rehap.

The detection of an over-represented sub-sequence in a set of (carefully chosen) DNA sequences is often the main clue leading to the investigation of a possible functional role for such a subsequence. Over-represented substrings (with possibly local mutations) in a biological string are termed motifs. A typical functional unit that can be modeled by a motif is a Transcription Factor Binding Site (TFBS), a portion of the DNA sequence apt to the binding of a protein that participates in complex transcriptomic biochemical reactions. In the literature it has been proposed a simplified combinatorial problem called the planted (l-d)-motif problem (known also as the (l-d) Challenge Problem) that captures the essential combinatorial nature of the motif finding problem. In this paper we propose a novel graph-based algorithm for solving a refinement of the (l-d) Challenge Problem. Experimental results show that instances of the (l-d) Challenge Problem considered difficult for competing state of the art methods in literature can be solved efficiently in our framework.

The World Wide Web (WWW) is rapidly becoming important for society as a medium for sharing data, information, and services, and there is a growing interest in tools for understanding collective behavior and emerging phenomena in the WWW. In this article we focus on the problem of searching and classifying communities in the Web. Loosely speaking a community is a group of pages related to a common interest. More formally, communities have been associated in the computer science literature with the existence of a locally dense subgraph of the Web graph (where Web pages are nodes and hyperlinks are arcs of the Web graph). The core of our contribution is a new scalable algorithm for finding relatively dense subgraphs in massive graphs. We apply our algorithm on Web graphs built on three publicly available large crawls of the Web (with raw sizes up to 120M nodes and 1G arcs). The effectiveness of our algorithm in finding dense subgraphs is demonstrated experimentally by embedding artificial communities in the Web graph and counting how many of these are blindly found. Effectiveness increases with the size and density of the communities: it is close to 100% for communities of thirty nodes or more (even at low density). It is still about 80% even for communities of twenty nodes with density over 50% of the arcs present. At the lower extremes the algorithm catches 35% of dense communities made of ten nodes. We also develop some sufficient conditions for the detection of a community under some local graph models and not-too-restrictive hypotheses. We complete our Community Watch system by clustering the communities found in the Web graph into homogeneous groups by topic and labeling each group by representative keywords.

A tool to provide an idea of the content of a given video is becoming a need in the current Web scenario, where the presence of videos is increasing day after day. Dynamic summarization techniques can be used to this aim as they set up a video abstract, by selecting and sequencing short video clips extracted from the original video. Needless to say, the selection process is critical. In this paper we focus our attention on clustering algorithms to provide such selection and we investigate the effects of their employment in the Web scenario. Clustering algorithms are very effective in producing static video summary, but few works consider them for video abstract production. For this reason, we set up an experimental scenario where we investigate their performance considering different categories of video, different abstract lengths and different low-level video analysis. Results show that clustering techniques can be useful only for some categories of videos and only if the selection process is based on video scene characteristics. Furthermore, the investigation also shows that to provide a customized service (user can freely decide the abstract time length), only fast clustering algorithm should be used.

Single nucleotide polymorphism (SNP) is the most frequent form of DNA variation. The set of SNP's present in a chromosome (called the haplotype) is of interest in a wide area of applications in molecular biology and biomedicine, including diagnostic and medical therapy. In this paper we propose a new heuristic method for the problem of haplotype reconstruction for (portions of) a pair of homologous human chromosomes from a single individual (SIH). The problem is well known in literature and exact algorithms have been proposed for the case when no (or few) gaps are allowed in the input fragments. These algorithms, though exact and of polynomial complexity, are slow in practice. When gaps are considered no exact method of polynomial complexity is known. The problem is also hard to approximate with guarantees. Therefore fast heuristics have been proposed. In this paper we describe SpeedHap, a new heuristic method that is able to tackle the case of many gapped fragments and retains its effectiveness even when the input fragments have high rate of reading errors (up to 20%) and low coverage (as low as 3). We test SpeedHap on real data from the HapMap Project.

The World Wide Web (WWW) is rapidly becoming important for society as a medium for sharing data, information and services, and there is a growing interest in tools for understanding collective behaviors and emerging phenomena in the WWW. In this paper we focus on the problem of searching and classifying communities in the web. Loosely speaking a community is a group of pages related to a common interest. More formally communities have been associated in the computer science literature with the existence of a locally dense sub-graph of the web-graph (where web pages are nodes and hyper-links are arcs of the web-graph). The core of our contribution is a new scalable algorithm for finding relatively dense subgraphs in massive graphs. We apply our algorithm on web-graphs built on three publicly available large crawls of the web (with raw sizes up to 120M nodes and 1G arcs). The effectiveness of our algorithm in finding dense subgraphs is demonstrated experimentally by embedding artificial communities in the web-graph and counting how many of these are blindly found. Effectiveness increases with the size and density of the communities: it is close to 100% for communities of a thirty nodes or more (even at low density). It is still about 80% even for communities of twenty nodes with density over 50% of the arcs present. At the lower extremes the algorithm catches 35% of dense communities made of ten nodes. We complete our Community Watch system by clustering the communities found in the web-graph into homogeneous groups by topic and labelling each group by representative keywords.

Packet classification is a common task in modern Internet routers. The goal is to classify packets into "classes" or "flows" according to some ruleset that looks at multiple fields of each packet. Differentiated actions can then be applied to the traffic depending on the result of the classification. Even though rulesets can be expressed in a relatively compact way by using high level languages, the resulting decision trees can partition the search space (the set of possible attribute values) in a potentially very large (106 and more) number of regions. This calls for methods that scale to such large problem sizes, though the only scalable proposal in the literature so far is the one based on a fat inverted segment tree (A. Feldmann and S. Muthukrishnan). In this paper we propose a new geometric technique called G-filter for packet classification on d dimensions. G-filter is based on an improved space decomposition technique. In addition to a theoretical analysis showing that classification in G-filter has O(1) time complexity and slightly super-linear space in the number of rules, we provide thorough experiments showing that the constants involved are extremely small on a wide range of problem sizes, and that G-filter improve the best results in the literature for large problem sizes, and is competitive for small sizes as well.