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: Huntington's disease (HD) is a hereditary neurodegenerative disorder characterized by brain atrophy particularly in striatum leading to personality changes, chorea and dementia. Glycogen synthase kinase-3 (GSK-3) is a serine/threonine kinase in the crossroad of many signaling pathways that is highly pleiotropic as it phosphorylates more than hundred substrates including structural, metabolic, and signaling proteins. Increased GSK-3 activity is believed to contribute to the pathogenesis of neurodegenerative diseases like Alzheimer's disease and GSK-3 inhibitors have been postulated as therapeutic agents for neurodegeneration. Regarding HD, GSK-3 inhibitors have shown beneficial effects in cell and invertebrate animal models but no evident efficacy in mouse models. Intriguingly, those studies were performed without interrogating GSK-3 level and activity in HD brain. Here we aim to explore the level and also the enzymatic activity of GSK-3 in the striatum and other less affected brain regions of HD patients and of the R6/1 mouse model to then elucidate the possible contribution of its alteration to HD pathogenesis by genetic manipulation in mice. We report a dramatic decrease in GSK-3 levels and activity in striatum and cortex of HD patients with similar results in the mouse model. Correction of the GSK-3 deficit in HD mice, by combining with transgenic mice with conditional GSK-3 expression, resulted in amelioration of their brain atrophy and behavioral motor and learning deficits. Thus, our results demonstrate that decreased brain GSK-3 contributes to HD neurological phenotype and open new therapeutic opportunities based on increasing GSK-3 activity or attenuating the harmful consequences of its decrease.

Description: Spinocerebellar ataxia type 6 (SCA6) is dominantly inherited neurodegenerative disease, caused by an expansion of CAG repeat encoding a polyglutamine (PolyQ) tract in the Ca v 2.1 voltage-gated calcium channel. Its key pathological features include selective degeneration of the cerebellar Purkinje cells (PCs), a common target for PolyQ-induced toxicity in various SCAs. Mutant Ca v 2.1 confers toxicity primarily through a toxic gain-of-function mechanism; however, its molecular basis remains elusive. Here, we studied the cerebellar gene expression patterns of young Sca6 -MPI 118Q/118Q knockin (KI) mice, which expressed mutant Ca v 2.1 from an endogenous locus and recapitulated many phenotypic features of human SCA6. Transcriptional signatures in the MPI 118Q/118Q mice were distinct from those in the Sca1 154Q/2Q mice, a faithful SCA1 KI mouse model. Temporal expression profiles of the candidate genes revealed that the up-regulation of genes associated with microglial activation was initiated before PC degeneration and was augmented as the disease progressed. Histological analysis of the MPI 118Q/118Q cerebellum showed the predominance of M1-like pro-inflammatory microglia and it was concomitant with elevated expression levels of tumor necrosis factor, interleukin-6, Toll-like receptor (TLR) 2 and 7. Genetic ablation of MyD88, a major adaptor protein conveying TLR signaling, altered expression patterns of M1/M2 microglial phenotypic markers in the MPI 118Q/118Q cerebellum. More importantly, it ameliorated PC loss and partially rescued motor impairments in the early disease phase. These results suggest that early neuroinflammatory response may play an important role in the pathogenesis of SCA6 and its modulation could pave the way for slowing the disease progression during the early stage of the disease.

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: 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: 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: Adaptor proteins (AP 1–5) are heterotetrameric complexes that facilitate specialized cargo sorting in vesicular-mediated trafficking. Mutations in AP5Z1 , encoding a subunit of the AP-5 complex, have been reported to cause hereditary spastic paraplegia (HSP), although their impact at the cellular level has not been assessed. Here we characterize three independent fibroblast lines derived from skin biopsies of patients harbouring nonsense mutations in AP5Z1 and presenting with spastic paraplegia accompanied by neuropathy, parkinsonism and/or cognitive impairment. In all three patient-derived lines, we show that there is complete loss of AP-5 protein and a reduction in the associated AP-5 µ5 protein. Using ultrastructural analysis, we show that these patient-derived lines consistently exhibit abundant multilamellar structures that are positive for markers of endolysosomes and are filled with aberrant storage material organized as exaggerated multilamellar whorls, striated belts and ‘fingerprint bodies’. This phenotype can be replicated in a HeLa cell culture model by siRNA knockdown of AP-5 . The cellular phenotype bears striking resemblance to features described in a number of lysosomal storage diseases (LSDs). Collectively, these findings reveal an emerging picture of the role of AP-5 in endosomal and lysosomal homeostasis, illuminates a potential pathomechanism that is relevant to the role of AP-5 in neurons and expands the understanding of recessive HSPs. Moreover, the resulting accumulation of storage material in endolysosomes leads us to propose that AP-5 deficiency represents a new type of LSDs.

Description: Cleft palate is a common birth defect in humans. Therefore, understanding the molecular genetics of palate development is important from both scientific and medical perspectives. Lhx6 and Lhx8 encode LIM homeodomain transcription factors, and inactivation of both genes in mice resulted in profound craniofacial defects including cleft secondary palate. The initial outgrowth of the palate was severely impaired in the mutant embryos, due to decreased cell proliferation. Through genome-wide transcriptional profiling, we discovered that p57 Kip2 ( Cdkn1c ), encoding a cell cycle inhibitor, was up-regulated in the prospective palate of Lhx6 –/– ;Lhx8 –/– mutants. p57 Kip2 has been linked to Beckwith–Wiedemann syndrome and IMAGe syndrome in humans, which are developmental disorders with increased incidents of palate defects among the patients. To determine the molecular mechanism underlying the regulation of p57 Kip2 by the Lhx genes, we combined chromatin immunoprecipitation, in silico search for transcription factor-binding motifs, and in vitro reporter assays with putative cis-regulatory elements. The results of these experiments indicated that LHX6 and LHX8 regulated p57 Kip2 via both direct and indirect mechanisms, with the latter mediated by Forkhead box (FOX) family transcription factors. Together, our findings uncovered a novel connection between the initiation of palate development and a cell cycle inhibitor via LHX. We propose a model in which Lhx6 and Lhx8 negatively regulate p57 Kip2 expression in the prospective palate area to allow adequate levels of cell proliferation and thereby promote normal palate development. This is the first report elucidating a molecular genetic pathway downstream of Lhx in palate development.

