ALBERT

Description: Motivation: Transmembrane β barrel proteins (TMBs) are found in the outer membrane of Gram-negative bacteria, chloroplast and mitochondria. They play a major role in the translocation machinery, pore formation, membrane anchoring and ion exchange. TMBs are also promising targets for antimicrobial drugs and vaccines. Given the difficulty in membrane protein structure determination, computational methods to identify TMBs and predict the topology of TMBs are important. Results: Here, we present BOCTOPUS; an improved method for the topology prediction of TMBs by employing a combination of support vector machines (SVMs) and Hidden Markov Models (HMMs). The SVMs and HMMs account for local and global residue preferences, respectively. Based on a 10-fold cross-validation test, BOCTOPUS performs better than all existing methods, reaching a Q3 accuracy of 87%. Further, BOCTOPUS predicted the correct number of strands for 83% proteins in the dataset. BOCTOPUS might also help in reliable identification of TMBs by using it as an additional filter to methods specialized in this task. Availability: BOCTOPUS is freely available as a web server at: http://boctopus.cbr.su.se/ . The datasets used for training and evaluations are also available from this site. Contact: arne@bioinfo.se Supplementary information: Supplementary data are available at Bioinformatics online.

Description: Motivation: High-dimensional data such as microarrays have created new challenges to traditional statistical methods. One such example is on class prediction with high-dimension, low-sample size data. Due to the small sample size, the sample mean estimates are usually unreliable. As a consequence, the performance of the class prediction methods using the sample mean may also be unsatisfactory. To obtain more accurate estimation of parameters some statistical methods, such as regularizations through shrinkage, are often desired. Results: In this article, we investigate the family of shrinkage estimators for the mean value under the quadratic loss function. The optimal shrinkage parameter is proposed under the scenario when the sample size is fixed and the dimension is large. We then construct a shrinkage-based diagonal discriminant rule by replacing the sample mean by the proposed shrinkage mean. Finally, we demonstrate via simulation studies and real data analysis that the proposed shrinkage-based rule outperforms its original competitor in a wide range of settings. Contact: tongt@hkbu.edu.hk

Description: Motivation: The advent of high-throughput sequencing technologies is revolutionizing our ability in discovering and genotyping DNA copy number variants (CNVs). Read count-based approaches are able to detect CNV regions with an unprecedented resolution. Although this computational strategy has been recently introduced in literature, much work has been already done for the preparation, normalization and analysis of this kind of data. Results: Here we face the many aspects that cover the detection of CNVs by using read count approach. We first study the characteristics and systematic biases of read count distributions, focusing on the normalization methods designed for removing these biases. Subsequently, we compare the algorithms designed to detect the boundaries of CNVs and we investigate the ability of read count data to predict the exact number of DNA copy. Finally, we review the tools publicly available for analysing read count data. To better understand the state of the art of read count approaches, we compare the performance of the three most widely used sequencing technologies (Illumina Genome Analyzer, Roche 454 and Life Technologies SOLiD) in all the analyses that we perform. Contact: albertomagi@gmail.com Supplementary information: Supplementary data are available at Bioinformatics online.

Description: Motivation: We investigate and quantify the generalizability of the white blood cell (WBC) transcriptome to the general, multiorgan transcriptome. We use data from the NCBI's Gene Expression Omnibus (GEO) public repository to define two datasets for comparison, WBC and OO (Other Organ) sets. Results: Comprehensive pair-wise correlation and expression level profiles are calculated for both datasets (with sizes of 81 and 1463, respectively). We have used mapping and ranking across the Gene Ontology (GO) categories to quantify similarity between the two sets. GO mappings of the most correlated and highly expressed genes from the two datasets tightly match, with the notable exceptions of components of the ribosome, cell adhesion and immune response. That is, 10 877 or 48.8% of all measured genes do not change 〉10% of rank range between WBC and OO; only 878 (3.9%) change rank 〉50%. Two trans -tissue gene lists are defined, the most changing and the least changing genes in expression rank. We also provide a general, quantitative measure of the probability of expression rank and correlation profile in the OO system given the expression rank and correlation profile in the WBC dataset. Contact: vvaltchinov@partners.org Supplementary information: Supplementary data are available at Bioinformatics online.

Description: Motivation: The understanding of the molecular sources for diseases like cancer can be significantly improved by computational models. Recently, Boolean networks have become very popular for modeling signaling and regulatory networks. However, such models rely on a set of Boolean functions that are in general not known. Unfortunately, while detailed information on the molecular interactions becomes available in large scale through electronic databases, the information on the Boolean functions does not become available simultaneously and has to be included manually into the models, if at all known. Results: We propose a new Boolean approach which can directly utilize the mechanistic network information available through modern databases. The Boolean function is implicitly defined by the reaction mechanisms. Special care has been taken for the treatment of kinetic features like inhibition. The method has been applied to a signaling model combining the Wnt and MAPK pathway. Availability: A sample C++ implementation of the proposed method is available for Linux and compatible systems through http://code.google.com/p/libscopes/wiki/Paper2011 Contact: handorf@physik.hu-berlin.de Supplementary information: Supplementary data are available at Bioinformatics online.

Description: Motivation: Multiple sequence alignment (MSA) is a core method in bioinformatics. The accuracy of such alignments may influence the success of downstream analyses such as phylogenetic inference, protein structure prediction, and functional prediction. The importance of MSA has lead to the proliferation of MSA methods, with different objective functions and heuristics to search for the optimal MSA. Different methods of inferring MSAs produce different results in all but the most trivial cases. By measuring the differences between inferred alignments, we may be able to develop an understanding of how these differences (i) relate to the objective functions and heuristics used in MSA methods, and (ii) affect downstream analyses. Results: We introduce four metrics to compare MSAs, which include the position in a sequence where a gap occurs or the location on a phylogenetic tree where an insertion or deletion (indel) event occurs. We use both real and synthetic data to explore the information given by these metrics and demonstrate how the different metrics in combination can yield more information about MSA methods and the differences between them. Availability: MetAl is a free software implementation of these metrics in Haskell. Source and binaries for Windows, Linux and Mac OS X are available from http://kumiho.smith.man.ac.uk/whelan/software/metal/ . Contact: simon.whelan@manchester.ac.uk

Description: Motivation: Peptide detection is a crucial step in mass spectrometry (MS) based proteomics. Most existing algorithms are based upon greedy isotope template matching and thus may be prone to error propagation and ineffective to detect overlapping peptides. In addition, existing algorithms usually work at different charge states separately, isolating useful information that can be drawn from other charge states, which may lead to poor detection of low abundance peptides. Results: BPDA2d models spectra as a mixture of candidate peptide signals and systematically evaluates all possible combinations of possible peptide candidates to interpret the given spectra. For each candidate, BPDA2d takes into account its elution profile, charge state distribution and isotope pattern, and it combines all evidence to infer the candidate's signal and existence probability. By piecing all evidence together—especially by deriving information across charge states—low abundance peptides can be better identified and peptide detection rates can be improved. Instead of local template matching, BPDA2d performs global optimization for all candidates and systematically optimizes their signals. Since BPDA2d looks for the optimal among all possible interpretations of the given spectra, it has the capability in handling complex spectra where features overlap. BPDA2d estimates the posterior existence probability of detected peptides, which can be directly used for probability-based evaluation in subsequent processing steps. Our experiments indicate that BPDA2d outperforms state-of-the-art detection methods on both simulated data and real liquid chromatography–mass spectrometry data, according to sensitivity and detection accuracy. Availability: The BPDA2d software package is available at http://gsp.tamu.edu/Publications/supplementary/sun11a/ Contact: Michelle.Zhang@utsa.edu ; edward@ece.tamu.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Description: Motivation: The continued progress in developing technological platforms, availability of many published experimental datasets, as well as different statistical methods to analyze those data have allowed approaching the same research question using various methods simultaneously. To get the best out of all these alternatives, we need to integrate their results in an unbiased manner. Prioritized gene lists are a common result presentation method in genomic data analysis applications. Thus, the rank aggregation methods can become a useful and general solution for the integration task. Results: Standard rank aggregation methods are often ill-suited for biological settings where the gene lists are inherently noisy. As a remedy, we propose a novel robust rank aggregation (RRA) method. Our method detects genes that are ranked consistently better than expected under null hypothesis of uncorrelated inputs and assigns a significance score for each gene. The underlying probabilistic model makes the algorithm parameter free and robust to outliers, noise and errors. Significance scores also provide a rigorous way to keep only the statistically relevant genes in the final list. These properties make our approach robust and compelling for many settings. Availability: All the methods are implemented as a GNU R package R obust R ank A ggreg , freely available at the Comprehensive R Archive Network http://cran.r-project.org/ . Contact: vilo@ut.ee Supplementary information Supplementary data are available at Bioinformatics online.

Description: : CLARE is a computational method designed to reveal sequence encryption of tissue-specific regulatory elements. Starting with a set of regulatory elements known to be active in a particular tissue/process, it learns the sequence code of the input set and builds a predictive model from features specific to those elements. The resulting model can then be applied to user-supplied genomic regions to identify novel candidate regulatory elements. CLARE's model also provides a detailed analysis of transcription factors that most likely bind to the elements, making it an invaluable tool for understanding mechanisms of tissue-specific gene regulation. Availability: CLARE is freely accessible at http://clare.dcode.org/ . Contact: taherl@ncbi.nlm.nih.gov ; ovcharen@nih.gov Supplementary information: Supplementary data are available at Bioinformatics online.

Description: Motivation: We present a pipeline for the pre-processing, quality assessment, read distribution and methylation estimation for methylated DNA immunoprecipitation (MeDIP)-sequence datasets. This is the first MeDIP-seq-specific analytic pipeline that starts at the output of the sequencers. This pipeline will reduce the data analysis load on staff and allows the easy and straightforward analysis of sequencing data for DNA methylation. The pipeline integrates customized scripting and several existing tools, which can deal with both paired and single end data. Availability: The package and extensive documentation, and comparison to public data is available at http://life.tongji.edu.cn/meqa/ Contact: jhuang@cephb.fr

Description: Motivation: A plethora of bioinformatics analysis has led to the discovery of numerous gene sets, which can be interpreted as discrete measurements emitted from latent signaling pathways. Their potential to infer signaling pathway structures, however, has not been sufficiently exploited. Existing methods accommodating discrete data do not explicitly consider signal cascading mechanisms that characterize a signaling pathway. Novel computational methods are thus needed to fully utilize gene sets and broaden the scope from focusing only on pairwise interactions to the more general cascading events in the inference of signaling pathway structures. Results: We propose a gene set based simulated annealing (SA) algorithm for the reconstruction of signaling pathway structures. A signaling pathway structure is a directed graph containing up to a few hundred nodes and many overlapping signal cascades, where each cascade represents a chain of molecular interactions from the cell surface to the nucleus. Gene sets in our context refer to discrete sets of genes participating in signal cascades, the basic building blocks of a signaling pathway, with no prior information about gene orderings in the cascades. From a compendium of gene sets related to a pathway, SA aims to search for signal cascades that characterize the optimal signaling pathway structure. In the search process, the extent of overlap among signal cascades is used to measure the optimality of a structure. Throughout, we treat gene sets as random samples from a first-order Markov chain model. We evaluated the performance of SA in three case studies. In the first study conducted on 83 KEGG pathways, SA demonstrated a significantly better performance than Bayesian network methods. Since both SA and Bayesian network methods accommodate discrete data, use a ‘search and score’ network learning strategy and output a directed network, they can be compared in terms of performance and computational time. In the second study, we compared SA and Bayesian network methods using four benchmark datasets from DREAM. In our final study, we showcased two context-specific signaling pathways activated in breast cancer. Availibility: Source codes are available from http://dl.dropbox.com/u/16000775/sa_sc.zip Contact: dzhu@wayne.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Description: : We provide a Bioconductor package with quality assessment, processing and visualization tools for high-throughput sequencing data, with emphasis in ChIP-seq and RNA-seq studies. It includes detection of outliers and biases, inefficient immuno-precipitation and overamplification artifacts, de novo identification of read-rich genomic regions and visualization of the location and coverage of genomic region lists. Availability: www.bioconductor.org Contact: david.rossell@irbbarcelona.org Supplementary information: Supplementary data available at Bioinformatics online.

