INBIA: a boosting methodology for proteomic network inference.

BACKGROUND: The analysis of tissue-specific protein interaction networks and their functional enrichment in pathological and normal tissues provides insights on the etiology of diseases. The Pan-cancer proteomic project, in The Cancer Genome Atlas, collects protein expressions in human cancers and it is a reference resource for the functional study of cancers. However, established protocols to infer interaction networks from protein expressions are still missing. RESULTS: We have developed a methodology called Inference Network Based on iRefIndex Analysis (INBIA) to accurately correlate proteomic inferred relations to protein-protein interaction (PPI) networks. INBIA makes use of 14 network inference methods on protein expressions related to 16 cancer types. It uses as reference model the iRefIndex human PPI network. Predictions are validated through non-interacting and tissue specific PPI networks resources. The first, Negatome, takes into account likely non-interacting proteins by combining both structure properties and literature mining. The latter, TissueNet and GIANT, report experimentally verified PPIs in more than 50 human tissues. The reliability of the proposed methodology is assessed by comparing INBIA with PERA, a tool which infers protein interaction networks from Pathway Commons, by both functional and topological analysis. CONCLUSION: Results show that INBIA is a valuable approach to predict proteomic interactions in pathological conditions starting from the current knowledge of human protein interactions.

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

MOTIVATION: Biological network querying is a problem requiring a considerable computational effort to be solved. Given a target and a query network, it aims to find occurrences of the query in the target by considering 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 because they provide meaningful results also in case of noisy data. In addition, as the size of available networks increases steadily, existing algorithms and tools are becoming unsuitable. This is rising new challenges for the design of more efficient and accurate solutions. RESULTS: This paper presents APPAGATO, a stochastic and parallel algorithm to find approximate occurrences of a query network in biological networks. APPAGATO handles node, edge and node label mismatches. Thanks to its randomic and parallel nature, it applies to large networks and, compared with existing 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 gene ontology terms. Case studies have been done by querying protein complexes among different species and tissues. AVAILABILITY AND IMPLEMENTATION: APPAGATO has been developed on top of CUDA-C ++ Toolkit 7.0 framework. The software is available online http://profs.sci.univr.it/∼bombieri/APPAGATO CONTACT: rosalba.giugno@univr.it SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Mechanistic Translation of Melanoma Genetic Landscape in Enriched Pathways and Oncogenic Protein-Protein Interactions.

BACKGROUND/AIM: Malignant melanoma is a skin cancer originating from the oncogenic transformation of melanocytes located in the epidermal layers. Usually, the patient's prognosis depends on timing of disease detection and molecular and genetic profiling, which may all significantly influence mortality rates. Genetic analyses often detect somatic BRAF, NRAS and cKIT mutations, germline substitutions in CDKN2A, and alterations of the PI3K-AKT-PTEN pathway. A peculiar molecular future of melanoma is its high immunogenicity, making this tumor targetable by programmed cell death protein 1-specific antibodies. MATERIALS AND METHODS: Ten formalin-fixed paraffin embedded samples derived from melanoma patients were subjected to next-generation sequencing (NGS) analysis using the FDA-approved FoundationOne CDx™ test. The molecular features of each case were then analyzed employing several in silico prediction tools. RESULTS: We analyzed the mutational landscape of patients with metastatic or relapsed cutaneous melanoma to define enriched pathways and protein-protein interactions. The analysis showed that both known genetic alterations and variants of unknown significance rely on redundant signaling converging on similar gene ontology biological processes. Complex informatics analyses of NGS-based genetic results identified pivotal signaling pathways that could provide additional targets for cancer treatment. CONCLUSION: Our data suggest an additional role for NGS in melanoma, as analysis of comprehensive genetic findings using innovative informatic tools may lengthen the list of druggable molecular targets that impact patient outcome.

Next generation sequencing in a cohort of patients with rare sarcoma histotypes: A single institution experience.