Description: Keratoconus is a degenerative eye condition which results from thinning of the cornea and causes vision distortion. Treatments such as ultraviolet (UV) cross-linking have proved effective for management of keratoconus when performed in early stages of the disease. The central corneal thickness (CCT) is a highly heritable endophenotype of keratoconus, and it is estimated that up to 95% of its phenotypic variance is due to genetics. Genome-wide association efforts of CCT have identified common variants (i.e. minor allele frequency (MAF) 〉5%). However, these studies typically ignore the large set of exonic variants whose MAF is usually low. In this study, we performed a CCT exome-wide association analysis in a sample of 1029 individuals from a population-based study in Western Australia. We identified a genome-wide significant exonic variant rs121908120 ( P = 6.63 x 10 –10 ) in WNT10A . This gene is 437 kb from a gene previously associated with CCT ( USP37 ). We showed in a conditional analysis that the WNT10A variant completely accounts for the signal previously seen at USP37 . We replicated our finding in independent samples from the Brisbane Adolescent Twin Study, Twin Eye Study in Tasmania and the Rotterdam Study. Further, we genotyped rs121908120 in 621 keratoconus cases and compared the frequency to a sample of 1680 unscreened controls from the Queensland Twin Registry. We found that rs121908120 increases the risk of keratoconus two times (odds ratio 2.03, P = 5.41 x 10 –5 ).

Description: Somatic cell cytokinesis was shown to involve the insertion of sphingolipids (SLs) to midbodies prior to abscission. Spermatogenic midbodies transform into stable intercellular bridges (ICBs) connecting clonal daughter cells in a syncytium. This process requires specialized SL structures. (1) Using high resolution-mass spectrometric imaging, we show in situ a biphasic pattern of SL synthesis with testis-specific anchors. This pattern correlates with and depends on ceramide synthase 3 (CerS3) localization in both, pachytene spermatocytes until completion of meiosis and elongating spermatids. (2) Blocking the pathways to germ cell-specific ceramides (CerS3-KO) and further to glycosphingolipids (glucosylceramide synthase-KO) in mice highlights the need for special SLs for spermatid ICB stability. In contrast to somatic mitosis these SLs require ultra-long polyunsaturated anchors with unique physico-chemical properties, which can only be provided by CerS3. Loss of these anchors causes enhanced apoptosis during meiosis, formation of multinuclear giant cells and spermatogenic arrest. Hence, testis-specific SLs, which we also link to CerS3 in human testis, are quintessential for male fertility.

Description: Therapy-responsive biomarkers are an important and unmet need in the muscular dystrophy field where new treatments are currently in clinical trials. By using a comprehensive high-resolution mass spectrometry approach and western blot validation, we found that two fragments of the myofibrillar structural protein myomesin-3 (MYOM3) are abnormally present in sera of Duchenne muscular dystrophy (DMD) patients, limb-girdle muscular dystrophy type 2D (LGMD2D) and their respective animal models. Levels of MYOM3 fragments were assayed in therapeutic model systems: (1) restoration of dystrophin expression by antisense oligonucleotide-mediated exon-skipping in mdx mice and (2) stable restoration of α-sarcoglycan expression in KO-SGCA mice by systemic injection of a viral vector. Following administration of the therapeutic agents MYOM3 was restored toward wild-type levels. In the LGMD model, where different doses of vector were used, MYOM3 restoration was dose-dependent. MYOM3 fragments showed lower inter-individual variability compared with the commonly used creatine kinase assay, and correlated better with the restoration of the dystrophin-associated protein complex and muscle force. These data suggest that the MYOM3 fragments hold promise for minimally invasive assessment of experimental therapies for DMD and other neuromuscular disorders.

Description: RNA interference (RNAi) offers a promising therapeutic approach for dominant genetic disorders that involve gain-of-function mechanisms. One candidate disease for RNAi therapy application is myotonic dystrophy type 1 (DM1), which results from toxicity of a mutant mRNA. DM1 is caused by expansion of a CTG repeat in the 3' UTR of the DMPK gene. The expression of DMPK mRNA containing an expanded CUG repeat (CUG exp ) leads to defects in RNA biogenesis and turnover. We designed miRNA-based RNAi hairpins to target the CUG exp mRNA in the human α-skeletal muscle actin long-repeat ( HSA LR ) mouse model of DM1. RNAi expression cassettes were delivered to HSA LR mice using recombinant adeno-associated viral (rAAV) vectors injected intravenously as a route to systemic gene therapy. Vector delivery significantly reduced disease pathology in muscles of the HSA LR mice, including a reduction in the CUG exp mRNA, a reduction in myotonic discharges, a shift toward adult pre-mRNA splicing patterns, reduced myofiber hypertrophy and a decrease in myonuclear foci containing the CUG exp mRNA. Significant reversal of hallmarks of DM1 in the rAAV RNAi-treated HSA LR mice indicate that defects characteristic of DM1 can be mitigated with a systemic RNAi approach targeting the nuclei of terminally differentiated myofibers. Efficient rAAV-mediated delivery of RNAi has the potential to provide a long-term therapy for DM1 and other dominant muscular dystrophies.

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: The heart is a muscle with high energy demands. Hence, most patients with mitochondrial disease produced by defects in the oxidative phosphorylation (OXPHOS) system are susceptible to cardiac involvement. The presentation of mitochondrial cardiomyopathy includes hypertrophic, dilated and left ventricular noncompaction, but the molecular mechanisms involved in cardiac impairment are unknown. One of the most frequent OXPHOS defects in humans frequently associated with cardiomyopathy is cytochrome c oxidase (COX) deficiency caused by mutations in COX assembly factors such as Sco1 and Sco2. To investigate the molecular mechanisms that underlie the cardiomyopathy associated with Sco deficiency, we have heart specifically interfered scox expression, the single Drosophila Sco orthologue. Cardiac-specific knockdown of scox reduces fly lifespan, and it severely compromises heart function and structure, producing dilated cardiomyopathy. Cardiomyocytes with low levels of scox have a significant reduction in COX activity and they undergo a metabolic switch from OXPHOS to glycolysis, mimicking the clinical features found in patients harbouring Sco mutations. The major cardiac defects observed are produced by a significant increase in apoptosis, which is dp53-dependent. Genetic and molecular evidence strongly suggest that dp53 is directly involved in the development of the cardiomyopathy induced by scox deficiency. Remarkably, apoptosis is enhanced in the muscle and liver of Sco2 knock-out mice, clearly suggesting that cell death is a key feature of the COX deficiencies produced by mutations in Sco genes in humans.

