ALBERT

Description: We present here a work that applies an automatic construction of ensembles based on the Clustering and Selection (CS) algorithm for time series forecasting. The automatic method, called CSELM, initially finds an optimum number of clusters for training data set and subsequently designates an Extreme Learning Machine (ELM) for each cluster found. For model evaluation, the testing data set are submitted to clustering technique and the nearest cluster to data input will give a supervised response through its associated ELM. Self-organizing maps were used in the clustering phase. Adaptive differential evolution was used to optimize the parameters and performance of the different techniques used in the clustering and prediction phases. The results obtained with the CSELM method are compared with results obtained by other methods in the literature. Five well-known time series were used to validate CSELM. Content Type Journal Article Pages 191-203 DOI 10.3233/HIS-130176 Authors Tiago P.F. Lima, Centro de Informática, Universidade Federal de Pernambuco, Recife, Brazil Teresa B. Ludermir, Centro de Informática, Universidade Federal de Pernambuco, Recife, Brazil Journal International Journal of Hybrid Intelligent Systems Online ISSN 1875-8819 Print ISSN 1448-5869 Journal Volume Volume 10 Journal Issue Volume 10, Number 4 / 2013

Description: There is much interest in using high-throughput DNA sequencing methodology to monitor microorganisms, complex plant and animal communities. However, there are experimental and analytical issues to consider before applying a sequencing technology, which was originally developed for genome projects, to ecological projects. Many of these issues have been highlighted by recent microbial studies. Understanding how high-throughput sequencing is best implemented is important for the interpretation of recent results and the success of future applications. Addressing complex biological questions with metagenomics requires the interaction of researchers who bring different skill sets to problem solving. Educators can help by nurturing a collaborative interdisciplinary approach to genome science, which is essential for effective problem solving. Educators are in a position to help students, teachers, the public and policy makers interpret the new knowledge that metagenomics brings. To do this, they need to understand, not only the excitement of the science but also the pitfalls and shortcomings of methodology and research designs. We review these issues and some of the research directions that are helping to move the field forward.

Description: We believe that undergraduate biology students must acquire a foundational background in computing including how to formulate a computational problem; develop an algorithmic solution; implement their solution in software and then test, document and use their code to explore biological phenomena. Moreover, by learning these skills in the first year, students acquire a powerful tool set that they can use and build on throughout their studies. To address this need, we have developed a first-year undergraduate course that teaches students the foundations of computational thinking and programming in the context of problems in biology. This article describes the structure and content of the course and summarizes assessment data on both affective and learning outcomes.

Description: Bioinformatics is an integral part of modern life sciences. It has revolutionized and redefined how research is carried out and has had an enormous impact on biotechnology, medicine, agriculture and related areas. Yet, it is only rarely integrated into high school teaching and learning programs, playing almost no role in preparing the next generation of information-oriented citizens. Here, we describe the design principles of bioinformatics learning environments, including our own, that are aimed at introducing bioinformatics into senior high school curricula through engaging learners in scientifically authentic inquiry activities. We discuss the bioinformatics-related benefits and challenges that high school teachers and students face in the course of the implementation process, in light of previous studies and our own experience. Based on these lessons, we present a new approach for characterizing the questions embedded in bioinformatics teaching and learning units, based on three criteria: the type of domain-specific knowledge required to answer each question (declarative knowledge, procedural knowledge, strategic knowledge, situational knowledge), the scientific approach from which each question stems (biological, bioinformatics, a combination of the two) and the associated cognitive process dimension (remember, understand, apply, analyze, evaluate, create). We demonstrate the feasibility of this approach using a learning environment, which we developed for the high school level, and suggest some of its implications. This review sheds light on unique and critical characteristics related to broader integration of bioinformatics in secondary education, which are also relevant to the undergraduate level, and especially on curriculum design, development of suitable learning environments and teaching and learning processes.

Description: Next-generation sequencing (NGS) is increasingly being adopted as the backbone of biomedical research. With the commercialization of various affordable desktop sequencers, NGS will be reached by increasing numbers of cellular and molecular biologists, necessitating community consensus on bioinformatics protocols to tackle the exponential increase in quantity of sequence data. The current resources for NGS informatics are extremely fragmented. Finding a centralized synthesis is difficult. A multitude of tools exist for NGS data analysis; however, none of these satisfies all possible uses and needs. This gap in functionality could be filled by integrating different methods in customized pipelines, an approach helped by the open-source nature of many NGS programmes. Drawing from community spirit and with the use of the Wikipedia framework, we have initiated a collaborative NGS resource: The NGS WikiBook. We have collected a sufficient amount of text to incentivize a broader community to contribute to it. Users can search, browse, edit and create new content, so as to facilitate self-learning and feedback to the community. The overall structure and style for this dynamic material is designed for the bench biologists and non-bioinformaticians. The flexibility of online material allows the readers to ignore details in a first read, yet have immediate access to the information they need. Each chapter comes with practical exercises so readers may familiarize themselves with each step. The NGS WikiBook aims to create a collective laboratory book and protocol that explains the key concepts and describes best practices in this fast-evolving field.

Description: Motivation: Pyrosequencing technology provides an important new approach to more extensively characterize diverse sequence populations and detect low frequency variants. However, the promise of this technology has been difficult to realize, as careful correction of sequencing errors is crucial to distinguish rare variants (~1%) in an infected host with high sensitivity and specificity. Results: We developed a new approach, referred to as Indel and Carryforward Correction (ICC), to cluster sequences without substitutions and locally correct only indel and carryforward sequencing errors within clusters to ensure that no rare variants are lost. ICC performs sequence clustering in the order of (i) homopolymer indel patterns only, (ii) indel patterns only and (iii) carryforward errors only, without the requirement of a distance cutoff value. Overall, ICC removed 93–95% of sequencing errors found in control datasets. On pyrosequencing data from a PCR fragment derived from 15 HIV-1 plasmid clones mixed at various frequencies as low as 0.1%, ICC achieved the highest sensitivity and similar specificity compared with other commonly used error correction and variant calling algorithms. Availability and implementation: Source code is freely available for download at http://indra.mullins.microbiol.washington.edu/ICC . It is implemented in Perl and supported on Linux, Mac OS X and MS Windows. Contact: jmullins@uw.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Description: In this dissertation, we propose solving methods for the job shop scheduling problem with sequence-dependent setup times, considering four different objective functions. The formal properties of the problem are studied. Two new neighborhood structures are proposed, with their respective feasibility and non-improving conditions, as well as an algorithm for fast neighbors' cost estimation. These structures are embedded in local search procedures, and their hybridization with a genetic algorithm is also studied. The experimental results show that the proposed methods obtain excellent results, improving in many cases the state of the art. Content Type Journal Article Category Thesis Pages 419-421 DOI 10.3233/AIC-130571 Authors Miguel A. González, Computing Technologies Group, Department of Computing, Artificial Intelligence Center, University of Oviedo, Campus of Viesques, 33271 Gijón, Spain. E-mail: mig@uniovi.es Journal AI Communications Online ISSN 1875-8452 Print ISSN 0921-7126 Journal Volume Volume 26 Journal Issue Volume 26, Number 4 / 2013

Description: : Tiki proteins appear to antagonize Wnt signalling pathway by acting as Wnt proteases, thereby affecting Wnt solubility by its amino-terminal cleavage. Tiki1 protease activity was shown to be metal ion-dependent and was inhibited by chelating agents and thus was tentatively proposed to be a metalloprotease. Nevertheless, Tiki proteins exhibit no detectable sequence similarity to previously described metalloproteases, but instead have been reported as being homologues of TraB proteins (Pfam ID: PF01963), a widely distributed family of unknown function and structure. Here, we show that Tiki proteins are members of a new superfamily of domains contained not just in TraB proteins, but also in erythromycin esterase (Pfam ID: PF05139), DUF399 (domain of unknown function 399; Pfam ID: PF04187) and MARTX toxins that contribute to host invasion and pathogenesis by bacteria. We establish the core fold of this enzymatic domain and its catalytic residues. Contact: luis.sanchezpulido@dpag.ox.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online.

Description: Motivation: The research area metabolomics achieved tremendous popularity and development in the last couple of years. Owing to its unique interdisciplinarity, it requires to combine knowledge from various scientific disciplines. Advances in the high-throughput technology and the consequently growing quality and quantity of data put new demands on applied analytical and computational methods. Exploration of finally generated and analyzed datasets furthermore relies on powerful tools for data mining and visualization. Results: To cover and keep up with these requirements, we have created MeltDB 2.0, a next-generation web application addressing storage, sharing, standardization, integration and analysis of metabolomics experiments. New features improve both efficiency and effectivity of the entire processing pipeline of chromatographic raw data from pre-processing to the derivation of new biological knowledge. First, the generation of high-quality metabolic datasets has been vastly simplified. Second, the new statistics tool box allows to investigate these datasets according to a wide spectrum of scientific and explorative questions. Availability: The system is publicly available at https://meltdb.cebitec.uni-bielefeld.de . A login is required but freely available. Contact: nkessler@cebitec.uni-bielefeld.de

Description: Motivation: High - throughput next - generation sequencing technologies enable increasingly fast and affordable sequencing of genomes and transcriptomes, with a broad range of applications. The quality of the sequencing data is crucial for all applications. A significant portion of the data produced contains errors, and ever more efficient error correction programs are needed. Results: We propose RACER (Rapid and Accurate Correction of Errors in Reads), a new software program for correcting errors in sequencing data. RACER has better error-correcting performance than existing programs, is faster and requires less memory. To support our claims, we performed extensive comparison with the existing leading programs on a variety of real datasets. Availability: RACER is freely available for non-commercial use at www.csd.uwo.ca/~ilie/RACER/ . Contact: ilie@csd.uwo.ca Supplementary information: Supplementary data are available at Bioinformatics online.

Description: : Interactions between various types of molecules that regulate crucial cellular processes are extensively investigated by high-throughput experiments and require dedicated computational methods for the analysis of the resulting data. In many cases, these data can be represented as a bipartite graph because it describes interactions between elements of two different types such as the influence of different experimental conditions on cellular variables or the direct interaction between receptors and their activators/inhibitors. One of the major challenges in the analysis of such noisy datasets is the statistical evaluation of the relationship between any two elements of the same type. Here, we present SICOP (significant co-interaction patterns), an implementation of a method that provides such an evaluation based on the number of their common interaction partners, their so-called co-interaction. This general network analytic method, proved successful in diverse fields, provides a framework for assessing the significance of this relationship by comparison with the expected co-interaction in a suitable null model of the same bipartite graph. SICOP takes into consideration up to two distinct types of interactions such as up- or downregulation. The tool is written in Java and accepts several common input formats and supports different output formats, facilitating further analysis and visualization. Its key features include a user-friendly interface, easy installation and platform independence. Availability: The software is open source and available at cna.cs.uni-kl.de/SICOP under the terms of the GNU General Public Licence (version 3 or later). Contact: agnes.horvat@iwr.uni-heidelberg.de or zweig@cs.uni-kl.de

Description: : Molecular recognition features (MoRFs) are small, intrinsically disordered regions in proteins that undergo a disorder-to-order transition on binding to their partners. MoRFs are involved in protein–protein interactions and may function as the initial step in molecular recognition. The aim of this work was to collect, organize and store all membrane proteins that contain MoRFs. Membrane proteins constitute ~30% of fully sequenced proteomes and are responsible for a wide variety of cellular functions. MoRFs were classified according to their secondary structure, after interacting with their partners. We identified MoRFs in transmembrane and peripheral membrane proteins. The position of transmembrane protein MoRFs was determined in relation to a protein’s topology. All information was stored in a publicly available mySQL database with a user-friendly web interface. A Jmol applet is integrated for visualization of the structures. mpMoRFsDB provides valuable information related to disorder-based protein–protein interactions in membrane proteins. Availability: http://bioinformatics.biol.uoa.gr/mpMoRFsDB Contact: shamodr@biol.uoa.gr