Description: Motivation: We study a stochastic method for approximating the set of local minima in partial RNA folding landscapes associated with a bounded-distance neighbourhood of folding conformations. The conformations are limited to RNA secondary structures without pseudoknots. The method aims at exploring partial energy landscapes p L induced by folding simulations and their underlying neighbourhood relations. It combines an approximation of the number of local optima devised by Garnier and Kallel (2002) with a run-time estimation for identifying sets of local optima established by Reeves and Eremeev (2004). Results: The method is tested on nine sequences of length between 50 nt and 400 nt, which allows us to compare the results with data generated by RNAsubopt and subsequent barrier tree calculations. On the nine sequences, the method captures on average 92% of local minima with settings designed for a target of 95%. The run-time of the heuristic can be estimated by O ( n 2 D ln), where n is the sequence length, is the number of local minima in the partial landscape p L under consideration and D is the maximum number of steepest descent steps in attraction basins associated with p L . Contact: a.albrecht@qub.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online.

Description: Motivation: RNA-seq is a powerful technology for the study of transcriptome profiles that uses deep-sequencing technologies. Moreover, it may be used for cellular phenotyping and help establishing the etiology of diseases characterized by abnormal splicing patterns. In RNA-Seq, the exact nature of splicing events is buried in the reads that span exon–exon boundaries. The accurate and efficient mapping of these reads to the reference genome is a major challenge. Results: We developed PASSion, a pattern growth algorithm-based pipeline for splice site detection in paired-end RNA-Seq reads. Comparing the performance of PASSion to three existing RNA-Seq analysis pipelines, TopHat, MapSplice and HMMSplicer, revealed that PASSion is competitive with these packages. Moreover, the performance of PASSion is not affected by read length and coverage. It performs better than the other three approaches when detecting junctions in highly abundant transcripts. PASSion has the ability to detect junctions that do not have known splicing motifs, which cannot be found by the other tools. Of the two public RNA-Seq datasets, PASSion predicted ~ 137 000 and 173 000 splicing events, of which on average 82 are known junctions annotated in the Ensembl transcript database and 18% are novel. In addition, our package can discover differential and shared splicing patterns among multiple samples. Availability: The code and utilities can be freely downloaded from https://trac.nbic.nl/passion and ftp://ftp.sanger.ac.uk/pub/zn1/passion Contact: y.zhang@lumc.nl ; k.ye@lumc.nl Supplementary information: Supplementary data are available at Bioinformatics online.

Description: Motivation: The completion of 168 genome sequences from a single population of Drosophila melanogaster provides a global view of genomic variation and an understanding of the evolutionary forces shaping the patterns of DNA polymorphism and divergence along the genome. Results: We present the ‘Population Drosophila Browser’ (PopDrowser), a new genome browser specially designed for the automatic analysis and representation of genetic variation across the D. melanogaster genome sequence. PopDrowser allows estimating and visualizing the values of a number of DNA polymorphism and divergence summary statistics, linkage disequilibrium parameters and several neutrality tests. PopDrowser also allows performing custom analyses on-the-fly using user-selected parameters. Availability: PopDrowser is freely available from http://PopDrowser.uab.cat . Contact: miquel.ramia@uab.cat

Description: Motivation: Probabilistic approaches for inferring transcription factor binding sites (TFBSs) and regulatory motifs from DNA sequences have been developed for over two decades. Previous work has shown that prediction accuracy can be significantly improved by incorporating features such as the competition of multiple transcription factors (TFs) for binding to nearby sites, the tendency of TFBSs for co-regulated TFs to cluster and form cis -regulatory modules and explicit evolutionary modeling of conservation of TFBSs across orthologous sequences. However, currently available tools only incorporate some of these features, and significant methodological hurdles hampered their synthesis into a single consistent probabilistic framework. Results: We present MotEvo, a integrated suite of Bayesian probabilistic methods for the prediction of TFBSs and inference of regulatory motifs from multiple alignments of phylogenetically related DNA sequences, which incorporates all features just mentioned. In addition, MotEvo incorporates a novel model for detecting unknown functional elements that are under evolutionary constraint, and a new robust model for treating gain and loss of TFBSs along a phylogeny. Rigorous benchmarking tests on ChIP-seq datasets show that MotEvo's novel features significantly improve the accuracy of TFBS prediction, motif inference and enhancer prediction. Availability: Source code, a user manual and files with several example applications are available at www.swissregulon.unibas.ch . Contact: erik.vannimwegen@unibas.ch Supplementary information: Supplementary data are available at Bioinformatics online.

Description: : We present LaTcOm, a new web tool, which offers several alternative methods for ‘rare codon cluster’ (RCC) identification from a single and simple graphical user interface. In the current version, three RCC detection schemes are implemented: the recently described %MinMax algorithm and a simplified sliding window approach, along with a novel modification of a linear-time algorithm for the detection of maximally scoring subsequences tailored to the RCC detection problem. Among a number of user tunable parameters, several codon-based scales relevant for RCC detection are available, including tRNA abundance values from Escherichia coli and several codon usage tables from a selection of genomes. Furthermore, useful scale transformations may be performed upon user request (e.g. linear, sigmoid). Users may choose to visualize RCC positions within the submitted sequences either with graphical representations or in textual form for further processing. Availability: LaTcOm is freely available online at the URL http://troodos.biol.ucy.ac.cy/latcom.html . Contact: vprobon@ucy.ac.cy Supplementary information: Supplementary data are available at Bioinformatics online.

Description: : Many existing databases annotate experimentally characterized single nucleotide polymorphisms (SNPs). Each non-synonymous SNP (nsSNP) changes one amino acid in the gene product (single amino acid substitution;SAAS). This change can either affect protein function or be neutral in that respect. Most polymorphisms lack experimental annotation of their functional impact. Here, we introduce SNPdbe—SNP database of effects, with predictions of computationally annotated functional impacts of SNPs. Database entries represent nsSNPs in dbSNP and 1000 Genomes collection, as well as variants from UniProt and PMD. SAASs come from 〉2600 organisms; ‘human’ being the most prevalent. The impact of each SAAS on protein function is predicted using the SNAP and SIFT algorithms and augmented with experimentally derived function/structure information and disease associations from PMD, OMIM and UniProt. SNPdbe is consistently updated and easily augmented with new sources of information. The database is available as an MySQL dump and via a web front end that allows searches with any combination of organism names, sequences and mutation IDs. Availability: http://www.rostlab.org/services/snpdbe Contact: schaefer@rostlab.org ; snpdbe@rostlab.org

Description: : We have implemented in a single package all the features required for extracting, visualizing and manipulating fully conserved positions as well as those with a family-dependent conservation pattern in multiple sequence alignments. The program allows, among other things, to run different methods for extracting these positions, combine the results and visualize them in protein 3D structures and sequence spaces. Availability and implementation: JDet is a multiplatform application written in Java. It is freely available, including the source code, at http://csbg.cnb.csic.es/JDet . The package includes two of our recently developed programs for detecting functional positions in protein alignments ( Xdet and S3Det ), and support for other methods can be added as plug-ins. A help file and a guided tutorial for JDet are also available. Contact: pazos@cnb.csic.es

Description: : VarSifter is a graphical software tool for desktop computers that allows investigators of varying computational skills to easily and quickly sort, filter, and sift through sequence variation data. A variety of filters and a custom query framework allow filtering based on any combination of sample and annotation information. By simplifying visualization and analyses of exome-scale sequence variation data, this program will help bring the power and promise of massively-parallel DNA sequencing to a broader group of researchers. Availability and Implementation: VarSifter is written in Java, and is freely available in source and binary versions, along with a User Guide, at http://research.nhgri.nih.gov/software/VarSifter/ . Contact: mullikin@mail.nih.gov Supplementary Information: Additional figures and methods available online at the journal's website.

Description: Motivation: Given the current costs of next-generation sequencing, large studies carry out low-coverage sequencing followed by application of methods that leverage linkage disequilibrium to infer genotypes. We propose a novel method that assumes study samples are sequenced at low coverage and genotyped on a genome-wide microarray, as in the 1000 Genomes Project (1KGP). We assume polymorphic sites have been detected from the sequencing data and that genotype likelihoods are available at these sites. We also assume that the microarray genotypes have been phased to construct a haplotype scaffold. We then phase each polymorphic site using an MCMC algorithm that iteratively updates the unobserved alleles based on the genotype likelihoods at that site and local haplotype information. We use a multivariate normal model to capture both allele frequency and linkage disequilibrium information around each site. When sequencing data are available from trios, Mendelian transmission constraints are easily accommodated into the updates. The method is highly parallelizable, as it analyses one position at a time. Results: We illustrate the performance of the method compared with other methods using data from Phase 1 of the 1KGP in terms of genotype accuracy, phasing accuracy and downstream imputation performance. We show that the haplotype panel we infer in African samples, which was based on a trio-phased scaffold, increases downstream imputation accuracy for rare variants (R2 increases by 〉0.05 for minor allele frequency 〈1%), and this will translate into a boost in power to detect associations. These results highlight the value of incorporating microarray genotypes when calling variants from next-generation sequence data. Availability: The method (called MVNcall) is implemented in a C++ program and is available from http://www.stats.ox.ac.uk/~marchini/#software . Contact: marchini@stats.ox.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online.

Description: Motivation: Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. Results: To align our large (〉80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of 〉50 in mapping speed, aligning to the human genome 550 million 2 x 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80–90% success rate, corroborating the high precision of the STAR mapping strategy. Availability and implementation: STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/ . Contact: dobin@cshl.edu .

Description: Motivation: Local motifs are patterns of DNA or protein sequences that occur within a sequence interval relative to a biologically defined anchor or landmark. Current protein motif discovery methods do not adequately consider such constraints to identify biologically significant motifs that are only weakly over-represented but spatially confined. Using negatives, i.e. sequences known to not contain a local motif, can further increase the specificity of their discovery. Results: This article introduces the method DLocalMotif that makes use of positional information and negative data for local motif discovery in protein sequences. DLocalMotif combines three scoring functions, measuring degrees of motif over-representation, entropy and spatial confinement, specifically designed to discriminatively exploit the availability of negative data. The method is shown to outperform current methods that use only a subset of these motif characteristics. We apply the method to several biological datasets. The analysis of peroxisomal targeting signals uncovers several novel motifs that occur immediately upstream of the dominant peroxisomal targeting signal-1 signal. The analysis of proline-tyrosine nuclear localization signals uncovers multiple novel motifs that overlap with C2H2 zinc finger domains. We also evaluate the method on classical nuclear localization signals and endoplasmic reticulum retention signals and find that DLocalMotif successfully recovers biologically relevant sequence properties. Availability: http://bioinf.scmb.uq.edu.au/dlocalmotif/ Contact: m.boden@uq.edu.au Supplementary information: Supplementary data are available at Bioinformatics online.

Description: Motivation: Protein–protein interaction (PPI) plays an important role in understanding gene functions, and many computational PPI prediction methods have been proposed in recent years. Despite the extensive efforts, PPI prediction still has much room to improve. Sequence-based co-evolution methods include the substitution rate method and the mirror tree method, which compare sequence substitution rates and topological similarity of phylogenetic trees, respectively. Although they have been used to predict PPI in species with small genomes like Escherichia coli , such methods have not been tested in large scale proteome like Homo sapiens . Result: In this study, we propose a novel sequence-based co-evolution method, co-evolutionary divergence (CD), for human PPI prediction. Built on the basic assumption that protein pairs with similar substitution rates are likely to interact with each other, the CD method converts the evolutionary information from 14 species of vertebrates into likelihood ratios and combined them together to infer PPI. We showed that the CD method outperformed the mirror tree method in three independent human PPI datasets by a large margin. With the arrival of more species genome information generated by next generation sequencing, the performance of the CD method can be further improved. Availability: Source code and support are available at http://mib.stat.sinica.edu.tw/LAP/tmp/CD.rar . Contact: syuan@stat.sinica.edu.tw Supplementary information: Supplementary data are available at Bioinformatics online.

Description: : In higher eukaryotes, the identification of translation initiation sites (TISs) has been focused on finding these signals in cDNA or mRNA sequences. Using Arabidopsis thaliana ( A.t. ) information, we developed a prediction tool for signals within genomic sequences of plants that correspond to TISs. Our tool requires only genome sequence, not expressed sequences. Its sensitivity/specificity is for A.t. (90.75%/92.2%), for Vitis vinifera (66.8%/94.4%) and for Populus trichocarpa (81.6%/94.4%), which suggests that our tool can be used in annotation of different plant genomes. We provide a list of features used in our model. Further study of these features may improve our understanding of mechanisms of the translation initiation. Availability and implementation: Our tool is implemented as an artificial neural network. It is available as a web-based tool and, together with the source code, the list of features, and data used for model development, is accessible at http://cbrc.kaust.edu.sa/dts . Contact: vladimir.bajic@kaust.edu.sa Supplementary information : Supplementary data are available at Bioinformatics online.

