ALBERT

Description: : As sequencing becomes cheaper and more widely available, there is a greater need to quickly and effectively analyze large-scale genomic data. While the functionality of AVIA v1.0, whose implementation was based on ANNOVAR, was comparable with other annotation web servers, AVIA v2.0 represents an enhanced web-based server that extends genomic annotations to cell-specific transcripts and protein-level functional annotations. With AVIA’s improved interface, users can better visualize their data, perform comprehensive searches and categorize both coding and non-coding variants. Availability and implementation : AVIA is freely available through the web at http://avia.abcc.ncifcrf.gov . Contact : Hue.Vuong@fnlcr.nih.gov Supplementary information: Supplementary data are available at Bioinformatics online.

Description: : As new methods for multivariate analysis of genome wide association studies become available, it is important to be able to combine results from different cohorts in a meta-analysis. The R package MultiMeta provides an implementation of the inverse-variance-based method for meta-analysis, generalized to an n -dimensional setting. Availability and implementation: The R package MultiMeta can be downloaded from CRAN. Contact: dragana.vuckovic@burlo.trieste.it ; vi1@sanger.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online.

Description: We prove a spectral flow formula for one-parameter families of Hamiltonian systems under homoclinic boundary conditions, which relates the spectral flow to the relative Maslov index of a pair of curves of Lagrangians induced by the stable and unstable subspaces, respectively. Finally, we deduce sufficient conditions for bifurcation of homoclinic trajectories of one-parameter families of non-autonomous Hamiltonian vector fields.

Description: We consider the Schur–Horn problem for normal operators in von Neumann algebras, which is the problem of characterizing the possible diagonal values of a given normal operator based on its spectral data. For normal matrices, this problem is well known to be extremely difficult, and in fact, it remains open for matrices of size greater than $3$ . We show that the infinite-dimensional version of this problem is more tractable, and establish approximate solutions for normal operators in von Neumann factors of type I $_\infty$ , II, and III. A key result is an approximation theorem that can be seen as an approximate multivariate analogue of Kadison's Carpenter Theorem.

Description: We study the rate of convergence to zero of the tail entropy of $C^\infty$ maps. We give an upper bound of this rate in terms of the growth in $k$ of the derivative of order $k$ and give examples showing the optimality of the established rate of convergence. We also consider the case of multimodal maps of the interval. Finally, we prove that homoclinic tangencies give rise to $C^r$ $(r\geqslant 2)$ robustly non- $h$ -expansive dynamical systems.

Description: Let ${{\mathscr {C}}}^0_{{{\mathfrak {g}}}}$ be the category of finite-dimensional integrable modules over the quantum affine algebra $U_{q}'({{\mathfrak {g}}})$ and let $R^{A_\infty }{\mbox {-}\mathrm {gmod}}$ denote the category of finite-dimensional graded modules over the quiver Hecke algebra of type $A_{\infty }$ . In this paper, we investigate the relationship between the categories ${{\mathscr {C}}}^0_{A_{N-1}^{(1)}}$ and ${{\mathscr {C}}}^0_{A_{N-1}^{(2)}}$ by constructing the generalized quantum affine Schur–Weyl duality functors ${\mathcal {F}}^{(t)}$ from $R^{A_\infty }{\mbox {-}\mathrm {gmod}}$ to ${{\mathscr {C}}}^0_{A_{N-1}^{(t)}}\ (t=1,2)$ .

Description: We present new constructions of complex and $p$ -adic Darmon points on elliptic curves over base fields of arbitrary signature. We conjecture that these points are global and present numerical evidence to support our conjecture.

Description: Motivation: Stem cell differentiation is largely guided by master transcriptional regulators, but it also depends on the expression of other types of genes, such as cell cycle genes, signaling genes, metabolic genes, trafficking genes, etc. Traditional approaches to understanding gene expression patterns across multiple conditions, such as principal components analysis or K-means clustering, can group cell types based on gene expression, but they do so without knowledge of the differentiation hierarchy. Hierarchical clustering can organize cell types into a tree, but in general this tree is different from the differentiation hierarchy itself. Methods: Given the differentiation hierarchy and gene expression data at each node, we construct a weighted Euclidean distance metric such that the minimum spanning tree with respect to that metric is precisely the given differentiation hierarchy. We provide a set of linear constraints that are provably sufficient for the desired construction and a linear programming approach to identify sparse sets of weights, effectively identifying genes that are most relevant for discriminating different parts of the tree. Results: We apply our method to microarray gene expression data describing 38 cell types in the hematopoiesis hierarchy, constructing a weighted Euclidean metric that uses just 175 genes. However, we find that there are many alternative sets of weights that satisfy the linear constraints. Thus, in the style of random-forest training, we also construct metrics based on random subsets of the genes and compare them to the metric of 175 genes. We then report on the selected genes and their biological functions. Our approach offers a new way to identify genes that may have important roles in stem cell differentiation. Contact: tperkins@ohri.ca Supplementary information: Supplementary data are available at Bioinformatics online.

Description: Motivation: Principal component analysis (PCA) is a basic tool often used in bioinformatics for visualization and dimension reduction. However, it is known that PCA may not consistently estimate the true direction of maximal variability in high-dimensional, low sample size settings, which are typical for molecular data. Assuming that the underlying signal is sparse, i.e. that only a fraction of features contribute to a principal component (PC), this estimation consistency can be retained. Most existing sparse PCA methods use L1-penalization, i.e. the lasso , to perform feature selection. But, the lasso is known to lack variable selection consistency in high dimensions and therefore a subsequent interpretation of selected features can give misleading results. Results: We present S4VDPCA, a sparse PCA method that incorporates a subsampling approach, namely stability selection. S4VDPCA can consistently select the truly relevant variables contributing to a sparse PC while also consistently estimate the direction of maximal variability. The performance of the S4VDPCA is assessed in a simulation study and compared to other PCA approaches, as well as to a hypothetical oracle PCA that ‘knows’ the truly relevant features in advance and thus finds optimal, unbiased sparse PCs. S4VDPCA is computationally efficient and performs best in simulations regarding parameter estimation consistency and feature selection consistency. Furthermore, S4VDPCA is applied to a publicly available gene expression data set of medulloblastoma brain tumors. Features contributing to the first two estimated sparse PCs represent genes significantly over-represented in pathways typically deregulated between molecular subgroups of medulloblastoma. Availability and implementation: Software is available at https://github.com/mwsill/s4vdpca . Contact: m.sill@dkfz.de Supplementary information: Supplementary data are available at Bioinformatics online.

Description: Motivation: Glycans play critical roles in many biological processes, and their structural diversity is key for specific protein-glycan recognition. Comparative structural studies of biological molecules provide useful insight into their biological relationships. However, most computational tools are designed for protein structure, and despite their importance, there is no currently available tool for comparing glycan structures in a sequence order- and size-independent manner. Results: A novel method, GS-align, is developed for glycan structure alignment and similarity measurement. GS-align generates possible alignments between two glycan structures through iterative maximum clique search and fragment superposition. The optimal alignment is then determined by the maximum structural similarity score, GS-score, which is size-independent. Benchmark tests against the Protein Data Bank (PDB) N -linked glycan library and PDB homologous/non-homologous N -glycoprotein sets indicate that GS-align is a robust computational tool to align glycan structures and quantify their structural similarity. GS-align is also applied to template-based glycan structure prediction and monosaccharide substitution matrix generation to illustrate its utility. Availability and implementation: http://www.glycanstructure.org/gsalign . Contact: wonpil@ku.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Description: Motivation: Impedance-based technologies are advancing methods for measuring proliferation of adherent cell cultures non-invasively and in real time. The analysis of the resulting data has so far been hampered by inappropriate computational methods and the lack of systematic data to evaluate the characteristics of the assay. Results: We used a commercially available system for impedance-based growth measurement (xCELLigence) and compared the reported cell index with data from microscopy. We found that the measured signal correlates linearly with the cell number throughout the time of an experiment with sufficient accuracy in subconfluent cell cultures. The resulting growth curves for various colon cancer cells could be well described with the empirical Richards growth model, which allows for extracting quantitative parameters (such as characteristic cycle times). We found that frequently used readouts like the cell index at a specific time or the area under the growth curve cannot be used to faithfully characterize growth inhibition. We propose to calculate the average growth rate of selected time intervals to accurately estimate time-dependent IC50 values of drugs from growth curves. Contact: nils.bluethgen@charite.de Supplementary information: Supplementary data are available at Bioinformatics online.

Description: Individualized treatment rules recommend treatments on the basis of individual patient characteristics. A high-quality treatment rule can produce better patient outcomes, lower costs and less treatment burden. If a treatment rule learned from data is to be used to inform clinical practice or provide scientific insight, it is crucial that it be interpretable; clinicians may be unwilling to implement models they do not understand, and black-box models may not be useful for guiding future research. The canonical example of an interpretable prediction model is a decision tree. We propose a method for estimating an optimal individualized treatment rule within the class of rules that are representable as decision trees. The class of rules we consider is interpretable but expressive. A novel feature of this problem is that the learning task is unsupervised, as the optimal treatment for each patient is unknown and must be estimated. The proposed method applies to both categorical and continuous treatments and produces favourable marginal mean outcomes in simulation experiments. We illustrate it using data from a study of major depressive disorder.

Description: Sufficient dimension reduction in regression aims to reduce the predictor dimension by replacing the original predictors with some set of linear combinations of them without loss of information. Numerous dimension reduction methods have been developed based on this paradigm. However, little effort has been devoted to diagnostic studies within the context of dimension reduction. In this paper we introduce methods to check goodness-of-fit for a given dimension reduction subspace. The key idea is to extend the so-called distance correlation to measure the conditional dependence relationship between the covariates and the response given a reduction subspace. Our methods require minimal assumptions, which are usually much less restrictive than the conditions needed to justify the original methods. Asymptotic properties of the test statistic are studied. Numerical examples demonstrate the effectiveness of the proposed approach.

Description: We propose a geometric framework to assess sensitivity of Bayesian procedures to modelling assumptions based on the nonparametric Fisher–Rao metric. While the framework is general, the focus of this article is on assessing local and global robustness in Bayesian procedures with respect to perturbations of the likelihood and prior, and on the identification of influential observations. The approach is based on a square-root representation of densities, which enables analytical computation of geodesic paths and distances, facilitating the definition of naturally calibrated local and global discrepancy measures. An important feature of our approach is the definition of a geometric $\epsilon$ -contamination class of sampling distributions and priors via intrinsic analysis on the space of probability density functions. We demonstrate the applicability of our framework to generalized mixed-effects models and to directional and shape data.