Sarcomas are mesenchymal-derived cancers with overlapping clinical and pathologic features and a remarkable histological heterogeneity. While a precise diagnosis is often challenging to achieve, systemic treatment of sarcomas is still quite uniform. In this scenario, next generation sequencing (NGS) may be exploited to assist diagnosis and to identify specific targetable alterations. However, the precise role of genomic characterization in these diseases is still debated. In the present study, we analyzed 18 samples from 11 low-incidence sarcomas using NGS technology. We also used an in-silico prediction tool to reclassify variants of unknown significance and then looked for potentially druggable alterations to match with targeted therapies. Our cohort presented several predictable findings (e.g. MYC amplification in radio-induced angio-sarcoma, COL1A1-PDGFB rearrangements in dermatofibrosarcoma protuberans) along with unexpected results (e.g. the reciprocal WT1-EWSR1 fusion in a desmoplastic small round cell tumor). One third of patients (6/18) displayed at least one actionable molecular alterations. Our experience confirms the potential role of NGS in the management of rare sarcomas. This tool may support the diagnostic process, but also detect targets for personalized therapies.

MODIT: MOtif DIscovery in Temporal Networks.

Temporal networks are graphs where each edge is linked with a timestamp, denoting when an interaction between two nodes happens. According to the most recently proposed definitions of the problem, motif search in temporal networks consists in finding and counting all connected temporal graphs Q (called motifs) occurring in a larger temporal network T, such that matched target edges follow the same chronological order imposed by edges in Q. In the last few years, several algorithms have been proposed to solve motif search, but most of them are limited to very small or specific motifs due to the computational complexity of the problem. In this paper, we present MODIT (MOtif DIscovery in Temporal Networks), an algorithm for counting motifs of any size in temporal networks, inspired by a very recent algorithm for subgraph isomorphism in temporal networks, called TemporalRI. Experiments show that for big motifs (more than 3 nodes and 3 edges) MODIT can efficiently retrieve them in reasonable time (up to few hours) in many networks of medium and large size and outperforms state-of-the art algorithms.

Computational Methods to Investigate the Impact of miRNAs on Pathways.

Pathway analysis is a wide class of methods allowing to determine the alteration of functional processes in complex diseases. However, biological pathways are still partial, and knowledge coming from posttranscriptional regulators has started to be considered in a systematic way only recently. Here we will give a global and updated view of the main pathway and subpathway analysis methodologies, focusing on the improvements obtained through the recent introduction of microRNAs as regulatory elements in these frameworks.

Fast Subgraph Matching Strategies Based on Pattern-Only Heuristics.

Many scientific applications entail solving the subgraph isomorphism problem, i.e., given an input pattern graph, find all the subgraphs of a (usually much larger) target graph that are structurally equivalent to that input. Because subgraph isomorphism is NP-complete, methods to solve it have to use heuristics. This work evaluates subgraph isomorphism methods to assess their computational behavior on a wide range of synthetic and real graphs. Surprisingly, our experiments show that, among the leading algorithms, certain heuristics based only on pattern graphs are the most efficient.

SPECTRA: An Integrated Knowledge Base for Comparing Tissue and Tumor-Specific PPI Networks in Human.

Protein-protein interaction (PPI) networks available in public repositories usually represent relationships between proteins within the cell. They ignore the specific set of tissues or tumors where the interactions take place. Indeed, proteins can form tissue-selective complexes, while they remain inactive in other tissues. For these reasons, a great attention has been recently paid to tissue-specific PPI networks, in which nodes are proteins of the global PPI network whose corresponding genes are preferentially expressed in specific tissues. In this paper, we present SPECTRA, a knowledge base to build and compare tissue or tumor-specific PPI networks. SPECTRA integrates gene expression and protein interaction data from the most authoritative online repositories. We also provide tools for visualizing and comparing such networks, in order to identify the expression and interaction changes of proteins across tissues, or between the normal and pathological states of the same tissue. SPECTRA is available as a web server at http://alpha.dmi.unict.it/spectra.

NetMatchStar: an enhanced Cytoscape network querying app.

We present NetMatchStar, a Cytoscape app to find all the occurrences of a query graph in a network and check for its significance as a motif with respect to seven different random models. The query can be uploaded or built from scratch using Cytoscape facilities. The app significantly enhances the previous NetMatch in style, performance and functionality. Notably NetMatchStar allows queries with wildcards.