Description: : The signaling Petri net (SPN) simulator, designed to provide insights into the trends of molecules’ activity levels in response to an external stimulus, contributes to the systems biology necessity of analyzing the dynamics of large-scale cellular networks. Implemented into the freely available software, BioLayout Express 3D , the simulator is publicly available and easy to use, provided the input files are prepared in the GraphML format, typically using the network editing software, yEd, and standards specific to the software. However, analysis of complex networks represented using other systems biology formatting languages (on which popular software, such as CellDesigner and Cytoscape, are based) requires manual manipulation, a step that is prone to error and limits the use of the SPN simulator in BioLayout Express 3D . To overcome this, we present a Cytoscape plug-in that enables users to automatically convert networks for analysis with the SPN simulator from the standard systems biology markup language. The automation of this step opens the SPN simulator to a far larger user group than has previously been possible. Availability and implementation: Distributed under the GNU General Public License Version 3 at http://apps.cytoscape.org/apps/spnconverter . Contact: christine@picb.ac.cn

Description: Motivation: Conformational diversity is a key concept in the understanding of different issues related with protein function such as the study of catalytic processes in enzymes, protein-protein recognition, protein evolution and the origins of new biological functions. Here, we present a database of proteins with different degrees of conformational diversity. Conformational Diversity of Native State (CoDNaS) is a redundant collection of three-dimensional structures for the same protein derived from protein data bank. Structures for the same protein obtained under different crystallographic conditions have been associated with snapshots of protein dynamism and consequently could characterize protein conformers. CoDNaS allows the user to explore global and local structural differences among conformers as a function of different parameters such as presence of ligand, post-translational modifications, changes in oligomeric states and differences in pH and temperature. Additionally, CoDNaS contains information about protein taxonomy and function, disorder level and structural classification offering useful information to explore the underlying mechanism of conformational diversity and its close relationship with protein function. Currently, CoDNaS has 122 122 structures integrating 12 684 entries, with an average of 9.63 conformers per protein. Availability: The database is freely available at http://www.codnas.com.ar/ . Contact: gusparisi@gmail.com

Description: Research in automated planning is getting more and more focused on empirical evaluation. Likewise the need for methodologies and benchmarks to build solid evaluations of planners is increasing. In 1998 the planning community made a move to address this need and initiated the International Planning Competition – or IPC for short. This competition has typically been conducted every two years in the context of the International Conference on Automated Planning and Scheduling (ICAPS) and tries to define standard metrics and benchmarks to reliably evaluate planners. In the sixth edition of the competition, IPC 2008, there was an attempt to automate the evaluation of all entries in the competition which was imitated to a large extent and extended in several ways in the seventh edition, IPC 2011. As a result, a software for automatically running planning experiments and inspecting the results is available, encouraging researchers to use it for their own research interests. The software allows researchers to reproduce and inspect the results of IPC 2011, but also to generate and analyze new experiments with private sets of planners and problems. In this paper we provide a gentle introduction to this software and examine the main difficulties, both from a scientific and engineering point of view, in assessing the performance of automated planners. Content Type Journal Article Pages 331-354 DOI 10.3233/AIC-130572 Authors Carlos Linares López, Computer Science Department, Universidad Carlos III de Madrid, Madrid, Spain. E-mails: {carlos.linares, sergio.jimenez}@uc3m.es Sergio Jiménez, Computer Science Department, Universidad Carlos III de Madrid, Madrid, Spain. E-mails: {carlos.linares, sergio.jimenez}@uc3m.es Malte Helmert, Department of Mathematics and Computer Science, Universität Basel, Basel, Switzerland. E-mail: malte.helmert@unibas.ch Journal AI Communications Online ISSN 1875-8452 Print ISSN 0921-7126 Journal Volume Volume 26 Journal Issue Volume 26, Number 4 / 2013

Description: Motivation: Recent experimental advancements allow determining positions of nucleosomes for complete genomes. However, the resulting nucleosome occupancy maps are averages of heterogeneous cell populations. Accordingly, they represent a snapshot of a dynamic ensemble at a single time point with an overlay of many configurations from different cells. To study the organization of nucleosomes along the genome and to understand the mechanisms of nucleosome translocation, it is necessary to retrieve features of specific conformations from the population average. Results: Here, we present a method for identifying non-overlapping nucleosome configurations that combines binary-variable analysis and a Monte Carlo approach with a simulated annealing scheme. In this manner, we obtain specific nucleosome configurations and optimized solutions for the complex positioning patterns from experimental data. We apply the method to compare nucleosome positioning at transcription factor binding sites in different mouse cell types. Our method can model nucleosome translocations at regulatory genomic elements and generate configurations for simulations of the spatial folding of the nucleosome chain. Availability: Source code, precompiled binaries, test data and a web-based test installation are freely available at http://bioinformatics.fh-stralsund.de/nucpos/ Contact: gero.wedemann@fh-stralsund.de Supplementary Information: Supplementary data are available at Bioinformatics online.

Description: As statistical data is inherently highly structured and comes with rich metadata (in form of code lists, data cubes etc.), it would be a missed opportunity to not tap into it from the Linked Data angle. At the time of this writing, there exists no simple way to transform statistical data into Linked Data since the raw data comes in different shapes and forms. Given that SDMX (Statistical Data and Metadata eXchange) is arguably the most widely used standard for statistical data exchange, a great amount of statistical data about our societies is yet to be discoverable and identifiable in a uniform way. In this article, we present the design and implementation of SDMX-ML to RDF/XML XSL transformations, as well as the publication of OECD, BFS, FAO, ECB, and IMF datasets with that tooling. Content Type Journal Article Pages - DOI 10.3233/SW-130123 Authors Sarven Capadisli, Universität Leipzig, Institut für Informatik, AKSW, Postfach 100920, D-04009 Leipzig, Germany. E-mail: info@csarven.ca, auer@informatik.uni-leipzig.de, ngonga@informatik.uni-leipzig.de Sören Auer, Universität Leipzig, Institut für Informatik, AKSW, Postfach 100920, D-04009 Leipzig, Germany. E-mail: info@csarven.ca, auer@informatik.uni-leipzig.de, ngonga@informatik.uni-leipzig.de Axel-Cyrille Ngonga Ngomo, Universität Leipzig, Institut für Informatik, AKSW, Postfach 100920, D-04009 Leipzig, Germany. E-mail: info@csarven.ca, auer@informatik.uni-leipzig.de, ngonga@informatik.uni-leipzig.de Journal Semantic Web Online ISSN 2210-4968 Print ISSN 1570-0844

Description: Arguments need to be judged against other arguments. The decision to accept or reject an argument is generally a global decision that involves examining the same question for other arguments that oppose or can defend the argument in question. This article presents the acceptability semantics for abstract argumentation that through a recursive definition gives a global assignment of the acceptable and non-acceptable subsets of arguments. This semantics stems from the aim to formalize directly the generally accepted intuition that: ‘An argument can be accepted if and only if all its challenging arguments can be rejected.’ The acceptability semantics tightly integrates the notion of defending against a challenging argument by counter-attacking it with the notion of self-defeating (or self-rejecting) arguments that (help to) bring about their own non-acceptability. The proposal is motivated by earlier studies of the semantics of Logic Programming (LP) in terms of argumentation, where the basic well founded and stable model semantics of LP can be uniformly captured using a recursively defined argumentation semantics for Negation as Failure and where these standard semantics of LP can be further extended through argumentation.

Description: This article is concerned with the design and analysis of polynomial time algorithms for determining whether a Planar Quantified Integer Program (PQIP) is feasible. A PQIP can be described briefly as an integer program involving two variables, in which each variable can be either universally or existentially quantified. There are four types of PQIPs, depending on how the variables are quantified (existentially or universally). In this article, we present two new, simple, and efficient algorithms for the case as well as a detailed account of the complexity of the other cases. Moreover, we discuss certification with respect to the provided algorithms.

Description: In the present article, the quantifiers over propositions are first introduced into the language for reasoning about probability, then the complexity issues for validity problems dealing with the corresponding hierarchy of probabilistic sentences are investigated. We prove, among other things, the $${\Pi }_{1}^{1}$$ -completeness for the general validity and also indicate the least level in the hierarchy for which the validity problem is undecidable.

Description: We use mosaics to provide a simple, sound, complete and terminating tableau reasoning procedure for the temporal logic of until and since over general linear time.

Description: The aim of what semantic science is to have scientific ontologies, data and hypotheses represented and published in machine understandable forms that enable predictions on new cases. There is much work on developing scientific ontologies and representing scientific data in terms of these ontologies. The next step is to publish hypotheses that can make (probabilistic) predictions on the published data and can be used for prediction on new cases. The published data can be used to evaluate hypotheses. To make a prediction in a particular case, hypotheses are combined to form models. This article considers feature-based semantic science where the data and new cases are described in terms of features. A prediction for a new case is made by building a model made up of hypotheses that fit together, are consistent with the ontologies used, and are adequate for the case. We give some desiderata for such models, and show how the construction of such models is a form of abduction. We provide a definition for models that satisfies these criteria and prove that it produces a coherent probability distribution over the values of interest.

Description: In this article we present methods of transition from one perspective on logic to others, and apply this in particular to obtain a coalgebraic presentation of logic. The central ingredient in this process is to view consequence relations as morphisms in a category.

Description: We consider the expressive power of the first-order structure 〈, C 〉 where is either of two of different domains of extended regions in Euclidean space, and C(x,y) is the topological relation ‘Region x is in contact with region y .’ We prove two main theorems: Let $$\mathcal{P}$$ [Q] be the domain of bounded, non-empty, rational polyhedra in two- or three-dimensional Euclidean space. A relation over $$\mathcal{P}$$ [Q] is definable in the structure 〈 $$\mathcal{P}$$ [Q], C 〉 if and only if is arithmetic and invariant under rational PL-homeomorphisms of the space to itself. We also extend this result to a number of other domains, including the domain of all polyhedra and the domain of semi-algebraic regions. Let $$\mathcal{R}$$ be the space of bounded, non-empty, closed regular regions in n -dimensional Euclidean space. Any analytical relation over lower dimensional (i.e. empty interior) compact point sets that is invariant under homeomorphism is implicitly definable in the structure 〈 $$\mathcal{R}$$ , C 〉.

Description: We proposed two multiparty quantum secret sharing schemes based on n -particle Greenberger–Horne–Zeilinger-states (GHZ states), which are transformed from Einstein–Podolsky–Rosen pairs by entanglement swapping. In our schemes, the dealer imposes messages by performing local unitary operations ( I, x, i y, z ) on the n -particle GHZ state she holds, and the agents collaborate to deduce the dealer's messages by performing local unitary operations on their own qubit. The amount of dealer's secret message is positively related with the number of agents. The need of qubits is one-third less than the former schemes and, also, they can be reused for the next new round. The scheme (II) does not have to pre-share the code table, which increases the security without the risk of being stolen.

Description: Automatic fare collection (AFC) systems calculate the fare that the users must pay depending on the time of service (time-based) or the points of entrance and exit of the system (distance-based). The progressive introduction of Information and Communication Technologies allows the use of electronic tickets, which helps us to reduce costs and improve the control of the infrastructures. Nevertheless, these systems must be secure against possible fraud and they must also preserve users’ privacy. Therefore, we have studied the security requirements for the time-based and distance-based systems and we have proposed a protocol for each of the AFC systems. 1 The protocols offer strong privacy for honest users, i.e. the service provider is not able to disclose the identity of its users and, moreover, different journeys of the same user are not linkable between them. However, anonymity for users could be revoked if they misbehave. The protocols have been implemented in Android and its performance has been evaluated in two Android smartphones. The results remark that protocols are suitable to be used on an AFC system with a medium class mobile device although they offer a better experience with a high-class smartphone. The appearance in the market of more powerful mobile devices suggests a better usability of our proposal in a near future.