Description: With the discovery of an increasing number of causal genes for complex human disorders, it is crucial to assess the genetic risk of disease onset for individuals who are carriers of these causal mutations and to compare the distribution of the age-at-onset for such individuals with the distribution for noncarriers. In many genetic epidemiological studies that aim to estimate causal gene effect on disease, the age-at-onset of disease is subject to censoring. In addition, the mutation carrier or noncarrier status of some individuals may be unknown, due to the high cost of in-person ascertainment by collecting DNA samples or because of the death of older individuals. Instead, the probability of such individuals’ mutation status can be obtained from various other sources. When mutation status is missing, the available data take the form of censored mixture data. Recently, various methods have been proposed for risk estimation using such data, but none is efficient for estimating a nonparametric distribution. We propose a fully efficient sieve maximum likelihood estimation method, in which we estimate the logarithm of the hazard ratio between genetic mutation groups using B-splines, while applying nonparametric maximum likelihood estimation to the reference baseline hazard function. Our estimator can be calculated via an expectation-maximization algorithm which is much faster than existing methods. We show that our estimator is consistent and semiparametrically efficient and establish its asymptotic distribution. Simulation studies demonstrate the superior performance of the proposed method, which is used to estimate the distribution of the age-at-onset of Parkinson's disease for carriers of mutations in the leucine-rich repeat kinase 2, LRRK2, gene.

Description: Smoothing splines provide flexible nonparametric regression estimators. However, the high computational cost of smoothing splines for large datasets has hindered their wide application. In this article, we develop a new method, named adaptive basis sampling, for efficient computation of smoothing splines in super-large samples. Except for the univariate case where the Reinsch algorithm is applicable, a smoothing spline for a regression problem with sample size n can be expressed as a linear combination of n basis functions and its computational complexity is generally O ( n 3 ). We achieve a more scalable computation in the multivariate case by evaluating the smoothing spline using a smaller set of basis functions, obtained by an adaptive sampling scheme that uses values of the response variable. Our asymptotic analysis shows that smoothing splines computed via adaptive basis sampling converge to the true function at the same rate as full basis smoothing splines. Using simulation studies and a large-scale deep earth core-mantle boundary imaging study, we show that the proposed method outperforms a sampling method that does not use the values of response variables.

Description: We propose tests for nonlinear serial dependence in time series under the null hypothesis of general linear dependence, in contrast to the more widely studied null hypothesis of independence. The approach is based on combining an entropy dependence metric, which possesses many desirable properties and is used as a test statistic, with a suitable extension of surrogate data methods, a class of Monte Carlo distribution-free tests for nonlinearity, and a smoothed sieve bootstrap scheme. We show how, in the same way as the autocorrelation function is used for linear models, our tests can in principle be employed to detect the lags at which a significant nonlinear relationship is present. We prove the asymptotic validity of the proposed procedures and the corresponding inferences. The small-sample performance of the tests in terms of power and size is assessed through a simulation study. Applications to real datasets of different kinds are also presented.

Description: Contamination caused by outliers is inevitable in data analysis, and robust statistical methods are often needed. In this paper we develop a new approach for robust data analysis on the basis of scoring rules. A scoring rule is a discrepancy measure to assess the quality of probabilistic forecasts. We propose a simple method of estimating not only parameters in the statistical model but also the contamination ratio, i.e., the ratio of outliers. The outliers are detected based on the estimated contamination ratio. For this purpose, we use scoring rules with extended statistical models called unnormalized models. Regression problems are also considered. We study complex heterogeneous contamination wherein the contamination ratio in a response variable may depend on covariate variables, and propose a simple method to estimate a robust regression function and expected contamination ratio. Simulation studies demonstrate the effectiveness of our method.

Description: The sample covariance matrix, which is well known to be highly nonrobust, plays a central role in many classical multivariate statistical methods. A popular way of making such multivariate methods more robust is to replace the sample covariance matrix with some robust scatter matrix. The aim of this paper is to point out that multivariate methods often require that certain properties of the covariance matrix hold also for the robust scatter matrix in order for the corresponding robust plug-in method to be a valid approach, but that not all scatter matrices possess the desired properties. Plug-in methods for independent components analysis, observational regression and graphical modelling are considered in more detail. For each case, it is shown that replacing the sample covariance matrix with a symmetrized robust scatter matrix yields a valid robust multivariate procedure.

Description: So-called big data are likely to have complex structure, in particular implying that estimates of precision obtained by applying standard statistical procedures are likely to be misleading, even if the point estimates of parameters themselves may be reasonably satisfactory. While this possibility is best explored in the context of each special case, here we outline a fairly general representation of the accretion of error in large systems and explore the possible implications for the estimation of regression coefficients. The discussion raises issues broadly parallel to the distinction between short-range and long-range dependence in time series theory.

Description: This paper extends the classical two-regime threshold autoregressive model by introducing hysteresis to its regime-switching structure, which leads to a new model: the hysteretic autoregressive model. The proposed model enjoys the piecewise linear structure of a threshold model but has a more flexible regime switching mechanism. A sufficient condition is given for geometric ergodicity. Conditional least squares estimation is discussed, and the asymptotic distributions of its estimators and information criteria for model selection are derived. Simulation results and an example support the model.

Description: This paper points out an error in Davidov and Iliopoulos's ( Biometrika 100 , 778–80) proof of convergence of an iterative algorithm for the proportional likelihood ratio model. It is shown that the iterative algorithm increases the likelihood in each iteration and converges under mild additional conditions when the odds ratio function is bounded.

Description: Motivation: The storage and transmission of high-throughput sequencing data consumes significant resources. As our capacity to produce such data continues to increase, this burden will only grow. One approach to reduce storage and transmission requirements is to compress this sequencing data. Results: We present a novel technique to boost the compression of sequencing that is based on the concept of bucketing similar reads so that they appear nearby in the file. We demonstrate that, by adopting a data-dependent bucketing scheme and employing a number of encoding ideas, we can achieve substantially better compression ratios than existing de novo sequence compression tools, including other bucketing and reordering schemes. Our method, Mince, achieves up to a 45% reduction in file sizes (28% on average) compared with existing state-of-the-art de novo compression schemes. Availability and implementation : Mince is written in C++11, is open source and has been made available under the GPLv3 license. It is available at http://www.cs.cmu.edu/~ckingsf/software/mince . Contact: carlk@cs.cmu.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Description: : Current methods for motif discovery from chromatin immunoprecipitation followed by sequencing (ChIP-seq) data often identify non-targeted transcription factor (TF) motifs, and are even further limited when peak sequences are similar due to common ancestry rather than common binding factors. The latter aspect particularly affects a large number of proteins from the Cys 2 His 2 zinc finger (C2H2-ZF) class of TFs, as their binding sites are often dominated by endogenous retroelements that have highly similar sequences. Here, we present recognition code-assisted discovery of regulatory elements (RCADE) for motif discovery from C2H2-ZF ChIP-seq data. RCADE combines predictions from a DNA recognition code of C2H2-ZFs with ChIP-seq data to identify models that represent the genuine DNA binding preferences of C2H2-ZF proteins. We show that RCADE is able to identify generalizable binding models even from peaks that are exclusively located within the repeat regions of the genome, where state-of-the-art motif finding approaches largely fail. Availability and implementation: RCADE is available as a webserver and also for download at http://rcade.ccbr.utoronto.ca/ . Supplementary information: Supplementary data are available at Bioinformatics online. Contact: t.hughes@utoronto.ca

Description: Motivation: Phylogenetic estimates from published studies can be archived using general platforms like Dryad (Vision, 2010) or TreeBASE (Sanderson et al. , 1994). Such services fulfill a crucial role in ensuring transparency and reproducibility in phylogenetic research. However, digital tree data files often require some editing (e.g. rerooting) to improve the accuracy and reusability of the phylogenetic statements. Furthermore, establishing the mapping between tip labels used in a tree and taxa in a single common taxonomy dramatically improves the ability of other researchers to reuse phylogenetic estimates. As the process of curating a published phylogenetic estimate is not error-free, retaining a full record of the provenance of edits to a tree is crucial for openness, allowing editors to receive credit for their work and making errors introduced during curation easier to correct. Results : Here, we report the development of software infrastructure to support the open curation of phylogenetic data by the community of biologists. The backend of the system provides an interface for the standard database operations of creating, reading, updating and deleting records by making commits to a git repository. The record of the history of edits to a tree is preserved by git’s version control features. Hosting this data store on GitHub ( http://github.com/ ) provides open access to the data store using tools familiar to many developers. We have deployed a server running the ‘phylesystem-api’, which wraps the interactions with git and GitHub. The Open Tree of Life project has also developed and deployed a JavaScript application that uses the phylesystem-api and other web services to enable input and curation of published phylogenetic statements. Availability and implementation : Source code for the web service layer is available at https://github.com/OpenTreeOfLife/phylesystem-api . The data store can be cloned from: https://github.com/OpenTreeOfLife/phylesystem . A web application that uses the phylesystem web services is deployed at http://tree.opentreeoflife.org/curator . Code for that tool is available from https://github.com/OpenTreeOfLife/opentree . Contact : mtholder@gmail.com

Description: Motivation: Chromatin Immunoprecipitation followed by sequencing (ChIP-seq) detects genome-wide DNA–protein interactions and chromatin modifications, returning enriched regions (ERs), usually associated with a significance score. Moderately significant interactions can correspond to true, weak interactions, or to false positives; replicates of a ChIP-seq experiment can provide co-localised evidence to decide between the two cases. We designed a general methodological framework to rigorously combine the evidence of ERs in ChIP-seq replicates, with the option to set a significance threshold on the repeated evidence and a minimum number of samples bearing this evidence. Results : We applied our method to Myc transcription factor ChIP-seq datasets in K562 cells available in the ENCODE project. Using replicates, we could extend up to 3 times the ER number with respect to single-sample analysis with equivalent significance threshold. We validated the ‘rescued’ ERs by checking for the overlap with open chromatin regions and for the enrichment of the motif that Myc binds with strongest affinity; we compared our results with alternative methods (IDR and jMOSAiCS), obtaining more validated peaks than the former and less peaks than latter, but with a better validation. Availability and implementation : An implementation of the proposed method and its source code under GPLv3 license are freely available at http://www.bioinformatics.deib.polimi.it/MSPC/ and http://mspc.codeplex.com/ , respectively. Contact : marco.morelli@iit.it Supplementary information: Supplementary Material are available at Bioinformatics online.