Description: Motivation: Pathway or gene set analysis has been widely applied to genomic data. Many current pathway testing methods use univariate test statistics calculated from individual genomic markers, which ignores the correlations and interactions between candidate markers. Random forests-based pathway analysis is a promising approach for incorporating complex correlation and interaction patterns, but one limitation of previous approaches is that pathways have been considered separately, thus pathway cross-talk information was not considered. Results: In this article, we develop a new pathway hunting algorithm for survival outcomes using random survival forests, which prioritize important pathways by accounting for gene correlation and genomic interactions. We show that the proposed method performs favourably compared with five popular pathway testing methods using both synthetic and real data. We find that the proposed methodology provides an efficient and powerful pathway modelling framework for high-dimensional genomic data. Availability: The R code for the analysis used in this article is available upon request. Contact: xi.steven.chen@gmail.com Supplementary information: Supplementary data are available at Bioinformatics online.

Description: Motivation: PacBio sequencers produce two types of characteristic reads (continuous long reads: long and high error rate and circular consensus sequencing: short and low error rate), both of which could be useful for de novo assembly of genomes. Currently, there is no available simulator that targets the specific generation of PacBio libraries. Results: Our analysis of 13 PacBio datasets showed characteristic features of PacBio reads (e.g. the read length of PacBio reads follows a log-normal distribution). We have developed a read simulator, PBSIM, that captures these features using either a model-based or sampling-based method. Using PBSIM, we conducted several hybrid error correction and assembly tests for PacBio reads, suggesting that a continuous long reads coverage depth of at least 15 in combination with a circular consensus sequencing coverage depth of at least 30 achieved extensive assembly results. Availability: PBSIM is freely available from the web under the GNU GPL v2 license ( http://code.google.com/p/pbsim/ ). Contact: mhamada@k.u-tokyo.ac.jp Supplementary information: Supplementary data are available at Bioinformatics online.

Description: : Drugster is a fully interactive pipeline designed to break the command line barrier and introduce a new user-friendly environment to perform drug design, lead and structure optimization experiments through an efficient combination of the PDB2PQR, Ligbuilder, Gromacs and Dock suites. Our platform features a novel workflow that guides the user through each logical step of the iterative 3D structural optimization setup and drug design process, by providing a seamless interface to all incorporated packages. Availability: Drugster can be freely downloaded via our dedicated server system at http://www.bioacademy.gr/bioinformatics/drugster/ . Contact: dvlachakis@bioacademy.gr .

Description: XiP (eXtensible integrative Pipeline) is a flexible, editable and modular environment with a user-friendly interface that does not require previous advanced programming skills to run, construct and edit workflows. XiP allows the construction of workflows by linking components written in both R and Java, the analysis of high-throughput data in grid engine systems and also the development of customized pipelines that can be encapsulated in a package and distributed. XiP already comes with several ready-to-use pipeline flows for the most common genomic and transcriptomic analysis and ~300 computational components. Availability: XiP is open source, freely available under the Lesser General Public License (LGPL) and can be downloaded from http://xip.hgc.jp . Contact: nagasaki@megabank.tohoku.ac.jp

Description: Existing repositories for experimental datasets typically capture snapshots of data acquired using a single experimental technique and often require manual population and continual curation. We present a storage system for heterogeneous research data that performs dynamic automated indexing to provide powerful search, discovery and collaboration features without the restrictions of a structured repository. ADAM is able to index many commonly used file formats generated by laboratory assays and therefore offers specific advantages to the experimental biology community. However, it is not domain specific and can promote sharing and re-use of working data across scientific disciplines. Availability and implementation: ADAM is implemented using Java and supported on Linux. It is open source under the GNU General Public License v3.0. Installation instructions, binary code, a demo system and virtual machine image and are available at http://www.imperial.ac.uk/bioinfsupport/resources/software/adam . Contact: m.woodbridge@imperial.ac.uk

Description: : Drug versus Disease (DvD) provides a pipeline, available through R or Cytoscape, for the comparison of drug and disease gene expression profiles from public microarray repositories. Negatively correlated profiles can be used to generate hypotheses of drug-repurposing, whereas positively correlated profiles may be used to infer side effects of drugs. DvD allows users to compare drug and disease signatures with dynamic access to databases Array Express, Gene Expression Omnibus and data from the Connectivity Map. Availability and implementation: R package (submitted to Bioconductor) under GPL 3 and Cytoscape plug-in freely available for download at www.ebi.ac.uk/saezrodriguez/DVD/ . Contact: saezrodriguez@ebi.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online.

Description: Motivation: Homology detection is a long-standing challenge in computational biology. To tackle this problem, typically all-versus-all BLAST results are coupled with data partitioning approaches resulting in clusters of putative homologous proteins. One of the main problems, however, has been widely neglected: all clustering tools need a density parameter that adjusts the number and size of the clusters. This parameter is crucial but hard to estimate without gold standard data at hand. Developing a gold standard, however, is a difficult and time consuming task. Having a reliable method for detecting clusters of homologous proteins between a huge set of species would open opportunities for better understanding the genetic repertoire of bacteria with different lifestyles. Results: Our main contribution is a method for identifying a suitable and robust density parameter for protein homology detection without a given gold standard. Therefore, we study the core genome of 89 actinobacteria. This allows us to incorporate background knowledge, i.e. the assumption that a set of evolutionarily closely related species should share a comparably high number of evolutionarily conserved proteins (emerging from phylum-specific housekeeping genes). We apply our strategy to find genes/proteins that are specific for certain actinobacterial lifestyles, i.e. different types of pathogenicity. The whole study was performed with transitivity clustering, as it only requires a single intuitive density parameter and has been shown to be well applicable for the task of protein sequence clustering. Note, however, that the presented strategy generally does not depend on our clustering method but can easily be adapted to other clustering approaches. Availability: All results are publicly available at http://transclust.mmci.uni-saarland.de/actino_core/ or as Supplementary Material of this article. Contact: roettger@mpi-inf.mpg.de Supplementary information: Supplementary data are available at Bioinformatics online.

Description: : Nuclear receptors (NRs) are a class of transcription factors playing important roles in various biological processes. An NR often impacts numerous genes and different NRs share overlapped target networks. To fulfil the need for a database incorporating binding sites of different NRs at various conditions for easy comparison and visualization to improve our understanding of NR binding mechanisms, we have developed NURBS, a database for experimental and predicted nuclear receptor binding sites of mouse (NURBS). NURBS currently contains binding sites across the whole-mouse genome of 8 NRs identified in 40 chromatin immunoprecipitation with massively parallel DNA sequencing experiments. All datasets are processed using a widely used procedure and same statistical criteria to ensure the binding sites derived from different datasets are comparable. NURBS also provides predicted binding sites using NR-HMM, a Hidden Markov Model (HMM) model. Availability: The GBrowse-based user interface of NURBS is freely accessible at http://shark.abl.ku.edu/nurbs/ . NR-HMM and all results can be downloaded for free at the website. Contact: jwfang@ku.edu

Description: Motivation: Phylogenetics, or reconstructing the evolutionary relationships of organisms, is critical for understanding evolution. A large number of heuristic algorithms for phylogenetics have been developed, some of which enable estimates of trees with tens of thousands of taxa. Such trees may not be robust, as small changes in the input data can cause major differences in the optimal topology. Tools that can assess the quality and stability of phylogenetic tree estimates and identify the most reliable parts of the tree are needed. Results: We define measures that assess the stability of trees, subtrees and individual taxa with respect to changes in the input sequences. Our measures consider changes at the finest granularity in the input data (i.e. individual nucleotides). We demonstrate the effectiveness of our measures on large published datasets. Our measures are computationally feasible for phylogenetic datasets consisting of tens of thousands of taxa. Availability: This software is available at http://bioinformatics.cise.ufl.edu/phylostab Contact: sheikh@cise.ufl.edu

Description: Motivation: There is a substantial body of works in the biology literature that seeks to characterize the cyclic behavior of genes during cell division. Gene expression microarrays made it possible to measure the expression profiles of thousands of genes simultaneously in time-course experiments to assess changes in the expression levels of genes over time. In this context, the commonly used procedures for testing include the permutation test by de Lichtenberg et al. and the Fisher’s G -test, both of which are designed to evaluate periodicity against noise. However, it is possible that a gene of interest may have expression that is neither cyclic nor just noise. Thus, there is a need for a new test for periodicity that can identify cyclic patterns against not only noise but also other non-cyclic patterns such as linear, quadratic or higher order polynomial patterns. Results: To address this weakness, we have introduced an empirical Bayes approach to test for periodicity and compare its performance in terms of sensitivity and specificity with that of the permutation test and Fisher’s G -test through extensive simulations and by application to a set of time-course experiments on the Schizosaccharomyces pombe cell-cycle gene expression. We use ‘conserved’ and ‘cycling’ genes by Lu et al. to assess the sensitivity and CESR genes by Chen et al. to assess the specificity of our new empirical Bayes method. Availability and implementation: The SAS Macro for our empirical Bayes test for periodicity is included in the supplementary materials along with a sample run of the MACRO program. Contact: mkocak1@uthsc.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Description: Motivation: Exploring the genetic basis of heritable traits remains one of the central challenges in biomedical research. In traits with simple Mendelian architectures, single polymorphic loci explain a significant fraction of the phenotypic variability. However, many traits of interest seem to be subject to multifactorial control by groups of genetic loci. Accurate detection of such multivariate associations is non-trivial and often compromised by limited statistical power. At the same time, confounding influences, such as population structure, cause spurious association signals that result in false-positive findings. Results: We propose linear mixed models LMM-Lasso, a mixed model that allows for both multi-locus mapping and correction for confounding effects. Our approach is simple and free of tuning parameters; it effectively controls for population structure and scales to genome-wide datasets. LMM-Lasso simultaneously discovers likely causal variants and allows for multi-marker–based phenotype prediction from genotype. We demonstrate the practical use of LMM-Lasso in genome-wide association studies in Arabidopsis thaliana and linkage mapping in mouse, where our method achieves significantly more accurate phenotype prediction for 91% of the considered phenotypes. At the same time, our model dissects the phenotypic variability into components that result from individual single nucleotide polymorphism effects and population structure. Enrichment of known candidate genes suggests that the individual associations retrieved by LMM-Lasso are likely to be genuine. Availability: Code available under http://webdav.tuebingen . mpg.de/u/karsten/Forschung/research.html. Contact: rakitsch@tuebingen.mpg.de , ippert@microsoft.com or stegle@ebi.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online.

Description: Motivation: Analyzing data from multi-platform genomics experiments combined with patients’ clinical outcomes helps us understand the complex biological processes that characterize a disease, as well as how these processes relate to the development of the disease. Current data integration approaches are limited in that they do not consider the fundamental biological relationships that exist among the data obtained from different platforms. Statistical Model: We propose an integrative Bayesian analysis of genomics data (iBAG) framework for identifying important genes/biomarkers that are associated with clinical outcome. This framework uses hierarchical modeling to combine the data obtained from multiple platforms into one model. Results: We assess the performance of our methods using several synthetic and real examples. Simulations show our integrative methods to have higher power to detect disease-related genes than non-integrative methods. Using the Cancer Genome Atlas glioblastoma dataset, we apply the iBAG model to integrate gene expression and methylation data to study their associations with patient survival. Our proposed method discovers multiple methylation-regulated genes that are related to patient survival, most of which have important biological functions in other diseases but have not been previously studied in glioblastoma. Availability: http://odin.mdacc.tmc.edu/~vbaladan/ . Contact: veera@mdanderson.org Supplementary information: Supplementary data are available at Bioinformatics online.