Description: Accurately locating unknown nodes is a critical issue in the study of wireless sensor networks (WSNs). Many localization approaches have been proposed based on anchor nodes, which are assumed to know their locations by manual placement or additional equipments such as global positioning system. However, none of these approaches can work properly under the adversarial scenario. In this paper, we propose a novel scheme called two-step secure localization (TSSL) stand against many typical malicious attacks, e.g. wormhole attack and location spoofing attack. TSSL detects malicious nodes step by step. First, anchor nodes collaborate with each other to identify suspicious nodes by checking their coordinates, identities and time of sending information. Then, by using a modified mesh generation scheme, malicious nodes are isolated and the WSN is divided into areas with different trust grades. Finally, a novel localization algorithm based on the arrival time difference of localization information is adopted to calculate locations of unknown nodes. Simulation results show that the TSSL detects malicious nodes effectively and the localization algorithm accomplishes localization with high localization accuracy.

Description: Distance-bounding protocols form a family of challenge–response authentication protocols that have been introduced to thwart relay attacks. They enable a verifier to authenticate and to establish an upper bound on the physical distance to an untrusted prover. We provide a detailed security analysis of a family of such protocols. More precisely, we show that the secret key shared between the verifier and the prover can be leaked after a number of nonce repetitions. The leakage probability, while exponentially decreasing with the nonce length, is only weakly dependent on the key length. Our main contribution is a high probability bound on the number of sessions required for the attacker to discover the secret, and an experimental analysis of the attack under noisy conditions. Both of these show that the attack's success probability mainly depends on the length of the used nonces rather than the length of the shared secret key. The theoretical bound could be used by practitioners to appropriately select their security parameters. While longer nonces can guard against this type of attack, we provide a possible countermeasure which successfully combats these attacks even when short nonces are used.

Description: In threshold public-key encryption (TPKE), the decryption key is divided into n shares, each one of which is given to a different decryption user in order to avoid single points of failure. A robust TPKE is that if threshold decryption of a valid ciphertext fails, the combiner can identify the decryption users that supplied invalid partial decryption shares. In this paper, we propose a practical and efficient TPKE scheme which is robust and non-interactive. Security against chosen-ciphertext attacks (CCAs) can be proved in the standard model under the hashed Diffie–Hellman assumption in bilinear groups. The security reduction is tight and simple. We use an instantiation hash function of the Kiltz's key encapsulation mechanism and Lai et al. 's chosen-ciphertext secure technique to construct a TPKE scheme. Moreover, our scheme is more simple and shown to be more efficient than currently existing CCA-secure TPKE schemes.

Description: The advances in the digital world (e.g. the Internet, Communications, etc.) are closing the gap between consumers and providers, in both Business to Consumer and Business to Business environments. Through the Internet, providers can offer their products directly to consumers, which increase their choices and allow them to contract a set of different services/products from different providers. But sometimes the consumer needs an all-or-nothing exchange with these providers: an atomic exchange. The consumer has negotiated/obtained a set of conditions that are only met if she acquires all the services/products. Thus, we need an Atomic Multi-Two-Party contract signing protocol. Even though digital signature of contracts is a topic that has been widely studied, it lacks a solution when applied to Atomic Multi-Two-Party scenarios. Here, we propose the first solution to solve this problem.

Description: Key-evolving cryptography is intended to mitigate the damage in case of a secret key compromise, one of the severest security threats to actual cryptographic schemes. In the public-key setting, the essential idea of key-evolving lies in updating the private key with time, while maintaining the same public key. Key evolution in encryption and signing has been well studied, especially in the identity-based (ID-based) setting where an entity's public key can be derived from that entity's identity information. From a more practical standpoint, however, one would like to use the primitive signcryption in the hierarchical ID-based setting. In this paper, we propose the first key-evolving hierarchical ID-based signcryption scheme that is scalable and joining-time-oblivious and allows secret keys to be updated autonomously. The security proofs of our construction depend on the bilinear Diffie–Hellman assumption and the computational Diffie–Hellman assumption in the random oracle model. To be specific, our proposal not only achieves the fundamental goals of confidentiality and authenticity, but also enjoys desirable properties of non-repudiation, ciphertext anonymity and strong forward security. Compared with the conventional sign-then-encrypt approach, our construction provides better efficiency in terms of the computation cost and the communication overhead.

Description: Cooperative Intelligent Transportation Systems (ITS) will turn up to us with safer and more efficient driving environments as well as convenient and infotainment features for future ITS stations. ETSI and ISO are completing the standardization of the building blocks of a reference communication architecture for cooperative ITS. Future ITS stations complying with this set of standards deployed in vehicles, at the roadside infrastructures and within the Internet are expected to communicate with each other through a combination of ITS dedicated communication protocols and legacy Internet protocols. However, in spite of the wide adoption of IPv6 for cooperative ITS communications, relatively little attention has been paid to the security issues related to IPv6 signaling and IPv6 transport communications. In this paper, we present our position on the emerging and urgent IPv6-related security issues that occur in communications between ITS stations complying with the ITS station reference architecture under standardization within ETSI TC ITS and ISO TC204.

Description: Motivation: Residue–residue contacts across the transmembrane helices dictate the three-dimensional topology of alpha-helical membrane proteins. However, contact determination through experiments is difficult because most transmembrane proteins are hard to crystallize. Results: We present a novel method (MemBrain) to derive transmembrane inter-helix contacts from amino acid sequences by combining correlated mutations and multiple machine learning classifiers. Tested on 60 non-redundant polytopic proteins using a strict leave-one-out cross-validation protocol, MemBrain achieves an average accuracy of 62%, which is 12.5% higher than the current best method from the literature. When applied to 13 recently solved G protein-coupled receptors, the MemBrain contact predictions helped increase the TM-score of the I-TASSER models by 37% in the transmembrane region. The number of foldable cases (TM-score 〉0.5) increased by 100%, where all G protein-coupled receptor templates and homologous templates with sequence identity 〉30% were excluded. These results demonstrate significant progress in contact prediction and a potential for contact-driven structure modeling of transmembrane proteins. Availability: www.csbio.sjtu.edu.cn/bioinf/MemBrain/ Contact: hbshen@sjtu.edu.cn or zhng@umich.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Description: Motivation: Identification of protein–ligand binding sites is critical to protein function annotation and drug discovery. However, there is no method that could generate optimal binding site prediction for different protein types. Combination of complementary predictions is probably the most reliable solution to the problem. Results: We develop two new methods, one based on binding-specific substructure comparison (TM-SITE) and another on sequence profile alignment (S-SITE), for complementary binding site predictions. The methods are tested on a set of 500 non-redundant proteins harboring 814 natural, drug-like and metal ion molecules. Starting from low-resolution protein structure predictions, the methods successfully recognize 〉51% of binding residues with average Matthews correlation coefficient (MCC) significantly higher (with P -value 〈10 –9 in student t -test) than other state-of-the-art methods, including COFACTOR, FINDSITE and ConCavity. When combining TM-SITE and S-SITE with other structure-based programs, a consensus approach (COACH) can increase MCC by 15% over the best individual predictions. COACH was examined in the recent community-wide COMEO experiment and consistently ranked as the best method in last 22 individual datasets with the Area Under the Curve score 22.5% higher than the second best method. These data demonstrate a new robust approach to protein–ligand binding site recognition, which is ready for genome-wide structure-based function annotations. Availability: http://zhanglab.ccmb.med.umich.edu/COACH/ Contact: zhng@umich.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Description: Motivation: The nucleosome is the basic repeating unit of chromatin. It contains two copies each of the four core histones H2A, H2B, H3 and H4 and about 147 bp of DNA. The residues of the histone proteins are subject to numerous post-translational modifications, such as methylation or acetylation. Chromatin immunoprecipitiation followed by sequencing (ChIP-seq) is a technique that provides genome-wide occupancy data of these modified histone proteins, and it requires appropriate computational methods. Results: We present NucHunter, an algorithm that uses the data from ChIP-seq experiments directed against many histone modifications to infer positioned nucleosomes. NucHunter annotates each of these nucleosomes with the intensities of the histone modifications. We demonstrate that these annotations can be used to infer nucleosomal states with distinct correlations to underlying genomic features and chromatin-related processes, such as transcriptional start sites, enhancers, elongation by RNA polymerase II and chromatin-mediated repression. Thus, NucHunter is a versatile tool that can be used to predict positioned nucleosomes from a panel of histone modification ChIP-seq experiments and infer distinct histone modification patterns associated to different chromatin states. Availability: The software is available at http://epigen.molgen.mpg.de/nuchunter/ . Contact: chung@molgen.mpg.de Supplementary information: Supplementary data are available at Bioinformatics online.

Description: Motivation: In biomedical research a growing number of platforms and technologies are used to measure diverse but related information, and the task of clustering a set of objects based on multiple sources of data arises in several applications. Most current approaches to multisource clustering either independently determine a separate clustering for each data source or determine a single ‘joint’ clustering for all data sources. There is a need for more flexible approaches that simultaneously model the dependence and the heterogeneity of the data sources. Results: We propose an integrative statistical model that permits a separate clustering of the objects for each data source. These separate clusterings adhere loosely to an overall consensus clustering, and hence they are not independent. We describe a computationally scalable Bayesian framework for simultaneous estimation of both the consensus clustering and the source-specific clusterings. We demonstrate that this flexible approach is more robust than joint clustering of all data sources, and is more powerful than clustering each data source independently. We present an application to subtype identification of breast cancer tumor samples using publicly available data from The Cancer Genome Atlas. Availability: R code with instructions and examples is available at http://people.duke.edu/%7Eel113/software.html . Contact: Eric.Lock@duke.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Description: Motivation: More and more evidences have indicated that long–non-coding RNAs (lncRNAs) play critical roles in many important biological processes. Therefore, mutations and dysregulations of these lncRNAs would contribute to the development of various complex diseases. Developing powerful computational models for potential disease-related lncRNAs identification would benefit biomarker identification and drug discovery for human disease diagnosis, treatment, prognosis and prevention. Results : In this article, we proposed the assumption that similar diseases tend to be associated with functionally similar lncRNAs. Then, we further developed the method of Laplacian Regularized Least Squares for LncRNA–Disease Association (LRLSLDA) in the semisupervised learning framework. Although known disease–lncRNA associations in the database are rare, LRLSLDA still obtained an AUC of 0.7760 in the leave-one-out cross validation, significantly improving the performance of previous methods. We also illustrated the performance of LRLSLDA is not sensitive (even robust) to the parameters selection and it can obtain a reliable performance in all the test classes. Plenty of potential disease–lncRNA associations were publicly released and some of them have been confirmed by recent results in biological experiments. It is anticipated that LRLSLDA could be an effective and important biological tool for biomedical research. Availability: The code of LRLSLDA is freely available at http://asdcd.amss.ac.cn/Software/Details/2 . Contact: xingchen@amss.ac.cn or yangy@amt.ac.cn Supplementary information: Supplementary data are available at Bioinformatics online.

Description: Motivation: Genomic repositories are rapidly growing, as witnessed by the 1000 Genomes or the UK10K projects. Hence, compression of multiple genomes of the same species has become an active research area in the past years. The well-known large redundancy in human sequences is not easy to exploit because of huge memory requirements from traditional compression algorithms. Results: We show how to obtain several times higher compression ratio than of the best reported results, on two large genome collections (1092 human and 775 plant genomes). Our inputs are variant call format files restricted to their essential fields. More precisely, our novel Ziv-Lempel-style compression algorithm squeezes a single human genome to ~400 KB. The key to high compression is to look for similarities across the whole collection, not just against one reference sequence, what is typical for existing solutions. Availability: http://sun.aei.polsl.pl/tgc (also as Supplementary Material) under a free license. Supplementary data: Supplementary data are available at Bioinformatics online. Contact: sebastian.deorowicz@polsl.pl

Description: Motivation:  Biological systems are understood through iterations of modeling and experimentation. Not all experiments, however, are equally valuable for predictive modeling. This study introduces an efficient method for experimental design aimed at selecting dynamical models from data. Motivated by biological applications, the method enables the design of crucial experiments: it determines a highly informative selection of measurement readouts and time points. Results:  We demonstrate formal guarantees of design efficiency on the basis of previous results. By reducing our task to the setting of graphical models, we prove that the method finds a near-optimal design selection with a polynomial number of evaluations. Moreover, the method exhibits the best polynomial-complexity constant approximation factor, unless P = NP. We measure the performance of the method in comparison with established alternatives, such as ensemble non-centrality, on example models of different complexity. Efficient design accelerates the loop between modeling and experimentation: it enables the inference of complex mechanisms, such as those controlling central metabolic operation. Availability:  Toolbox ‘NearOED’ available with source code under GPL on the Machine Learning Open Source Software Web site (mloss.org). Contact:   busettoa@inf.ethz.ch Supplementary information:   Supplementary data are available at Bioinformatics online.