Description: : We announce the release of kSNP3.0, a program for SNP identification and phylogenetic analysis without genome alignment or the requirement for reference genomes. kSNP3.0 is a significantly improved version of kSNP v2. Availability and implementation : kSNP3.0 is implemented as a package of stand-alone executables for Linux and Mac OS X under the open-source BSD license. The executable packages, source code and a full User Guide are freely available at https://sourceforge.net/projects/ksnp/files/ Contact: barryghall@gmail.com

Description: Motivation: We have created an R package named phylogeo that provides a set of geographic utilities for sequencing-based microbial ecology studies. Although the geographic location of samples is an important aspect of environmental microbiology, none of the major software packages used in processing microbiome data include utilities that allow users to map and explore the spatial dimension of their data. phylogeo solves this problem by providing a set of plotting and mapping functions that can be used to visualize the geographic distribution of samples, to look at the relatedness of microbiomes using ecological distance, and to map the geographic distribution of particular sequences. By extending the popular phyloseq package and using the same data structures and command formats, phylogeo allows users to easily map and explore the geographic dimensions of their data from the R programming language. Availability and Implementation: phylogeo is documented and freely available http://zachcp.github.io/phylogeo Contact : zcharlop@rockefeller.edu

Description: : Gener is a development module for programming chemical controllers based on DNA strand displacement. Gener is developed with the aim of providing a simple interface that minimizes the opportunities for programming errors: Gener allows the user to test the computations of the DNA programs based on a simple two-domain strand displacement algebra, the minimal available so far. The tool allows the user to perform stepwise computations with respect to the rules of the algebra as well as exhaustive search of the computation space with different options for exploration and visualization. Gener can be used in combination with existing tools, and in particular, its programs can be exported to Microsoft Research’s DSD tool as well as to LaTeX. Availability and implementation : Gener is available for download at the Cosbi website at http://www.cosbi.eu/research/prototypes/gener as a windows executable that can be run on Mac OS X and Linux by using Mono. Contact : ozan@cosbi.eu

Description: Motivation: Molecular dynamics simulations provide atomic insight into the physicochemical characteristics of lipid membranes and hence, a wide range of force field families capable of modelling various lipid types have been developed in recent years. To model membranes in a biologically realistic lipid composition, simulation systems containing multiple different lipids must be assembled. Results: We present a new web service called MemGen that is capable of setting up simulation systems of heterogenous lipid membranes. MemGen is not restricted to certain lipid force fields or lipid types, but instead builds membranes from uploaded structure files which may contain any kind of amphiphilic molecule. MemGen works with any all-atom or united-atom lipid representation. Availability and implementation: MemGen is freely available without registration at http://memgen.uni-goettingen.de . Contact: jhub@gwdg.de Supplementary information: Supplementary data are available at Bioinformatics online.

Description: Choosing the number of components in a finite mixture model is a challenging task. In this article, we study the behaviour of information criteria for selecting the mixture order, based on either the observed likelihood or the complete likelihood including component labels. We propose a new observed likelihood criterion called aic mix , which is shown to be order consistent. We further show that when there is a nontrivial level of classification uncertainty in the true model, complete likelihood criteria asymptotically underestimate the true number of components. A simulation study illustrates the potentially poor finite-sample performance of complete likelihood criteria, while aic mix and the Bayesian information criterion perform strongly regardless of the level of classification uncertainty.

Description: Outlier detection is an integral component of statistical modelling and estimation. For high-dimensional data, classical methods based on the Mahalanobis distance are usually not applicable. We propose an outlier detection procedure that replaces the classical minimum covariance determinant estimator with a high-breakdown minimum diagonal product estimator. The cut-off value is obtained from the asymptotic distribution of the distance, which enables us to control the Type I error and deliver robust outlier detection. Simulation studies show that the proposed method behaves well for high-dimensional data.

Description: This paper adopts a nonparametric Bayesian approach to testing whether a function is monotone. Two new families of tests are constructed. The first uses constrained smoothing splines with a hierarchical stochastic-process prior that explicitly controls the prior probability of monotonicity. The second uses regression splines together with two proposals for the prior over the regression coefficients. Via simulation, the finite-sample performance of the tests is shown to improve upon existing frequentist and Bayesian methods. The asymptotic properties of the Bayes factor for comparing monotone versus nonmonotone regression functions in a Gaussian model are also studied. Our results significantly extend those currently available, which chiefly focus on determining the dimension of a parametric linear model.

Description: The paper develops hierarchical empirical Bayes and benchmarked hierarchical empirical Bayes estimators of positive small area means under multiplicative models. The usual benchmarking requirement is that the small area estimates, when aggregated, should equal the direct estimates for the larger geographical areas. However, while estimating positive small area parameters, the conventional squared error or weighted squared error loss subject to the usual benchmark constraint may not produce positive estimators, so it is necessary to seek other loss functions. We consider a multiplicative model for the original data for estimating positive small area means, and suggest a variant of the Kullback–Leibler divergence as a loss function. The prediction errors of the suggested hierarchical empirical Bayes estimators are investigated asymptotically, and their second-order unbiased estimators are provided. Bootstrapped estimators of these prediction errors for both hierarchical empirical Bayes and benchmarked hierarchical empirical Bayes estimators are also given. The performance of the suggested procedures is investigated through simulation as well as with an example.

Description: Odds ratios can be estimated in case-control studies using standard logistic regression, ignoring the outcome-dependent sampling. In this paper we discuss an analogous result for treatment effects on the treated in matched cohort studies. Specifically, in studies where a sample of treated subjects is observed along with a separate sample of possibly matched controls, we show that efficient and doubly robust estimators of effects on the treated are computationally equivalent to standard estimators, which ignore the matching and exposure-based sampling. This is not the case for general average effects. We also show that matched cohort studies are often more efficient than random sampling for estimating effects on the treated, and derive the optimal number of matches for a given set of matching variables. We illustrate our results via simulation and in a matched cohort study of the effect of hysterectomy on the risk of cardiovascular disease.

Description: Current status data occur in contexts including demographic studies and tumorigenicity experiments. In such cases, each subject is observed only once and the failure time of interest is either left- or right-censored (Kalbfleisch & Prentice, 2002). Many methods have been developed for the analysis of such data (Huang, 1996; Sun, 2006), most of which assume that the failure time and the observation time are independent completely or given covariates. In this paper, we present a sieve maximum likelihood approach for current status data when independence does not hold. A copula model and monotone I-splines are used and the asymptotic properties of the resulting estimators are established. In particular, the estimated regression parameters are shown to be semiparametrically efficient. An illustrative example is provided.

Description: Motivation: In practice, identifying and interpreting the functional impacts of the regulatory relationships between micro-RNA and messenger-RNA is non-trivial. The sheer scale of possible micro-RNA and messenger-RNA interactions can make the interpretation of results difficult. Results: We propose a supervised framework, pMim, built upon concepts of significance combination, for jointly ranking regulatory micro-RNA and their potential functional impacts with respect to a condition of interest. Here, pMim directly tests if a micro-RNA is differentially expressed and if its predicted targets, which lie in a common biological pathway, have changed in the opposite direction. We leverage the information within existing micro-RNA target and pathway databases to stabilize the estimation and annotation of micro-RNA regulation making our approach suitable for datasets with small sample sizes. In addition to outputting meaningful and interpretable results, we demonstrate in a variety of datasets that the micro-RNA identified by pMim, in comparison to simpler existing approaches, are also more concordant with what is described in the literature. Availability and implementation: This framework is implemented as an R function, pMim , in the package sydSeq available from http://www.ellispatrick.com/r-packages . Contact: jean.yang@sydney.edu.au Supplementary information: Supplementary data are available at Bioinformatics online.

Description: Motivation: Cellular mRNA levels originate from the combined action of multiple regulatory processes, which can be recapitulated by the rates of pre-mRNA synthesis, pre-mRNA processing and mRNA degradation. Recent experimental and computational advances set the basis to study these intertwined levels of regulation. Nevertheless, software for the comprehensive quantification of RNA dynamics is still lacking. Results: INSPEcT is an R package for the integrative analysis of RNA- and 4sU-seq data to study the dynamics of transcriptional regulation. INSPEcT provides gene-level quantification of these rates, and a modeling framework to identify which of these regulatory processes are most likely to explain the observed mRNA and pre-mRNA concentrations. Software performance is tested on a synthetic dataset, instrumental to guide the choice of the modeling parameters and the experimental design. Availability and implementation: INSPEcT is submitted to Bioconductor and is currently available as Supplementary Additional File S1 . Contact: mattia.pelizzola@iit.it Supplementary Information: Supplementary data are available at Bioinformatics online.

Description: Motivation: Experimentally determined gene regulatory networks can be enriched by computational inference from high-throughput expression profiles. However, the prediction of regulatory interactions is severely impaired by indirect and spurious effects, particularly for eukaryotes. Recently, published methods report improved predictions by exploiting the a priori known targets of a regulator (its local topology) in addition to expression profiles. Results: We find that methods exploiting known targets show an unexpectedly high rate of false discoveries. This leads to inflated performance estimates and the prediction of an excessive number of new interactions for regulators with many known targets. These issues are hidden from common evaluation and cross-validation setups, which is due to Simpson’s paradox. We suggest a confidence score recalibration method (CoRe) that reduces the false discovery rate and enables a reliable performance estimation. Conclusions: CoRe considerably improves the results of network inference methods that exploit known targets. Predictions then display the biological process specificity of regulators more correctly and enable the inference of accurate genome-wide regulatory networks in eukaryotes. For yeast, we propose a network with more than 22 000 confident interactions. We point out that machine learning approaches outside of the area of network inference may be affected as well. Availability and implementation: Results, executable code and networks are available via our website http://www.bio.ifi.lmu.de/forschung/CoRe . Contact: robert.kueffner@helmholtz-muenchen.de Supplementary information: Supplementary data are available at Bioinformatics online.

Description: Motivation: Stoichiometric and constraint-based methods of computational strain design have become an important tool for rational metabolic engineering. One of those relies on the concept of constrained minimal cut sets (cMCSs). However, as most other techniques, cMCSs may consider only reaction (or gene) knockouts to achieve a desired phenotype. Results : We generalize the cMCSs approach to constrained regulatory MCSs (cRegMCSs), where up/downregulation of reaction rates can be combined along with reaction deletions. We show that flux up/downregulations can virtually be treated as cuts allowing their direct integration into the algorithmic framework of cMCSs. Because of vastly enlarged search spaces in genome-scale networks, we developed strategies to (optionally) preselect suitable candidates for flux regulation and novel algorithmic techniques to further enhance efficiency and speed of cMCSs calculation. We illustrate the cRegMCSs approach by a simple example network and apply it then by identifying strain designs for ethanol production in a genome-scale metabolic model of Escherichia coli. The results clearly show that cRegMCSs combining reaction deletions and flux regulations provide a much larger number of suitable strain designs, many of which are significantly smaller relative to cMCSs involving only knockouts. Furthermore, with cRegMCSs, one may also enable the fine tuning of desired behaviours in a narrower range. The new cRegMCSs approach may thus accelerate the implementation of model-based strain designs for the bio-based production of fuels and chemicals. Availability and implementation: MATLAB code and the examples can be downloaded at http://www.mpi-magdeburg.mpg.de/projects/cna/etcdownloads.html . Contact : krishna.mahadevan@utoronto.ca or klamt@mpi-magdeburg.mpg.de Supplementary information: Supplementary data are available at Bioinformatics online.

Description: Motivation: Lipids are a large and diverse group of biological molecules with roles in membrane formation, energy storage and signaling. Cellular lipidomes may contain tens of thousands of structures, a staggering degree of complexity whose significance is not yet fully understood. High-throughput mass spectrometry-based platforms provide a means to study this complexity, but the interpretation of lipidomic data and its integration with prior knowledge of lipid biology suffers from a lack of appropriate tools to manage the data and extract knowledge from it. Results: To facilitate the description and exploration of lipidomic data and its integration with prior biological knowledge, we have developed a knowledge resource for lipids and their biology—SwissLipids. SwissLipids provides curated knowledge of lipid structures and metabolism which is used to generate an in silico library of feasible lipid structures. These are arranged in a hierarchical classification that links mass spectrometry analytical outputs to all possible lipid structures, metabolic reactions and enzymes. SwissLipids provides a reference namespace for lipidomic data publication, data exploration and hypothesis generation. The current version of SwissLipids includes over 244 000 known and theoretically possible lipid structures, over 800 proteins, and curated links to published knowledge from over 620 peer-reviewed publications. We are continually updating the SwissLipids hierarchy with new lipid categories and new expert curated knowledge. Availability: SwissLipids is freely available at http://www.swisslipids.org/ . Contact: alan.bridge@isb-sib.ch Supplementary information: Supplementary data are available at Bioinformatics online.