Description: Motivation: The ready availability of next-generation sequencing has led to a situation where it is easy to produce very fragmentary genome assemblies. We present a pipeline, SWiPS (Scaffolding With Protein Sequences), that uses orthologous proteins to improve low quality genome assemblies. The protein sequences are used as guides to scaffold existing contigs, while simultaneously allowing the gene structure to be predicted by homology. Results: To perform, SWiPS does not depend on a high N50 or whole proteins being encoded on a single contig. We tested our algorithm on simulated next-generation data from Ciona intestinalis , real next-generation data from Drosophila melanogaster , a complex genome assembly of Homo sapiens and the low coverage Sanger sequence assembly of Callorhinchus milii . The improvements in N50 are of the order of ~20% for the C.intestinalis and H.sapiens assemblies, which is significant, considering the large size of intergenic regions in these eukaryotes. Using the CEGMA pipeline to assess the gene space represented in the genome assemblies, the number of genes retrieved increased by 〉110% for C.milii and from 20 to 40% for C.intestinalis . The scaffold error rates are low: 85–90% of scaffolds are fully correct, and 〉95% of local contig joins are correct. Availability: SWiPS is available freely for download at http://www.well.ox.ac.uk/~yli142/swips.html . Contact: yang.li@well.ox.ac.uk or copley@well.ox.ac.uk

Description: Motivation: Biological reality can in silico only be comprehensively represented in multi-scaled models. To this end, cell behavioural models addressing the multi-cellular level have to be semantically linked with mechanistic molecular models. These requirements have to be met by flexible software workflows solving the issues of different time scales, inter-model variable referencing and flexible sub-model embedding. Results: We developed a novel software workflow (EPISIM) for the semantic integration of Systems Biology Markup Language (SBML)-based quantitative models in multi-scaled tissue models and simulations. This workflow allows to import and access SBML-based models. SBML model species, reactions and parameters are semantically integrated in cell behavioural models (CBM) represented by graphical process diagrams. By this, cellular states like proliferation and differentiation can be flexibly linked to gene-regulatory or biochemical reaction networks. For a multi-scale agent-based tissue simulation executable code is automatically generated where different time scales of imported SBML models and CBM have been mapped. We demonstrate the capabilities of the novel software workflow by integrating Tyson’s cell cycle model in our model of human epidermal tissue homeostasis. Finally, we show the semantic interplay of the different biological scales during tissue simulation. Availability: The EPISIM platform is available as binary executables for Windows, Linux and Mac OS X at http://www.tiga.uni-hd.de . Supplementary data are available at http://www.tiga.uni-hd.de/supplements/SemSBMLIntegration.html . Contact: niels.grabe@bioquant.uni-heidelberg.de

Description: Motivation: The Illumina Infinium 450 k DNA Methylation Beadchip is a prime candidate technology for Epigenome-Wide Association Studies (EWAS). However, a difficulty associated with these beadarrays is that probes come in two different designs, characterized by widely different DNA methylation distributions and dynamic range, which may bias downstream analyses. A key statistical issue is therefore how best to adjust for the two different probe designs. Results: Here we propose a novel model-based intra-array normalization strategy for 450 k data, called BMIQ (Beta MIxture Quantile dilation), to adjust the beta-values of type2 design probes into a statistical distribution characteristic of type1 probes. The strategy involves application of a three-state beta-mixture model to assign probes to methylation states, subsequent transformation of probabilities into quantiles and finally a methylation-dependent dilation transformation to preserve the monotonicity and continuity of the data. We validate our method on cell-line data, fresh frozen and paraffin-embedded tumour tissue samples and demonstrate that BMIQ compares favourably with two competing methods. Specifically, we show that BMIQ improves the robustness of the normalization procedure, reduces the technical variation and bias of type2 probe values and successfully eliminates the type1 enrichment bias caused by the lower dynamic range of type2 probes. BMIQ will be useful as a preprocessing step for any study using the Illumina Infinium 450 k platform. Availability: BMIQ is freely available from http://code.google.com/p/bmiq/ . Contact: a.teschendorff@ucl.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online

Description: Motivation: In silico methods provide efficient ways to predict possible interactions between drugs and targets. Supervised learning approach, bipartite local model (BLM), has recently been shown to be effective in prediction of drug–target interactions. However, for drug-candidate compounds or target-candidate proteins that currently have no known interactions available, its pure ‘local’ model is not able to be learned and hence BLM may fail to make correct prediction when involving such kind of new candidates . Results: We present a simple procedure called neighbor-based interaction-profile inferring (NII) and integrate it into the existing BLM method to handle the new candidate problem. Specifically, the inferred interaction profile is treated as label information and is used for model learning of new candidates. This functionality is particularly important in practice to find targets for new drug-candidate compounds and identify targeting drugs for new target-candidate proteins. Consistent good performance of the new BLM–NII approach has been observed in the experiment for the prediction of interactions between drugs and four categories of target proteins. Especially for nuclear receptors, BLM–NII achieves the most significant improvement as this dataset contains many drugs/targets with no interactions in the cross-validation. This demonstrates the effectiveness of the NII strategy and also shows the great potential of BLM–NII for prediction of compound–protein interactions. Contact: jpmei@ntu.edu.sg Supplementary information: Supplementary data are available at Bioinformatics online.

Description: Motivation: Local similarity analysis of biological time series data helps elucidate the varying dynamics of biological systems. However, its applications to large scale high-throughput data are limited by slow permutation procedures for statistical significance evaluation. Results: We developed a theoretical approach to approximate the statistical significance of local similarity analysis based on the approximate tail distribution of the maximum partial sum of independent identically distributed (i.i.d.) random variables. Simulations show that the derived formula approximates the tail distribution reasonably well (starting at time points with no delay and with delay) and provides P -values comparable with those from permutations. The new approach enables efficient calculation of statistical significance for pairwise local similarity analysis, making possible all-to-all local association studies otherwise prohibitive. As a demonstration, local similarity analysis of human microbiome time series shows that core operational taxonomic units (OTUs) are highly synergetic and some of the associations are body-site specific across samples. Availability: The new approach is implemented in our eLSA package, which now provides pipelines for faster local similarity analysis of time series data. The tool is freely available from eLSA’s website: http://meta.usc.edu/softs/lsa . Supplementary information: Supplementary data are available at Bioinformatics online. Contact: fsun@usc.edu

Description: Motivation: The use of dense single nucleotide polymorphism (SNP) data in genetic linkage analysis of large pedigrees is impeded by significant technical, methodological and computational challenges. Here we describe Superlink-Online SNP, a new powerful online system that streamlines the linkage analysis of SNP data. It features a fully integrated flexible processing workflow comprising both well-known and novel data analysis tools, including SNP clustering, erroneous data filtering, exact and approximate LOD calculations and maximum-likelihood haplotyping. The system draws its power from thousands of CPUs, performing data analysis tasks orders of magnitude faster than a single computer. By providing an intuitive interface to sophisticated state-of-the-art analysis tools coupled with high computing capacity, Superlink-Online SNP helps geneticists unleash the potential of SNP data for detecting disease genes. Results: Computations performed by Superlink-Online SNP are automatically parallelized using novel paradigms, and executed on unlimited number of private or public CPUs. One novel service is large-scale approximate Markov Chain–Monte Carlo (MCMC) analysis. The accuracy of the results is reliably estimated by running the same computation on multiple CPUs and evaluating the Gelman–Rubin Score to set aside unreliable results. Another service within the workflow is a novel parallelized exact algorithm for inferring maximum-likelihood haplotyping. The reported system enables genetic analyses that were previously infeasible. We demonstrate the system capabilities through a study of a large complex pedigree affected with metabolic syndrome. Availability: Superlink-Online SNP is freely available for researchers at http://cbl-hap.cs.technion.ac.il/superlink-snp . The system source code can also be downloaded from the system website. Contact: omerw@cs.technion.ac.il Supplementary information: Supplementary data are available at Bioinformatics online.

Description: : Velvet is a popular open-source de novo genome assembly software tool, which is run from the Unix command line. Most of the problems experienced by new users of Velvet revolve around constructing syntactically and semantically correct command lines, getting input files into acceptable formats and assessing the output. Here, we present Velvet Assembler Graphical User Environment (VAGUE), a multi-platform graphical front-end for Velvet. VAGUE aims to make sequence assembly accessible to a wider audience and to facilitate better usage amongst existing users of Velvet. Availability and implementation: VAGUE is implemented in JRuby and targets the Java Virtual Machine. It is available under an open-source GPLv2 licence from http://www.vicbioinformatics.com/ . Contact: torsten.seemann@monash.edu

Description: : The sequencing of bisulfite-treated DNA (Bi-Seq) is becoming a gold standard for methylation studies. The mapping of Bi-Seq reads is complex and requires special alignment algorithms. This problem is particularly relevant for SOLiD color space, where the bisulfite conversion C/T changes two adjacent colors into 16 possible combinations. Here, we present an algorithm that efficiently aligns Bi-Seq reads obtained either from SOLiD or Illumina. An accompanying methylation-caller program creates a genomic view of methylated and unmethylated Cs on both DNA strands. Availability and implementation: The algorithm has been implemented as an option of the program PASS, freely available at http://pass.cribi.unipd.it . Contact: pass@cribi.unipd.it Supplementary information: Supplementary data are available at Bioinformatics online.

Description: Motivation: The structures of homologous proteins are generally better conserved than their sequences. This phenomenon is demonstrated by the prevalence of structurally conserved regions (SCRs) even in highly divergent protein families. Defining SCRs requires the comparison of two or more homologous structures and is affected by their availability and divergence, and our ability to deduce structurally equivalent positions among them. In the absence of multiple homologous structures, it is necessary to predict SCRs of a protein using information from only a set of homologous sequences and (if available) a single structure. Accurate SCR predictions can benefit homology modelling and sequence alignment. Results: Using pairwise DaliLite alignments among a set of homologous structures, we devised a simple measure of structural conservation, termed structural conservation index (SCI). SCI was used to distinguish SCRs from non-SCRs. A database of SCRs was compiled from 386 SCOP superfamilies containing 6489 protein domains. Artificial neural networks were then trained to predict SCRs with various features deduced from a single structure and homologous sequences. Assessment of the predictions via a 5-fold cross-validation method revealed that predictions based on features derived from a single structure perform similarly to ones based on homologous sequences, while combining sequence and structural features was optimal in terms of accuracy (0.755) and Matthews correlation coefficient (0.476). These results suggest that even without information from multiple structures, it is still possible to effectively predict SCRs for a protein. Finally, inspection of the structures with the worst predictions pinpoints difficulties in SCR definitions. Availability: The SCR database and the prediction server can be found at http://prodata.swmed.edu/SCR . Contact: 91huangi@gmail.com or grishin@chop.swmed.edu Supplementary information: Supplementary data are available at Bioinformatics Online

Description: Motivation: Systems Genetics approaches, in particular those relying on genetical genomics data, put forward a new paradigm of large-scale genome and network analysis. These methods use naturally occurring multi-factorial perturbations (e.g. polymorphisms) in properly controlled and screened genetic crosses to elucidate causal relationships in biological networks. However, although genetical genomics data contain rich information, a clear dissection of causes and effects as required for reconstructing gene regulatory networks is not easily possible. Results: We present a framework for reconstructing gene regulatory networks from genetical genomics data where genotype and phenotype correlation measures are used to derive an initial graph which is subsequently reduced by pruning strategies to minimize false positive predictions. Applied to realistic simulated genetic data from a recent DREAM challenge, we demonstrate that our approach is simple yet effective and outperforms more complex methods (including the best performer) with respect to (i) reconstruction quality (especially for small sample sizes) and (ii) applicability to large data sets due to relatively low computational costs. We also present reconstruction results from real genetical genomics data of yeast. Availability: A MATLAB implementation (script) of the reconstruction framework is available at www.mpi-magdeburg.mpg.de/projects/cna/etcdownloads.html Contact: klamt@mpi-magdeburg.mpg.de

Description: Motivation: Non-coding RNA (ncRNA) genes are increasingly acknowledged for their importance in the human genome. However, there is no comprehensive non-redundant database for all such human genes. Results: We leveraged the effective platform of GeneCards, the human gene compendium, together with the power of fRNAdb and additional primary sources, to judiciously unify all ncRNA gene entries obtainable from 15 different primary sources. Overlapping entries were clustered to unified locations based on an algorithm employing genomic coordinates. This allowed GeneCards’ gamut of relevant entries to rise ~5-fold, resulting in ~80 000 human non-redundant ncRNAs, belonging to 14 classes. Such ‘grand unification’ within a regularly updated data structure will assist future ncRNA research. Availability and implementation: All of these non-coding RNAs are included among the ~122 500 entries in GeneCards V3.09, along with pertinent annotation, automatically mined by its built-in pipeline from 100 data sources. This information is available at www.genecards.org . Contact : Frida.Belinky@weizmann.ac.il Supplementary information: Supplementary data are available at Bioinformatics online.

Description: Motivation: DOOSS (Data Overlaid On Secondary Structures) is a tool for visualizing annotated secondary structures of large single-stranded nucleotide sequences (such as full-length virus genomes). The purpose of this tool is to assist investigators in evaluating the biological relevance of secondary structures within particular sequences. Availability and implementation: DOOSS is written in Java and is available from: http://dooss.computingforbiology.org Contact: michaelgolden0@gmail.com Supplementary information : Supplementary data are available at Bioinformatics online.