Description: : Small RNA deep sequencing is widely used to characterize non-coding RNAs (ncRNAs) differentially expressed between two conditions, e.g. healthy and diseased individuals and to reveal insights into molecular mechanisms underlying condition-specific phenotypic traits. The ncRNAome is composed of a multitude of RNAs, such as transfer RNA, small nucleolar RNA and microRNA (miRNA), to name few. Here we present omiRas, a Web server for the annotation, comparison and visualization of interaction networks of ncRNAs derived from next-generation sequencing experiments of two different conditions. The Web tool allows the user to submit raw sequencing data and results are presented as: (i) static annotation results including length distribution, mapping statistics, alignments and quantification tables for each library as well as lists of differentially expressed ncRNAs between conditions and (ii) an interactive network visualization of user-selected miRNAs and their target genes based on the combination of several miRNA–mRNA interaction databases. Availability and Implementation: The omiRas Web server is implemented in Python, PostgreSQL, R and can be accessed at: http://tools.genxpro.net/omiras/ . Contact: rotter@genxpro.de Supplementary Information: Supplementary data are available at Bioinformatics online.

Description: : We present PARSEC (PAtteRn Search and Contextualization), a new open source platform for guided discovery, allowing localization and biological characterization of short genomic sites in entire eukaryotic genomes. PARSEC can search for a sequence or a degenerated pattern. The retrieved set of genomic sites can be characterized in terms of (i) conservation in model organisms, (ii) genomic context (proximity to genes) and (iii) function of neighboring genes. These modules allow the user to explore, visualize, filter and extract biological knowledge from a set of short genomic regions such as transcription factor binding sites. Availability: Web site implemented in Java, JavaScript and C++, with all major browsers supported. Freely available at lbgi.fr/parsec. Source code is freely available at sourceforge.net/projects/genomicparsec. Contact: odile.lecompte@unistra.fr Supplementary information: Supplementary data are available at Bioinformatics online.

Description: : At the rate that prokaryotic genomes can now be generated, comparative genomics studies require a flexible method for quickly and accurately predicting orthologs among the rapidly changing set of genomes available. SPOCS implements a graph-based ortholog prediction method to generate a simple tab-delimited table of orthologs and in addition, html files that provide a visualization of the predicted ortholog/paralog relationships to which gene/protein expression metadata may be overlaid. Availability and Implementation: A SPOCS web application is freely available at http://cbb.pnnl.gov/portal/tools/spocs.html . Source code for Linux systems is also freely available under an open source license at http://cbb.pnnl.gov/portal/software/spocs.html ; the Boost C++ libraries and BLAST are required. Contact: leeann.mccue@pnnl.gov

Description: : Automated image processing has allowed cell migration research to evolve to a high-throughput research field. As a consequence, there is now an unmet need for data management in this domain. The absence of a generic management system for the quantitative data generated in cell migration assays results in each dataset being treated in isolation, making data comparison across experiments difficult. Moreover, by integrating quality control and analysis capabilities into such a data management system, the common practice of having to manually transfer data across different downstream analysis tools will be markedly sped up and made more robust. In addition, access to a data management solution creates gateways for data standardization, meta-analysis and structured public data dissemination. We here present CellMissy, a cross-platform data management system for cell migration data with a focus on wound healing data. CellMissy simplifies and automates data management, storage and analysis from the initial experimental set-up to data exploration. Availability and implementation: CellMissy is a cross-platform open-source software developed in Java. Source code and cross-platform binaries are freely available under the Apache2 open source license at http://cellmissy.googlecode.com . Contact: lennart.martens@ugent.be Supplementary Information: Supplementary data are available at Bioinformatics online.

Description: Motivation: With the expansion of high-throughput technologies, understanding different kinds of genome-level data is a common task. MicroRNA (miRNA) is increasingly profiled using high-throughput technologies (microarrays or next-generation sequencing). The downstream analysis of miRNA targets can be difficult. Although there are many databases and algorithms to predict miRNA targets, there are few tools to integrate miRNA–gene interaction data into high-throughput genomic analyses. Results: We present targetHub, a CouchDB database of miRNA–gene interactions. TargetHub provides a programmer-friendly interface to access miRNA targets. The Web site provides RESTful access to miRNA–gene interactions with an assortment of gene and miRNA identifiers. It can be a useful tool to integrate miRNA target interaction data directly into high-throughput bioinformatics analyses. Availability: TargetHub is available on the web at http://app1.bioinformatics.mdanderson.org/tarhub/_design/basic/index.html . Contact: coombes.3@osu.edu

Description: With the advent of YouTube channels in bioinformatics, open platforms for problem solving in bioinformatics, active web forums in computing analyses and online resources for learning to code or use a bioinformatics tool, the more traditional continuing education bioinformatics training programs have had to adapt. Bioinformatics training programs that solely rely on traditional didactic methods are being superseded by these newer resources. Yet such face-to-face instruction is still invaluable in the learning continuum. Bioinformatics.ca, which hosts the Canadian Bioinformatics Workshops, has blended more traditional learning styles with current online and social learning styles. Here we share our growing experiences over the past 12 years and look toward what the future holds for bioinformatics training programs.

Description: Teaching students with very diverse backgrounds can be extremely challenging. This article uses the Bioinformatics and Systems Biology MSc in Amsterdam as a case study to describe how the knowledge gap for students with heterogeneous backgrounds can be bridged. We show that a mix in backgrounds can be turned into an advantage by creating a stimulating learning environment for the students. In the MSc Programme, conversion classes help to bridge differences between students, by mending initial knowledge and skill gaps. Mixing students from different backgrounds in a group to solve a complex task creates an opportunity for the students to reflect on their own abilities. We explain how a truly interdisciplinary approach to teaching helps students of all backgrounds to achieve the MSc end terms. Moreover, transferable skills obtained by the students in such a mixed study environment are invaluable for their later careers.

Description: The number of bioinformatics tools and resources that support molecular and cell biology approaches is continuously expanding. Moreover, systems and network biology analyses are accompanied more and more by integrated bioinformatics methods. Traditional information-centered university teaching methods often fail, as (1) it is impossible to cover all existing approaches in the frame of a single course, and (2) a large segment of the current bioinformation can become obsolete in a few years. Signaling network offers an excellent example for teaching bioinformatics resources and tools, as it is both focused and complex at the same time. Here, we present an outline of a university bioinformatics course with four sample practices to demonstrate how signaling network studies can integrate biochemistry, genetics, cell biology and network sciences. We show that several bioinformatics resources and tools, as well as important concepts and current trends, can also be integrated to signaling network studies. The research-type hands-on experiences we show enable the students to improve key competences such as teamworking, creative and critical thinking and problem solving. Our classroom course curriculum can be re-formulated as an e-learning material or applied as a part of a specific training course. The multi-disciplinary approach and the mosaic setup of the course have the additional benefit to support the advanced teaching of talented students.

Description: Today, Bioinformatics has become a scientific discipline with great relevance for the Molecular Biosciences and for the Omics sciences in general. Although developed countries have progressed with large strides in Bioinformatics education and research, in other regions, such as Central America, the advances have occurred in a gradual way and with little support from the Academia, either at the undergraduate or graduate level. To address this problem, the University of Costa Rica’s Medical School, a regional leader in Bioinformatics in Central America, has been conducting a series of Bioinformatics workshops, seminars and courses, leading to the creation of the region’s first Bioinformatics Master’s Degree. The recent creation of the Central American Bioinformatics Network (BioCANET), associated to the deployment of a supporting computational infrastructure (HPC Cluster) devoted to provide computing support for Molecular Biology in the region, is providing a foundational stone for the development of Bioinformatics in the area. Central American bioinformaticians have participated in the creation of as well as co-founded the Iberoamerican Bioinformatics Society (SOIBIO). In this article, we review the most recent activities in education and research in Bioinformatics from several regional institutions. These activities have resulted in further advances for Molecular Medicine, Agriculture and Biodiversity research in Costa Rica and the rest of the Central American countries. Finally, we provide summary information on the first Central America Bioinformatics International Congress, as well as the creation of the first Bioinformatics company (Indromics Bioinformatics), spin-off the Academy in Central America and the Caribbean.

Description: Pattern recognition is concerned with the development of systems that learn to solve a given problem using a set of example instances, each represented by a number of features. These problems include clustering, the grouping of similar instances; classification, the task of assigning a discrete label to a given instance; and dimensionality reduction, combining or selecting features to arrive at a more useful representation. The use of statistical pattern recognition algorithms in bioinformatics is pervasive. Classification and clustering are often applied to high-throughput measurement data arising from microarray, mass spectrometry and next-generation sequencing experiments for selecting markers, predicting phenotype and grouping objects or genes. Less explicitly, classification is at the core of a wide range of tools such as predictors of genes, protein function, functional or genetic interactions, etc., and used extensively in systems biology. A course on pattern recognition (or machine learning) should therefore be at the core of any bioinformatics education program. In this review, we discuss the main elements of a pattern recognition course, based on material developed for courses taught at the BSc, MSc and PhD levels to an audience of bioinformaticians, computer scientists and life scientists. We pay attention to common problems and pitfalls encountered in applications and in interpretation of the results obtained.

Description: Argumentation is a powerful paradigm able to formalize commonsense reasoning, finding application in different domains such as automated reasoning, decision making, legal dispute, automated negotiation, etc. However, most of these argumentation-based formalizations do not model the notion of argument accrual, which has been recently gaining importance. This thesis defines a novel formalization of argument accrual, including a declarative characterization of this notion and an associated operational characterization addressing computation. The proposed formalization makes contributions to the existing accrual approaches, mainly concerning the answers obtained, answer explanation, accrual evaluation and comparison, and efficiency of computation. Content Type Journal Article Category Thesis Pages 413-415 DOI 10.3233/AIC-130569 Authors Mauro J. Gómez Lucero, National Council of Scientific and Technical Research, Artificial Intelligence Research & Development Laboratory, Universidad Nacional del Sur, Bahía Blanca, Argentina. E-mail: mjg@cs.uns.edu.ar Journal AI Communications Online ISSN 1875-8452 Print ISSN 0921-7126 Journal Volume Volume 26 Journal Issue Volume 26, Number 4 / 2013

Description: This work proposes a novel algorithm to extract biclusters from binary datasets: the Bit-Pattern Biclustering Algorithm (BiBit). The selective search performed by BiBit, based on a very fast bits words processing technique, provides very satisfactory results in quality and computational cost. Besides, a new software tool, named CarGene (Characterization of Genes), that helps scientists to validate sets of genes using biological knowledge is introduced too. Content Type Journal Article Category Thesis Pages 417-418 DOI 10.3233/AIC-130570 Authors Domingo S. Rodriguez-Baena, School of Engineering, Pablo de Olavide University, Seville, Spain. E-mail: dsrodbae@upo.es Journal AI Communications Online ISSN 1875-8452 Print ISSN 0921-7126 Journal Volume Volume 26 Journal Issue Volume 26, Number 4 / 2013

Description: Motivation: Human miRNAs have recently been found to have important roles in viral replication. Understanding the patterns and details of human miRNA interactions during virus–host interactions may help uncover novel antiviral therapies. Based on the abundance of knowledge available regarding protein–protein interactions (PPI), virus–host protein interactions, experimentally validated human miRNA-target pairs and transcriptional regulation of human miRNAs, it is possible to explore the complex regulatory network that exists between viral proteins and human miRNAs at the system level. Results: By integrating current data regarding the virus–human interactome and human miRNA-target pairs, the overlap between targets of viral proteins and human miRNAs was identified and found to represent topologically important proteins (e.g. hubs or bottlenecks) at the global center of the human PPI network. Viral proteins and human miRNAs were also found to significantly target human PPI pairs. Furthermore, an overlap analysis of virus targets and transcription factors (TFs) of human miRNAs revealed that viral proteins preferentially target human miRNA TFs, representing a new pattern of virus–host interactions. Potential feedback loops formed by viruses, human miRNAs and miRNA TFs were also identified, and these may be exploited by viruses resulting in greater virulence and more effective replication strategies. Contact: boxc@bmi.ac.cn or ni.ming@163.com or sqwang@bmi.ac.cn Supplementary information: Supplementary data are available at Bioinformatics online.