Description: Motivation: Both the quantitative real-time polymerase chain reaction (qPCR) and quantitative isothermal amplification (qIA) are standard methods for nucleic acid quantification. Numerous real-time read-out technologies have been developed. Despite the continuous interest in amplification-based techniques, there are only few tools for pre-processing of amplification data. However, a transparent tool for precise control of raw data is indispensable in several scenarios, for example, during the development of new instruments. Results: chipPCR is an R package for the pre-processing and quality analysis of raw data of amplification curves. The package takes advantage of R ’s S 4 object model and offers an extensible environment. chipPCR contains tools for raw data exploration: normalization, baselining, imputation of missing values, a powerful wrapper for amplification curve smoothing and a function to detect the start and end of an amplification curve. The capabilities of the software are enhanced by the implementation of algorithms unavailable in R , such as a 5-point stencil for derivative interpolation. Simulation tools, statistical tests, plots for data quality management, amplification efficiency/quantification cycle calculation, and datasets from qPCR and qIA experiments are part of the package. Core functionalities are integrated in GUIs (web-based and standalone shiny applications), thus streamlining analysis and report generation. Availability and implementation: http://cran.r-project.org/web/packages/chipPCR . Source code: https://github.com/michbur/chipPCR . Contact : stefan.roediger@b-tu.de Supplementary information: Supplementary data are available at Bioinformatics online.

Description: : A key to understanding RNA function is to uncover its complex 3D structure. Experimental methods used for determining RNA 3D structures are technologically challenging and laborious, which makes the development of computational prediction methods of substantial interest. Previously, we developed the iFoldRNA server that allows accurate prediction of short (〈50 nt) tertiary RNA structures starting from primary sequences. Here, we present a new version of the iFoldRNA server that permits the prediction of tertiary structure of RNAs as long as a few hundred nucleotides. This substantial increase in the server capacity is achieved by utilization of experimental information such as base-pairing and hydroxyl-radical probing. We demonstrate a significant benefit provided by integration of experimental data and computational methods. Availability and implementation: http://ifoldrna.dokhlab.org Contact: dokh@unc.eu

Description: : The ms-data-core-api is a free, open-source library for developing computational proteomics tools and pipelines. The Application Programming Interface, written in Java, enables rapid tool creation by providing a robust, pluggable programming interface and common data model. The data model is based on controlled vocabularies/ontologies and captures the whole range of data types included in common proteomics experimental workflows, going from spectra to peptide/protein identifications to quantitative results. The library contains readers for three of the most used Proteomics Standards Initiative standard file formats: mzML, mzIdentML, and mzTab. In addition to mzML, it also supports other common mass spectra data formats: dta, ms2, mgf, pkl, apl (text-based), mzXML and mzData (XML-based). Also, it can be used to read PRIDE XML, the original format used by the PRIDE database, one of the world-leading proteomics resources. Finally, we present a set of algorithms and tools whose implementation illustrates the simplicity of developing applications using the library. Availability and implementation: The software is freely available at https://github.com/PRIDE-Utilities/ms-data-core-api . Supplementary information: Supplementary data are available at Bioinformatics online Contact: juan@ebi.ac.uk

Description: : Scanning probe microscopy (SPM) is already a relevant tool in biological research at the nanoscale. We present ‘Flatten plus’, a recent and helpful implementation in the well-known WSxM free software package. ‘Flatten plus’ allows reducing low-frequency noise in SPM images in a semi-automated way preventing the appearance of typical artifacts associated with such filters. Availability and implementation: WSxM is a free software implemented in C++ supported on MS Windows, but it can also be run under Mac or Linux using emulators such as Wine or Parallels. WSxM can be downloaded from http://www.wsxmsolutions.com/ . Contact: ignacio.horcas@wsxmsolutions.com

Description: : Despite the plethora of methods available for the functional analysis of omics data, obtaining comprehensive-yet detailed understanding of the results remains challenging. This is mainly due to the lack of publicly available tools for the visualization of this type of information. Here we present an R package called GOplot, based on ggplot2, for enhanced graphical representation. Our package takes the output of any general enrichment analysis and generates plots at different levels of detail: from a general overview to identify the most enriched categories (bar plot, bubble plot) to a more detailed view displaying different types of information for molecules in a given set of categories (circle plot, chord plot, cluster plot). The package provides a deeper insight into omics data and allows scientists to generate insightful plots with only a few lines of code to easily communicate the findings. Availability and Implementation: The R package GOplot is available via CRAN-The Comprehensive R Archive Network: http://cran.r-project.org/web/packages/GOplot . The shiny web application of the Venn diagram can be found at: https://wwalter.shinyapps.io/Venn/ . A detailed manual of the package with sample figures can be found at https://wencke.github.io/ Contact: fscabo@cnic.es or mricote@cnic.es

Description: Motivation: Horizontal transfer of transposable (HTT) elements among eukaryotes was discovered in the mid-1980s. As then, 〉300 new cases have been described. New findings about HTT are revealing the evolutionary impact of this phenomenon on host genomes. In order to provide an up to date, interactive and expandable database for such events, we developed the HTT-DB database. Results: HTT-DB allows easy access to most of HTT cases reported along with rich information about each case. Moreover, it allows the user to generate tables and graphs based on searches using Transposable elements and/or host species classification and export them in several formats. Availability and implementation: This database is freely available on the web at http://lpa.saogabriel.unipampa.edu.br:8080/httdatabase . HTT-DB was developed based on Java and MySQL with all major browsers supported. Tools and software packages used are free for personal or non-profit projects. Contact: bdotto82@gmail.com or gabriel.wallau@gmail.com

Description: We introduce a new framework for the analysis of the stability of solitons for the one-dimensional Gross–Pitaevskii equation. In particular, we establish the asymptotic stability of the black soliton with zero speed.

Description: Let $k$ and $n$ be positive even integers. For a cuspidal Hecke eigenform $h$ in the Kohnen plus space of weight $k-n/2+1/2$ for $\varGamma _0(4),$ let $I_n(h)$ be the Duke–Imamo $\bar {{\text {g}}}$ lu–Ikeda lift of $h$ in the space of cusp forms of weight $k$ for ${\rm Sp}_n({{\bf{Z}}}),$ and $f$ be the primitive form of weight $2k-n$ for ${\rm SL}_2({{\bf{Z}}})$ corresponding to $h$ under the Shimura correspondence. We then express the ratio $\displaystyle {\langle I_n(h), I_n(h) \rangle / \langle h, h \rangle }$ of the period of $I_n(h)$ to that of $h$ in terms of special values of certain $L$ -functions of $f$ . This proves the conjecture proposed by Ikeda concerning the period of the Duke–Imamo $\bar {{\text {g}}}$ lu–Ikeda lift.

Description: Motivation : The majority of variation identified by genome wide association studies falls in non-coding genomic regions and is hypothesized to impact regulatory elements that modulate gene expression. Here we present a statistically rigorous software tool GREGOR (Genomic Regulatory Elements and Gwas Overlap algoRithm) for evaluating enrichment of any set of genetic variants with any set of regulatory features. Using variants from five phenotypes, we describe a data-driven approach to determine the tissue and cell types most relevant to a trait of interest and to identify the subset of regulatory features likely impacted by these variants. Last, we experimentally evaluate six predicted functional variants at six lipid-associated loci and demonstrate significant evidence for allele-specific impact on expression levels. GREGOR systematically evaluates enrichment of genetic variation with the vast collection of regulatory data available to explore novel biological mechanisms of disease and guide us toward the functional variant at trait-associated loci. Availability and implementation : GREGOR, including source code, documentation, examples, and executables, is available at http://genome.sph.umich.edu/wiki/GREGOR . Contact : cristen@umich.edu Supplementary information : Supplementary data are available at Bioinformatics online.

Description: Motivation: Genome and transcriptome analyses can be used to explore cancers comprehensively, and it is increasingly common to have multiple omics data measured from each individual. Furthermore, there are rich functional data such as predicted impact of mutations on protein coding and gene/protein networks. However, integration of the complex information across the different omics and functional data is still challenging. Clinical validation, particularly based on patient outcomes such as survival, is important for assessing the relevance of the integrated information and for comparing different procedures. Results: An analysis pipeline is built for integrating genomic and transcriptomic alterations from whole-exome and RNA sequence data and functional data from protein function prediction and gene interaction networks. The method accumulates evidence for the functional implications of mutated potential driver genes found within and across patients. A driver-gene score (DGscore) is developed to capture the cumulative effect of such genes. To contribute to the score, a gene has to be frequently mutated, with high or moderate mutational impact at protein level, exhibiting an extreme expression and functionally linked to many differentially expressed neighbors in the functional gene network. The pipeline is applied to 60 matched tumor and normal samples of the same patient from The Cancer Genome Atlas breast-cancer project. In clinical validation, patients with high DGscores have worse survival than those with low scores ( P = 0.001). Furthermore, the DGscore outperforms the established expression-based signatures MammaPrint and PAM50 in predicting patient survival. In conclusion, integration of mutation, expression and functional data allows identification of clinically relevant potential driver genes in cancer. Availability and implementation: The documented pipeline including annotated sample scripts can be found in http://fafner.meb.ki.se/biostatwiki/driver-genes/ . Contact: yudi.pawitan@ki.se Supplementary information: Supplementary data are available at Bioinformatics online.

Description: Motivation: With improvements in next-generation sequencing technologies and reductions in price, ordered RNA-seq experiments are becoming common. Of primary interest in these experiments is identifying genes that are changing over time or space, for example, and then characterizing the specific expression changes. A number of robust statistical methods are available to identify genes showing differential expression among multiple conditions, but most assume conditions are exchangeable and thereby sacrifice power and precision when applied to ordered data. Results: We propose an empirical Bayes mixture modeling approach called EBSeq-HMM. In EBSeq-HMM, an auto-regressive hidden Markov model is implemented to accommodate dependence in gene expression across ordered conditions. As demonstrated in simulation and case studies, the output proves useful in identifying differentially expressed genes and in specifying gene-specific expression paths. EBSeq-HMM may also be used for inference regarding isoform expression. Availability and implementation: An R package containing examples and sample datasets is available at Bioconductor. Contact: kendzior@biostat.wisc.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Description: Motivation: Sequence discovery tools play a central role in several fields of computational biology. In the framework of Transcription Factor binding studies, most of the existing motif finding algorithms are computationally demanding, and they may not be able to support the increasingly large datasets produced by modern high-throughput sequencing technologies. Results: We present FastMotif, a new motif discovery algorithm that is built on a recent machine learning technique referred to as Method of Moments. Based on spectral decompositions, our method is robust to model misspecifications and is not prone to locally optimal solutions. We obtain an algorithm that is extremely fast and designed for the analysis of big sequencing data. On HT-Selex data, FastMotif extracts motif profiles that match those computed by various state-of-the-art algorithms, but one order of magnitude faster. We provide a theoretical and numerical analysis of the algorithm’s robustness and discuss its sensitivity with respect to the free parameters. Availability and implementation: The Matlab code of FastMotif is available from http://lcsb-portal.uni.lu/bioinformatics . Contact: vlassis@adobe.com Supplementary information: Supplementary data are available at Bioinformatics online.

