Post-transcriptional knowledge in pathway analysis increases the accuracy of phenotypes classification

Motivation: Prediction of phenotypes from high-dimensional data is a crucial task in precision biology and medicine. Many technologies employ genomic biomarkers to characterize phenotypes. However, such elements are not sufficient to explain the underlying biology. To improve this, pathway analysis techniques have been proposed. Nevertheless, such methods have shown lack of accuracy in phenotypes classification. Results: Here we propose a novel methodology called MITHrIL (Mirna enrIched paTHway Impact anaLysis) for the analysis of signaling pathways, which has built on top of the work of Tarca et al., 2009. MITHrIL extends pathways by adding missing regulatory elements, such as microRNAs, and their interactions with genes. The method takes as input the expression values of genes and/or microRNAs and returns a list of pathways sorted according to their deregulation degree, together with the corresponding statistical significance (p-values). Our analysis shows that MITHrIL outperforms its competitors even in the worst case. In addition, our method is able to correctly classify sets of tumor samples drawn from TCGA. Availability: MITHrIL is freely available at the following URL: http://alpha.dmi.unict.it/mithril

Exploring the Role of Interdisciplinarity in Physics: Success, Talent and Luck

Although interdisciplinarity is often touted as a necessity for modern research, the evidence on the relative impact of sectorial versus to interdisciplinary science is qualitative at best. In this paper we leverage the bibliographic data set of the American Physical Society to quantify the role of interdisciplinarity in physics, and that of talent and luck in achieving success in scientific careers. We analyze a period of 30 years (1980-2009) tagging papers and their authors by means of the Physics and Astronomy Classification Scheme (PACS), to show that some degree of interdisciplinarity is quite helpful to reach success, measured as a proxy of either the number of articles or the citations score. We also propose an agent-based model of the publication-reputation-citation dynamics reproduces the trends observed in the APS data set. On the one hand, the results highlight the crucial role of randomness and serendipity in real scientific research; on the other, they shed light on a counter-intuitive effect indicating that the most talented authors are not necessarily the most successful ones.Comment: 21 pages, 19 figure

Establish the expected number of induced motifs on unlabeled graphs through analytical models

AbstractComplex networks are usually characterized by the presence of small and recurrent patterns of interactions between nodes, called network motifs. These small modules can help to elucidate the structure and the functioning of complex systems. Assessing the statistical significance of a pattern as a motif in a network G is a time consuming task which entails the computation of the expected number of occurrences of the pattern in an ensemble of random graphs preserving some features of G, such as the degree distribution. Recently, few models have been devised to analytically compute expectations of the number of non-induced occurrences of a motif. Less attention has been payed to the harder analysis of induced motifs. Here, we illustrate an analytical model to derive the mean number of occurrences of an induced motif in an unlabeled network with respect to a random graph model. A comprehensive experimental analysis shows the effectiveness of our approach for the computation of the expected number of induced motifs up to 10 nodes. Finally, the proposed method is helpful when running subgraph counting algorithms to get the number of occurrences of a topology become unfeasible

TemporalRI: subgraph isomorphism in temporal networks with multiple contacts

AbstractTemporal networks are graphs where each edge is associated with a timestamp denoting when two nodes interact. Temporal Subgraph Isomorphism (TSI) aims at retrieving all the subgraphs of a temporal network (called target) matching a smaller temporal network (called query), such that matched target edges appear in the same chronological order of corresponding query edges. Few algorithms have been proposed to solve the TSI problem (or variants of it) and most of them are applicable only to small or specific queries. In this paper we present TemporalRI, a new subgraph isomorphism algorithm for temporal networks with multiple contacts between nodes, which is inspired by RI algorithm. TemporalRI introduces the notion of temporal flows and uses them to filter the search space of candidate nodes for the matching. Our algorithm can handle queries of any size and any topology. Experiments on real networks of different sizes show that TemporalRI is very efficient compared to the state-of-the-art, especially for large queries and targets

Prediction of human targets for viral-encoded microRNAs by thermodynamics and empirical constraints

MicroRNAs (miRNAs) are small RNA molecules that modulate gene expression through degradation of specific mRNAs and/or repression of their translation. miRNAs are involved in both physiological and pathological processes, such as apoptosis and cancer. Their presence has been demonstrated in several organisms as well as in viruses. Virus encoded miRNAs can act as viral gene expression regulators, but they may also interfere with the expression of host genes. Viral miRNAs may control host cell proliferation by targeting cell-cycle and apoptosis regulators. Therefore, they could be involved in cancer pathogenesis. Computational prediction of miRNA/target pairs is a fundamental step in these studies. Here, we describe the use of miRiam, a novel program based on both thermodynamics features and empirical constraints, to predict viral miRNAs/human targets interactions. miRiam exploits target mRNA secondary structure accessibility and interaction rules, inferred from validated miRNA/mRNA pairs. A set of genes involved in apoptosis and cell-cycle regulation was identified as target for our studies. This choice was supported by the knowledge that DNA tumor viruses interfere with the above processes in humans. miRNAs were selected from two cancer-related viruses, Epstein-Barr Virus (EBV) and Kaposi-Sarcoma-Associated Herpes Virus (KSHV). Results show that several transcripts possess potential binding sites for these miRNAs. This work has produced a set of plausible hypotheses of involvement of v-miRNAs and human apoptosis genes in cancer development. Our results suggest that during viral infection, besides the protein-based host regulation mechanism, a post-transcriptional level interference may exist. miRiam is freely available for downloading at http://ferrolab.dmi.unict.it/miriam