Description: Motivation: In sequencing studies of common diseases and quantitative traits, power to test rare and low frequency variants individually is weak. To improve power, a common approach is to combine statistical evidence from several genetic variants in a region. Major challenges are how to do the combining and which statistical framework to use. General approaches for testing association between rare variants and quantitative traits include aggregating genotypes and trait values, referred to as ‘collapsing’, or using a score-based variance component test. However, little attention has been paid to alternative models tailored for protein truncating variants. Recent studies have highlighted the important role that protein truncating variants, commonly referred to as ‘loss of function’ variants, may have on disease susceptibility and quantitative levels of biomarkers. We propose a Bayesian modelling framework for the analysis of protein truncating variants and quantitative traits. Results: Our simulation results show that our models have an advantage over the commonly used methods. We apply our models to sequence and exome-array data and discover strong evidence of association between low plasma triglyceride levels and protein truncating variants at APOC3 (Apolipoprotein C3). Availability: Software is available from http://www.well.ox.ac.uk/~rivas/mamba Contact: donnelly@well.ox.ac.uk

Description: Motivation: Structural information of macromolecular complexes provides key insights into the way they carry out their biological functions. Achieving high-resolution structural details with electron microscopy requires the identification of a large number (up to hundreds of thousands) of single particles from electron micrographs, which is a laborious task if it has to be manually done and constitutes a hurdle towards high-throughput. Automatic particle selection in micrographs is far from being settled and new and more robust algorithms are required to reduce the number of false positives and false negatives. Results: In this article, we introduce an automatic particle picker that learns from the user the kind of particles he is interested in. Particle candidates are quickly and robustly classified as particles or non-particles. A number of new discriminative shape-related features as well as some statistical description of the image grey intensities are used to train two support vector machine classifiers. Experimental results demonstrate that the proposed method: (i) has a considerably low computational complexity and (ii) provides results better or comparable with previously reported methods at a fraction of their computing time. Availability: The algorithm is fully implemented in the open-source Xmipp package and downloadable from http://xmipp.cnb.csic.es . Contact: vabrishami@cnb.csic.es or coss@cnb.csic.es Supplementary Information: Supplementary data are available at Bioinformatics online.

Description: : Recent major cancer genome sequencing studies have used whole-genome sequencing to detect various types of genomic variation. However, a number of these studies have continued to rely on SNP array information to provide additional results for copy number and loss-of-heterozygosity estimation and assessing tumour purity. OncoSNP-SEQ is a statistical model-based approach for inferring copy number profiles directly from high-coverage whole genome sequencing data that is able to account for unknown tumour purity and ploidy. Availability: MATLAB code is available at the following URL: https://sites.google.com/site/oncosnpseq/ . Contact : c.yau@imperial.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online.

Description: : Multi-Image Genome (MIG) viewer is a web-based application for visualizing, querying and filtering many thousands of genome browser regions as well as for exporting the data in a variety of formats. This methodology has been used successfully to analyze ChIP-Seq data and RNA-Seq data and to detect somatic mutations in genome resequencing projects. Availability: MIG is available at https://mig.molbiol.ox.ac.uk/mig/ Contact: simon.mcgowan@imm.ox.ac.uk

Description: : Unlike DNA, RNA abundances can vary over several orders of magnitude. Thus, identification of RNA–protein binding sites from high-throughput sequencing data presents unique challenges. Although peak identification in ChIP-Seq data has been extensively explored, there are few bioinformatics tools tailored for peak calling on analogous datasets for RNA-binding proteins. Here we describe ASPeak (abundance sensitive peak detection algorithm), an implementation of an algorithm that we previously applied to detect peaks in exon junction complex RNA immunoprecipitation in tandem experiments. Our peak detection algorithm yields stringent and robust target sets enabling sensitive motif finding and downstream functional analyses. Availability: ASPeak is implemented in Perl as a complete pipeline that takes bedGraph files as input. ASPeak implementation is freely available at https://sourceforge.net/projects/as-peak under the GNU General Public License. ASPeak can be run on a personal computer, yet is designed to be easily parallelizable. ASPeak can also run on high performance computing clusters providing efficient speedup. The documentation and user manual can be obtained from http://master.dl.sourceforge.net/project/as-peak/manual.pdf . Contact: alper.kucukural@umassmed.edu or ccenik@stanford.edu

Description: Motivation: Kinases of the eukaryotic protein kinase superfamily are key regulators of most aspects eukaryotic cellular behavior and have provided several drug targets including kinases dysregulated in cancers. The rapid increase in the number of genomic sequences has created an acute need to identify and classify members of this important class of enzymes efficiently and accurately. Results: Kinannote produces a draft kinome and comparative analyses for a predicted proteome using a single line command, and it is currently the only tool that automatically classifies protein kinases using the controlled vocabulary of Hanks and Hunter [Hanks and Hunter (1995)]. A hidden Markov model in combination with a position-specific scoring matrix is used by Kinannote to identify kinases, which are subsequently classified using a BLAST comparison with a local version of KinBase, the curated protein kinase dataset from www.kinase.com . Kinannote was tested on the predicted proteomes from four divergent species. The average sensitivity and precision for kinome retrieval from the test species are 94.4 and 96.8%. The ability of Kinannote to classify identified kinases was also evaluated, and the average sensitivity and precision for full classification of conserved kinases are 71.5 and 82.5%, respectively. Kinannote has had a significant impact on eukaryotic genome annotation, providing protein kinase annotations for 36 genomes made public by the Broad Institute in the period spanning 2009 to the present. Availability: Kinannote is freely available at http://sourceforge.net/projects/kinannote . Contact: jmgold@broadinstitute.org Supplementary information: Supplementary data are available at Bioinformatics online.

Description: Motivation: Quantification of lipids is a primary goal in lipidomics. In direct infusion/injection (or shotgun) lipidomics, accurate downstream identification and quantitation requires accurate summarization of repetitive peak measurements. Imprecise peak summarization multiplies downstream error by propagating into species identification and intensity estimation. To our knowledge, this is the first analysis of direct infusion peak summarization in the literature. Results: We present two novel peak summarization algorithms for direct infusion samples and compare them with an off-machine ad hoc summarization algorithm as well as with the propriety Xcalibur algorithm. Our statistical agglomeration algorithm reduces peakwise error by 38% mass/charge (m/z) and 44% (intensity) compared with the ad hoc method over three datasets. Pointwise error is reduced by 23% (m/z). Compared with Xcalibur, our statistical agglomeration algorithm produces 68% less m/z error and 51% less intensity error on average on two comparable datasets. Availability: The source code for Statistical Agglomeration and the datasets used are freely available for non-commercial purposes at https://github.com/optimusmoose/statistical_agglomeration . Modified Bin Aggolmeration is freely available in MSpire, an open source mass spectrometry package at https://github.com/princelab/mspire/ . Contact: 2robsmith@gmail.com or jtprince@chem.byu.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Description: Liquid chromatography coupled to mass spectrometry (LC-MS) is the dominant technological platform for proteomics. An LC-MS analysis of a complex biological sample can be visualized as a ‘map’ of which the positional coordinates are the mass-to-charge ratio (m/z) and chromatographic retention time (RT) of the chemical species profiled. Label-free quantitative proteomics requires the alignment and comparison of multiple LC-MS maps to ascertain the reproducibility of experiments or reveal proteome changes under different conditions. The main challenge in this task lies in correcting inevitable RT shifts. Similar, but not identical, LC instruments and settings can cause peptides to elute at very different times and sometimes in a different order, violating the assumptions of many state-of-the-art alignment tools. To meet this challenge, we developed LWBMatch, a new algorithm based on weighted bipartite matching. Unlike existing tools, which search for accurate warping functions to correct RT shifts, we directly seek a peak-to-peak mapping by maximizing a global similarity function between two LC-MS maps. For alignment tasks with large RT shifts (〉500 s), an approximate warping function is determined by locally weighted scatterplot smoothing of potential matched features, detected using a novel voting scheme based on co-elution. For validation, we defined the ground truth for alignment success based on tandem mass spectrometry identifications from sequence searching. We showed that our method outperforms several existing tools in terms of precision and recall, and is capable of aligning maps from different instruments and settings. Availability: Available at https://sourceforge.net/projects/rt-alignment/ . Contact: kehlam@ust.hk Supplementary information: Supplementary data are available at Bioinformatics online.

Description: : We propose SW#, a new CUDA graphical processor unit-enabled and memory-efficient implementation of dynamic programming algorithm, for local alignment. It can be used as either a stand-alone application or a library. Although there are other graphical processor unit implementations of the Smith–Waterman algorithm, SW# is the only one publicly available that can produce sequence alignments on genome-wide scale. For long sequences, it is at least a few hundred times faster than a CPU version of the same algorithm. Availability: Source code and installation instructions freely available for download at http://complex.zesoi.fer.hr/SW.html . Contact: mile.sikic@fer.hr Supplementary information: Supplementary results are available at Bioinformatics online.

Description: argumentation frameworks nowadays provide the most popular formalization of argumentation on a conceptual level. Numerous semantics for this paradigm have been proposed, whereby the cf2 semantics has shown to solve particular problems concerned with odd-length cycles in such frameworks. Due to the complicated definition of this semantics it has somehow been neglected in the literature. In this article, we introduce an alternative characterization of the cf2 semantics which, roughly speaking, avoids the recursive computation of subframeworks. This facilitates further investigation steps, like a complete complexity analysis. Furthermore, we show how the notion of strong equivalence can be characterized in terms of the cf2 semantics. In contrast to other semantics, it turns out that for the cf2 semantics strong equivalence coincides with syntactical equivalence. We make this particular behaviour more explicit by defining a new property for argumentation semantics, called the succinctness property. If a semantics satisfies the succinctness property, then for every framework F , all its attacks contribute to the evaluation of at least one framework F ' containing F . We finally characterize strong equivalence also for the stage and the naive semantics. Together with known results these characterizations imply that none of the prominent semantics for abstract argumentation, except the cf2 semantics, satisfies the succinctness property.