Description: Motivation: Next-generation high-throughput sequencing has become a state-of-the-art technique in genome assembly. Scaffolding is one of the main stages of the assembly pipeline. During this stage, contigs assembled from the paired-end reads are merged into bigger chains called scaffolds. Because of a high level of statistical noise, chimeric reads, and genome repeats the problem of scaffolding is a challenging task. Current scaffolding software packages widely vary in their quality and are highly dependent on the read data quality and genome complexity. There are no clear winners and multiple opportunities for further improvements of the tools still exist. Results: This article presents an efficient scaffolding algorithm ScaffMatch that is able to handle reads with both short (〈600 bp) and long (〉35 000 bp) insert sizes producing high-quality scaffolds. We evaluate our scaffolding tool with the F score and other metrics (N50, corrected N50) on eight datasets comparing it with the most available packages. Our experiments show that ScaffMatch is the tool of preference for the most datasets. Availability and implementation: The source code is available at http://alan.cs.gsu.edu/NGS/?q=content/scaffmatch . Contact: mandric@cs.gsu.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Description: Motivation: Identifying protein subchloroplast localization in chloroplast organelle is very helpful for understanding the function of chloroplast proteins. There have existed a few computational prediction methods for protein subchloroplast localization. However, these existing works have ignored proteins with multiple subchloroplast locations when constructing prediction models, so that they can predict only one of all subchloroplast locations of this kind of multilabel proteins. Results: To address this problem, through utilizing label-specific features and label correlations simultaneously, a novel multilabel classifier was developed for predicting protein subchloroplast location(s) with both single and multiple location sites. As an initial study, the overall accuracy of our proposed algorithm reaches 55.52%, which is quite high to be able to become a promising tool for further studies. Availability and implementation: An online web server for our proposed algorithm named MultiP-SChlo was developed, which are freely accessible at http://biomed.zzuli.edu.cn/bioinfo/multip-schlo/ . Contact: pandaxiaoxi@gmail.com or gzli@tongji.edu.cn Supplementary information: Supplementary data are available at Bioinformatics online.

Description: Motivation: Loops in proteins are often involved in biochemical functions. Their irregularity and flexibility make experimental structure determination and computational modeling challenging. Most current loop modeling methods focus on modeling single loops. In protein structure prediction, multiple loops often need to be modeled simultaneously. As interactions among loops in spatial proximity can be rather complex, sampling the conformations of multiple interacting loops is a challenging task. Results: In this study, we report a new method called m ulti-loop Di stance-guided S equential chain- Gro wth Monte Carlo ( M -D i SG ro ) for prediction of the conformations of multiple interacting loops in proteins. Our method achieves an average RMSD of 1.93 Å for lowest energy conformations of 36 pairs of interacting protein loops with the total length ranging from 12 to 24 residues. We further constructed a data set containing proteins with 2, 3 and 4 interacting loops. For the most challenging target proteins with four loops, the average RMSD of the lowest energy conformations is 2.35 Å. Our method is also tested for predicting multiple loops in β-barrel membrane proteins. For outer-membrane protein G, the lowest energy conformation has a RMSD of 2.62 Å for the three extracellular interacting loops with a total length of 34 residues (12, 12 and 10 residues in each loop). Availability and implementation : The software is freely available at: tanto.bioe.uic.edu/m-DiSGro. Contact: jinfeng@stat.fsu.edu or jliang@uic.edu Supplementary information : Supplementary data are available at Bioinformatics online.

Description: Motivation: Network comparison is a computationally intractable problem with important applications in systems biology and other domains. A key challenge is to properly quantify similarity between wiring patterns of two networks in an alignment-free fashion. Also, alignment-based methods exist that aim to identify an actual node mapping between networks and as such serve a different purpose. Various alignment-free methods that use different global network properties (e.g. degree distribution) have been proposed. Methods based on small local subgraphs called graphlets perform the best in the alignment-free network comparison task, due to high level of topological detail that graphlets can capture. Among different graphlet-based methods, Graphlet Correlation Distance (GCD) was shown to be the most accurate for comparing networks. Recently, a new graphlet-based method called NetDis was proposed, which was claimed to be superior. We argue against this, as the performance of NetDis was not properly evaluated to position it correctly among the other alignment-free methods. Results : We evaluate the performance of available alignment-free network comparison methods, including GCD and NetDis. We do this by measuring accuracy of each method (in a systematic precision-recall framework) in terms of how well the method can group (cluster) topologically similar networks. By testing this on both synthetic and real-world networks from different domains, we show that GCD remains the most accurate, noise-tolerant and computationally efficient alignment-free method. That is, we show that NetDis does not outperform the other methods, as originally claimed, while it is also computationally more expensive. Furthermore, since NetDis is dependent on the choice of a network null model (unlike the other graphlet-based methods), we show that its performance is highly sensitive to the choice of this parameter. Finally, we find that its performance is not independent on network sizes and densities, as originally claimed. Contact : natasha@imperial.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online.

Description: Motivation: The functional impact of small molecules is increasingly being assessed in different eukaryotic species through large-scale phenotypic screening initiatives. Identifying the targets of these molecules is crucial to mechanistically understand their function and uncover new therapeutically relevant modes of action. However, despite extensive work carried out in model organisms and human, it is still unclear to what extent one can use information obtained in one species to make predictions in other species. Results: Here, for the first time, we explore and validate at a large scale the use of protein homology relationships to predict the targets of small molecules across different species. Our results show that exploiting target homology can significantly improve the predictions, especially for molecules experimentally tested in other species. Interestingly, when considering separately orthology and paralogy relationships, we observe that mapping small molecule interactions among orthologs improves prediction accuracy, while including paralogs does not improve and even sometimes worsens the prediction accuracy. Overall, our results provide a novel approach to integrate chemical screening results across multiple species and highlight the promises and remaining challenges of using protein homology for small molecule target identification. Availability and implementation: Homology-based predictions can be tested on our website http://www.swisstargetprediction.ch . Contact: david.gfeller@unil.ch or vincent.zoete@isb-sib.ch . Supplementary information: Supplementary data are available at Bioinformatics online.

Description: We study the homological finiteness properties ${\rm FP}_m$ of wreath products $\Gamma = H \wr _X G$ . We show that, when $H$ has infinite abelianization, $\Gamma$ is of type ${\rm FP}_m$ if and only if both $G$ and $H$ have type ${\rm FP}_m$ and $G$ acts (diagonally) on $X^i$ with stabilizers of type ${\rm FP}_{m-i}$ and with finitely many orbits for all $1 \leq i \leq m$ . If furthermore $H$ is torsion-free, we give a criterion for $\Gamma$ to be Bredon- ${\rm FP}_m$ with respect to the class of finite subgroups of $\Gamma$ . Finally, when $H$ has infinite abelianization and $\chi : \Gamma \to \mathbb {R}$ is a non-zero homomorphism with $\chi (H) = 0$ , we classify when $[\chi ]$ belongs to the Bieri–Neumann–Strebel–Renz invariant $\Sigma ^m(\Gamma , \mathbb {Z})$ .

Description: We investigate the logarithmic bundles associated to arrangements of hypersurfaces with a fixed degree in a smooth projective variety. We then specialize to the case when the variety is a quadric hypersurface and a multiprojective space to prove a Torelli-type theorem in some cases.

Description: We study the higher differentiability of the very weak solution of the Cauchy–Dirichlet problem associated to the linear parabolic system with Hölder continuous coefficients and L 1 right-hand side of the form $$\begin{align*} \dfrac{\partial u}{\partial t} -{\rm div}(A(x,t)Du) & = \mu \quad \mbox{in } \Omega \,{\times}\,]0, T[,\\ u & = 0 \quad \mbox{on } \partial \Omega \,{\times}\,]0, T[,\\ u & = 0 \quad \mbox{on } \Omega \times \{ 0\}. \end{align*}$$

Description: This article evaluates 186 papers, published between 1978 and 2013 in 16 representative scientific journals, related to maintenance and reliability problems that were tackled from a multi-criteria (MC) perspective. An overview and insights are presented. This study may be useful to researchers and others concerned with maintenance and reliability who seek not only to understand the potential of MC and multi-objective models but also to develop and apply an MC decision model to help solve a real problem in these areas. There are some discussions on some principles for the application of MC in maintenance and reliability and some guidance on how to choose a suitable MC method is given, based on previous applications.

Description: A credit-linked note (CLN) is a type of credit derivative, constructed with a bond and an embedded credit default swap, which allows the issuer to transfer a specific credit risk to credit investors. In this paper, we model CLNs with and without counterparty risk in the reduced-form framework. For CLNs with counterparty risk, we consider two different scenarios, i.e. the issuer of CLNs and reference assets have either positive correlation or negative correlation. Assuming the interest rate follows the Cox–Ingersoll–Ross (CIR) model (Cox et al ., 1985) and the default events mainly depend on the interest rate, we model the two different correlations. Explicit formulas for value functions are obtained through a partial differential equation approach. In addition, counterparty valuation adjustment and the dependence on related parameters are also investigated.

Description: Motivation: Very large studies are required to provide sufficiently big sample sizes for adequately powered association analyses. This can be an expensive undertaking and it is important that an accurate sample size is identified. For more realistic sample size calculation and power analysis, the impact of unmeasured aetiological determinants and the quality of measurement of both outcome and explanatory variables should be taken into account. Conventional methods to analyse power use closed-form solutions that are not flexible enough to cater for all of these elements easily. They often result in a potentially substantial overestimation of the actual power. Results: In this article, we describe the Estimating Sample-size and Power in R by Exploring Simulated Study Outcomes tool that allows assessment errors in power calculation under various biomedical scenarios to be incorporated. We also report a real world analysis where we used this tool to answer an important strategic question for an existing cohort. Availability and implementation: The software is available for online calculation and downloads at http://espresso-research.org . The code is freely available at https://github.com/ESPRESSO-research . Contact: louqman@gmail.com Supplementary information: Supplementary data are available at Bioinformatics online.

