ALBERT

Description: Motivation: High-throughput sequencing (HTS) technologies have made low-cost sequencing of large numbers of samples commonplace. An explosion in the type, not just number, of sequencing experiments has also taken place including genome re-sequencing, population-scale variation detection, whole transcriptome sequencing and genome-wide analysis of protein-bound nucleic acids. Results: We present Artemis as a tool for integrated visualization and computational analysis of different types of HTS datasets in the context of a reference genome and its corresponding annotation. Availability: Artemis is freely available (under a GPL licence) for download (for MacOSX, UNIX and Windows) at the Wellcome Trust Sanger Institute websites: http://www.sanger.ac.uk/resources/software/artemis/ . Contact: artemis@sanger.ac.uk ; tjc@sanger.ac.uk

Description: : microRibonucleic acid (miRNAs) are small regulatory molecules that act by mRNA degradation or via translational repression. Although many miRNAs are ubiquitously expressed, a small subset have differential expression patterns that may give rise to tissue-specific complexes. Motivation: This work studies gene targeting patterns amongst miRNAs with differential expression profiles, and links this to control and regulation of protein complexes. Results: We find that, when a pair of miRNAs are not expressed in the same tissues, there is a higher tendency for them to target the direct partners of the same hub proteins. At the same time, they also avoid targeting the same set of hub-spokes. Moreover, the complexes corresponding to these hub-spokes tend to be specific and nonoverlapping. This suggests that the effect of miRNAs on the formation of complexes is specific. Contact: wongls@comp.nus.edu.sg Supplementary information: Supplementary data are available at Bioinformatics online.

Description: Motivation: Small interfering RNAs (siRNAs) are produced from much longer sequences of double-stranded RNA precursors through cleavage by Dicer or a Dicer-like protein. These small RNAs play a key role in genetic and epigenetic regulation; however, a full understanding of the mechanisms by which they operate depends on the characterization of the precursors from which they are derived. Results: High-throughput sequencing of small RNA populations allows the locations of the double-stranded RNA precursors to be inferred. We have developed methods to analyse small RNA sequencing data from multiple biological sources, taking into account replicate information, to identify robust sets of siRNA precursors. Our methods show good performance on both a set of small RNA sequencing data in Arabidopsis thaliana and simulated datasets. Availability: Our methods are available as the Bioconductor ( www.bioconductor.org ) package segmentSeq (version 1.5.6 and above). Contact: tjh48@cam.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online.

Description: Motivation: Intrinsically disordered regions are key for the function of numerous proteins, and the scant available experimental annotations suggest the existence of different disorder flavors. While efficient predictions are required to annotate entire genomes, most existing methods require sequence profiles for disorder prediction, making them cumbersome for high-throughput applications. Results: In this work, we present an ensemble of protein disorder predictors called ESpritz. These are based on bidirectional recursive neural networks and trained on three different flavors of disorder, including a novel NMR flexibility predictor. ESpritz can produce fast and accurate sequence-only predictions, annotating entire genomes in the order of hours on a single processor core. Alternatively, a slower but slightly more accurate ESpritz variant using sequence profiles can be used for applications requiring maximum performance. Two levels of prediction confidence allow either to maximize reasonable disorder detection or to limit expected false positives to 5%. ESpritz performs consistently well on the recent CASP9 data, reaching a S w measure of 54.82 and area under the receiver operator curve of 0.856. The fast predictor is four orders of magnitude faster and remains better than most publicly available CASP9 methods, making it ideal for genomic scale predictions. Conclusions: ESpritz predicts three flavors of disorder at two distinct false positive rates, either with a fast or slower and slightly more accurate approach. Given its state-of-the-art performance, it can be especially useful for high-throughput applications. Availability: Both a web server for high-throughput analysis and a Linux executable version of ESpritz are available from: http://protein.bio.unipd.it/espritz/ Contact: silvio.tosatto@unipd.it Supplementary information: Supplementary data are available at Bioinformatics online.

Description: Motivation: Clustering protein structures is an important task in structural bioinformatics. De novo structure prediction, for example, often involves a clustering step for finding the best prediction. Other applications include assigning proteins to fold families and analyzing molecular dynamics trajectories. Results: We present Pleiades, a novel approach to clustering protein structures with a rigorous mathematical underpinning. The method approximates clustering based on the root mean square deviation by first mapping structures to Gauss integral vectors—which were introduced by Røgen and co-workers—and subsequently performing K-means clustering. Conclusions: Compared to current methods, Pleiades dramatically improves on the time needed to perform clustering, and can cluster a significantly larger number of structures, while providing state-of-the-art results. The number of low energy structures generated in a typical folding study, which is in the order of 50 000 structures, can be clustered within seconds to minutes. Contact: thamelry@binf.ku.dk ; harder@binf.ku.dk Supplementary Information: Supplementary data are available at Bioinformatics online.