Proteins comparison through probabilistic optimal structure local alignment.

Multiple local structure comparison helps to identify common structural motifs or conserved binding sites in 3D structures in distantly related proteins. Since there is no best way to compare structures and evaluate the alignment, a wide variety of techniques and different similarity scoring schemes have been proposed. Existing algorithms usually compute the best superposition of two structures or attempt to solve it as an optimization problem in a simpler setting (e.g., considering contact maps or distance matrices). Here, we present PROPOSAL (PROteins comparison through Probabilistic Optimal Structure local ALignment), a stochastic algorithm based on iterative sampling for multiple local alignment of protein structures. Our method can efficiently find conserved motifs across a set of protein structures. Only the distances between all pairs of residues in the structures are computed. To show the accuracy and the effectiveness of PROPOSAL we tested it on a few families of protein structures. We also compared PROPOSAL with two state-of-the-art tools for pairwise local alignment on a dataset of manually annotated motifs. PROPOSAL is available as a Java 2D standalone application or a command line program at http://ferrolab.dmi.unict.it/proposal/proposal.html.

GASOLINE: a Greedy And Stochastic algorithm for optimal Local multiple alignment of Interaction NEtworks.

The analysis of structure and dynamics of biological networks plays a central role in understanding the intrinsic complexity of biological systems. Biological networks have been considered a suitable formalism to extend evolutionary and comparative biology. In this paper we present GASOLINE, an algorithm for multiple local network alignment based on statistical iterative sampling in connection to a greedy strategy. GASOLINE overcomes the limits of current approaches by producing biologically significant alignments within a feasible running time, even for very large input instances. The method has been extensively tested on a database of real and synthetic biological networks. A comprehensive comparison with state-of-the art algorithms clearly shows that GASOLINE yields the best results in terms of both reliability of alignments and running time on real biological networks and results comparable in terms of quality of alignments on synthetic networks. GASOLINE has been developed in Java, and is available, along with all the computed alignments, at the following URL: http://ferrolab.dmi.unict.it/gasoline/gasoline.html.

GASOLINE: a Cytoscape app for multiple local alignment of PPI networks.

Comparing protein interaction networks can reveal interesting patterns of interactions for a specific function or process in distantly related species. In this paper we present GASOLINE, a Cytoscape app for multiple local alignments of PPI (protein-protein interaction) networks. The app is based on the homonymous greedy and stochastic algorithms. To the authors knowledge, it is the first Cytoscape app for computing and visualizing local alignments, without requiring any post-processing operations. GO terms can be easily attached to the aligned proteins for further functional analysis of alignments. GASOLINE can perform the alignment task in few minutes, even for a large number of input networks.

Atopy and risk factors for latex sensitization in a selected population.

The value of recommending latex allergy screening in allergy departments of the Army's Hospital was studied. The purpose of the study was to evaluate whether atopy was a risk factor for latex sensitization in a specific population such as the young male soldiers of the Italian Army. The study was also aimed to assess the role of other risk factors. One thousand five hundred male subjects (1000 subjects who were atopic and 500 subjects who were nonatopic), visiting the Department of Allergology and Respiratory Physiopathology of the Army's Hospital in Bari, Italy, were enrolled into the study. The protocol included a questionnaire (symptoms of atopy, use of latex gloves and condoms and possible reactions previous surgical procedures), a clinical examination, a skin-prick test to latex and common allergens to evaluate atopy, and in part a latex challenge. Among the 1000 subjects who were atopic, 2.8% had evidence for sensitization to latex compared with 1.2% in the 500 subjects in the nonatopic group. The risk of latex sensitization was 19 times higher for subjects with a history of reactions to latex exposure and had a twofold increase for each surgical procedure and for each skin test positivity for inhalant allergens. Another risk factor was positivity to skin-prick tests for Artemisia vulgaris, cypress, and molds. Atopy significantly relates to an increased risk of latex sensitization. Screening is recommended in the Army's Hospital to identify latex-sensitized subjects and inform them about the risks connected with this condition.