Description: Motivation: Given the importance of non-coding RNAs to cellular regulatory functions, it would be highly desirable to have accurate computational prediction of RNA 3D structure, a task which remains challenging. Even for a short RNA sequence, the space of tertiary conformations is immense; existing methods to identify native-like conformations mostly resort to random sampling of conformations to achieve computational feasibility. However, native conformations may not be examined and prediction accuracy may be compromised due to sampling. State-of-the-art methods have yet to deliver satisfactory predictions for RNAs of length beyond 50 nucleotides. Results: This paper presents a method to tackle a key step in the RNA 3D structure prediction problem, the prediction of the nucleotide interactions that constitute the desired 3D structure. The research is based on a novel graph model, called a backbone k-tree , to tightly constrain the nucleotide interaction relationships considered for RNA 3D structures. It is shown that the new model makes it possible to efficiently predict the optimal set of nucleotide interactions (including the non-canonical interactions in all recently revealed families) from the query sequence along with known or predicted canonical basepairs. The preliminary results indicate that in most cases the new method can predict with a high accuracy the nucleotide interactions that constitute the 3D structure of the query sequence. It thus provides a useful tool for the accurate prediction of RNA 3D structure. Availability and Implementation: The source package for BkTree is available at http://rna-informatics.uga.edu/index.php?f=software&p=BkTree . Contact: lding@uga.edu or cai@cs.uga.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Description: Motivation: RNAs fold into complex structures that are integral to the diverse mechanisms underlying RNA regulation of gene expression. Recent development of transcriptome-wide RNA structure profiling through the application of structure-probing enzymes or chemicals combined with high-throughput sequencing has opened a new field that greatly expands the amount of in vitro and in vivo RNA structural information available. The resultant datasets provide the opportunity to investigate RNA structural information on a global scale. However, the analysis of high-throughput RNA structure profiling data requires considerable computational effort and expertise. Results: We present a new platform, StructureFold, that provides an integrated computational solution designed specifically for large-scale RNA structure mapping and reconstruction across any transcriptome. StructureFold automates the processing and analysis of raw high-throughput RNA structure profiling data, allowing the seamless incorporation of wet-bench structural information from chemical probes and/or ribonucleases to restrain RNA secondary structure prediction via the RNAstructure and ViennaRNA package algorithms. StructureFold performs reads mapping and alignment, normalization and reactivity derivation, and RNA structure prediction in a single user-friendly web interface or via local installation. The variation in transcript abundance and length that prevails in living cells and consequently causes variation in the counts of structure-probing events between transcripts is accounted for. Accordingly, StructureFold is applicable to RNA structural profiling data obtained in vivo as well as to in vitro or in silico datasets. StructureFold is deployed via the Galaxy platform. Availability and Implementation: StructureFold is freely available as a component of Galaxy available at: https://usegalaxy.org/ . Contact: yxt148@psu.edu or sma3@psu.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Description: : Structural variations (SVs) are large genomic rearrangements that vary significantly in size, making them challenging to detect with the relatively short reads from next-generation sequencing (NGS). Different SV detection methods have been developed; however, each is limited to specific kinds of SVs with varying accuracy and resolution. Previous works have attempted to combine different methods, but they still suffer from poor accuracy particularly for insertions. We propose MetaSV, an integrated SV caller which leverages multiple orthogonal SV signals for high accuracy and resolution. MetaSV proceeds by merging SVs from multiple tools for all types of SVs. It also analyzes soft-clipped reads from alignment to detect insertions accurately since existing tools underestimate insertion SVs. Local assembly in combination with dynamic programming is used to improve breakpoint resolution. Paired-end and coverage information is used to predict SV genotypes. Using simulation and experimental data, we demonstrate the effectiveness of MetaSV across various SV types and sizes. Availability and implementation: Code in Python is at http://bioinform.github.io/metasv/ . Contact: rd@bina.com Supplementary information: Supplementary data are available at Bioinformatics online.

Description: : We present a web server to predict the functional effect of single or multiple amino acid substitutions, insertions and deletions using the prediction tool PROVEAN. The server provides rapid analysis of protein variants from any organisms, and also supports high-throughput analysis for human and mouse variants at both the genomic and protein levels. Availability and implementation : The web server is freely available and open to all users with no login requirements at http://provean.jcvi.org . Contact: achan@jcvi.org Supplementary information: Supplementary data are available at Bioinformatics online.

Description: Motivation: In attempts to determine the genetic causes of human disease, researchers are often faced with a large number of candidate genes. Linkage studies can point to a genomic region containing hundreds of genes, while the high-throughput sequencing approach will often identify a great number of non-synonymous genetic variants. Since systematic experimental verification of each such candidate gene is not feasible, a method is needed to decide which genes are worth investigating further. Computational gene prioritization presents itself as a solution to this problem, systematically analyzing and sorting each gene from the most to least likely to be the disease-causing gene, in a fraction of the time it would take a researcher to perform such queries manually. Results: Here, we present Gene TIssue Expression Ranker (GeneTIER), a new web-based application for candidate gene prioritization. GeneTIER replaces knowledge-based inference traditionally used in candidate disease gene prioritization applications with experimental data from tissue-specific gene expression datasets and thus largely overcomes the bias toward the better characterized genes/diseases that commonly afflict other methods. We show that our approach is capable of accurate candidate gene prioritization and illustrate its strengths and weaknesses using case study examples. Availability and Implementation: Freely available on the web at http://dna.leeds.ac.uk/GeneTIER/. Contact: umaan@leeds.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online.

Description: Motivation: The role of personalized medicine and target treatment in the clinical management of cancer patients has become increasingly important in recent years. This has made the task of precise histological substratification of cancers crucial. Increasingly, genomic data are being seen as a valuable classifier. Specifically, copy number alteration (CNA) profiles generated by next-generation sequencing (NGS) can become a determinant for tumours subtyping. The principle purpose of this study is to devise a model with good prediction capability for the tumours histological subtypes as a function of both the patients covariates and their genome-wide CNA profiles from NGS data. Results: We investigate a logistic regression for modelling tumour histological subtypes as a function of the patients’ covariates and their CNA profiles, in a mixed model framework. The covariates, such as age and gender, are considered as fixed predictors and the genome-wide CNA profiles are considered as random predictors. We illustrate the application of this model in lung and oral cancer datasets, and the results indicate that the tumour histological subtypes can be modelled with a good fit. Our cross-validation indicates that the logistic regression exhibits the best prediction relative to other classification methods we considered in this study. The model also exhibits the best agreement in the prediction between smooth-segmented and circular binary-segmented CNA profiles. Availability and implementation: An R package to run a logistic regression is available in http://www1.maths.leeds.ac.uk/~arief/R/CNALR/ . Contact: a.gusnanto@leeds.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online.

Description: : Metabolic network mapping is a widely used approach for integration of metabolomic experimental results with biological domain knowledge. However, current approaches can be limited by biochemical domain or pathway knowledge which results in sparse disconnected graphs for real world metabolomic experiments. MetaMapR integrates enzymatic transformations with metabolite structural similarity, mass spectral similarity and empirical associations to generate richly connected metabolic networks. This open source, web-based or desktop software, written in the R programming language, leverages KEGG and PubChem databases to derive associations between metabolites even in cases where biochemical domain or molecular annotations are unknown. Network calculation is enhanced through an interface to the Chemical Translation System, which allows metabolite identifier translation between 〉200 common biochemical databases. Analysis results are presented as interactive visualizations or can be exported as high-quality graphics and numerical tables which can be imported into common network analysis and visualization tools. Availability and Implementation: Freely available at http://dgrapov.github.io/MetaMapR/ . Requires R and a modern web browser. Installation instructions, tutorials and application examples are available at http://dgrapov.github.io/MetaMapR/ . Contact: ofiehn@ucdavis.edu

Description: Motivation: The Cellular Phenotype Database (CPD) is a repository for data derived from high-throughput systems microscopy studies. The aims of this resource are: (i) to provide easy access to cellular phenotype and molecular localization data for the broader research community; (ii) to facilitate integration of independent phenotypic studies by means of data aggregation techniques, including use of an ontology and (iii) to facilitate development of analytical methods in this field. Results: In this article we present CPD, its data structure and user interface, propose a minimal set of information describing RNA interference experiments, and suggest a generic schema for management and aggregation of outputs from phenotypic or molecular localization experiments. The database has a flexible structure for management of data from heterogeneous sources of systems microscopy experimental outputs generated by a variety of protocols and technologies and can be queried by gene, reagent, gene attribute, study keywords, phenotype or ontology terms. Availability and implementation: CPD is developed as part of the Systems Microscopy Network of Excellence and is accessible at http://www.ebi.ac.uk/fg/sym . Contact: jes@ebi.ac.uk or ugis@ebi.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online.

Description: : Specific recognition of DNA by proteins is a crucial step of many biological processes. PDIviz is a plugin for the PyMOL molecular visualization system that analyzes protein–DNA binding interfaces by comparing the solvent accessible surface area of the complex against the free protein and free DNA. The plugin provides three distinct three-dimensional visualization modes to highlight interactions with DNA bases and backbone, major and minor groove, and with atoms of different pharmacophoric type (hydrogen bond donors/acceptors, hydrophobic and thymine methyl). Each mode comes in three styles to focus the visual analysis on the protein or DNA side of the interface, or on the nucleotide sequence. PDIviz allows for the generation of publication quality images, all calculated data can be written to disk, and a command line interface is provided for automating tasks. The plugin may be helpful for the detailed identification of regions involved in DNA base and shape readout, and can be particularly useful in rapidly pinpointing the overall mode of interaction. Availability and implementation: Freely available at http://melolab.org/pdiviz/ as a PyMOL plugin. Tested with incentive, educational, and open source versions of PyMOL on Windows, Mac and Linux systems. Contact: aschueller@bio.puc.cl Supplementary Information: Supplementary data are available at Bioinformatics online.

Description: : In next generation sequencing (NGS)-based genetic studies, researchers typically perform genotype calling first and then apply standard genotype-based methods for association testing. However, such a two-step approach ignores genotype calling uncertainty in the association testing step and may incur power loss and/or inflated type-I error. In the recent literature, a few robust and efficient likelihood based methods including both likelihood ratio test (LRT) and score test have been proposed to carry out association testing without intermediate genotype calling. These methods take genotype calling uncertainty into account by directly incorporating genotype likelihood function (GLF) of NGS data into association analysis. However, existing LRT methods are computationally demanding or do not allow covariate adjustment; while existing score tests are not applicable to markers with low minor allele frequency (MAF). We provide an LRT allowing flexible covariate adjustment, develop a statistically more powerful score test and propose a combination strategy (UNC combo) to leverage the advantages of both tests. We have carried out extensive simulations to evaluate the performance of our proposed LRT and score test. Simulations and real data analysis demonstrate the advantages of our proposed combination strategy: it offers a satisfactory trade-off in terms of computational efficiency, applicability (accommodating both common variants and variants with low MAF) and statistical power, particularly for the analysis of quantitative trait where the power gain can be up to ~60% when the causal variant is of low frequency (MAF 〈 0.01). Availability and implementation : UNC combo and the associated R files, including documentation, examples, are available at http://www.unc.edu/~yunmli/UNCcombo/ Contact: yunli@med.unc.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Description: Motivation : Recombined T- and B-cell receptor repertoires are increasingly being studied using next generation sequencing (NGS) in order to interrogate the repertoire composition as well as changes in the distribution of receptor clones under different physiological and disease states. This type of analysis requires efficient and unambiguous clonotype assignment to a large number of NGS read sequences, including the identification of the incorporated V and J gene segments and the CDR3 sequence. Current tools have deficits with respect to performance, accuracy and documentation of their underlying algorithms and usage. Results : We present IMSEQ, a method to derive clonotype repertoires from NGS data with sophisticated routines for handling errors stemming from PCR and sequencing artefacts. The application can handle different kinds of input data originating from single- or paired-end sequencing in different configurations and is generic regarding the species and gene of interest. We have carefully evaluated our method with simulated and real world data and show that IMSEQ is superior to other tools with respect to its clonotyping as well as standalone error correction and runtime performance. Availability and implementation: IMSEQ was implemented in C++ using the SeqAn library for efficient sequence analysis. It is freely available under the GPLv2 open source license and can be downloaded at www.imtools.org . Supplementary information : Supplementary data are available at Bioinformatics online. Contact: lkuchenb@inf.fu-berlin.de or peter.robinson@charite.de