Description: Maximal frequent pattern mining has been suggested for data mining to avoid generating a huge set of frequent patterns. Conversely, weighted frequent pattern mining has been proposed to discover important frequent patterns by considering the weighted support. We propose two mining algorithms of maximal correlated weight frequent pattern (MCWP), termed MCWP(WA) (based on Weight Ascending order) and MCWP(SD) (based on Support Descending order), to mine a compact and meaningful set of frequent patterns. MCWP(SD) obtains an advantage in conditional database access, but may not obtain the highest weighted item of the conditional database to mine highly correlated weight frequent patterns. Thus, we suggest a technique that uses additional conditions to prune lowly correlated weight items before the subsets checking process. Analyses show that our algorithms are efficient and scalable. Content Type Journal Article Pages 917-939 DOI 10.3233/IDA-130612 Authors Unil Yun, Department of Computer Engineering, Sejong University, Seoul, Korea Keun Ho Ryu, Department of Computer Engineering, Sejong University, Seoul, Korea Journal Intelligent Data Analysis Online ISSN 1571-4128 Print ISSN 1088-467X Journal Volume Volume 17 Journal Issue Volume 17, Number 5 / 2013

Description: Log information describing the items the users have selected from the set of answers a query engine returns to their queries constitute an excellent form of indirect user feedback that has been extensively used in the web to improve the effectiveness of search engines. In this work we study how the logs can be exploited to improve the ranking of the results returned by an entity search engine. Entity search engines are becoming more and more popular as the web is changing from a web of documents into a "web of things". We show that entity search engines pose new challenges since their model is different than the one documents are based on. We present a novel framework for feature extraction that is based on the notions of entity matching and attribute frequencies. The extracted features are then used to train a ranking classifier. We introduce different methods and metrics for ranking, we combine them with existing traditional techniques and we study their performance using real and synthetic data. The experiments show that our technique provides better results in terms of accuracy. Content Type Journal Article Pages 837-856 DOI 10.3233/IDA-130609 Authors Davide Mottin, University of Trento, Trento, Italy Themis Palpanas, University of Trento, Trento, Italy Yannis Velegrakis, University of Trento, Trento, Italy Journal Intelligent Data Analysis Online ISSN 1571-4128 Print ISSN 1088-467X Journal Volume Volume 17 Journal Issue Volume 17, Number 5 / 2013

Description: A typical problem in the field of moving object (MO) databases consists in discovering interesting trajectory patterns. To solve this problem, data mining techniques are commonly used. Due to the huge volume of these trajectory data, some form of compression facilitates the data processing. One of such compression techniques is based on the notion of stops and moves. In this approach, a set of places that are relevant to the application, denoted Places of Interest (POIs) is selected. If a moving object spends a pre-defined amount of time in a place of interest, this place is considered a stop for the object's trajectory. Thus, raw trajectories given by (O_{id}, t, x, y)-tuples can be replaced by a sequence of application-relevant stops. This leads to the concept of semantic trajectory, in short, a trajectory obtained by replacing raw trajectory data with a sequence of stops, and enriched with metadata of the POIs corresponding to such stops. We present a language based on regular expressions over constraints, denoted RE-SPaM, that can intensionally express sequential patterns. The constraints in RE-SPaM are defined as conjunctions of equalities over metadata of the POIs. In addition, we introduce a data mining algorithm, based on sequential pattern mining techniques, where uninteresting sequences are pruned in advance making use of the automaton that accepts a RE-SPaM expression. This makes the task of the analyst easier, and the mining algorithm more efficient. We also show that RE-SPaM can be extended to support spatial functions, thus integrating spatial data in a moving object setting (proposals so far only account for the MO trajectories themselves). We denote the resulting language RE-SPaM^{+S}. We show that the overhead of this extension is negligible, due to caching techniques that we explain in the paper. We close the paper with a case study over which we perform experiments to study the main variables that impact the performance of the mining algorithm. Content Type Journal Article Pages 857-898 DOI 10.3233/IDA-130610 Authors Leticia Gómez, Instituto Tecnológico de Buenos Aires, Buenos Aires, Argentina Alejandro A. Vaisman, Department of Computer and Decision Engineering (CoDE) CP 165/15, Université Libre de Bruxelles, Bruxelles, Belgium Journal Intelligent Data Analysis Online ISSN 1571-4128 Print ISSN 1088-467X Journal Volume Volume 17 Journal Issue Volume 17, Number 5 / 2013

Description: A probabilistic radial basis function (PRBF) network is an effective non-linear classifier. However, similar to most other neural network models it is non-transparent, which makes its predictions difficult to interpret. In this paper we show how a one-variable-at-a-time and an all-subsets explanation method can be modified for an equivalent and more efficient use with PRBF network classifiers. We use several artificial and real-life data sets to demonstrate the usefulness of the visualizations and explanations of the PRBF network classifier. Content Type Journal Article Pages 791-802 DOI 10.3233/IDA-130607 Authors Marko Robnik-Šikonja, Faculty of computer and information science, University of Ljubljana, Tržaska, Ljubljana, Slovenia Erik Štrumbelj, Faculty of computer and information science, University of Ljubljana, Tržaska, Ljubljana, Slovenia Igor Kononenko, Faculty of computer and information science, University of Ljubljana, Tržaska, Ljubljana, Slovenia Journal Intelligent Data Analysis Online ISSN 1571-4128 Print ISSN 1088-467X Journal Volume Volume 17 Journal Issue Volume 17, Number 5 / 2013

Description: Customers usually change their purchase interests in the short product life cycle of the e-commerce environment. Therefore, recent transaction patterns should have a greater effect on the customer preferences. From the seller's point of view, an e-commerce recommender system should focus on the profit of recommendation. This study proposes a new sequential pattern mining algorithm that incorporates the concepts of frequency, recency, and profit to discover frequent, recent, and profitable sequential patterns, called FRP-sequences. Based on the discovered sequential patterns, this study develops a collaborative recommender system to improve recommendation accuracy for customers and the profit of recommendation from the seller's perspective. The proposed recommender system clusters customers, discovers FRP-sequences for each cluster, and then recommends items to the target customers based on their frequent, recent, and profitable FRP-sequences. In the stage of discovering FRP-sequences, the transaction patterns near the current time period and profitable items are weighted more heavily to improve profit. This study uses a public food mart database to determine the performance of the proposed approach, and compares it with traditional recommendation models. The proposed system performs better than traditional recommendation models in both recommendation accuracy and profit. Content Type Journal Article Pages 899-916 DOI 10.3233/IDA-130611 Authors Cheng-Lung Huang, Department of Information Management, National Kaohsiung First University of Science and Technology, Kaohsiung, Taiwan Mu-Chen Chen, Institute of Traffic and Transportation, National Chiao Tung University, Taipei, Taiwan Wen-Chen Huang, Department of Information Management, National Kaohsiung First University of Science and Technology, Kaohsiung, Taiwan Sheng-Huang Huang, Department of Information Management, National Kaohsiung First University of Science and Technology, Kaohsiung, Taiwan Journal Intelligent Data Analysis Online ISSN 1571-4128 Print ISSN 1088-467X Journal Volume Volume 17 Journal Issue Volume 17, Number 5 / 2013

Description: It is very important when search methods are being designed to know which parameters have the greatest influence on the behaviour and performance of the algorithm. To this end, algorithm parameters are commonly calibrated by means of either theoretic analysis or intensive experimentation. However, due to the importance of parameters and its effect on the results, finding appropriate parameter values should be carried out using robust tools to determine the way they operate and influence the results. When undertaking a detailed statistical analysis of the influence of each parameter, the designer should pay attention mostly to the parameters that are statistically significant. In this paper the ANOVA (ANalysis Of the VAriance) method is used to carry out an exhaustive analysis of an evolutionary algorithm method and the different parameters it requires. Following this idea, the significance and relative importance of the parameters regarding the obtained results, as well as suitable values for each of these, were obtained using ANOVA and post-hoc Tukey's Honestly Significant Difference tests on four well known function optimization problems. Through this statistical study we have verified the adequacy of parameter values available in the bibliography using parametric hypothesis tests. Content Type Journal Article Pages 771-789 DOI 10.3233/IDA-130606 Authors M.G. Arenas, ETSIIT. CITIC. University of Granada, Granada, Spain N. Rico, ETSIIT. CITIC. University of Granada, Granada, Spain A.M. Mora, ETSIIT. CITIC. University of Granada, Granada, Spain P.A. Castillo, ETSIIT. CITIC. University of Granada, Granada, Spain J.J. Merelo, ETSIIT. CITIC. University of Granada, Granada, Spain Journal Intelligent Data Analysis Online ISSN 1571-4128 Print ISSN 1088-467X Journal Volume Volume 17 Journal Issue Volume 17, Number 5 / 2013

Description: In this paper, a novel feature selection algorithm FEAST is proposed based on association rule mining. The proposed algorithm first mines association rules from a data set; then, it identifies the relevant and interactive feature values with the constraint association rules whose consequent is the target concept, detects and eliminates the redundant feature values with the constraint association rules whose consequent and antecedent are both of single feature value. Finally, it obtains the feature subset by mapping the feature values to the corresponding features. As the support and confidence thresholds are two important parameters in association rule mining and play a vital role in FEAST, a partial least square regression (PLSR) based threshold prediction method is presented as well. The effectiveness of FEAST is tested on both synthetic and real world data sets, and the classification results of five different types of classifiers with seven representative feature selection algorithms are compared. The results on the synthetic data sets show that FEAST can effectively identify irrelevant and redundant features while reserving interactive ones. The results on the real world data sets show that FEAST outperforms other feature selection algorithms in terms of classification accuracies. In addition, the PLSR based threshold prediction method is performed on the real world data sets, and the results show it works well in recommending proper support and confidence thresholds for FEAST. Content Type Journal Article Pages 803-835 DOI 10.3233/IDA-130608 Authors Guangtao Wang, Department of Computer Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, China Qinbao Song, Department of Computer Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, China Journal Intelligent Data Analysis Online ISSN 1571-4128 Print ISSN 1088-467X Journal Volume Volume 17 Journal Issue Volume 17, Number 5 / 2013

Description: Data preprocessing is a main step in data mining because real data can be corrupted for different causes and high performance data mining systems require high quality data. When a database is used for training a neural network, a fuzzy system or a neuro-fuzzy system, a suitable data selection and pre-processing stage can be very useful in order to obtain a reliable result. For instance, when the final aim of a system trained through a supervised learning procedure is to approximate an existing functional relationship between input and output variables, the database that is exploited in the system training phase should not contain input-output patterns for which the same input or similar input sets are associated to very different values of the output variable. In this paper a procedure is proposed for detecting non-coherent associations between input and output patterns: by comparing two distance matrices associated to the input and output patterns, the elements of the available dataset, where similar values of input variables are associated to quite different output values can be pointed out. The efficiency of the proposed algorithm when pre-processing data coming from an industrial database is presented and discussed together with a statistical assessment of the obtained results. Content Type Journal Article Pages 737-751 DOI 10.3233/IDA-130604 Authors Nicola Matarese, Istituto TeCIP, Scuola Superiore Sant'Anna, Ghezzano (PI), Italy Valentina Colla, Istituto TeCIP, Scuola Superiore Sant'Anna, Ghezzano (PI), Italy Marco Vannucci, Istituto TeCIP, Scuola Superiore Sant'Anna, Ghezzano (PI), Italy Leonardo M. Reyneri, Istituto TeCIP, Scuola Superiore Sant'Anna, Ghezzano (PI), Italy Journal Intelligent Data Analysis Online ISSN 1571-4128 Print ISSN 1088-467X Journal Volume Volume 17 Journal Issue Volume 17, Number 5 / 2013

Description: Complex emergent systems are known to be ill-managed because of their complex nature. This article introduces a novel interdisciplinary approach towards their study. In this sense, the DeciMaS methodological approach to mining and simulating data in complex information systems is introduced. The DeciMaS framework consists of three principal phases, preliminary domain and system analysis, system design and coding, and simulation and decision making. The framework offers a sequence of steps in order to support a domain expert who is not a specialist in data mining during the knowledge discovery process. With this aim a generalized structure of a decision support system (DSS) has been worked out. The DSS is virtually and logically organized into a three-leveled architecture. The first layer is dedicated to data retrieval, fusion and pre-processing, the second one discovers knowledge from data, and the third layer deals with making decisions and generating output information. Data mining is aimed to solve the following problems: association, classification, function approximation, and clustering. DeciMaS populates the second logical level of the DSS with agents which are aimed to complete these tasks. The agents use a wide range of data mining procedures that include approaches for estimation and prediction: regression analysis, artificial networks (ANNs), self-organizational methods, in particular, Group Method of Data Handling, and hybrid methods. The association task is solved with artificial neural networks. The ANNs are trained with different training algorithms such as backpropagation, resilient propagation and genetic algorithms. In order to assess the proposal an exhaustive experiment, designed to evaluate the possible harm caused by environmental contamination upon public health, is introduced in detail. Content Type Journal Article Pages 753-769 DOI 10.3233/IDA-130605 Authors Marina V. Sokolova, Instituto de Investigación en Informática de Albacete (i3A) and Departamento de Sistemas Informáticos, Universidad de Castilla-La Mancha, Albacete, Spain Antonio Fernández-Caballero, Instituto de Investigación en Informática de Albacete (i3A) and Departamento de Sistemas Informáticos, Universidad de Castilla-La Mancha, Albacete, Spain Journal Intelligent Data Analysis Online ISSN 1571-4128 Print ISSN 1088-467X Journal Volume Volume 17 Journal Issue Volume 17, Number 5 / 2013

Description: The objectives of this special issue are advanced security technologies and services for future computing environments, including, but not limited to, security primitives, protocols and security applications and services. Recent advances in security technologies and services for future computing environments have created a new class of the following: (i) Wireless sensor networks and radio-frequency identification security and privacy; (ii) security architectures for distributed network systems, P2P systems, cloud and grid systems; and (iii) security in e-commerce, mobile and wireless networks, and finally, security standards and assurance methods. All papers are expected to focus on novel approaches for advanced security technologies and services for future computing environments and to present high-quality results for tackling problems arising from the ever-growing advanced security technologies and services for future computing environments.