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: Purpose   To investigate whether general practitioners, hospital physicians and specialized practitioners in psychiatry have similar preferences for initiating treatment with expensive serotonin-specific reuptake inhibitors (SSRIs). Methods   All first-time prescriptions for the SSRIs escitalopram, citalopram and sertraline reported to the Danish National Register of Medicinal Product Statistics from April 1, 2009 until March 31, 2010 were analysed with regard to treatment naivety and type of prescriber. A prescription was considered as first time if the patient had not received a prescription for the same drug within the last 2 years. Patients who had not received a prescription for an antidepressant within 6 months prior to the date of redemption were classified as treatment-naïve. Results   We included 82,702 first-time prescriptions, 65,313 (79 %) of which were for treatment-naïve patients. Of the treatment-naïve patients, 19 % were initially prescribed escitalopram. Hospital physicians prescribed escitalopram to 34 % of their treatment-naïve patients, while practitioners specialized in psychiatry prescribed it to 25 %, and general practitioners prescribed it to 17 %. General practitioners, however, were responsible for initiating 87 % of all treatment-naïve patients. Conclusion   The most expensive SSRI, escitalopram, is prescribed as first choice to one in five patients receiving their first antidepressant of escitalopram, citalopram or sertraline. General practitioners made the bulk of all first-time SSRI prescriptions to treatment-naïve patients. Content Type Journal Article Category Pharmacoepidemiology and Prescription Pages 1-5 DOI 10.1007/s00228-012-1447-7 Authors Karen Killerup Poulsen, Faculty of Health and Medicines Sciences, University of Copenhagen, Copenhagen, Denmark Dorte Glintborg, Institute for Rational Pharmacotherapy, Copenhagen, Denmark Søren Ilsøe Moreno, Institute for Rational Pharmacotherapy, Copenhagen, Denmark Steffen Thirstrup, Danish Health and Medicines Authority, Copenhagen, Denmark Lise Aagaard, Institute of Public Health, Clinical Pharmacology, Faculty of Health Sciences, University of Southern Denmark, JB Winsløws Vej 19, 5000 Odense C, Denmark Stig Ejdrup Andersen, Department of Clinical Pharmacology, Bispebjerg Hospital, Copenhagen, Denmark Journal European Journal of Clinical Pharmacology Online ISSN 1432-1041 Print ISSN 0031-6970

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

Description: Bringing pharmacogenetics to the bedside Content Type Journal Article Category Letter to the Editors Pages 1-2 DOI 10.1007/s00228-012-1444-x Authors Ankur Gupta, Department of Cardiology, Hartford Hospital, Cardiology, 80 Seymour Street, Hartford, 06106, CT, USA Journal European Journal of Clinical Pharmacology Online ISSN 1432-1041 Print ISSN 0031-6970

Description: Purpose   It is recognised that paediatric prescribing errors are prevalent, and that most are made by junior doctors; however, detecting errors in order to demonstrate actual error rates can be difficult. There is evidence to suggest that dosing errors are the most common type of prescribing error in practice, but there has been little research on whether prescribing assessments are an effective reflection of actual practice.This article aims to determine if prescribing error types in a paediatric prescribing competency assessment reflects error types seen in actual practice. Methods   This study was conducted in Royal Manchester Children’s Hospital (RMCH) and the participants were junior doctors working at RMCH in 2010-2011. The intervention was a prescribing competency assessment package at RMCH.The main outcome measurement was the category and rate of prescribing errors. Results were taken from the junior doctors’ prescribing competency assessment. The assessment papers were analysed for errors and the errors were then broken down into pre-defined categories. Results   Rates of prescribing errors in the competency assessment are higher than published results shown in practice (23.1 %). The most common type of prescribing error (incorrect calculation of dose) reflects results seen in actual practice. Conclusion   The types of prescribing errors made in the competency assessment are reflective of errors made in actual practice. Prescribing teaching can be tailored according to the types of errors noted; and the prescribing competency package as a whole can be used to educate junior doctors on good prescribing practice and reduce prescribing errors. Content Type Journal Article Category Pharmacoepidemiology and Prescription Pages 1-4 DOI 10.1007/s00228-012-1440-1 Authors Tessa Davis, Medical Leadership Programme, North Western Deanery, Manchester, UK Hong Thoong, Royal Manchester Children’s Hospital, Pharmacy Department, Central Manchester University Hospitals NHS Foundation Trust, Manchester, UK Anna Kelsey, Central Manchester University Hospitals NHS Foundation Trust, Manchester, UK Guy Makin, Institute of Cancer Sciences, Manchester Cancer Research Centre, Manchester Academic Health Sciences Centre, University of Manchester, Manchester, UK Journal European Journal of Clinical Pharmacology Online ISSN 1432-1041 Print ISSN 0031-6970