Description: Understanding the regulatory landscape of the human genome is a central question in complex trait genetics. Most single-nucleotide polymorphisms (SNPs) associated with cancer risk lie in non-protein-coding regions, implicating regulatory DNA elements as functional targets of susceptibility variants. Here, we describe genome-wide annotation of regions of open chromatin and histone modification in fallopian tube and ovarian surface epithelial cells (FTSECs, OSECs), the debated cellular origins of high-grade serous ovarian cancers (HGSOCs) and in endometriosis epithelial cells (EECs), the likely precursor of clear cell ovarian carcinomas (CCOCs). The regulatory architecture of these cell types was compared with normal human mammary epithelial cells and LNCaP prostate cancer cells. We observed similar positional patterns of global enhancer signatures across the three different ovarian cancer precursor cell types, and evidence of tissue-specific regulatory signatures compared to non-gynecological cell types. We found significant enrichment for risk-associated SNPs intersecting regulatory biofeatures at 17 known HGSOC susceptibility loci in FTSECs ( P = 3.8 x 10 –30 ), OSECs ( P = 2.4 x 10 –23 ) and HMECs ( P = 6.7 x 10 –15 ) but not for EECs ( P = 0.45) or LNCaP cells ( P = 0.88). Hierarchical clustering of risk SNPs conditioned on the six different cell types indicates FTSECs and OSECs are highly related (96% of samples using multi-scale bootstrapping) suggesting both cell types may be precursors of HGSOC. These data represent the first description of regulatory catalogues of normal precursor cells for different ovarian cancer subtypes, and provide unique insights into the tissue specific regulatory variation with respect to the likely functional targets of germline genetic susceptibility variants for ovarian cancer.

Description: The gene mapt codes for the microtubule-associated protein Tau. The R406W amino acid substitution in Tau is associated with frontotemporal dementia with parkinsonism linked to chromosome 17 (FTDP-17) characterized by Tau-positive filamentous inclusions. These filamentous Tau inclusions are present in a group of neurodegenerative diseases known as tauopathies, including Alzheimer's disease (AD). To gain more insights into the pathomechanism of tauopathies, we performed an RNAi-based large-scale screen in Drosophila melanogaster to identify genetic modifiers of Tau[R406W]-induced toxicity. A collection of RNAi lines, putatively silencing more than 7000 genes, was screened for the ability to modify Tau[R406W]-induced toxicity in vivo . This collection covered more than 50% of all protein coding fly genes and more than 90% of all fly genes known to have a human ortholog. Hereby, we identified 62 genes that, when silenced by RNAi, modified Tau-induced toxicity specifically. Among these 62 modifiers were three subunits of the Dynein/Dynactin complex. Analysis on segmental nerves of fly larvae showed that pan neural Tau[R406W] expression and concomitant silencing of Dynein/Dynactin complex members synergistically caused strong pathological changes within the axonal compartment, but only minor changes at synapses. At the larval stage, these alterations did not cause locomotion deficits, but became evident in adult flies. Our data suggest that Tau-induced detrimental effects most likely originate from axonal rather than synaptic dysfunction and that impaired retrograde transport intensifies detrimental effects of Tau in axons. In conclusion, our findings contribute to the elucidation of disease mechanisms in tauopathies like FTDP-17 or AD.

Description: Retinitis pigmentosa (RP), the most common form of inherited retinal degeneration, is clinically and genetically heterogeneous and can appear as syndromic or non-syndromic. Mucopolysaccharidosis type IIIC (MPS IIIC) is a lethal disorder, caused by mutations in the heparan-alpha-glucosaminide N-acetyltransferase ( HGSNAT ) gene and characterized by progressive neurological deterioration, with retinal degeneration as a prominent feature. We identified HGSNAT mutations in six patients with non-syndromic RP. Whole exome sequencing (WES) in an Ashkenazi Jewish Israeli RP patient revealed a novel homozygous HGSNAT variant, c.370A〉T, which leads to partial skipping of exon 3. Screening of 66 Ashkenazi RP index cases revealed an additional family with two siblings homozygous for c.370A〉T. WES in three Dutch siblings with RP revealed a complex HGSNAT variant, c.[398G〉C; 1843G〉A] on one allele, and c.1843G〉A on the other allele. HGSNAT activity levels in blood leukocytes of patients were reduced compared with healthy controls, but usually higher than those in MPS IIIC patients. All patients were diagnosed with non-syndromic RP and did not exhibit neurological deterioration, or any phenotypic features consistent with MPS IIIC. Furthermore, four of the patients were over 60 years old, exceeding by far the life expectancy of MPS IIIC patients. HGSNAT is highly expressed in the mouse retina, and we hypothesize that the retina requires higher HGSNAT activity to maintain proper function, compared with other tissues associated with MPS IIIC, such as the brain. This report broadens the spectrum of phenotypes associated with HGSNAT mutations and highlights the critical function of HGSNAT in the human retina.

Description: Increased age, BMI and HbA1c levels are risk factors for several non-communicable diseases. However, the impact of these factors on the genome-wide DNA methylation pattern in human adipose tissue remains unknown. We analyzed the DNA methylation of ~480 000 sites in human adipose tissue from 96 males and 94 females and related methylation to age, BMI and HbA1c. We also compared epigenetic signatures in adipose tissue and blood. Age was significantly associated with both altered DNA methylation and expression of 1050 genes (e.g. FHL2 , NOX4 and PLG ). Interestingly, many reported epigenetic biomarkers of aging in blood, including ELOVL2 , FHL2 , KLF14 and GLRA1 , also showed significant correlations between adipose tissue DNA methylation and age in our study. The most significant association between age and adipose tissue DNA methylation was found upstream of ELOVL2 . We identified 2825 genes (e.g. FTO , ITIH5 , CCL18 , MTCH2 , IRS1 and SPP1 ) where both DNA methylation and expression correlated with BMI. Methylation at previously reported HIF3A sites correlated significantly with BMI in females only. HbA1c (range 28–46 mmol/mol) correlated significantly with the methylation of 711 sites, annotated to, for example, RAB37 , TICAM1 and HLA-DPB1 . Pathway analyses demonstrated that methylation levels associated with age and BMI are overrepresented among genes involved in cancer, type 2 diabetes and cardiovascular disease. Our results highlight the impact of age, BMI and HbA1c on epigenetic variation of candidate genes for obesity, type 2 diabetes and cancer in human adipose tissue. Importantly, we demonstrate that epigenetic biomarkers in blood can mirror age-related epigenetic signatures in target tissues for metabolic diseases such as adipose tissue.