Description: In this paper, we present a statistical analysis of six traffic features based on entropy and distinct feature number at the packet level, and we find that, although these traffic features are unstable and show seasonal patterns like traffic volume in a long-time period, they are stable and consistent with Gaussian distribution in a short-time period. However, this equilibrium property will be violated by some anomalies. Based on this observation, we propose a Multi-dimensional Box plot method for Short-time scale Traffic (MBST) to classify abnormal and normal traffic. We compare our new method with the MCST method proposed in our prior work and the well-known wavelet-based and A Short-Timescale Uncorrelated-Traffic Equilibrium (ASTUTE) techniques. The detection result on synthetic anomaly traffic shows that MBST can better detect the low-rate attacks than wavelet-based and MCST methods, and detection result on real traffic demonstrates that MBST can detect more anomalies with lower false alarm rate than the two methods. Especially compared with ASTUTE, MBST performs much better for detecting anomalies involving a few large flows despite a little poor for detecting anomalies involving large number of small flows.

Description: : MicroRNAs (miRNAs) have been extensively studied owing to their important regulatory roles in genic expression. An increasingly number of reports are performing extensive data mining in small RNA sequencing libraries to detect miRNAs isoforms and also 5' and 3' post-transcriptional nucleotide additions, as well as edited miRNAs sequences. A ready to use pipeline, isomiRID, was developed to standardize and automatize the search for miRNAs isoforms in high-throughput small RNA sequencing libraries. Availability: isomiRID is a command line Python script available at http://www.ufrgs.br/RNAi/isomiRID/ . Contact: rogerio.margis@ufrgs.br Supplementary information: Supplementary Date are available at Bioinformatics online.

Description: Motivation: Understanding the details of protein–RNA interactions is important to reveal the functions of both the RNAs and the proteins. In these interactions, the secondary structures of the RNAs play an important role. Because RNA secondary structures in protein–RNA complexes are variable, considering the ensemble of RNA secondary structures is a useful approach. In particular, recent studies have supported the idea that, in the analysis of RNA secondary structures, the base-pairing probabilities (BPPs) of RNAs (i.e. the probabilities of forming a base pair in the ensemble of RNA secondary structures) provide richer and more robust information about the structures than a single RNA secondary structure, for example, the minimum free energy structure or a snapshot of structures in the Protein Data Bank. However, there has been no investigation of the BPPs in protein–RNA interactions. Results: In this study, we analyzed BPPs of RNA molecules involved in known protein–RNA complexes in the Protein Data Bank. Our analysis suggests that, in the tertiary structures, the BPPs (which are computed using only sequence information) for unpaired nucleotides with intermolecular hydrogen bonds (hbonds) to amino acids were significantly lower than those for unpaired nucleotides without hbonds. On the other hand, no difference was found between the BPPs for paired nucleotides with and without intermolecular hbonds. Those findings were commonly supported by three probabilistic models, which provide the ensemble of RNA secondary structures, including the McCaskill model based on Turner’s free energy of secondary structures. Contact: iwakiri@cb.k.u-tokyo.ac.jp or mhamada@cb.k.u-tokyo.ac.jp Supplementary information: Supplementary data are available at Bioinformatics online.

Description: Motivation: Gene fusions resulting from chromosomal aberrations are an important cause of cancer. The complexity of genomic changes in certain cancer types has hampered the identification of gene fusions by molecular cytogenetic methods, especially in carcinomas. This is changing with the advent of next-generation sequencing, which is detecting a substantial number of new fusion transcripts in individual cancer genomes. However, this poses the challenge of identifying those fusions with greater oncogenic potential amid a background of ‘passenger’ fusion sequences. Results: In the present work, we have used some recently identified genomic hallmarks of oncogenic fusion genes to develop a pipeline for the classification of fusion sequences, namely, Oncofuse. The pipeline predicts the oncogenic potential of novel fusion genes, calculating the probability that a fusion sequence behaves as ‘driver’ of the oncogenic process based on features present in known oncogenic fusions. Cross-validation and extensive validation tests on independent datasets suggest a robust behavior with good precision and recall rates. We believe that Oncofuse could become a useful tool to guide experimental validation studies of novel fusion sequences found during next-generation sequencing analysis of cancer transcriptomes. Availability and implementation: Oncofuse is a naive Bayes Network Classifier trained and tested using Weka machine learning package. The pipeline is executed by running a Java/Groovy script, available for download at www.unav.es/genetica/oncofuse.html . Contact: fnovo@unav.es Supplementary information: Supplementary data are available at Bioinformatics online.

Description: Motivation: High-throughput sequencing of mRNA (RNA-Seq) has led to tremendous improvements in the detection of expressed genes and reconstruction of RNA transcripts. However, the extensive dynamic range of gene expression, technical limitations and biases, as well as the observed complexity of the transcriptional landscape, pose profound computational challenges for transcriptome reconstruction. Results: We present the novel framework MITIE (Mixed Integer Transcript IdEntification) for simultaneous transcript reconstruction and quantification. We define a likelihood function based on the negative binomial distribution, use a regularization approach to select a few transcripts collectively explaining the observed read data and show how to find the optimal solution using Mixed Integer Programming. MITIE can (i) take advantage of known transcripts, (ii) reconstruct and quantify transcripts simultaneously in multiple samples, and (iii) resolve the location of multi-mapping reads. It is designed for genome- and assembly-based transcriptome reconstruction. We present an extensive study based on realistic simulated RNA-Seq data. When compared with state-of-the-art approaches, MITIE proves to be significantly more sensitive and overall more accurate. Moreover, MITIE yields substantial performance gains when used with multiple samples. We applied our system to 38 Drosophila melanogaster modENCODE RNA-Seq libraries and estimated the sensitivity of reconstructing omitted transcript annotations and the specificity with respect to annotated transcripts. Our results corroborate that a well-motivated objective paired with appropriate optimization techniques lead to significant improvements over the state-of-the-art in transcriptome reconstruction. Availability: MITIE is implemented in C++ and is available from http://bioweb.me/mitie under the GPL license. Contact: Jonas_Behr@web.de and raetsch@cbio.mskcc.org Supplementary information: Supplementary data are available at Bioinformatics online.

Description: Motivation : Due to rapid technological advances, a wide range of different measurements can be obtained from a given biological sample including single nucleotide polymorphisms, copy number variation, gene expression levels, DNA methylation and proteomic profiles. Each of these distinct measurements provides the means to characterize a certain aspect of biological diversity, and a fundamental problem of broad interest concerns the discovery of shared patterns of variation across different data types. Such data types are heterogeneous in the sense that they represent measurements taken at different scales or represented by different data structures. Results : We propose a distance-based statistical test, the generalized RV (GRV) test, to assess whether there is a common and non-random pattern of variability between paired biological measurements obtained from the same random sample. The measurements enter the test through the use of two distance measures, which can be chosen to capture a particular aspect of the data. An approximate null distribution is proposed to compute P -values in closed-form and without the need to perform costly Monte Carlo permutation procedures. Compared with the classical Mantel test for association between distance matrices, the GRV test has been found to be more powerful in a number of simulation settings. We also demonstrate how the GRV test can be used to detect biological pathways in which genetic variability is associated to variation in gene expression levels in an ovarian cancer sample, and present results obtained from two independent cohorts. Availability : R code to compute the GRV test is freely available from http://www2.imperial.ac.uk/~gmontana Contact : g.montana@imperial.ac.uk Supplementary data : Supplementary data are available at Bioinformatics online.

Description: Motivation: Alzheimer’s disease (AD) is a severe neurodegenerative disease of the central nervous system that may be caused by perturbation of regulatory pathways rather than the dysfunction of a single gene. However, the pathology of AD has yet to be fully elucidated. Results: In this study, we systematically analyzed AD-related mRNA and miRNA expression profiles as well as curated transcription factor (TF) and miRNA regulation to identify active TF and miRNA regulatory pathways in AD. By mapping differentially expressed genes and miRNAs to the curated TF and miRNA regulatory network as active seed nodes, we obtained a potential active subnetwork in AD. Next, by using the breadth-first-search technique, potential active regulatory pathways, which are the regulatory cascade of TFs, miRNAs and their target genes, were identified. Finally, based on the known AD-related genes and miRNAs, the hypergeometric test was used to identify active pathways in AD. As a result, nine pathways were found to be significantly activated in AD. A comprehensive literature review revealed that eight out of nine genes and miRNAs in these active pathways were associated with AD. In addition, we inferred that the pathway hsa-miR-146a-〉STAT1-〉MYC, which is the source of all nine significantly active pathways, may play an important role in AD progression, which should be further validated by biological experiments. Thus, this study provides an effective approach to finding active TF and miRNA regulatory pathways in AD and can be easily applied to other complex diseases. Contact: lixia@hrbmu.edu.cn or lw2247@gmail.com . Supplementary information: Supplementary data are available at Bioinformatics online.

Description: Motivation: 3' end processing is important for transcription termination, mRNA stability and regulation of gene expression. To identify 3' ends, most techniques use an oligo-dT primer to construct deep sequencing libraries. However, this approach can lead to identification of artifactual polyadenylation sites due to internal priming in homopolymeric stretches of adenines. Although heuristic filters have been applied in these cases, they typically result in a high proportion of both false-positive and -negative classifications. Therefore, there is a need to develop improved algorithms to better identify mis-priming events in oligo-dT primed sequences. Results: By analyzing sequence features flanking 3' ends derived from oligo-dT-based sequencing, we developed a naïve Bayes classifier to classify them as true or false/internally primed. The resulting algorithm is highly accurate, outperforms previous heuristic filters and facilitates identification of novel polyadenylation sites. Contact: nathan.lawson@umassmed.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Description: Motivation: The lack of reliable, comprehensive gold standards complicates the development of many bioinformatics tools, particularly for the analysis of expression data and biological networks. Simulation approaches can provide provisional gold standards, such as regulatory networks, for the assessment of network inference methods. However, this just defers the problem, as it is difficult to assess how closely simulators emulate the properties of real data. Results: In analogy to Turing’s test discriminating humans and computers based on responses to questions, we systematically compare real and artificial systems based on their gene expression output. Different expression data analysis techniques such as clustering are applied to both types of datasets. We define and extract distributions of properties from the results, for instance, distributions of cluster quality measures or transcription factor activity patterns. Distributions of properties are represented as histograms to enable the comparison of artificial and real datasets. We examine three frequently used simulators that generate expression data from parameterized regulatory networks. We identify features distinguishing real from artificial datasets that suggest how simulators could be adapted to better emulate real datasets and, thus, become more suitable for the evaluation of data analysis tools. Availability: See http://www2.bio.ifi.lmu.de/~kueffner/attfad/ and the supplement for precomputed analyses; other compendia can be analyzed via the CRAN package attfad. The full datasets can be obtained from http://www2.bio.ifi.lmu.de/~kueffner/attfad/data.tar.gz . Contact: robert.kueffner@bio.ifi.lmu.de Supplementary information: Supplementary data are available at Bioinformatics online.