Description: Background/aim   Timed interval cerebrospinal fluid (CSF) sampling by indwelling catheterization can be a valuable corroborative tool for the pharmacokinetic and pharmacodynamic assessment of drugs. CSF sampling in studies on drug candidates for Alzheimer’s disease have been conducted in evaluations of the biomarkers acetylcholine (ACh), tau proteins, amyloid precursor protein and beta-amyloid fragments. The primary aim of this study was to study the feasibility and the burden on the healthy volunteers of serial CSF sampling within the contract research organization environment in order to establish a standardized research tool for future drug development studies. Materials and methods   This study is a validation study in healthy subjects: eight healthy male subjects aged 55–75 years were enrolled. After eligibility had been confirmed, the subjects were admitted to the clinical pharmacology unit 2 days before starting the CSF sampling procedure. Hydration by drip infusion of 2 L saline was performed for 24 h before starting the CSF sampling procedure, and for antithrombotic purposes, Fraxiparine (nadroparine calcium) was given 12 and 36 h after intradural catheterization. CSF catheterization was performed by board-certified anesthesiologists with experience in inserting indwelling intrathecal catheters. Subjects only required to remain in a horizontal position for the first 24 h after removal of the catheter. CSF and blood samples were collected by interval sampling over a 30-h period. Results   The study was completed by seven of the eight subjects. Six subjects who completed the study reported adverse effects (AEs) which were all mild and from which they recovered during their stay in the clinic. A total of 25 AEs were reported of which 13 were considered to be procedure-related. The procedure was well tolerated by all participating subjects, and the VAS scale scores for headache and back pain were low. CSF samples were analyzed for ACh. All values were above the lowest limit of quantification. On average, the ACh concentration started at a low level but rose between 1 and 2 h after insertion of the catheter and then remained high during the whole sampling period up to 30 h. Conclusion   Serial sampling of CSF in seven healthy volunteers up to 30 h occurred without serious complications and was well tolerated. The CSF collected was of good quality and facilitated the assessment of an Alzheimer’s disease-sensitive biomarker. We conclude that this validation study can form the basis for future patient studies aimed at elucidating disease mechanisms and the pharmacodynamics of drugs in the developmental stage. Content Type Journal Article Category Clinical Trial Pages 1-8 DOI 10.1007/s00228-012-1443-y Authors Izaak den Daas, QPS Netherlands BV, Groningen, The Netherlands Johan Wemer, QPS Netherlands BV, Groningen, The Netherlands Khalid Abou Farha, QPS Netherlands BV, Groningen, The Netherlands Wim Tamminga, QPS Netherlands BV, Groningen, The Netherlands Theo de Boer, QPS Netherlands BV, Groningen, The Netherlands Rob Spanjersberg, Department of Anesthesiology, University Groningen, University Medical Center Groningen, Groningen, The Netherlands Michel M. R. F. Struys, Department of Anesthesiology, University Groningen, University Medical Center Groningen, Groningen, The Netherlands Anthony R. Absalom, Department of Anesthesiology, University Groningen, University Medical Center Groningen, Groningen, The Netherlands Journal European Journal of Clinical Pharmacology Online ISSN 1432-1041 Print ISSN 0031-6970

Description: Motivation: Hepatitis B virus and hepatitis C virus are the two leading causes resulting in hepatocellular carcinoma (HCC). It is observed that hepatitis C virus (HCV) is relatively difficult to induce HCC compared with hepatitis B virus (HBV). This motivates us to reveal the reasons behind this from the viewpoint of immune genes. Results: To distinguish the immune genes with low-level expression in HBV-induced HCC, but high-level expression in HCV-induced HCC, the concept of distinction immune gene is proposed. A filter is then designed to screen these genes. By using gene positive network with strong correlations between genes, the genes are further filtered to form the set of key distinction immune genes. The 23 key distinction immune genes are screened, which are divided into four clusters, T cells, B cells, immune signalling and major histocompatibility complex. It is evident that the screened genes are important immune genes, which are activated in HCV-induced HCC, but inactivated in HBV-induced HCC. In HCV-induced HCC, the structures of HCV adaptively update, so that they are difficult to be identified by antigens. Therefore, the clinic advice is either to increase the update speed of antigens or reduce the update speed of the viruses during the treatment of HCV-induced HCC. Moreover, it is also advised to add T cells or add the expression levels of T cells to strengthen the ability to kill cancer cells. In contrast, HBV updates slowly, but the immunity system in HBV-induced HCC has been damaged seriously. As a result, the clinic advice is to improve the immune ability of patients subjected to HBV-induced HCC, such as increasing immunoglobulin, T cells and B cells and so forth. Contact: zhiwei.gao@northumbria.ac.uk