Description: Interstitial lung disease, nephrotic syndrome and junctional epidermolysis bullosa is an autosomal recessive multiorgan disorder caused by mutations in the gene for the integrin α3 subunit ( ITGA3 ). The full spectrum of manifestations and genotype–phenotype correlations is still poorly characterized. Here, we uncovered the disease-causing role and the molecular mechanisms underlying a homozygous ITGA3 mutation leading to the single amino acid substitution, p.R463W. The patient suffered from respiratory distress and episodes of cyanosis with onset in the first week of life and had a nephrotic syndrome. Although there was no clinical evidence for cutaneous fragility, the analysis of a skin sample and of skin epithelial cells enabled the direct assessment of the authentic mutant protein. We show that the mutation altered the conformation of the extracellular β-propeller domain of the integrin α3 subunit preventing correct processing of N-linked oligosaccharides, heterodimerization with β1 integrin and maturation through cleavage into heavy and light chains in the Golgi. Confocal microscopy demonstrated that the mutant protein accumulated intracellularly, but it was not present in focal adhesions or on the cell membrane as shown by flow cytometry. These findings highlight that single amino acid changes in the integrin α3 subunit may crucially alter the structure and complex processing of this integrin, completely preventing its functionality. The present report also underscores that ITGA3 mutations may account for atypical cases solely with early onset respiratory and renal involvement.

Description: Gestational age (GA) and birth weight have been implicated in the determination of long-term health. It has been hypothesized that changes in DNA methylation may mediate these long-term effects. We obtained DNA methylation profiles from cord blood and peripheral blood at ages 7 and 17 in the same children from the Avon Longitudinal Study of Parents and Children. Repeated-measures data were used to investigate changes in birth-related methylation during childhood and adolescence. Ten developmental phenotypes (e.g. height) were analysed to identify possible mediation of health effects by DNA methylation. In cord blood, methylation at 224 CpG sites was found to be associated with GA and 23 CpG sites with birth weight. Methylation changed in the majority of these sites over time, but neither birth characteristic was strongly associated with methylation at age 7 or 17 (using a conservative correction for multiple testing of P 〈 1.03 x 10 –7 ), suggesting resolution of differential methylation by early childhood. Associations were observed between birth weight-associated CpG sites and phenotypic characteristics in childhood. One strong association involved birth weight, methylation of a CpG site proximal to the NFIX locus and bone mineral density at age 17. Analysis of serial methylation from birth to adolescence provided evidence for a lack of persistence of methylation differences beyond early childhood. Sites associated with birth weight were linked to developmental genes and have methylation levels which are associated with developmental phenotypes. Replication and interrogation of causal relationships are needed to substantiate whether methylation differences at birth influence the association between birth weight and development.

Description: Fragile X-associated tremor/ataxia syndrome (FXTAS) is a late-onset neurodegenerative disorder affecting carriers of the fragile X-premutation, who have an expanded CGG repeat in the 5'-UTR of the FMR1 gene. FXTAS is characterized by progressive development of intention tremor, ataxia, parkinsonism and neuropsychological problems. The disease is thought to be caused by a toxic RNA gain-of-function mechanism, and the major hallmark of the disease is ubiquitin-positive intranuclear inclusions in neurons and astrocytes. We have developed a new transgenic mouse model in which we can induce expression of an expanded repeat in the brain upon doxycycline (dox) exposure (i.e. Tet-On mice). This Tet-On model makes use of the PrP-rtTA driver and allows us to study disease progression and possibilities of reversibility. In these mice, 8 weeks of dox exposure was sufficient to induce the formation of ubiquitin-positive intranuclear inclusions, which also stain positive for the RAN translation product FMRpolyG. Formation of these inclusions is reversible after stopping expression of the expanded CGG RNA at an early developmental stage. Furthermore, we observed a deficit in the compensatory eye movements of mice with inclusions, a functional phenotype that could be reduced by stopping expression of the expanded CGG RNA early in the disease development. Taken together, this study shows, for the first time, the potential of disease reversibility and suggests that early intervention might be beneficial for FXTAS patients.

Description: Mutations affecting specific splicing regulatory elements offer suitable models to better understand their interplay and to devise therapeutic strategies. Here we characterize a meaningful splicing model in which numerous Hemophilia B-causing mutations, either missense or at the donor splice site (5'ss) of coagulation F9 exon 2, promote aberrant splicing by inducing the usage of a strong exonic cryptic 5'ss. Splicing assays with natural and artificial F9 variants indicated that the cryptic 5'ss is regulated, among a network of regulatory elements, by an exonic splicing silencer (ESS). This finding and the comparative analysis of the F9 sequence across species showing that the cryptic 5'ss is always paralleled by the conserved ESS support a compensatory mechanism aimed at minimizing unproductive splicing. To recover splicing we tested antisense oligoribonucleotides masking the cryptic 5'ss, which were effective on exonic changes but promoted exon 2 skipping in the presence of mutations at the authentic 5'ss. On the other hand, we observed a very poor correction effect by small nuclear RNA U1 (U1snRNA) variants with increased or perfect complementarity to the defective 5'ss, a strategy previously exploited to rescue splicing. Noticeably, the combination of the mutant-specific U1snRNAs with antisense oligonucleotides produced appreciable amounts of correctly spliced transcripts (from 0 to 20–40%) from several mutants of the exon 2 5'ss. Based on the evidence of an altered interplay among ESS, cryptic and the authentic 5'ss as a disease-causing mechanism, we provide novel experimental insights into the combinatorial correction activity of antisense molecules and compensatory U1snRNAs.

Description: Facioscapulohumeral muscular dystrophy (FSHD) is caused by the aberrant expression of the DUX4 transcription factor in skeletal muscle. The DUX4 retrogene is encoded in the D4Z4 macrosatellite repeat array, and smaller array size or a mutation in the SMCHD1 gene results in inefficient epigenetic repression of DUX4 in skeletal muscle, causing FSHD1 and FSHD2, respectively. Previously we showed that the entire D4Z4 repeat is bi-directionally transcribed with the generation of small si- or miRNA-like fragments and suggested that these might suppress DUX4 expression through the endogenous RNAi pathway. Here we show that exogenous siRNA targeting the region upstream of the DUX4 transcription start site suppressed DUX4 mRNA expression and increased both H3K9 methylation and AGO2 recruitment. In contrast, similarly targeted MOE-gapmer antisense oligonucleotides that degrade RNA but do not engage the RNAi pathway did not repress DUX4 expression. In addition, knockdown of DICER or AGO2 using either siRNA or MOE-gapmer chemistries resulted in the induction of DUX4 expression in control muscle cells that normally do not express DUX4 , indicating that the endogenous RNAi pathway is necessary to maintain repression of DUX4 in control muscle cells. Together these data demonstrate a role of the endogenous RNAi pathway in repeat-mediated epigenetic repression of the D4Z4 macrosatellite repeat, and show that enhancing the activity of this pathway by supplying exogenous siRNA oligonucleotides represents a potential therapeutic approach to silencing DUX4 in FSHD.