Description: Motivation: The two major epigenetic modifications of cytosines, 5-methylcytosine (5-mC) and 5-hydroxymethylcytosine (5-hmC), coexist with each other in a range of mammalian cell populations. Increasing evidence points to important roles of 5-hmC in demethylation of 5-mC and epigenomic regulation in development. Recently developed experimental methods allow direct single-base profiling of either 5-hmC or 5-mC. Meaningful analyses seem to require combining these experiments with bisulfite sequencing, but doing so naively produces inconsistent estimates of 5-mC or 5-hmC levels. Results: We present a method to jointly model read counts from bisulfite sequencing, oxidative bisulfite sequencing and Tet-Assisted Bisulfite sequencing, providing simultaneous estimates of 5-hmC and 5-mC levels that are consistent across experiment types. Availability: http://smithlab.usc.edu/software/mlml Contact: andrewds@usc.edu Supplementary information: Supplementary material is available at Bioinformatics online.

Description: Motivation: A major goal in genomic research is to identify genes that may jointly influence a biological response. From many years of intensive biomedical research, a large body of biological knowledge, or pathway information, has accumulated in available databases. There is a strong interest in leveraging these pathways to improve the statistical power and interpretability in studying gene networks associated with complex phenotypes. This prior information is a valuable complement to large-scale genomic data such as gene expression data generated from microarrays. However, it is a non-trivial task to effectively integrate available biological knowledge into gene expression data when reconstructing gene networks. Results: In this article, we developed and applied a Lasso method from a Bayesian perspective, a method we call prior Lasso (pLasso), for the reconstruction of gene networks. In this method, we partition edges between genes into two subsets: one subset of edges is present in known pathways, whereas the other has no prior information associated. Our method assigns different prior distributions to each subset according to a modified Bayesian information criterion that incorporates prior knowledge on both the network structure and the pathway information. Simulation studies have indicated that the method is more effective in recovering the underlying network than a traditional Lasso method that does not use the prior information. We applied pLasso to microarray gene expression datasets, where we used information from the Pathway Commons (PC) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) as prior information for the network reconstruction, and successfully identified network hub genes associated with clinical outcome in cancer patients. Availability: The source code is available at http://nba.uth.tmc.edu/homepage/liu/pLasso . Contact: Yin.Liu@uth.tmc.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Description: : One approach to infer functions of new proteins from their homologs utilizes visualization of an all-against-all pairwise similarity network (A2ApsN) that exploits the speed of BLAST and avoids the complexity of multiple sequence alignment. However, identifying functions of the protein clusters in A2ApsN is never trivial, due to a lack of linking characterized proteins to their relevant information in current software packages. Given the database errors introduced by automatic annotation transfer, functional deduction should be made from proteins with experimental studies, i.e. ‘reference proteins’. Here, we present a web server, termed Pclust, which provides a user-friendly interface to visualize the A2ApsN, placing emphasis on such ‘reference proteins’ and providing access to their full information in source databases, e.g. articles in PubMed. The identification of ‘reference proteins’ and the ease of cross-database linkage will facilitate understanding the functions of protein clusters in the network, thus promoting interpretation of proteins of interest. Availability: The Pclust server is freely available at http://prodata.swmed.edu/pclust Contact: grishin@chop.swmed.edu Supplementary Information: Supplementary data are available at Bioinformatics online.

Description: : The understanding of the biological role of RNA molecules has changed. Although it is widely accepted that RNAs play important regulatory roles without necessarily coding for proteins, the functions of many of these non-coding RNAs are unknown. Thus, determining or modeling the 3D structure of RNA molecules as well as assessing their accuracy and stability has become of great importance for characterizing their functional activity. Here, we introduce a new web application, WebRASP, that uses knowledge-based potentials for scoring RNA structures based on distance-dependent pairwise atomic interactions. This web server allows the users to upload a structure in PDB format, select several options to visualize the structure and calculate the energy profile. The server contains online help, tutorials and links to other related resources. We believe this server will be a useful tool for predicting and assessing the quality of RNA 3D structures. Availability and implementation: The web server is available at http://melolab.org/webrasp . It has been tested on the most popular web browsers and requires Java plugin for Jmol visualization. Contact: fmelo@bio.puc.cl

Description: Contact: Wenhua.Wei@igmm.ed.ac.uk

Description: : Scaffold network generator (SNG) is an open-source command-line utility that computes the hierarchical network of scaffolds that define a large set of input molecules. Scaffold networks are useful for visualizing, analysing and understanding the chemical data that is increasingly available through large public repositories like PubChem. For example, some groups have used scaffold networks to identify missed-actives in high-throughput screens of small molecules with bioassays. Substantially improving on existing software, SNG is robust enough to work on millions of molecules at a time with a simple command-line interface. Availability and implementation: SNG is accessible at http://swami.wustl.edu/sng Contact: swamidass@wustl.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Description: : BioPAX is a community-developed standard language for biological pathway data. A key functionality required for efficient BioPAX data exchange is validation— detecting errors and inconsistencies in BioPAX documents. The BioPAX Validator is a command-line tool, Java library and online web service for BioPAX that performs 〉100 classes of consistency checks. Availability and implementation: The validator recognizes common syntactic errors and semantic inconsistencies and reports them in a customizable human readable format. It can also automatically fix some errors and normalize BioPAX data. Since its release, the validator has become a critical tool for the pathway informatics community, detecting thousands of errors and helping substantially increase the conformity and uniformity of BioPAX-formatted data. The BioPAX Validator is open source and released under LGPL v3 license. All sources, binaries and documentation can be found at sf.net/p/biopax, and the latest stable version of the web application is available at biopax.org/validator. Contact: igor.rodchenkov@utoronto.ca or gary.bader@utoronto.ca

Description: : Non-linear calibration is a widely used method for quantifying biomarkers wherein concentration-response curves estimated using samples of known concentrations are used to predict the biomarker concentrations in the samples of interest. The R package nCal fills an important gap in the open source, stand-alone software for performing non-linear calibration. For curve fitting, nCal provides a new implementation of a robust, Bayesian hierarchical five-parameter logistic model. nCal supports a simple graphical user interface that can be used by laboratory scientists, and contains functionality for importing data from the multiplex bead array assay instrumentation. Availability: The R package ‘nCal’ is available from http://cran.r-project.org/web/packages/nCal/ under GPL-2 or later. Contact: yfong@fhcrc.org Supplementary information: Supplementary information is available in the form of an R package vignette at the above repository and an FAQ at http://research.fhcrc.org/youyifong/en/resources/ncal.html .

Description: Motivation: Extensive DNA sequencing of tumor and matched normal samples using exome and whole-genome sequencing technologies has enabled the discovery of recurrent genetic alterations in cancer cells, but variability in stromal contamination and subclonal heterogeneity still present a severe challenge to available detection algorithms. Results: Here, we describe publicly available software, Shimmer, which accurately detects somatic single-nucleotide variants using statistical hypothesis testing with multiple testing correction. This program produces somatic single-nucleotide variant predictions with significantly higher sensitivity and accuracy than other available software when run on highly contaminated or heterogeneous samples, and it gives comparable sensitivity and accuracy when run on samples of high purity. Availability: http://www.github.com/nhansen/Shimmer Contact: nhansen@mail.nih.gov Supplementary information: Supplementary data are available at Bioinformatics online.

Description: : Mendel is one of the few statistical genetics packages that provide a full spectrum of gene mapping methods, ranging from parametric linkage in large pedigrees to genome-wide association with rare variants. Our latest additions to Mendel anticipate and respond to the needs of the genetics community. Compared with earlier versions, Mendel is faster and easier to use and has a wider range of applications. Supported platforms include Linux, MacOS and Windows. Availability : Free from www.genetics.ucla.edu/software/mendel Contact: klange@ucla.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Description: Motivation: cis -regulatory DNA sequence elements, such as enhancers and silencers, function to control the spatial and temporal expression of their target genes. Although the overall levels of gene expression in large cell populations seem to be precisely controlled, transcription of individual genes in single cells is extremely variable in real time. It is, therefore, important to understand how these cis -regulatory elements function to dynamically control transcription at single-cell resolution. Recently, statistical methods have been proposed to back calculate the rates involved in mRNA transcription using parameter estimation of a mathematical model of transcription and translation. However, a major complication in these approaches is that some of the parameters, particularly those corresponding to the gene copy number and transcription rate, cannot be distinguished; therefore, these methods cannot be used when the copy number is unknown. Results: Here, we develop a hierarchical Bayesian model to estimate biokinetic parameters from live cell enhancer–promoter reporter measurements performed on a population of single cells. This allows us to investigate transcriptional dynamics when the copy number is variable across the population. We validate our method using synthetic data and then apply it to quantify the function of two known developmental enhancers in real time and in single cells. Availability: Supporting information is submitted with the article. Contact: d.j.woodcock@warwick.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online.

Description: Motivation: The number of missense mutations being identified in cancer genomes has greatly increased as a consequence of technological advances and the reduced cost of whole-genome/whole-exome sequencing methods. However, a high proportion of the amino acid substitutions detected in cancer genomes have little or no effect on tumour progression (passenger mutations). Therefore, accurate automated methods capable of discriminating between driver (cancer-promoting) and passenger mutations are becoming increasingly important. In our previous work, we developed the Functional Analysis through Hidden Markov Models (FATHMM) software and, using a model weighted for inherited disease mutations, observed improved performances over alternative computational prediction algorithms. Here, we describe an adaptation of our original algorithm that incorporates a cancer-specific model to potentiate the functional analysis of driver mutations. Results: The performance of our algorithm was evaluated using two separate benchmarks. In our analysis, we observed improved performances when distinguishing between driver mutations and other germ line variants (both disease-causing and putatively neutral mutations). In addition, when discriminating between somatic driver and passenger mutations, we observed performances comparable with the leading computational prediction algorithms: SPF-Cancer and TransFIC. Availability and implementation: A web-based implementation of our cancer-specific model, including a downloadable stand-alone package, is available at http://fathmm.biocompute.org.uk . Contact: fathmm@biocompute.org.uk Supplementary information: Supplementary data are available at Bioinformatics online.

Description: Motivation: Fragmented RNA immunoprecipitation combined with RNA sequencing enabled the unbiased study of RNA epigenome at a near single-base resolution; however, unique features of this new type of data call for novel computational techniques. Result: Through examining the connections of RNA epigenome sequencing data with two well-studied data types, ChIP-Seq and RNA-Seq, we unveiled the salient characteristics of this new data type. The computational strategies were discussed accordingly, and a novel data processing pipeline was proposed that combines several existing tools with a newly developed exome-based approach ‘exomePeak’ for detecting, representing and visualizing the post-transcriptional RNA modification sites on the transcriptome. Availability: The MATLAB package ‘exomePeak’ and additional details are available at http://compgenomics.utsa.edu/exomePeak/ . Contact: yufei.huang@utsa.edu or jmeng@mit.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Description: Contact: 2robsmith@gmail.com

Description: Motivation: Comparative studies are encouraged by the fast increase of data availability from the latest high-throughput techniques, in particular from functional genomic studies. Yet, the size of datasets, the challenge of complete orthologs findings and not last, the variety of identification formats, make information integration challenging. With HOMECAT, we aim to facilitate cross-species relationship identification and data mapping, by combining orthology predictions from several publicly available sources, a convenient interface for high-throughput data download and automatic identifier conversion into a Cytoscape plug-in, that provides both an integration with a large set of bioinformatics tools, as well as a user-friendly interface. Availability: HOMECAT and the Supplementary Materials are freely available at http://www.cbmc.it/homecat/ . Contact: simone.zorzan@univr.it Supplementary information: Supplementary data are available at Bioinformatics online.

Description: : INstruct is a database of high-quality, 3D, structurally resolved protein interactome networks in human and six model organisms. INstruct combines the scale of available high-quality binary protein interaction data with the specificity of atomic-resolution structural information derived from co-crystal evidence using a tested interaction interface inference method. Its web interface is designed to allow for flexible search based on standard and organism-specific protein and gene-naming conventions, visualization of protein architecture highlighting interaction interfaces and viewing and downloading custom 3D structurally resolved interactome datasets. Availability: INstruct is freely available on the web at http://instruct.yulab.org with all major browsers supported. Contact: haiyuan.yu@cornell.edu Supplementary information: Supplementary data are available at Bioinformatics online.