Description: Motivation: Novel technologies brought in unprecedented amounts of high-throughput sequencing data along with great challenges in their analysis and interpretation. The percent-spliced-in (PSI, ) metric estimates the incidence of single-exon–skipping events and can be computed directly by counting reads that align to known or predicted splice junctions. However, the majority of human splicing events are more complex than single-exon skipping. Results: In this short report, we present a framework that generalizes the metric to arbitrary classes of splicing events. We change the view from exon centric to intron centric and split the value of into two indices, and , measuring the rate of splicing at the 5' and 3' end of the intron, respectively. The advantage of having two separate indices is that they deconvolute two distinct elementary acts of the splicing reaction. The completeness of splicing index is decomposed in a similar way. This framework is implemented as bam2ssj , a BAM-file–processing pipeline for strand-specific counting of reads that align to splice junctions or overlap with splice sites. It can be used as a consistent protocol for quantifying splice junctions from RNA-seq data because no such standard procedure currently exists. Availability: The C code of bam2ssj is open source and is available at https://github.com/pervouchine/bam2ssj Contact: dp@crg.eu

Description: : The largenet2 C++ library provides an infrastructure for the simulation of large dynamic and adaptive networks with discrete node and link states. Availability: The library is released as free software. It is available at http://biond.github.com/largenet2 . Largenet2 is licensed under the Creative Commons Attribution-NonCommercial 3.0 Unported License. Contact: gerd@biond.org .

Description: : Copy number variants (CNVs) are a major source of genetic variation. Comparing CNVs between samples is important in elucidating their potential effects in a wide variety of biological contexts. HD-CNV (hotspot detector for copy number variants) is a tool for downstream analysis of previously identified CNV regions from multiple samples, and it detects recurrent regions by finding cliques in an interval graph generated from the input. It creates a unique graphical representation of the data, as well as summary spreadsheets and UCSC (University of California, Santa Cruz) Genome Browser track files. The interval graph, when viewed with other software or by automated graph analysis, is useful in identifying genomic regions of interest for further study. Availability and implementation: HD-CNV is an open source Java code and is freely available, with tutorials and sample data from http://daleylab.org . Contact: jcamer7@uwo.ca

Description: Motivation: Stable isotope - labelling experiments have recently gained increasing popularity in metabolomics studies, providing unique insights into the dynamics of metabolic fluxes, beyond the steady-state information gathered by routine mass spectrometry. However, most liquid chromatography–mass spectrometry data analysis software lacks features that enable automated annotation and relative quantification of labelled metabolite peaks. Here , we describe mzMatch–ISO, a new extension to the metabolomics analysis pipeline mzMatch.R. Results: Targeted and untargeted isotope profiling using mzMatch–ISO provides a convenient visual summary of the quality and quantity of labelling for every metabolite through four types of diagnostic plots that show (i) the chromatograms of the isotope peaks of each compound in each sample group; (ii) the ratio of mono-isotopic and labelled peaks indicating the fraction of labelling; (iii) the average peak area of mono - isotopic and labelled peaks in each sample group; and (iv) the trend in the relative amount of labelling in a predetermined isotopomer. To aid further statistical analyses, the values used for generating these plots are also provided as a tab - delimited file. We demonstrate the power and versatility of mzMatch–ISO by analysing a 13 C - labelled metabolome dataset from trypanosomal parasites. Availability: mzMatch.R and mzMatch–ISO are available free of charge from http://mzmatch.sourceforge.net and can be used on Linux and Windows platforms running the latest version of R. Contact: rainer.breitling@manchester.ac.uk . Supplementary information: Supplementary data are available at Bioinformatics online

Description: Motivation: Establishing phospholipid identities in large lipidomic datasets is a labour-intensive process. Where genomics and proteomics capitalize on sequence-based signatures, glycerophospholipids lack easily definable molecular fingerprints. Carbon chain length, degree of unsaturation, linkage, and polar head group identity must be calculated from mass to charge (m/z) ratios under defined mass spectrometry (MS) conditions. Given increasing MS sensitivity, many m/z values are not represented in existing prediction engines. To address this need, Visualization and Phospholipid Identification is a web-based application that returns all theoretically possible phospholipids for any m/z value and MS condition. Visualization algorithms produce multiple chemical structure files for each species. Curated lipids detected by the Canadian Institutes of Health Research Training Program in Neurodegenerative Lipidomics are provided as high-resolution structures. Availability: VaLID is available through the Canadian Institutes of Health Research Training Program in Neurodegenerative Lipidomics resources web site at https://www.med.uottawa.ca/lipidomics/resources.html . Contacts: lipawrd@uottawa.ca Supplementary Information: Supplementary data are available at Bioinformatics online.

Description: : High-accuracy mass values detected by high-resolution mass spectrometry analysis enable prediction of elemental compositions, and thus are used for metabolite annotations in metabolomic studies. Here, we report an application of a relational database to significantly improve the rate of elemental composition predictions. By searching a database of pre-calculated elemental compositions with fixed kinds and numbers of atoms, the approach eliminates redundant evaluations of the same formula that occur in repeated calculations with other tools. When our approach is compared with HR2, which is one of the fastest tools available, our database search times were at least 109 times shorter than those of HR2. When a solid-state drive (SSD) was applied, the search time was 488 times shorter at 5 ppm mass tolerance and 1833 times at 0.1 ppm. Even if the search by HR2 was performed with 8 threads in a high-spec Windows 7 PC, the database search times were at least 26 and 115 times shorter without and with the SSD. These improvements were enhanced in a low spec Windows XP PC. We constructed a web service ‘MFSearcher’ to query the database in a RESTful manner. Availability and implementation: Available for free at http://webs2.kazusa.or.jp/mfsearcher . The web service is implemented in Java, MySQL, Apache and Tomcat, with all major browsers supported. Contact: sakurai@kazusa.or.jp Supplementary information: Supplementary data are available at Bioinformatics online.

Description: : We developed a new algorithmic method, VirusSeq, for detecting known viruses and their integration sites in the human genome using next-generation sequencing data. We evaluated VirusSeq on whole-transcriptome sequencing (RNA-Seq) data of 256 human cancer samples from The Cancer Genome Atlas. Using these data, we showed that VirusSeq accurately detects the known viruses and their integration sites with high sensitivity and specificity. VirusSeq can also perform this function using whole-genome sequencing data of human tissue. Availability: VirusSeq has been implemented in PERL and is available at http://odin.mdacc.tmc.edu/~xsu1/VirusSeq.html . Contact: xsu1@mdanderson.org Supplementary information: Supplementary data are available at Bioinformatics online.

Description: : Although in silico drug discovery approaches are crucial for the development of pharmaceuticals, their potential advantages in agrochemical industry have not been realized. The challenge for computer-aided methods in agrochemical arena is a lack of sufficient information for both pesticides and their targets. Therefore, it is important to establish such knowledge repertoire that contains comprehensive pesticides’ profiles, which include physicochemical properties, environmental fates, toxicities and mode of actions. Here, we present an integrated platform called Pesticide-Target interaction database (PTID), which comprises a total of 1347 pesticides with rich annotation of ecotoxicological and toxicological data as well as 13 738 interactions of pesticide-target and 4245 protein terms via text mining. Additionally, through the integration of ChemMapper, an in-house computational approach to polypharmacology, PTID can be used as a computational platform to identify pesticides targets and design novel agrochemical products. Availability: http://lilab.ecust.edu.cn/ptid/ . Contact: hlli@ecust.edu.cn ; xhqian@ecust.edu.cn Supplementary information: Supplementary data are available at Bioinformatics online.

Description: : Bioclipse, a graphical workbench for the life sciences, provides functionality for managing and visualizing life science data. We introduce Bioclipse-R, which integrates Bioclipse and the statistical programming language R. The synergy between Bioclipse and R is demonstrated by the construction of a decision support system for anticancer drug screening and mutagenicity prediction, which shows how Bioclipse-R can be used to perform complex tasks from within a single software system. Availability and implementation: Bioclipse-R is implemented as a set of Java plug-ins for Bioclipse based on the R-package rj. Source code and binary packages are available from https://github.com/bioclipse and http://www.bioclipse.net/bioclipse-r , respectively. Contact: martin.eklund@farmbio.uu.se Supplementary information: Supplementary data are available at Bioinformatics online.

Description: : The systematic and unbiased charting of protein–protein interaction (PPI) networks relevant to health or diseases has become an important and burgeoning challenge in systems biology. Further, current reports have supported that good correlation exists between the topological properties and biological function of protein nodes in networks. Coronary artery disease (CAD, also called coronary heart disease) is the most common type of heart disease worldwide. Traditional approaches of studying individual gene or protein have shown their weakness in such complex disease. Here, we provide NetCAD, a web-based tool for systematic investigation of CAD-specific proteins in human PPI network. The features of NetCAD includes the following: proposing a novel method combining biological principles and graph theory, quantified topological analysis tools, build-in PPI information database consolidated from major public databases, creating CAD-associated subnetwork and visualizing network graph with good visual effects. NetCAD may provide important biological information for uncovering the molecular mechanisms and potential targets for therapies of CAD, which could not be found merely through molecular biology methods. Availability and implementation: NetCAD is freely available at: http://www.herbbol.org/netcad/ . Contact: zhliu@implad.ac.cn or zhliu.liulab@foxmail.com

Description: : We have developed a software package, Cortex, designed for the analysis of genetic variation by de novo assembly of multiple samples. This allows direct comparison of samples without using a reference genome as intermediate and incorporates discovery and genotyping of single-nucleotide polymorphisms, indels and larger events in a single framework. We introduce pipelines which simplify the analysis of microbial samples and increase discovery power; these also enable the construction of a graph of known sequence and variation in a species, against which new samples can be compared rapidly. We demonstrate the ease-of-use and power by reproducing the results of studies using both long and short reads. Availability: http://cortexassembler.sourceforge.net (GPLv3 license). Contact: zam@well.ox.ac.uk , mcvean@well.ox.ac.uk Supplementary information : Supplementary data are available at Bioinformatics online.

Description: Motivation: Research interests in microRNAs have increased rapidly in the past decade. Many studies have showed that microRNAs have close relationships with various human cancers, and they potentially could be used as cancer indicators in diagnosis or as a suppressor for treatment purposes. There are several databases that contain microRNA–cancer associations predicted by computational methods but few from empirical results. Despite the fact that abundant experiments investigating microRNA expressions in cancer cells have been carried out, the results have remain scattered in the literature. We propose to extract microRNA–cancer associations by text mining and store them in a database called miRCancer. Results: The text mining is based on 75 rules we have constructed, which represent the common sentence structures typically used to state microRNA expressions in cancers. The microRNA–cancer association database, miRCancer, is updated regularly by running the text mining algorithm against PubMed. All miRNA–cancer associations are confirmed manually after automatic extraction. miRCancer currently documents 878 relationships between 236 microRNAs and 79 human cancers through the processing of 〉26 000 published articles. Availability: miRCancer is freely available on the web at http://mircancer.ecu.edu/ Contact: dingq@ecu.edu

Description: : The R/Bioconductor package RamiGO is an R interface to AmiGO that enables visualization of Gene Ontology (GO) trees. Given a list of GO terms, RamiGO uses the AmiGO visualize API to import Graphviz-DOT format files into R, and export these either as images (SVG, PNG) or into Cytoscape for extended network analyses. RamiGO provides easy customization of annotation, highlighting of specific GO terms, colouring of terms by P -value or export of a simplified summary GO tree. We illustrate RamiGO functionalities in a genome-wide gene set analysis of prognostic genes in breast cancer. Availability and implementation: RamiGO is provided in R/Bioconductor, is open source under the Artistic-2.0 License and is available with a user manual containing installation, operating instructions and tutorials. It requires R version 2.15.0 or higher. URL: http://bioconductor.org/packages/release/bioc/html/RamiGO.html Contact: markus.schroeder@ucdconnect.ie Supplementary information: Supplementary data are available at Bioinformatics online.

Description: Motivation: Horizontal gene transfer (HGT) plays a crucial role in the evolution of prokaryotic species. Typically, no more than a few genes are horizontally transferred between any two species. However, several studies identified pairs of species (or linages) between which many different genes were horizontally transferred. Such a pair is said to be linked by a highway of gene sharing . Inferring such highways is crucial to understanding the evolution of prokaryotes and for inferring past symbiotic and ecological associations among different species. Results: We present a new improved method for systematically detecting highways of gene sharing. As we demonstrate using a variety of simulated datasets, our method is highly accurate and efficient, and robust to noise and high rates of HGT. We further validate our method by applying it to a published dataset of 〉22 000 gene trees from 144 prokaryotic species. Our method makes it practical, for the first time, to perform accurate highway analysis quickly and easily even on large datasets with high rates of HGT. Availability and implementation: An implementation of the method can be freely downloaded from: http://acgt.cs.tau.ac.il/hide . Contact: rshamir@tau.ac.il Supplementary information: Supplementary data are available at Bioinformatics online.