Description: Overgrowth syndromes comprise a group of heterogeneous disorders characterised by excessive growth parameters, often in association with intellectual disability. To identify new causes of human overgrowth, we have been undertaking trio-based exome sequencing studies in overgrowth patients and their unaffected parents. Prioritisation of functionally relevant genes with multiple unique de novo mutations revealed four mutations in protein phosphatase 2A (PP2A) regulatory subunit B family genes protein phosphatase 2, regulatory Subunit B’, beta (PPP2R5B) ; protein phosphatase 2, regulatory Subunit B’, gamma (PPP2R5C) ; and protein phosphatase 2, regulatory Subunit B’, delta (PPP2R5D). This observation in 3 related genes in 111 individuals with a similar phenotype is greatly in excess of the expected number, as determined from gene-specific de novo mutation rates ( P = 1.43 x 10 –10 ). Analysis of exome-sequencing data from a follow-up series of overgrowth probands identified a further pathogenic mutation, bringing the total number of affected individuals to 5. Heterozygotes shared similar phenotypic features including increased height, increased head circumference and intellectual disability. The mutations clustered within a region of nine amino acid residues in the aligned protein sequences ( P = 1.6 x 10 –5 ). We mapped the mutations onto the crystal structure of the PP2A holoenzyme complex to predict their molecular and functional consequences. These studies suggest that the mutations may affect substrate binding, thus perturbing the ability of PP2A to dephosphorylate particular protein substrates. PP2A is a major negative regulator of v-akt murine thymoma viral oncogene homolog 1 (AKT). Thus, our data further expand the list of genes encoding components of the phosphatidylinositol-4,5-bisphosphate 3-kinase (PI3K)/AKT signalling cascade that are disrupted in human overgrowth conditions.

Description: Miles–Carpenter syndrome (MCS) was described in 1991 as an XLID syndrome with fingertip arches and contractures and mapped to proximal Xq. Patients had microcephaly, short stature, mild spasticity, thoracic scoliosis, hyperextendable MCP joints, rocker-bottom feet, hyperextended elbows and knees. A mutation, p.L66H, in ZC4H2 , was identified in a XLID re-sequencing project. Additional screening of linked families and next generation sequencing of XLID families identified three ZC4H2 mutations: p.R18K, p.R213W and p.V75in15aa. The families shared some relevant clinical features. In silico modeling of the mutant proteins indicated all alterations would destabilize the protein. Knockout mutations in zc4h2 were created in zebrafish and homozygous mutant larvae exhibited abnormal swimming, increased twitching, defective eye movement and pectoral fin contractures. Because several of the behavioral defects were consistent with hyperactivity, we examined the underlying neuronal defects and found that sensory neurons and motoneurons appeared normal. However, we observed a striking reduction in GABAergic interneurons. Analysis of cell-type-specific markers showed a specific loss of V2 interneurons in the brain and spinal cord, likely arising from mis-specification of neural progenitors. Injected human wt ZC4H2 rescued the mutant phenotype. Mutant zebrafish injected with human p.L66H or p.R213W mRNA failed to be rescued, while the p.R18K mRNA was able to rescue the interneuron defect. Our findings clearly support ZC4H2 as a novel XLID gene with a required function in interneuron development. Loss of function of ZC4H2 thus likely results in altered connectivity of many brain and spinal circuits.

Description: Alterations in oxidative metabolism are considered to be one of the major contributors to Huntington's disease (HD) pathogenesis. However, existing data about oxidative metabolism in HD are contradictory. Here, we investigated the effect of mutant huntingtin (mHtt) on oxidative metabolism in YAC128 mice. Both mHtt and wild-type huntingtin (Htt) were associated with mitochondria and the amount of bound Htt was four-times higher than the amount of bound mHtt. Percoll gradient-purified brain synaptic and non-synaptic mitochondria as well as unpurified brain, liver and heart mitochondria, isolated from 2- and 10-month-old YAC128 mice and age-matched WT littermates had similar respiratory rates. There was no difference in mitochondrial membrane potential or ADP and ATP levels. Expression of selected nuclear-encoded mitochondrial proteins in 2- and 10-month-old YAC128 and WT mice was similar. Cultured striatal and cortical neurons from YAC128 and WT mice had similar respiratory and glycolytic activities as measured with Seahorse XF24 analyzer in medium containing 10 m m glucose and 15 m m pyruvate. In the medium with 2.5 m m glucose, YAC128 striatal neurons had similar respiration, but slightly lower glycolytic activity. Striatal neurons had lower maximal respiration compared with cortical neurons. In vivo experiments with YAC128 and WT mice showed similar O 2 consumption, CO 2 release, physical activity, food consumption and fasted blood glucose. However, YAC128 mice were heavier and had more body fat compared with WT mice. Overall, our data argue against respiratory deficiency in YAC128 mice and, consequently, suggest that mitochondrial respiratory dysfunction is not essential for HD pathogenesis.

Description: Leucine-rich repeat kinase 2 (LRRK2) is the causative molecule of the autosomal dominant hereditary form of Parkinson's disease (PD), PARK8, which was originally defined in a study of a Japanese family (the Sagamihara family) harboring the I2020T mutation in the kinase domain. Although a number of reported studies have focused on cell death mediated by mutant LRRK2, details of the pathogenetic effect of LRRK2 still remain to be elucidated. In the present study, to elucidate the mechanism of neurodegeneration in PD caused by LRRK2, we generated induced pluripotent stem cells (iPSC) derived from fibroblasts of PD patients with I2020T LRRK2 in the Sagamihara family. We found that I2020T mutant LRRK2 iPSC-derived neurons released less dopamine than control-iPSC-derived neurons. Furthermore, we demonstrated that patient iPSC-derived neurons had a lower phospho-AKT level than control-iPSC-derived neurons, and that the former showed an increased incidence of apoptosis relative to the controls. Interestingly, patient iPSC-derived neurons exhibited activation of glycogen synthase kinase-3β (GSK-3β) and high Tau phosphorylation. In addition, the postmortem brain of the patient from whom the iPSC had been established exhibited deposition of neurofibrillary tangles as well as increased Tau phosphorylation in neurons. These results suggest that I2020T LRRK2-iPSC could be a promising new tool for reproducing the pathology of PD in the brain caused by the I2020T mutation, and applicable as a model in studies of targeted therapeutics.