SING: Subgraph search In Non-homogeneous Graphs

<p>Abstract</p> <p>Background</p> <p>Finding the subgraphs of a graph database that are isomorphic to a given query graph has practical applications in several fields, from cheminformatics to image understanding. Since subgraph isomorphism is a computationally hard problem, indexing techniques have been intensively exploited to speed up the process. Such systems filter out those graphs which cannot contain the query, and apply a subgraph isomorphism algorithm to each residual candidate graph. The applicability of such systems is limited to databases of small graphs, because their filtering power degrades on large graphs.</p> <p>Results</p> <p>In this paper, SING (Subgraph search In Non-homogeneous Graphs), a novel indexing system able to cope with large graphs, is presented. The method uses the notion of <it>feature</it>, which can be a small subgraph, subtree or path. Each graph in the database is annotated with the set of all its features. The key point is to make use of feature locality information. This idea is used to both improve the filtering performance and speed up the subgraph isomorphism task.</p> <p>Conclusions</p> <p>Extensive tests on chemical compounds, biological networks and synthetic graphs show that the proposed system outperforms the most popular systems in query time over databases of medium and large graphs. Other specific tests show that the proposed system is effective for single large graphs.</p

Comprehensive reconstruction and visualization of non-coding regulatory networks in human

Research attention has been powered to understand the functional roles of non-coding RNAs (ncRNAs). Many studies have demonstrated their deregulation in cancer and other human disorders. ncRNAs are also present in extracellular human body fluids such as serum and plasma, giving them a great potential as non-invasive biomarkers. However, non-coding RNAs have been relatively recently discovered and a comprehensive database including all of them is still missing. Reconstructing and visualizing the network of ncRNAs interactions are important steps to understand their regulatory mechanism in complex systems. This work presents ncRNA-DB, a NoSQL database that integrates ncRNAs data interactions from a large number of well established online repositories. The interactions involve RNA, DNA, proteins and diseases. ncRNA-DB is available at http://ncrnadb.scienze.univr.it/ncrnadb/. It is equipped with three interfaces: web based, command line and a Cytoscape app called ncINetView. By accessing only one resource, users can search for ncRNAs and their interactions, build a network annotated with all known ncRNAs and associated diseases, and use all visual and mining features available in Cytoscape

Similarity Measures and Dimensionality Reduction Techniques for Time Series Data Mining

The chapter is organized as follows. Section 2 will introduce the similarity matching problem on time series. We will note the importance of the use of efficient data structures to perform search, and the choice of an adequate distance measure. Section 3 will show some of the most used distance measure for time series data mining. Section 4 will review the above mentioned dimensionality reduction techniques

NetMatch: a Cytoscape plugin for searching biological networks.

Abstract Summary: NetMatch is a Cytoscape plugin which allows searching biological networks for subcomponents matching a given query. Queries may be approximate in the sense that certain parts of the subgraph-query may be left unspecified. To make the query creation process easy, a drawing tool is provided. Cytoscape is a bioinformatics software platform for the visualization and analysis of biological networks. Availability: The full package, a tutorial and associated examples are available at the following web sites: http://alpha.dmi.unict.it/~ctnyu/netmatch.html, http://baderlab.org/Software/NetMatch Contact: [email protected]

APPAGATO: an APproximate PArallel and stochastic GrAph querying TOol for biological networks

Motivation: Biological network querying is a problem requiring a considerable computational effort tobe solved. Given a target and a query network, it aims to find occurrences of the query in the target byconsidering topological and node similarities (i.e. mismatches between nodes, edges, or node labels).Querying tools that deal with similarities are crucial in biological network analysis since they providemeaningful results also in case of noisy data. In addition, since the size of available networks increasessteadily, existing algorithms and tools are becoming unsuitable. This is rising new challenges for the designof more efficient and accurate solutions.Results: This paper presents APPAGATO, a stochastic and parallel algorithm to find approximateoccurrences of a query network in biological networks. APPAGATO handles node, edge, and node labelmismatches. Thanks to its randomic and parallel nature, it applies to large networks and, compared toexisting tools, it provides higher performance as well as statistically significant more accurate results.Tests have been performed on protein-protein interaction networks annotated with synthetic and real geneontology terms. Case studies have been done by querying protein complexes among different species andtissue