Description: Motivation: Most proteins interact with small-molecule ligands such as metabolites or drug compounds. Over the past several decades, many of these interactions have been captured in high-resolution atomic structures. From a geometric point of view, most interaction sites for grasping these small-molecule ligands, as revealed in these structures, form concave shapes, or ‘pockets’, on the protein’s surface. An efficient method for comparing these pockets could greatly assist the classification of ligand-binding sites, prediction of protein molecular function and design of novel drug compounds. Results: We introduce a computational method, APoc ( A lignment of Poc kets), for the large-scale, sequence order-independent, structural comparison of protein pockets. A scoring function, the Pocket Similarity Score (PS-score), is derived to measure the level of similarity between pockets. Statistical models are used to estimate the significance of the PS-score based on millions of comparisons of randomly related pockets. APoc is a general robust method that may be applied to pockets identified by various approaches, such as ligand-binding sites as observed in experimental complex structures, or predicted pockets identified by a pocket-detection method. Finally, we curate large benchmark datasets to evaluate the performance of APoc and present interesting examples to demonstrate the usefulness of the method. We also demonstrate that APoc has better performance than the geometric hashing-based method SiteEngine. Availability and implementation: The APoc software package including the source code is freely available at http://cssb.biology.gatech.edu/APoc . Contact: skolnick@gatech.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Description: Motivation: The avalanche of data arriving since the development of NGS technologies have prompted the need for developing fast, accurate and easily automated bioinformatic tools capable of dealing with massive datasets. Among the most productive applications of NGS technologies is the sequencing of cellular RNA, known as RNA-Seq. Although RNA-Seq provides similar or superior dynamic range than microarrays at similar or lower cost, the lack of standard and user-friendly pipelines is a bottleneck preventing RNA-Seq from becoming the standard for transcriptome analysis. Results: In this work we present a pipeline for processing and analyzing RNA-Seq data, that we have named Grape (Grape RNA-Seq Analysis Pipeline Environment). Grape supports raw sequencing reads produced by a variety of technologies, either in FASTA or FASTQ format, or as prealigned reads in SAM/BAM format. A minimal Grape configuration consists of the file location of the raw sequencing reads, the genome of the species and the corresponding gene and transcript annotation. Grape first runs a set of quality control steps, and then aligns the reads to the genome, a step that is omitted for prealigned read formats. Grape next estimates gene and transcript expression levels, calculates exon inclusion levels and identifies novel transcripts. Grape can be run on a single computer or in parallel on a computer cluster. It is distributed with specific mapping and quantification tools, but given its modular design, any tool supporting popular data interchange formats can be integrated. Availability: Grape can be obtained from the Bioinformatics and Genomics website at: http://big.crg.cat/services/grape . Contact: david.gonzalez@crg.eu or roderic.guigo@crg.eu

Description: Motivation: Multiply correlated datasets have become increasingly common in genome-wide location analysis of regulatory proteins and epigenetic modifications. Their correlation can be directly incorporated into a statistical model to capture underlying biological interactions, but such modeling quickly becomes computationally intractable. Results: We present sparsely correlated hidden Markov models (scHMM), a novel method for performing simultaneous hidden Markov model (HMM) inference for multiple genomic datasets. In scHMM, a single HMM is assumed for each series, but the transition probability in each series depends on not only its own hidden states but also the hidden states of other related series. For each series, scHMM uses penalized regression to select a subset of the other data series and estimate their effects on the odds of each transition in the given series. Following this, hidden states are inferred using a standard forward–backward algorithm, with the transition probabilities adjusted by the model at each position, which helps retain the order of computation close to fitting independent HMMs (iHMM). Hence, scHMM is a collection of inter-dependent non-homogeneous HMMs, capable of giving a close approximation to a fully multivariate HMM fit. A simulation study shows that scHMM achieves comparable sensitivity to the multivariate HMM fit at a much lower computational cost. The method was demonstrated in the joint analysis of 39 histone modifications, CTCF and RNA polymerase II in human CD4+ T cells. scHMM reported fewer high-confidence regions than iHMM in this dataset, but scHMM could recover previously characterized histone modifications in relevant genomic regions better than iHMM. In addition, the resulting combinatorial patterns from scHMM could be better mapped to the 51 states reported by the multivariate HMM method of Ernst and Kellis. Availability: The scHMM package can be freely downloaded from http://sourceforge.net/p/schmm/ and is recommended for use in a linux environment. Contact: ghoshd@psu.edu or zhaohui.qin@emory.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Description: : Advances in sequencing technology have greatly reduced the costs incurred in collecting raw sequencing data. Academic laboratories and researchers therefore now have access to very large datasets of genomic alterations but limited time and computational resources to analyse their potential biological importance. Here, we provide a web-based application, Cancer-Related Analysis of Variants Toolkit, designed with an easy-to-use interface to facilitate the high-throughput assessment and prioritization of genes and missense alterations important for cancer tumorigenesis. Cancer-Related Analysis of Variants Toolkit provides predictive scores for germline variants, somatic mutations and relative gene importance, as well as annotations from published literature and databases. Results are emailed to users as MS Excel spreadsheets and/or tab-separated text files. Availability: http://www.cravat.us/ Contact: karchin@jhu.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Description: : Counting all the k -mers (substrings of length k ) in DNA/RNA sequencing reads is the preliminary step of many bioinformatics applications. However, state of the art k -mer counting methods require that a large data structure resides in memory. Such structure typically grows with the number of distinct k -mers to count. We present a new streaming algorithm for k -mer counting, called DSK (disk streaming of k-mers), which only requires a fixed user-defined amount of memory and disk space. This approach realizes a memory, time and disk trade-off. The multi-set of all k -mers present in the reads is partitioned, and partitions are saved to disk. Then, each partition is separately loaded in memory in a temporary hash table. The k -mer counts are returned by traversing each hash table. Low-abundance k -mers are optionally filtered. DSK is the first approach that is able to count all the 27-mers of a human genome dataset using only 4.0 GB of memory and moderate disk space (160 GB), in 17.9 h. DSK can replace a popular k -mer counting software (Jellyfish) on small-memory servers. Availability: http://minia.genouest.org/dsk Contact: rayan.chikhi@ens-cachan.org

Description: Motivation: Many studies have investigated the relationship between structural properties and dynamic behaviors in biological networks. In particular, feedback loop (FBL) and feedforward loop (FFL) structures have received a great deal of attention. One interesting and common property of FBL and FFL structures is their coherency of coupling. However, the role of coherent FFLs in relation to network robustness is not fully known, whereas that of coherent FBLs has been well established. Results: To establish that coherent FFLs are abundant in biological networks, we examined gene regulatory and signaling networks and found that FFLs are ubiquitous, and are in a coherently coupled form. This result was also observed in the species-based signaling networks that are integrated from KEGG database. By using a random Boolean network model, we demonstrated that these coherent FFLs can improve network robustness against update-rule perturbations. In particular, we found that coherent FFLs increase robustness because these structures induce downstream nodes to be robust against update-rule perturbations. Therefore, coherent FFLs can be considered as a design principle of human signaling networks that improve network robustness against update-rule perturbations. Contact: kwonyk@ulsan.ac.kr Supplementary information: Supplementary data are available at Bioinformatics online.

Description: : gKaKs is a codon-based genome-level Ka/Ks computation pipeline developed and based on programs from four widely used packages: BLAT, BLASTALL (including bl2seq, formatdb and fastacmd), PAML (including codeml and yn00) and KaKs_Calculator (including 10 substitution rate estimation methods). gKaKs can automatically detect and eliminate frameshift mutations and premature stop codons to compute the substitution rates (Ka, Ks and Ka/Ks) between a well-annotated genome and a non-annotated genome or even a poorly assembled scaffold dataset. It is especially useful for newly sequenced genomes that have not been well annotated. We applied gKaKs to estimate the genome-wide substitution rates in five pairs of closely related species. The average Ka and Ks computed by gKaKs were consistent with previous studies. We also compared the Ka, Ks and Ka/Ks of mouse and rat orthologous protein-coding genes estimated by gKaKs and based on the alignments generated by PAL2NAL. Results from two methods are compatible. Availability and implementation: gKaKs is implemented in Perl and is freely available on http://longlab.uchicago.edu/?q=gKaKs . The detailed user manual is available on the website. Contact: cfan@wayne.edu Supplementary information : Supplementary data are available at Bioinformatics online.

Description: : Insertional mutagenesis from virus infection is an important pathogenic risk for the development of cancer. Despite the advent of high-throughput sequencing, discovery of viral integration sites and expressed viral fusion events are still limited. Here, we present ViralFusionSeq (VFS), which combines soft-clipping information, read-pair analysis and targeted de novo assembly to discover and annotate viral–human fusions. VFS was used in an RNA-Seq experiment, simulated DNA-Seq experiment and re-analysis of published DNA-Seq datasets. Our experiments demonstrated that VFS is both sensitive and highly accurate. Availability : VFS is distributed under GPL version 3 at http://hkbic.cuhk.edu.hk/software/viralfusionseq Contact : tf.chan@cuhk.edu.hk Supplementary information : Supplementary data are available at Bioinformatics Online

Description: Motivation: Comparative modeling of RNA is known to be important for making accurate secondary structure predictions. RNA structure prediction tools such as PPfold or RNAalifold use an aligned set of sequences in predictions. Obtaining a multiple alignment from a set of sequences is quite a challenging problem itself, and the quality of the alignment can affect the quality of a prediction. By implementing RNA secondary structure prediction in a statistical alignment framework, and predicting structures from multiple alignment samples instead of a single fixed alignment, it may be possible to improve predictions. Results: We have extended the program StatAlign to make use of RNA-specific features, which include RNA secondary structure prediction from multiple alignments using either a thermodynamic approach (RNAalifold) or a Stochastic Context-Free Grammars (SCFGs) approach (PPfold). We also provide the user with scores relating to the quality of a secondary structure prediction, such as information entropy values for the combined space of secondary structures and sampled alignments, and a reliability score that predicts the expected number of correctly predicted base pairs. Finally, we have created RNA secondary structure visualization plugins and automated the process of setting up Markov Chain Monte Carlo runs for RNA alignments in StatAlign. Availability and implementation: The software is available from http://statalign.github.com/statalign/ . Contact : novak@stats.ox.ac.uk Supplementary information : Supplementary data are available at Bioinformatics online.

Description: : CePa is an R package aiming to find significant pathways through network topology information. The package has several advantages compared with current pathway enrichment tools. First, pathway node instead of single gene is taken as the basic unit when analysing networks to meet the fact that genes must be constructed into complexes to hold normal functions. Second, multiple network centralities are applied simultaneously to measure importance of nodes from different aspects to make a full view on the biological system. CePa extends standard pathway enrichment methods, which include both over-representation analysis procedure and gene-set analysis procedure. CePa has been evaluated with high performance on real-world data, and it can provide more information directly related to current biological problems. Availability: CePa is available at the Comprehensive R Archive Network (CRAN): http://cran.r-project.org/web/packages/CePa/ Contact: jwang@nju.edu.cn Supplementary information: Supplementary Data are available at Bioinformatics online.

Description: Motivation : Many techniques have been developed to compute the response network of a cell. A recent trend in this area is to compute response networks of small size, with the rationale that only part of a pathway is often changed by disease and that interpreting small subnetworks is easier than interpreting larger ones. However, these methods may not uncover the spectrum of pathways perturbed in a particular experiment or disease. Results : To avoid these difficulties, we propose to use algorithms that reconcile case-control DNA microarray data with a molecular interaction network by modifying per-gene differential expression P -values such that two genes connected by an interaction show similar changes in their gene expression values. We provide a novel evaluation of four methods from this class of algorithms. We enumerate three desirable properties that this class of algorithms should address. These properties seek to maintain that the returned gene rankings are specific to the condition being studied. Moreover, to ease interpretation, highly ranked genes should participate in coherent network structures and should be functionally enriched with relevant biological pathways. We comprehensively evaluate the extent to which each algorithm addresses these properties on a compendium of gene expression data for 54 diverse human diseases. We show that the reconciled gene rankings can identify novel disease-related functions that are missed by analyzing expression data alone. Availability : C++ software implementing our algorithms is available in the NetworkReconciliation package as part of the Biorithm software suite under the GNU General Public License: http://bioinformatics.cs.vt.edu/~murali/software/biorithm-docs . Contact : murali@cs.vt.edu Supplementary information : Supplementary data are available at Bioinformatics online.