Description: SOX10 is a transcription factor with well-known functions in neural crest and oligodendrocyte development. Mutations in SOX10 were first associated with Waardenburg–Hirschsprung disease (WS4; deafness, pigmentation defects and intestinal aganglionosis). However, variable phenotypes that extend beyond the WS4 definition are now reported. The neurological phenotypes associated with some truncating mutations are suggested to be the result of escape from the nonsense-mediated mRNA decay pathway; but, to date, no mechanism has been suggested for missense mutations, of which approximately 20 have now been reported, with about half of the latter shown to be redistributed to nuclear bodies of undetermined nature and function in vitro . Here, we report that p54NRB, which plays a crucial role in the regulation of gene expression during many cellular processes including differentiation, interacts synergistically with SOX10 to regulate several target genes. Interestingly, this paraspeckle protein, as well as two other members of the Drosophila behavior human splicing (DBHS) protein family, co-localize with SOX10 mutants in nuclear bodies, suggesting the possible paraspeckle nature of these foci or re-localization of the DBHS members to other subnuclear compartments. Remarkably, the co-transfection of wild-type and mutant SOX10 constructs led to the sequestration of wild-type protein in mutant-induced foci. In contrast to mutants presenting with additional cytoplasmic re-localization, those exclusively found in the nucleus alter synergistic activity between SOX10 and p54NRB. We propose that such a dominant negative effect may contribute to or be at the origin of the unique progressive and severe neurological phenotype observed in affected patients.

Description: Huntington's disease (HD) is a hereditary neurodegenerative disorder characterized by motor and cognitive impairments, involving striatum, cortex and hippocampus. Synaptic and memory dysfunction in HD mouse models have been related to low levels of brain-derived neurotrophic factor (BDNF) and imbalance between TrkB and p75 NTR receptors. In addition, astrocyte over-activation has also been suggested to contribute to HD cognitive deficits. Fingolimod (FTY720), a modulator of sphingosine-1 phosphate (S1P) receptors, has been shown to increase BDNF levels and to reduce astrogliosis, proving its potential to regulate trophic support and inflammatory response. In this view, we have investigated whether FTY720 improves synaptic plasticity and memory in the R6/1 mouse model of HD, through regulation of BDNF signaling and astroglial reactivity. Chronic administration of FTY720 from pre-symptomatic stages ameliorated long-term memory deficits and dendritic spine loss in CA1 hippocampal neurons from R6/1 mice. Furthermore, FTY720 delivery prevented astrogliosis and over-activation of nuclear factor kappa beta (NF-B) signaling in the R6/1 hippocampus, reducing tumor necrosis factor alpha (TNFα) and induced nitric oxide synthase (iNOS) levels. TNFα decrease correlated with the normalization of p75 NTR expression in the hippocampus of FTY720-treated R6/1 mice, thus preventing p75 NTR /TrkB imbalance. In addition, FTY720 increased cAMP levels and promoted phosphorylation of CREB and RhoA in the hippocampus of R6/1 mice, further supporting its role in the enhancement of synaptic plasticity. Our findings provide new insights into the mechanism of action of FTY720 and reveal a novel therapeutic strategy to treat memory deficits in HD.

Description: DDX11 was recently identified as a cause of Warsaw breakage syndrome (WABS). However, the functional mechanism of DDX11 and the contribution of clinically described mutations to the pathogenesis of WABS are elusive. Here, we show that DDX11 is a novel nucleolar protein that preferentially binds to hypomethylated active ribosomal DNA (rDNA) gene loci, where it interacts with upstream binding factor (UBF) and the RNA polymerase I (Pol I). DDX11 knockdown changed the epigenetic state of rDNA loci from euchromatic structures to more heterochromatic structures, reduced the activity of UBF, decreased the recruitment of UBF and RPA194 (a subunit of Pol I) to rDNA promoter, suppressed rRNA transcription and thereby inhibited growth and proliferation of HeLa cells. Importantly, two indentified WABS-derived mutants, R263Q and K897del, and a Fe–S deletion construct demonstrated significantly reduced binding abilities to rDNA promoters and lowered DNA-dependent ATPase activities compared with wild-type DDX11. Knockdown of the zebrafish ortholog of human DDX11 by morpholinos resulted in growth retardation and vertebral and craniofacial malformations in zebrafish, concomitant with the changes in histone epigenetic modifications at rDNA loci, the reduction of Pol I recruitment to the rDNA promoter and a significant decrease in nascent pre-RNA levels. These growth disruptions in zebrafish in response to DDX11 reduction showed similarities to the clinically described developmental abnormalities found in WABS patients for the first time in any vertebrate. Thus, our results indicate that DDX11 functions as a positive regulator of rRNA transcription and provides a novel insight into the pathogenesis of WABS.

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: Genome-wide association studies (GWAS) have identified several common loci contributing to non-obstructive azoospermia (NOA). However, a substantial fraction of NOA heritability remains undefined, especially those low-frequency [defined here as having a minor allele frequency (MAF) between 0.5 and 5%] and rare (MAF below 0.5%) variants. Here, we performed a 3-stage exome-wide association study in Han Chinese men to evaluate the role of low-frequency or rare germline variants in NOA development. The discovery stage included 962 NOA cases and 1348 healthy male controls genotyped by exome chips and was followed by a 2-stage replication with an additional 2168 cases and 5248 controls. We identified three low-frequency variants located at 6p22.2 (rs2298090 in HIST1H1E encoding p.Lys152Arg: OR = 0.30, P = 2.40 x 10 –16 ) and 6p21.33 (rs200847762 in FKBPL encoding p.Pro137Leu: OR = 0.11, P = 3.77 x 10 –16 ; rs11754464 in MSH5 : OR = 1.78, P = 3.71 x 10 –7 ) associated with NOA risk after Bonferroni correction. In summary, we report an instance of newly identified signals for NOA risk in genes previously undetected through GWAS on 6p22.2–6p21.33 in a Chinese population and highlight the role of low-frequency variants with a large effect in the process of spermatogenesis.