Description: Motivation: Interactions between amino acids are important determinants of the structure, stability and function of proteins. Several tools have been developed for the identification and analysis of such interactions in proteins based on the extensive studies carried out on high-resolution structures from Protein Data Bank (PDB). Although these tools allow users to identify and analyze interactions, analysis can only be performed on one structure at a time. This makes it difficult and time consuming to study the significance of these interactions on a large scale. Results: SpeeDB is a web-based tool for the identification of protein structures based on structural properties. SpeeDB queries are executed on all structures in the PDB at once, quickly enough for interactive use. SpeeDB includes standard queries based on published criteria for identifying various structures: disulphide bonds, catalytic triads and aromatic–aromatic, sulphur–aromatic, cation– and ionic interactions. Users can also construct custom queries in the user interface without any programming. Results can be downloaded in a Comma Separated Value (CSV) format for further analysis with other tools. Case studies presented in this article demonstrate how SpeeDB can be used to answer various biological questions. Analysis of human proteases revealed that disulphide bonds are the predominant type of interaction and are located close to the active site, where they promote substrate specificity. When comparing the two homologous G protein-coupled receptors and the two protein kinase paralogs analyzed, the differences in the types of interactions responsible for stability accounts for the differences in specificity and functionality of the structures. Availability and implementation: SpeeDB is available at http://www.parallelcomputing.ca as a web service. Contact: d@drobilla.net Supplementary Information: Supplementary data are available at Bioinformatics online.

Description: : Seq2pathway is an R/Python wrapper for pathway (or functional gene-set) analysis of genomic loci, adapted for advances in genome research. Seq2pathway associates the biological significance of genomic loci with their target transcripts and then summarizes the quantified values on the gene-level into pathway scores. It is designed to isolate systematic disturbances and common biological underpinnings from next-generation sequencing (NGS) data. Seq2pathway offers Bioconductor users enhanced capability in discovering collective pathway effects caused by both coding genes and cis-regulation of non-coding elements. Availability and implementation: The package is freely available at http://www.bioconductor.org/packages/release/bioc/html/seq2pathway.html . Contact : xyang2@uchicago.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Description: : Aggregation plots are frequently used to evaluate signal distributions at user-interested points in ChIP-Seq data analysis. agplus, a new and simple command-line tool, enables rapid and flexible generation of text tables tailored for aggregation plots from which users can easily design multiple groups based on user-definitions such as regulatory regions or transcription initiation sites. Availability and Implementation: This software is implemented in Ruby, supported on Linux and Mac OSX, and freely available at http://github.com/kazumits/agplus Contact: yohkawa@epigenetics.med.kyushu-u.ac.jp

Description: : We devise a novel inference algorithm to effectively solve the cancer progression model reconstruction problem. Our empirical analysis of the accuracy and convergence rate of our algorithm, CAncer PRogression Inference (CAPRI), shows that it outperforms the state-of-the-art algorithms addressing similar problems. Motivation: Several cancer-related genomic data have become available (e.g. The Cancer Genome Atlas , TCGA) typically involving hundreds of patients. At present, most of these data are aggregated in a cross-sectional fashion providing all measurements at the time of diagnosis. Our goal is to infer cancer ‘progression’ models from such data. These models are represented as directed acyclic graphs (DAGs) of collections of ‘selectivity’ relations, where a mutation in a gene A ‘selects’ for a later mutation in a gene B. Gaining insight into the structure of such progressions has the potential to improve both the stratification of patients and personalized therapy choices. Results: The CAPRI algorithm relies on a scoring method based on a probabilistic theory developed by Suppes, coupled with bootstrap and maximum likelihood inference. The resulting algorithm is efficient, achieves high accuracy and has good complexity, also, in terms of convergence properties. CAPRI performs especially well in the presence of noise in the data, and with limited sample sizes. Moreover CAPRI, in contrast to other approaches, robustly reconstructs different types of confluent trajectories despite irregularities in the data. We also report on an ongoing investigation using CAPRI to study atypical Chronic Myeloid Leukemia , in which we uncovered non trivial selectivity relations and exclusivity patterns among key genomic events. Availability and implementation: CAPRI is part of the TRanslational ONCOlogy R package and is freely available on the web at: http://bimib.disco.unimib.it/index.php/Tronco Contact: daniele.ramazzotti@disco.unimib.it Supplementary information: Supplementary data are available at Bioinformatics online.

Description: Motivation: Model organisms play critical roles in biomedical research of human diseases and drug development. An imperative task is to translate information/knowledge acquired from model organisms to humans. In this study, we address a trans-species learning problem: predicting human cell responses to diverse stimuli, based on the responses of rat cells treated with the same stimuli. Results: We hypothesized that rat and human cells share a common signal-encoding mechanism but employ different proteins to transmit signals, and we developed a bimodal deep belief network and a semi-restricted bimodal deep belief network to represent the common encoding mechanism and perform trans-species learning. These ‘deep learning’ models include hierarchically organized latent variables capable of capturing the statistical structures in the observed proteomic data in a distributed fashion. The results show that the models significantly outperform two current state-of-the-art classification algorithms. Our study demonstrated the potential of using deep hierarchical models to simulate cellular signaling systems. Availability and implementation: The software is available at the following URL: http://pubreview.dbmi.pitt.edu/TransSpeciesDeepLearning/ . The data are available through SBV IMPROVER website, https://www.sbvimprover.com/challenge-2/overview , upon publication of the report by the organizers. Contact : xinghua@pitt.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Description: Motivation: Identification of differentially expressed genes is an important step in extracting knowledge from gene expression profiling studies. The raw expression data from microarray and other high-throughput technologies is deposited into the Gene Expression Omnibus (GEO) and served as Simple Omnibus Format in Text (SOFT) files. However, to extract and analyze differentially expressed genes from GEO requires significant computational skills. Results: Here we introduce GEO2Enrichr, a browser extension for extracting differentially expressed gene sets from GEO and analyzing those sets with Enrichr, an independent gene set enrichment analysis tool containing over 70 000 annotated gene sets organized into 75 gene-set libraries. GEO2Enrichr adds JavaScript code to GEO web-pages; this code scrapes user selected accession numbers and metadata, and then, with one click, users can submit this information to a web-server application that downloads the SOFT files, parses, cleans and normalizes the data, identifies the differentially expressed genes, and then pipes the resulting gene lists to Enrichr for downstream functional analysis. GEO2Enrichr opens a new avenue for adding functionality to major bioinformatics resources such GEO by integrating tools and resources without the need for a plug-in architecture. Importantly, GEO2Enrichr helps researchers to quickly explore hypotheses with little technical overhead, lowering the barrier of entry for biologists by automating data processing steps needed for knowledge extraction from the major repository GEO. Availability and implementation: GEO2Enrichr is an open source tool, freely available for installation as browser extensions at the Chrome Web Store and FireFox Add-ons. Documentation and a browser independent web application can be found at http://amp.pharm.mssm.edu/g2e/ . Contact: avi.maayan@mssm.edu

Description: : We report the creation of Drug Signatures Database (DSigDB), a new gene set resource that relates drugs/compounds and their target genes, for gene set enrichment analysis (GSEA). DSigDB currently holds 22 527 gene sets, consists of 17 389 unique compounds covering 19 531 genes. We also developed an online DSigDB resource that allows users to search, view and download drugs/compounds and gene sets. DSigDB gene sets provide seamless integration to GSEA software for linking gene expressions with drugs/compounds for drug repurposing and translational research. Availability and implementation: DSigDB is freely available for non-commercial use at http://tanlab.ucdenver.edu/DSigDB . Supplementary information: Supplementary data are available at Bioinformatics online. Contact: aikchoon.tan@ucdenver.edu

Description: : We describe the implementation of the method introduced by Chambaz et al. in 2012. We also demonstrate its genome-wide application to the integrative search of new regions with strong association between DNA copy number and gene expression accounting for DNA methylation in breast cancers. Availability and implementation: An open-source R package tmle.npvi is available from CRAN ( http://cran.r-project.org/ ). Contact: pierre.neuvial@genopole.cnrs.fr

Description: Motivation: Genes with indispensable functions are identified as essential; however, the traditional gene-level studies of essentiality have several limitations. In this study, we characterized gene essentiality from a new perspective of protein domains, the independent structural or functional units of a polypeptide chain. Results: To identify such essential domains, we have developed an Expectation–Maximization (EM) algorithm-based Essential Domain Prediction (EDP) Model. With simulated datasets, the model provided convergent results given different initial values and offered accurate predictions even with noise. We then applied the EDP model to six microbial species and predicted 1879 domains to be essential in at least one species, ranging 10–23% in each species. The predicted essential domains were more conserved than either non-essential domains or essential genes. Comparing essential domains in prokaryotes and eukaryotes revealed an evolutionary distance consistent with that inferred from ribosomal RNA. When utilizing these essential domains to reproduce the annotation of essential genes, we received accurate results that suggest protein domains are more basic units for the essentiality of genes. Furthermore, we presented several examples to illustrate how the combination of essential and non-essential domains can lead to genes with divergent essentiality. In summary, we have described the first systematic analysis on gene essentiality on the level of domains. Contact: huilu.bioinfo@gmail.com or Long.Lu@cchmc.org Supplementary Information: Supplementary data are available at Bioinformatics online.

Description: Motivation: Information-theoretic and compositional analysis of biological sequences, in terms of k -mer dictionaries, has a well established role in genomic and proteomic studies. Much less so in epigenomics, although the role of k -mers in chromatin organization and nucleosome positioning is particularly relevant. Fundamental questions concerning the informational content and compositional structure of nucleosome favouring and disfavoring sequences with respect to their basic building blocks still remain open. Results: We present the first analysis on the role of k -mers in the composition of nucleosome enriched and depleted genomic regions (NER and NDR for short) that is: (i) exhaustive and within the bounds dictated by the information-theoretic content of the sample sets we use and (ii) informative for comparative epigenomics. We analize four different organisms and we propose a paradigmatic formalization of k -mer dictionaries, providing two different and complementary views of the k -mers involved in NER and NDR. The first extends well known studies in this area, its comparative nature being its major merit. The second, very novel, brings to light the rich variety of k -mers involved in influencing nucleosome positioning, for which an initial classification in terms of clusters is also provided. Although such a classification offers many insights, the following deserves to be singled-out: short poly(dA:dT) tracts are reported in the literature as fundamental for nucleosome depletion, however a global quantitative look reveals that their role is much less prominent than one would expect based on previous studies. Availability and implementation: Dictionaries, clusters and Supplementary Material are available online at http://math.unipa.it/rombo/epigenomics/ . Contact: simona.rombo@unipa.it Supplementary information: Supplementary data are available at Bioinformatics online.