Description: Motivation: RNA-Seq uses the high-throughput sequencing technology to identify and quantify transcriptome at an unprecedented high resolution and low cost. However, RNA-Seq reads are usually not uniformly distributed and biases in RNA-Seq data post great challenges in many applications including transcriptome assembly and the expression level estimation of genes or isoforms. Much effort has been made in the literature to calibrate the expression level estimation from biased RNA-Seq data, but the effect of biases on transcriptome assembly remains largely unexplored. Results: Here, we propose a statistical framework for both transcriptome assembly and isoform expression level estimation from biased RNA-Seq data. Using a quasi-multinomial distribution model, our method is able to capture various types of RNA-Seq biases, including positional, sequencing and mappability biases. Our experimental results on simulated and real RNA-Seq datasets exhibit interesting effects of RNA-Seq biases on both transcriptome assembly and isoform expression level estimation. The advantage of our method is clearly shown in the experimental analysis by its high sensitivity and precision in transcriptome assembly and the high concordance of its estimated expression levels with quantitative reverse transcription–polymerase chain reaction data. Availability: CEM is freely available at http://www.cs.ucr.edu/~liw/cem.html . Contact: liw@cs.ucr.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Description: Motivation: A number of studies of individual proteins have shown that post-translational modifications (PTMs) are associated with structural rearrangements of their target proteins. Although such studies provide critical insights into the mechanics behind the dynamic regulation of protein function, they usually feature examples with relatively large conformational changes. However, with the steady growth of Protein Data Bank (PDB) and available PTM sites, it is now possible to more systematically characterize the role of PTMs as conformational switches. In this study, we ask (1) what is the expected extent of structural change upon PTM, (2) how often are those changes in fact substantial, (3) whether the structural impact is spatially localized or global and (4) whether different PTMs have different signatures. Results: We exploit redundancy in PDB and, using root-mean-square deviation, study the conformational heterogeneity of groups of protein structures corresponding to identical sequences in their unmodified and modified forms. We primarily focus on the two most abundant PTMs in PDB, glycosylation and phosphorylation, but show that acetylation and methylation have similar tendencies. Our results provide evidence that PTMs induce conformational changes at both local and global level. However, the proportion of large changes is unexpectedly small; only 7% of glycosylated and 13% of phosphorylated proteins undergo global changes 〉2 Å. Further analysis suggests that phosphorylation stabilizes protein structure by reducing global conformational heterogeneity by 25%. Overall, these results suggest a subtle but common role of allostery in the mechanisms through which PTMs affect regulatory and signaling pathways. Contact: predrag@indiana.edu Supplementary Information : Supplementary data are available at Bioinformatics online.

Description: Motivation: Current methods in diagnostic microbiology typically focus on the detection of a single genomic locus or protein in a candidate agent. The presence of the entire microbe is then inferred from this isolated result. Problematically, the presence of recombination in microbial genomes would go undetected unless other genomic loci or protein components were specifically assayed. Microarrays lend themselves well to the detection of multiple loci from a given microbe; furthermore, the inherent nature of microarrays facilitates highly parallel interrogation of multiple microbes. However, none of the existing methods for analyzing diagnostic microarray data has the capacity to specifically identify recombinant microbes. In previous work, we developed a novel algorithm, VIPR, for analyzing diagnostic microarray data. Results: We have expanded upon our previous implementation of VIPR by incorporating a hidden Markov model (HMM) to detect recombinant genomes. We trained our HMM on a set of non-recombinant parental viruses and applied our method to 11 recombinant alphaviruses and 4 recombinant flaviviruses hybridized to a diagnostic microarray in order to evaluate performance of the HMM. VIPR HMM correctly identified 95% of the 62 inter-species recombination breakpoints in the validation set and only two false-positive breakpoints were predicted. This study represents the first description and validation of an algorithm capable of detecting recombinant viruses based on diagnostic microarray hybridization patterns. Availability: VIPR HMM is freely available for academic use and can be downloaded from http://ibridgenetwork.org/wustl/vipr . Contact: davewang@borcim.wustl.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Description: Motivation: The boost of next-generation sequencing technologies provides us with an unprecedented opportunity for elucidating genetic mysteries, yet the short-read length hinders us from better assembling the genome from scratch. New protocols now exist that can generate overlapping pair-end reads. By joining the 3' ends of each read pair, one is able to construct longer reads for assembling. However, effectively joining two overlapped pair-end reads remains a challenging task. Result: In this article, we present an efficient tool called Connecting Overlapped Pair-End (COPE) reads, to connect overlapping pair-end reads using k -mer frequencies. We evaluated our tool on 30 x simulated pair-end reads from Arabidopsis thaliana with 1% base error. COPE connected over 99% of reads with 98.8% accuracy, which is, respectively, 10 and 2% higher than the recently published tool FLASH. When COPE is applied to real reads for genome assembly, the resulting contigs are found to have fewer errors and give a 14-fold improvement in the N50 measurement when compared with the contigs produced using unconnected reads. Availability and implementation: COPE is implemented in C++ and is freely available as open-source code at ftp://ftp.genomics.org.cn/pub/cope . Contact: twlam@cs.hku.hk or luoruibang@genomics.org.cn

Description: Motivation: It becomes widely accepted that human cancer is a disease involving dynamic changes in the genome and that the missense mutations constitute the bulk of human genetic variations. A multitude of computational algorithms, especially the machine learning-based ones, has consequently been proposed to distinguish missense changes that contribute to the cancer progression (‘driver’ mutation) from those that do not (‘passenger’ mutation). However, the existing methods have multifaceted shortcomings, in the sense that they either adopt incomplete feature space or depend on protein structural databases which are usually far from integrated. Results: In this article, we investigated multiple aspects of a missense mutation and identified a novel feature space that well distinguishes cancer-associated driver mutations from passenger ones. An index (DX score) was proposed to evaluate the discriminating capability of each feature, and a subset of these features which ranks top was selected to build the SVM classifier. Cross-validation showed that the classifier trained on our selected features significantly outperforms the existing ones both in precision and robustness. We applied our method to several datasets of missense mutations culled from published database and literature and obtained more reasonable results than previous studies. Availability : The software is available online at http://www.methodisthealth.com/software and https://sites.google.com/site/drivermutationidentification/ . Contact : xzhou@tmhs.org Supplementary information : Supplementary data are available at Bioinformatics online.

Description: Motivation: Assembling peptides identified from tandem mass spectra into a list of proteins, referred to as protein inference, is an important issue in shotgun proteomics. The objective of protein inference is to find a subset of proteins that are truly present in the sample. Although many methods have been proposed for protein inference, several issues such as peptide degeneracy still remain unsolved. Results: In this article, we present a linear programming model for protein inference. In this model, we use a transformation of the joint probability that each peptide/protein pair is present in the sample as the variable. Then, both the peptide probability and protein probability can be expressed as a formula in terms of the linear combination of these variables. Based on this simple fact, the protein inference problem is formulated as an optimization problem: minimize the number of proteins with non-zero probabilities under the constraint that the difference between the calculated peptide probability and the peptide probability generated from peptide identification algorithms should be less than some threshold. This model addresses the peptide degeneracy issue by forcing some joint probability variables involving degenerate peptides to be zero in a rigorous manner. The corresponding inference algorithm is named as ProteinLP. We test the performance of ProteinLP on six datasets. Experimental results show that our method is competitive with the state-of-the-art protein inference algorithms. Availability: The source code of our algorithm is available at: https://sourceforge.net/projects/prolp/ . Contact: zyhe@dlut.edu.cn Supplementary information: Supplementary data are available at Bioinformatics Online.

Description: Motivation: Methylation of cytosines in DNA is an important epigenetic mechanism involved in transcriptional regulation and preservation of genome integrity in a wide range of eukaryotes. Immunoprecipitation of methylated DNA followed by hybridization to genomic tiling arrays (MeDIP-chip) is a cost-effective and sensitive method for methylome analyses. However, existing bioinformatics methods only enable a binary classification into unmethylated and methylated genomic regions, which limit biological interpretations. Indeed, DNA methylation levels can vary substantially within a given DNA fragment depending on the number and degree of methylated cytosines. Therefore, a method for the identification of more than two methylation states is highly desirable. Results: Here, we present a three-state hidden Markov model (MeDIP-HMM) for analyzing MeDIP-chip data. MeDIP-HMM uses a higher-order state-transition process improving modeling of spatial dependencies between chromosomal regions, allows a simultaneous analysis of replicates and enables a differentiation between unmethylated, methylated and highly methylated genomic regions. We train MeDIP-HMM using a Bayesian Baum–Welch algorithm, integrating prior knowledge on methylation levels. We apply MeDIP-HMM to the analysis of the Arabidopsis root methylome and systematically investigate the benefit of using higher-order HMMs. Moreover, we also perform an in-depth comparison study with existing methods and demonstrate the value of using MeDIP-HMM by comparisons to current knowledge on the Arabidopsis methylome. We find that MeDIP-HMM is a fast and precise method for the analysis of methylome data, enabling the identification of distinct DNA methylation levels. Finally, we provide evidence for the general applicability of MeDIP-HMM by analyzing promoter DNA methylation data obtained for chicken. Availability: MeDIP-HMM is available as part of the open-source Java library Jstacs ( www.jstacs.de/index.php/MeDIP-HMM ). Data files are available from the Jstacs website. Contact: seifert@ipk-gatersleben.de Supplementary information: Supplementary data are available at Bioinformatics online.

Description: Motivation: Automated annotation of neuroanatomical connectivity statements from the neuroscience literature would enable accessible and large-scale connectivity resources. Unfortunately, the connectivity findings are not formally encoded and occur as natural language text. This hinders aggregation, indexing, searching and integration of the reports. We annotated a set of 1377 abstracts for connectivity relations to facilitate automated extraction of connectivity relationships from neuroscience literature. We tested several baseline measures based on co-occurrence and lexical rules. We compare results from seven machine learning methods adapted from the protein interaction extraction domain that employ part-of-speech, dependency and syntax features. Results: Co-occurrence based methods provided high recall with weak precision. The shallow linguistic kernel recalled 70.1% of the sentence-level connectivity statements at 50.3% precision. Owing to its speed and simplicity, we applied the shallow linguistic kernel to a large set of new abstracts. To evaluate the results, we compared 2688 extracted connections with the Brain Architecture Management System (an existing database of rat connectivity). The extracted connections were connected in the Brain Architecture Management System at a rate of 63.5%, compared with 51.1% for co-occurring brain region pairs. We found that precision increases with the recency and frequency of the extracted relationships. Availability and implementation: The source code, evaluations, documentation and other supplementary materials are available at http://www.chibi.ubc.ca/WhiteText . Contact: paul@chibi.ubc.ca Supplementary information: Supplementary data are available at Bioinformatics Online.

Description: Motivation: The first step for clinical diagnostics, prognostics and targeted therapeutics of cancer is to comprehensively understand its molecular mechanisms. Large-scale cancer genomics projects are providing a large volume of data about genomic, epigenomic and gene expression aberrations in multiple cancer types. One of the remaining challenges is to identify driver mutations, driver genes and driver pathways promoting cancer proliferation and filter out the unfunctional and passenger ones. Results: In this study, we propose two methods to solve the so-called maximum weight submatrix problem, which is designed to de novo identify mutated driver pathways from mutation data in cancer. The first one is an exact method that can be helpful for assessing other approximate or/and heuristic algorithms. The second one is a stochastic and flexible method that can be employed to incorporate other types of information to improve the first method. Particularly, we propose an integrative model to combine mutation and expression data. We first apply our methods onto simulated data to show their efficiency. We further apply the proposed methods onto several real biological datasets, such as the mutation profiles of 74 head and neck squamous cell carcinomas samples, 90 glioblastoma tumor samples and 313 ovarian carcinoma samples. The gene expression profiles were also considered for the later two data. The results show that our integrative model can identify more biologically relevant gene sets. We have implemented all these methods and made a package called mutated driver pathway finder, which can be easily used for other researchers. Availability: A MATLAB package of MDPFinder is available at http://zhangroup.aporc.org/ShiHuaZhang Contact: zsh@amss.ac.cn Supplementary information: Supplementary data are available at Bioinformatics online.