Description: Loss-of-function mutations in the X-linked gene Methyl-CpG-binding protein 2 ( MECP2 ) cause a devastating pediatric neurological disorder called Rett syndrome. In males, these mutations typically result in severe neonatal encephalopathy and early lethality. On the other hand, owing to expression of the normal allele in ~50% of cells, females do not suffer encephalopathy but instead develop Rett syndrome. Typically females with Rett syndrome exhibit a delayed onset of neurologic dysfunction that manifests around the child's first birthday and progresses over the next few years. Features of this disorder include loss of acquired language and motor skills, intellectual impairment and hand stereotypies. The developmental regression observed in patients with Rett syndrome arises from altered neuronal function and is not the result of neurodegeneration. Maintenance of an appropriate level of MeCP2 appears integral to the function of healthy neurons as patients with increased levels of MeCP2, owing to duplication of the Xq28 region encompassing the MECP2 locus, also present with intellectual disability and progressive neurologic symptoms. Despite major efforts over the past two decades to elucidate the molecular functions of MeCP2, the mechanisms underlying the delayed appearance of symptoms remain unclear. In this review, we will highlight recent findings that have expanded our knowledge of MeCP2's functions, and we will discuss how epigenetic regulation, chromatin organization and circuit dynamics may contribute to the postnatal onset of Rett syndrome.

Description: Motivation: Chromatin Interaction Analysis by Paired-End Tag sequencing (ChIA-PET) is an established method for detecting genome-wide looping interactions at high resolution. Current ChIA-PET analysis software packages either fail to correct for non-specific interactions due to genomic proximity or only address a fraction of the steps required for data processing. We present Mango, a complete ChIA-PET data analysis pipeline that provides statistical confidence estimates for interactions and corrects for major sources of bias including differential peak enrichment and genomic proximity. Results: Comparison to the existing software packages, ChIA-PET Tool and ChiaSig revealed that Mango interactions exhibit much better agreement with high-resolution Hi-C data. Importantly, Mango executes all steps required for processing ChIA-PET datasets, whereas ChiaSig only completes 20% of the required steps. Application of Mango to multiple available ChIA-PET datasets permitted the independent rediscovery of known trends in chromatin loops including enrichment of CTCF, RAD21, SMC3 and ZNF143 at the anchor regions of interactions and strong bias for convergent CTCF motifs. Availability and implementation: Mango is open source and distributed through github at https://github.com/dphansti/mango . Contact: mpsnyder@standford.edu Supplementary information : Supplementary data are available at Bioinformatics online.

Description: Motivation: To increase the signal resolution for large-scale meta-analyses of genome-wide association studies, genotypes at unmeasured single nucleotide polymorphisms (SNPs) are commonly imputed using large multi-ethnic reference panels. However, the ever increasing size and ethnic diversity of both reference panels and cohorts makes genotype imputation computationally challenging for moderately sized computer clusters. Moreover, genotype imputation requires subject-level genetic data, which unlike summary statistics provided by virtually all studies, is not publicly available. While there are much less demanding methods which avoid the genotype imputation step by directly imputing SNP statistics, e.g. D irectly I mputing summary ST atistics (DIST) proposed by our group, their implicit assumptions make them applicable only to ethnically homogeneous cohorts. Results: To decrease computational and access requirements for the analysis of cosmopolitan cohorts, we propose DISTMIX, which extends DIST capabilities to the analysis of mixed ethnicity cohorts. The method uses a relevant reference panel to directly impute unmeasured SNP statistics based only on statistics at measured SNPs and estimated/user-specified ethnic proportions. Simulations show that the proposed method adequately controls the Type I error rates. The 1000 Genomes panel imputation of summary statistics from the ethnically diverse Psychiatric Genetic Consortium Schizophrenia Phase 2 suggests that, when compared to genotype imputation methods, DISTMIX offers comparable imputation accuracy for only a fraction of computational resources. Availability and implementation: DISTMIX software, its reference population data, and usage examples are publicly available at http://code.google.com/p/distmix . Contact: dlee4@vcu.edu Supplementary information: Supplementary Data are available at Bioinformatics online.

Description: Motivation: Finding somatic mutations from massively parallel sequencing data is becoming a standard process in genome-based biomedical studies. There are a number of robust methods developed for detecting somatic single nucleotide variations However, detection of somatic copy number alteration has been substantially less explored and remains vulnerable to frequently raised sampling issues: low frequency in cell population and absence of the matched control samples. Results: We developed a novel computational method SoloDel that accurately classifies low-frequent somatic deletions from germline ones with or without matched control samples. We first constructed a probabilistic, somatic mutation progression model that describes the occurrence and propagation of the event in the cellular lineage of the sample. We then built a Gaussian mixture model to represent the mixed population of somatic and germline deletions. Parameters of the mixture model could be estimated using the expectation-maximization algorithm with the observed distribution of read-depth ratios at the points of discordant-read based initial deletion calls. Combined with conventional structural variation caller, SoloDel greatly increased the accuracy in classifying somatic mutations. Even without control, SoloDel maintained a comparable performance in a wide range of mutated subpopulation size (10–70%). SoloDel could also successfully recall experimentally validated somatic deletions from previously reported neuropsychiatric whole-genome sequencing data. Availability and implementation: Java-based implementation of the method is available at http://sourceforge.net/projects/solodel/ Contact: swkim@yuhs.ac or dhlee@biosoft.kaist.ac.kr Supplementary information: Supplementary data are available at Bioinformatics online.