Description: Motivation: The number of reported genetic variants is rapidly growing, empowered by ever faster accumulation of next-generation sequencing data. A major issue is comparability. Standards that address the combined problem of inaccurately predicted breakpoints and repeat-induced ambiguities are missing. This decisively lowers the quality of ‘consensus’ callsets and hampers the removal of duplicate entries in variant databases, which can have deleterious effects in downstream analyses. Results: We introduce a sound framework for comparison of deletions that captures both tool-induced inaccuracies and repeat-induced ambiguities. We present a maximum matching algorithm that outputs virtual duplicates among two sets of predictions/annotations. We demonstrate that our approach is clearly superior over ad hoc criteria, like overlap, and that it can reduce the redundancy among callsets substantially. We also identify large amounts of duplicate entries in the Database of Genomic Variants, which points out the immediate relevance of our approach. Availability and implementation: Implementation is open source and available from https://bitbucket.org/readdi/readdi Contact: roland.wittler@uni-bielefeld.de or t.marschall@mpi-inf.mpg.de Supplementary information: Supplementary data are available at Bioinformatics online.

Description: Motivation: Deep sequencing of clinical samples is now an established tool for the detection of infectious pathogens, with direct medical applications. The large amount of data generated produces an opportunity to detect species even at very low levels, provided that computational tools can effectively profile the relevant metagenomic communities. Data interpretation is complicated by the fact that short sequencing reads can match multiple organisms and by the lack of completeness of existing databases, in particular for viral pathogens. Here we present metaMix, a Bayesian mixture model framework for resolving complex metagenomic mixtures. We show that the use of parallel Monte Carlo Markov chains for the exploration of the species space enables the identification of the set of species most likely to contribute to the mixture. Results: We demonstrate the greater accuracy of metaMix compared with relevant methods, particularly for profiling complex communities consisting of several related species. We designed metaMix specifically for the analysis of deep transcriptome sequencing datasets, with a focus on viral pathogen detection; however, the principles are generally applicable to all types of metagenomic mixtures. Availability and implementation: metaMix is implemented as a user friendly R package, freely available on CRAN: http://cran.r-project.org/web/packages/metaMix Contact: sofia.morfopoulou.10@ucl.ac.uk Supplementary information: Supplementary data are available at Bionformatics online.

Description: Motivation: A detailed analysis of multidimensional NMR spectra of macromolecules requires the identification of individual resonances (peaks). This task can be tedious and time-consuming and often requires support by experienced users. Automated peak picking algorithms were introduced more than 25 years ago, but there are still major deficiencies/flaws that often prevent complete and error free peak picking of biological macromolecule spectra. The major challenges of automated peak picking algorithms is both the distinction of artifacts from real peaks particularly from those with irregular shapes and also picking peaks in spectral regions with overlapping resonances which are very hard to resolve by existing computer algorithms. In both of these cases a visual inspection approach could be more effective than a ‘blind’ algorithm. Results: We present a novel approach using computer vision (CV) methodology which could be better adapted to the problem of peak recognition. After suitable ‘training’ we successfully applied the CV algorithm to spectra of medium-sized soluble proteins up to molecular weights of 26 kDa and to a 130 kDa complex of a tetrameric membrane protein in detergent micelles. Our CV approach outperforms commonly used programs. With suitable training datasets the application of the presented method can be extended to automated peak picking in multidimensional spectra of nucleic acids or carbohydrates and adapted to solid-state NMR spectra. Availability and implementation: CV-Peak Picker is available upon request from the authors. Contact : gsw@mol.biol.ethz.ch ; michal.walczak@mol.biol.ethz.ch ; adam.gonczarek@pwr.edu.pl Supplementary information : Supplementary data are available at Bioinformatics online.

Description: : PsyGeNET (Psychiatric disorders and Genes association NETwork) is a knowledge platform for the exploratory analysis of psychiatric diseases and their associated genes. PsyGeNET is composed of a database and a web interface supporting data search, visualization, filtering and sharing. PsyGeNET integrates information from DisGeNET and data extracted from the literature by text mining, which has been curated by domain experts. It currently contains 2642 associations between 1271 genes and 37 psychiatric disease concepts. In its first release, PsyGeNET is focused on three psychiatric disorders: major depression, alcohol and cocaine use disorders. PsyGeNET represents a comprehensive, open access resource for the analysis of the molecular mechanisms underpinning psychiatric disorders and their comorbidities. Availability and implementation: The PysGeNET platform is freely available at http://www.psygenet.org/ . The PsyGeNET database is made available under the Open Database License ( http://opendatacommons.org/licenses/odbl/1.0/ ). Contact: lfurlong@imim.es Supplementary information : Supplementary data are available at Bioinformatics online.

Description: This paper presents the development of an adaptive state estimator and an output controller based on the attractive ellipsoid method (AEM) for a class of uncertain non-linear systems (quasi-Lipschitz with external perturbations). The proposed method guarantees that, under a specific persistent excitation condition, the controlled system trajectories converge to an ellipsoid having a smaller trace of the corresponding inverse ellipsoidal matrix comparing to classical AEM without adaptation. An underactuated vertical double pendulum is considered as an experimental illustrative example of the suggested approach.

Description: In this paper, non-linear time-varying systems with slowly varying parameters are considered. Using slowly varying control Lyapunov function and considering a cost function, a controller (with closed-form structure) is presented which guarantees asymptotic stability of the closed-loop non-linear slowly varying system. Moreover, the Hamilton–Jacobi–Bellman equation is analysed to show that the proposed controller is a sub-optimal controller and the response of the closed-loop system may be very close to its optimal solution. Additionally, in order to show the applicability of the proposed method, it is applied to the time-varying inertia pendulum, which is one of the famous benchmarks among the non-linear time-varying systems. Then, the efficiency of the designed controller is compared with that of a numerical optimal controller, which is called, ‘receding horizon generalization of point-wise min-norm controller’. Simulation results demonstrate the applicability and efficiency of the proposed method.

Description: This paper addresses the problem of designing a static output feedback sliding mode control algorithm for linear MIMO systems with mismatched parameter uncertainties and matched perturbations. Once the system is in the sliding mode, the proposed output-dependent sliding variable can guarantee robust stability of the closed-loop system. The existence condition of the sliding variable is determined by solving the coupled algebraic Riccati equations which are involved with the original system parameters. The developed control law having the min–max property can guarantee that the system states are globally driven to and are finally maintained on the sliding variable in finite time. The high-gain control problem can be effectively avoided. The feasibility of the proposed method is demonstrated by simulation results.

Description: The objective of this paper is to study the notion of regional observability of a distributed parameter system governed by semilinear hyperbolic equations. This original concept is interested in the reconstruction of the state only on a subregion of the boundary of the system evolution domain . We give definition and some properties of this notion and we show that under some hypothesis, the regional boundary observability is guarantied. We use the analytical case by sectorial property of the considered dynamic operator and converted to a fixed point problem, and we show that it is possible to reconstruct such a state on a desired subregion . This approach leads to interesting results which are performed through numerical example and simulations.

Description: This paper deals with the distributed robust output regulation problem for the multi-agent systems with general nonlinear dynamics and uncertainty parameters. Assume that the information of active leaders or environmental disturbances cannot be completely measurable for all other agents. By employing the centre manifold theory and characteristics of the agents’ communication, the nonlinear distributed dynamic feedback controllers are designed to make the agents asymptotically track the references or reject the disturbance signals, which are generated by the exosystem. Two numerical examples are provided to show the effectiveness of the main results.

Description: This paper investigates event-triggered leaderless and leader-following consensus problems of multi-agent systems under jointly connected topology. A state-dependent event condition is proposed for each agent, which only relies on its own state and the last sampled data of itself and its neighbours. Based on the Lyapunov technique and algebraic graph theory, the designed event-triggered control strategies are proved to solve the leaderless and leader-following consensus problems when the topology is jointly connected. Moreover, such strategies can exclude Zeno-behaviour. Simulations illustrate the effectiveness of the theoretical results.

Description: By the theory of Sturm–Liouville eigenvalue problems, it is shown that the stability of one-dimensional wave equations with variable coefficients coupled with an ordinary differential equation (ODE) system on general tree-shaped networks is equivalent to that of its subsystem (called the base system). Thus, it is proved that the coupled system can arrive at asymptotical stability, if, for every interior vertex of the subsystem, the spectra of any two edges joined one common interior vertex of the subsystem are disjoint. Especially, the coupled system with one fixed root is exponentially stable. In the end, a star with three edges and a tree-shaped networks with 10 edges are given to verify the theoretical results.

Description: This paper studies the H consensus problem of multi-agent systems by means of a simultaneous stabilization approach. It is shown that the H consensus design problem for n coupled agents can be equivalently characterized as a problem of the simultaneous H stabilization of n – 1 independent subsystems. A new consensus analysis condition is obtained by investigating the corresponding simultaneous H stabilization problem. Based upon the analysis condition, a necessary and sufficient condition is derived to guarantee the consensus of multi-agent systems with a prescribed H performance, where the controller gain matrix is not coupled with the Lyapunov matrices, but parametrized by a positive-definite matrix. Iterative convex optimization approaches are further developed to solve the synthesis condition and to choose the initial values. Finally, a numerical example is given to show the applicability of the results.

Description: This article is concerned with the controllability of damped second-order impulsive neutral integrodifferential systems with infinite delay in Banach spaces. A set of sufficient conditions for the controllability of such systems are formulated by using a fixed point analysis approach. In particular, the compactness condition on the cosine family of operators appearing in various literatures is not required here. At last, an example is given to illustrate the obtained result.

Description: We consider an approximation scheme using hybrid functions for solving time-delayed optimal control problems with terminal inequality constraints. Using a Páde approximation, the problem is first transformed into one without a time-delayed argument. A computational method based on hybrid functions in time-domain is then proposed for solving the obtained non-delay optimal control problem. Hybrid functions integral operational matrix and direct collocation method are utilized to find the approximated optimal trajectory and the optimal control law of the original problem. Numerical results are also given to demonstrate the efficiency of the method.

Description: This paper investigates the problem of D-stability performance of an L 2 point formation control system with parametric uncertainties. A Quasi linear parameter varying (QLPV) model is introduced for the L 2 point formation flying, based on which a nonlinear control strategy is developed via the polynomial eigenstructure assignment (PEA) approach. The designed controller can guarantee the closed-loop system with invariant performance over a wide range of conditions due to its special structure with varying parameters. The robust D-stability of the multiple input and multiple output (MIMO) system is also addressed. It is shown that the D-stability performance of the L 2 point formation control system with parametric uncertainties can be ensured by using PEA controller. Finally, a simulation of formation flying is performed to illustrate the effectiveness of the proposed control approach.