Description: Motivation: Biologistics provides data for quantitative analysis of transport (diffusion) processes and their spatio-temporal correlations in cells. Mobility of proteins is one of the few parameters necessary to describe reaction rates for gene regulation. Although understanding of diffusion-limited biochemical reactions in vivo requires mobility data for the largest possible number of proteins in their native forms, currently, there is no database that would contain the complete information about the diffusion coefficients (DCs) of proteins in a given cell type. Results: We demonstrate a method for the determination of in vivo DCs for any molecule—regardless of its molecular weight, size and structure—in any type of cell. We exemplify the method with the database of in vivo DC for all proteins (4302 records) from the proteome of K12 strain of Escherichia coli , together with examples of DC of amino acids, sugars, RNA and DNA. The database follows from the scale-dependent viscosity reference curve (sdVRC). Construction of sdVRC for prokaryotic or eukaryotic cell requires ~20 in vivo measurements using techniques such as fluorescence correlation spectroscopy (FCS), fluorescence recovery after photobleaching (FRAP), nuclear magnetic resonance (NMR) or particle tracking. The shape of the sdVRC would be different for each organism, but the mathematical form of the curve remains the same. The presented method has a high predictive power, as the measurements of DCs of several inert, properly chosen probes in a single cell type allows to determine the DCs of thousands of proteins. Additionally, obtained mobility data allow quantitative study of biochemical interactions in vivo . Contact: rholyst@ichf.edu.pl Supplementary information: Supplementary data are available at Bioinformatics Online.

Description: Motivation: In modern sequencing studies, one can improve the confidence of genotype calls by phasing haplotypes using information from an external reference panel of fully typed unrelated individuals. However, the computational demands are so high that they prohibit researchers with limited computational resources from haplotyping large-scale sequence data. Results: Our graphics processing unit based software delivers haplotyping and imputation accuracies comparable to competing programs at a fraction of the computational cost and peak memory demand. Availability: Mendel-GPU , our OpenCL software, runs on Linux platforms and is portable across AMD and nVidia GPUs. Users can download both code and documentation at http://code.google.com/p/mendel-gpu/ . Contact: gary.k.chen@usc.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Description: : Phylogenetics, likelihood, evolution and complexity ( PLEX ) is a flexible and fast Bayesian Markov chain Monte Carlo software program for large-scale analysis of nucleotide and amino acid data using complex evolutionary models in a phylogenetic framework. The program gains large speed improvements over standard approaches by implementing ‘partial sampling of substitution histories’, a data augmentation approach that can reduce data analysis times from months to minutes on large comparative datasets. A variety of nucleotide and amino acid substitution models are currently implemented, including non-reversible and site-heterogeneous mixture models. Due to efficient algorithms that scale well with data size and model complexity, PLEX can be used to make inferences from hundreds to thousands of taxa in only minutes on a desktop computer. It also performs probabilistic ancestral sequence reconstruction. Future versions will support detection of co-evolutionary interactions between sites, probabilistic tests of convergent evolution and rigorous testing of evolutionary hypotheses in a Bayesian framework. Availability and implementation: PLEX v1.0 is licensed under GPL. Source code and documentation will be available for download at www.evolutionarygenomics.com/ProgramsData/PLEX . PLEX is implemented in C++ and supported on Linux, Mac OS X and other platforms supporting standard C++ compilers. Example data, control files, documentation and accessory Perl scripts are available from the website. Contact: David.Pollock@UCDenver.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Description: : PrIME-DLRS (or colloquially: ‘Delirious’) is a phylogenetic software tool to simultaneously infer and reconcile a gene tree given a species tree. It accounts for duplication and loss events, a relaxed molecular clock and is intended for the study of homologous gene families, for example in a comparative genomics setting involving multiple species. PrIME-DLRS uses a Bayesian MCMC framework, where the input is a known species tree with divergence times and a multiple sequence alignment, and the output is a posterior distribution over gene trees and model parameters. Availability and implementation : PrIME-DLRS is available for Java SE 6+ under the New BSD License, and JAR files and source code can be downloaded from http://code.google.com/p/jprime/ . There is also a slightly older C++ version available as a binary package for Ubuntu, with download instructions at http://prime.sbc.su.se . The C++ source code is available upon request. Contact: joel.sjostrand@scilifelab.se or jens.lagergren@scilifelab.se . Supplementary Information : PrIME-DLRS is based on a sound probabilistic model (Åkerborg et al. , 2009) and has been thoroughly validated on synthetic and biological datasets ( Supplementary Material online ).

Description: : Computational Structural Biology Toolbox (CSB) is a cross-platform Python class library for reading, storing and analyzing biomolecular structures with rich support for statistical analyses. CSB is designed for reusability and extensibility and comes with a clean, well-documented API following good object-oriented engineering practice. Availability: Stable release packages are available for download from the Python Package Index (PyPI) as well as from the project’s website http://csb.codeplex.com . Contacts: ivan.kalev@gmail.com or michael.habeck@tuebingen.mpg.de

Description: : GREVE has been developed to assist with the identification of recurrent genomic aberrations across cancer samples. The exact characterization of such aberrations remains a challenge despite the availability of increasing amount of data, from SNParray to next-generation sequencing. Furthermore, genomic aberrations in cancer are especially difficult to handle because they are, by nature, unique to the patients. However, their recurrence in specific regions of the genome has been shown to reflect their relevance in the development of tumors. GREVE makes use of previously characterized events to identify such regions and focus any further analysis. Availability: GREVE is available through a web interface and open-source application ( http://www.well.ox.ac.uk/GREVE ).

Description: : Protein interaction networks are widely used to depict the relationships between proteins. These networks often lack the information on physical binary interactions, and they do not inform whether there is incompatibility of structure between binding partners. Here, we introduce SAPIN, a framework dedicated to the structural analysis of protein interaction networks. SAPIN first identifies the protein parts that could be involved in the interaction and provides template structures. Next, SAPIN performs structural superimpositions to identify compatible and mutually exclusive interactions. Finally, the results are displayed using Cytoscape Web. Availability: The SAPIN server is available at http://sapin.crg.es . Contact: jae-seong.yang@crg.eu or christina.kiel@crg.eu Supplementary information: Supplementary data are available at Bioinformatics Online.

Description: : ChemBioServer is a publicly available web application for effectively mining and filtering chemical compounds used in drug discovery. It provides researchers with the ability to (i) browse and visualize compounds along with their properties, (ii) filter chemical compounds for a variety of properties such as steric clashes and toxicity, (iii) apply perfect match substructure search, (iv) cluster compounds according to their physicochemical properties providing representative compounds for each cluster, (v) build custom compound mining pipelines and (vi) quantify through property graphs the top ranking compounds in drug discovery procedures. ChemBioServer allows for pre-processing of compounds prior to an in silico screen, as well as for post-processing of top-ranked molecules resulting from a docking exercise with the aim to increase the efficiency and the quality of compound selection that will pass to the experimental test phase. Availability: The ChemBioServer web application is available at: http://bioserver-3.bioacademy.gr/Bioserver/ChemBioServer/ . Contact: gspyrou@bioacademy.gr

Description: Motivation: Cell growth and division affect the kinetics of internal cellular processes and the phenotype diversity of cell populations. Since the effects are complex, e.g. different cellular components are partitioned differently in cell division, to account for them in silico, one needs to simulate these processes in great detail. Results : We present SGNS2, a simulator of chemical reaction systems according to the Stochastic Simulation Algorithm with multi-delayed reactions within hierarchical, interlinked compartments which can be created, destroyed and divided at runtime. In division, molecules are randomly segregated into the daughter cells following a specified distribution corresponding to one of several partitioning schemes, applicable on a per-molecule-type basis. We exemplify its use with six models including a stochastic model of the disposal mechanism of unwanted protein aggregates in Escherichia coli , a model of phenotypic diversity in populations with different levels of synchrony, a model of a bacteriophage’s infection of a cell population and a model of prokaryotic gene expression at the nucleotide and codon levels. Availability : SGNS2, instructions and examples available at www.cs.tut.fi/~lloydpri/sgns2/ (open source under New BSD license). Contact : jason.lloyd-price@tut.fi Supplementary information: Supplementary data are available at Bioinformatics online.

Description: : There is an immediate need for tools to both analyse and visualize in real-time single-nucleotide polymorphisms, insertions and deletions, and other structural variants from new sequence file formats. We have developed VarB software that can be used to visualize variant call format files in real time, as well as identify regions under balancing selection and informative markers to differentiate user-defined groups (e.g. populations). We demonstrate its utility using sequence data from 50 Plasmodium falciparum isolates comprising two different continents and confirm known signals from genomic regions that contain important antigenic and anti-malarial drug-resistance genes. Availability and implementation: The C++-based software VarB and user manual are available from www.pathogenseq.org/varb . Contact: taane.clark@lshtm.ac.uk

Description: : comb-p is a command-line tool and a python library that manipulates BED files of possibly irregularly spaced P -values and (1) calculates auto-correlation, (2) combines adjacent P -values, (3) performs false discovery adjustment, (4) finds regions of enrichment (i.e. series of adjacent low P -values) and (5) assigns significance to those regions. In addition, tools are provided for visualization and assessment. We provide validation and example uses on bisulfite-seq with P -values from Fisher’s exact test, tiled methylation probes using a linear model and Dam-ID for chromatin binding using moderated t -statistics. Because the library accepts input in a simple, standardized format and is unaffected by the origin of the P -values, it can be used for a wide variety of applications. Availability: comb-p is maintained under the BSD license. The documentation and implementation are available at https://github.com/brentp/combined-pvalues . Contact: bpederse@gmail.com

Description: : ImgLib2 is an open-source Java library for n -dimensional data representation and manipulation with focus on image processing. It aims at minimizing code duplication by cleanly separating pixel-algebra, data access and data representation in memory. Algorithms can be implemented for classes of pixel types and generic access patterns by which they become independent of the specific dimensionality, pixel type and data representation. ImgLib2 illustrates that an elegant high-level programming interface can be achieved without sacrificing performance. It provides efficient implementations of common data types, storage layouts and algorithms. It is the data model underlying ImageJ2, the KNIME Image Processing toolbox and an increasing number of Fiji-Plugins. Availability : ImgLib2 is licensed under BSD. Documentation and source code are available at http://imglib2.net and in a public repository at https://github.com/imagej/imglib . Supplementary Information: Supplementary data are available at Bioinformatics Online. Contact : saalfeld@mpi-cbg.de

Description: : We have established an RNA mapping database (RMDB) to enable structural, thermodynamic and kinetic comparisons across single-nucleotide-resolution RNA structure mapping experiments. The volume of structure mapping data has greatly increased since the development of high-throughput sequencing techniques, accelerated software pipelines and large-scale mutagenesis. For scientists wishing to infer relationships between RNA sequence/structure and these mapping data, there is a need for a database that is curated, tagged with error estimates and interfaced with tools for sharing, visualization, search and meta-analysis. Through its on-line front-end, the RMDB allows users to explore single-nucleotide-resolution mapping data in heat-map, bar-graph and colored secondary structure graphics; to leverage these data to generate secondary structure hypotheses; and to download the data in standardized and computer-friendly files, including the RDAT and community-consensus SNRNASM formats. At the time of writing, the database houses 53 entries, describing more than 2848 experiments of 1098 RNA constructs in several solution conditions and is growing rapidly. Availability: Freely available on the web at http://rmdb.stanford.edu Contact: rhiju@stanford.edu Supplementary information: Supplementary data are available at Bioinformatics Online.

Description: : Spoligotyping is a well-established genotyping technique based on the presence of unique DNA sequences in Mycobacterium tuberculosis ( Mtb ), the causal agent of tuberculosis disease (TB). Although advances in sequencing technologies are leading to whole-genome bacterial characterization, tens of thousands of isolates have been spoligotyped, giving a global view of Mtb strain diversity. To bridge the gap, we have developed SpolPred , a software to predict the spoligotype from raw sequence reads. Our approach is compared with experimentally and de novo assembly determined strain types in a set of 44 Mtb isolates. In silico and experimental results are identical for almost all isolates (39/44). However, SpolPred detected five experimentally false spoligotypes and was more accurate and faster than the assembling strategy. Application of SpolPred to an additional seven isolates with no laboratory data led to types that clustered with identical experimental types in a phylogenetic analysis using single-nucleotide polymorphisms. Our results demonstrate the usefulness of the tool and its role in revealing experimental limitations. Availability and implementation : SpolPred is written in C and is available from www.pathogenseq.org/spolpred . Contact: francesc.coll@lshtm.ac.uk Supplementary information: Supplementary data are available at Bioinformatics Online.

Description: : NetworkView is an application for the display and analysis of protein·RNA interaction networks derived from structure and/or dynamics. These networks typically model individual protein residues and nucleic acid monomers as nodes and their pairwise contacts as edges with associated weights. NetworkView projects the network onto the underlying 3D molecular structure so that visualization and analysis of the network can be coupled to physical and biological properties. NetworkView is implemented as a plugin to the molecular visualization software VMD. Availability and implementation : NetworkView is included with VMD, which is available at http://www.ks.uiuc.edu/Research/vmd/ . Documentation, tutorials and supporting programs are available at http://www.scs.illinois.edu/schulten/software/ . Contact : networkview@scs.illinois.edu