Description: Motivation: Recent advancements in sequencing technology have led to a drastic reduction in the cost of sequencing a genome. This has generated an unprecedented amount of genomic data that must be stored, processed and transmitted. To facilitate this effort, we propose a new lossy compressor for the quality values presented in genomic data files (e.g. FASTQ and SAM files), which comprise roughly half of the storage space (in the uncompressed domain). Lossy compression allows for compression of data beyond its lossless limit. Results: The proposed algorithm QVZ exhibits better rate-distortion performance than the previously proposed algorithms, for several distortion metrics and for the lossless case. Moreover, it allows the user to define any quasi-convex distortion function to be minimized, a feature not supported by the previous algorithms. Finally, we show that QVZ-compressed data exhibit better performance in the genotyping than data compressed with previously proposed algorithms, in the sense that for a similar rate, a genotyping closer to that achieved with the original quality values is obtained. Availability and implementation: QVZ is written in C and can be downloaded from https://github.com/mikelhernaez/qvz . Contact: mhernaez@stanford.edu or gmalysa@stanford.edu or iochoa@stanford.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Description: Motivation: DNA methylation analysis suffers from very long processing time, as the advent of Next-Generation Sequencers has shifted the bottleneck of genomic studies from the sequencers that obtain the DNA samples to the software that performs the analysis of these samples. The existing software for methylation analysis does not seem to scale efficiently neither with the size of the dataset nor with the length of the reads to be analyzed. As it is expected that the sequencers will provide longer and longer reads in the near future, efficient and scalable methylation software should be developed. Results: We present a new software tool, called HPG-Methyl, which efficiently maps bisulphite sequencing reads on DNA, analyzing DNA methylation. The strategy used by this software consists of leveraging the speed of the Burrows–Wheeler Transform to map a large number of DNA fragments (reads) rapidly, as well as the accuracy of the Smith–Waterman algorithm, which is exclusively employed to deal with the most ambiguous and shortest reads. Experimental results on platforms with Intel multicore processors show that HPG-Methyl significantly outperforms in both execution time and sensitivity state-of-the-art software such as Bismark, BS-Seeker or BSMAP, particularly for long bisulphite reads. Availability and implementation: Software in the form of C libraries and functions, together with instructions to compile and execute this software. Available by sftp to anonymous@clariano.uv.es (password ‘anonymous’). Contact: juan.orduna@uv.es or jdopazo@cipf.es

Description: Motivation: During the evolution, functional sites on the surface of the protein as well as the hydrophobic core maintaining the structural integrity are well-conserved. However, available protein structure alignment methods align protein structures based solely on the 3D geometric similarity, limiting their ability to detect functionally relevant correspondences between the residues of the proteins, especially for distantly related homologous proteins. Results: In this article, we propose a new protein pairwise structure alignment algorithm (UniAlign) that incorporates additional evolutionary information captured in the form of sequence similarity, sequence profiles and residue conservation. We define a per-residue score (UniScore) as a weighted sum of these and other features and develop an iterative optimization procedure to search for an alignment with the best overall UniScore. Our extensive experiments on CDD, HOMSTRAD and BAliBASE benchmark datasets show that UniAlign outperforms commonly used structure alignment methods. We further demonstrate UniAlign's ability to develop family-specific models to drastically improve the quality of the alignments. Availability and implementation: UniAlign is available as a web service at: http://sacan.biomed.drexel.edu/unialign Contact: ahmet.sacan@drexel.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Description: Motivation: Circularized Chromosome Conformation Capture (4C) is a powerful technique for studying the spatial interactions of a specific genomic region called the ‘viewpoint’ with the rest of the genome, both in a single condition or comparing different experimental conditions or cell types. Observed ligation frequencies typically show a strong, regular dependence on genomic distance from the viewpoint, on top of which specific interaction peaks are superimposed. Here, we address the computational task to find these specific peaks and to detect changes between different biological conditions. Results: We model the overall trend of decreasing interaction frequency with genomic distance by fitting a smooth monotonically decreasing function to suitably transformed count data. Based on the fit, z -scores are calculated from the residuals, and high z -scores are interpreted as peaks providing evidence for specific interactions. To compare different conditions, we normalize fragment counts between samples, and call for differential contact frequencies using the statistical method DESeq2 adapted from RNA-Seq analysis. Availability and implementation: A full end-to-end analysis pipeline is implemented in the R package FourCSeq available at www.bioconductor.org . Contact: felix.klein@embl.de or whuber@embl.de Supplementary information: Supplementary data are available at Bioinformatics online.

Description: Motivation: Proteomic mass spectrometry analysis is becoming routine in clinical diagnostics, for example to monitor cancer biomarkers using blood samples. However, differential proteomics and identification of peaks relevant for class separation remains challenging. Results: Here, we introduce a simple yet effective approach for identifying differentially expressed proteins using binary discriminant analysis. This approach works by data-adaptive thresholding of protein expression values and subsequent ranking of the dichotomized features using a relative entropy measure. Our framework may be viewed as a generalization of the ‘peak probability contrast’ approach of Tibshirani et al. (2004) and can be applied both in the two-group and the multi-group setting. Our approach is computationally inexpensive and shows in the analysis of a large-scale drug discovery test dataset equivalent prediction accuracy as a random forest. Furthermore, we were able to identify in the analysis of mass spectrometry data from a pancreas cancer study biological relevant and statistically predictive marker peaks unrecognized in the original study. Availability and implementation: The methodology for binary discriminant analysis is implemented in the R package binda, which is freely available under the GNU General Public License (version 3 or later) from CRAN at URL http://cran.r-project.org/web/packages/binda/ . R scripts reproducing all described analyzes are available from the web page http://strimmerlab.org/software/binda/ . Contact: k.strimmer@imperial.ac.uk

Description: Motivation: Circadian oscillations have been observed in animals, plants, fungi and cyanobacteria and play a fundamental role in coordinating the homeostasis and behavior of biological systems. Genetically encoded molecular clocks found in nearly every cell, based on negative transcription/translation feedback loops and involving only a dozen genes, play a central role in maintaining these oscillations. However, high-throughput gene expression experiments reveal that in a typical tissue, a much larger fraction ( ~10% ) of all transcripts oscillate with the day–night cycle and the oscillating species vary with tissue type suggesting that perhaps a much larger fraction of all transcripts, and perhaps also other molecular species, may bear the potential for circadian oscillations. Results: To better quantify the pervasiveness and plasticity of circadian oscillations, we conduct the first large-scale analysis aggregating the results of 18 circadian transcriptomic studies and 10 circadian metabolomic studies conducted in mice using different tissues and under different conditions. We find that over half of protein coding genes in the cell can produce transcripts that are circadian in at least one set of conditions and similarly for measured metabolites. Genetic or environmental perturbations can disrupt existing oscillations by changing their amplitudes and phases, suppressing them or giving rise to novel circadian oscillations. The oscillating species and their oscillations provide a characteristic signature of the physiological state of the corresponding cell/tissue. Molecular networks comprise many oscillator loops that have been sculpted by evolution over two trillion day–night cycles to have intrinsic circadian frequency. These oscillating loops are coupled by shared nodes in a large network of coupled circadian oscillators where the clock genes form a major hub. Cells can program and re-program their circadian repertoire through epigenetic and other mechanisms. Availability and implementation: High-resolution and tissue/condition specific circadian data and networks available at http://circadiomics.igb.uci.edu . Contact: pfbaldi@ics.uci.edu Supplementary information : Supplementary data are available at Bioinformatics online.