Description: Motivation: Evolutionary expansion of gene regulatory circuits seems to boost morphological complexity. However, the expansion patterns and the quantification relationships have not yet been identified. In this study, we focus on the regulatory circuits at the post-transcriptional level, investigating whether and how this principle may apply. Results: By analysing the structure of mRNA transcripts in multiple metazoan species, we observed a striking exponential correlation between the length of 3' untranslated regions (3'UTR) and morphological complexity as measured by the number of cell types in each organism. Cellular diversity was similarly associated with the accumulation of microRNA genes and their putative targets. We propose that the lengthening of 3'UTRs together with a commensurate exponential expansion in post-transcriptional regulatory circuits can contribute to the emergence of new cell types during animal evolution. Contact: yukijuan@ntu.edu.tw or hsuancheng@ym.edu.tw . Supplementary information: Supplementary data are available at Bioinformatics online.

Description: Motivation: Comparing genomes of individual organisms using next-generation sequencing data is, until now, mostly performed using a reference genome. This is challenging when the reference is distant and introduces bias towards the exact sequence present in the reference. Recent improvements in both sequencing read length and efficiency of assembly algorithms have brought direct comparison of individual genomes by de novo assembly, rather than through a reference genome, within reach. Results: Here, we develop and test an algorithm, named Magnolya, that uses a Poisson mixture model for copy number estimation of contigs assembled from sequencing data. We combine this with co-assembly to allow de novo detection of copy number variation (CNV) between two individual genomes, without mapping reads to a reference genome. In co-assembly, multiple sequencing samples are combined, generating a single contig graph with different traversal counts for the nodes and edges between the samples. In the resulting ‘coloured’ graph, the contigs have integer copy numbers; this negates the need to segment genomic regions based on depth of coverage, as required for mapping-based detection methods. Magnolya is then used to assign integer copy numbers to contigs, after which CNV probabilities are easily inferred. The copy number estimator and CNV detector perform well on simulated data. Application of the algorithms to hybrid yeast genomes showed allotriploid content from different origin in the wine yeast Y12, and extensive CNV in aneuploid brewing yeast genomes. Integer CNV was also accurately detected in a short-term laboratory-evolved yeast strain. Availability: Magnolya is implemented in Python and available at: http://bioinformatics.tudelft.nl/ Contact: d.deridder@tudelft.nl Supplementary information: Supplementary data are available at Bioinformatics online.

Description: Motivation: It is well known that the accuracy of RNA secondary structure prediction from a single sequence is limited, and thus a comparative approach that predicts a common secondary structure from aligned sequences is a better choice if homologous sequences with reliable alignments are available. However, correct secondary structure information is needed to produce reliable alignments of RNA sequences. To tackle this dilemma, we require a fast and accurate aligner that takes structural information into consideration to yield reliable structural alignments, which are suitable for common secondary structure prediction. Results: We develop DAFS , a novel algorithm that simultaneously aligns and folds RNA sequences based on maximizing expected accuracy of a predicted common secondary structure and its alignment. DAFS decomposes the pairwise structural alignment problem into two independent secondary structure prediction problems and one pairwise (non-structural) alignment problem by the dual decomposition technique, and maintains the consistency of a pairwise structural alignment by imposing penalties on inconsistent base pairs and alignment columns that are iteratively updated. Furthermore, we extend DAFS to consider pseudoknots in RNA structural alignments by integrating IPknot for predicting a pseudoknotted structure. The experiments on publicly available datasets showed that DAFS can produce reliable structural alignments from unaligned sequences in terms of accuracy of common secondary structure prediction. Availability: The program of DAFS and the datasets are available at http://www.ncrna.org/software/dafs/ . Contact: satoken@bio.keio.ac.jp Supplementary information: Supplementary data are available at Bioinformatics online.

Description: Motivation: Protein sequence searching and alignment are fundamental tools of modern biology. Alignments are assessed using their similarity scores, essentially the sum of substitution matrix scores over all pairs of aligned amino acids. We previously proposed a generative probabilistic method that yields scores that take the sequence context around each aligned residue into account. This method showed drastically improved sensitivity and alignment quality compared with standard substitution matrix-based alignment. Results: Here, we develop an alternative discriminative approach to predict sequence context-specific substitution scores. We applied our approach to compute context-specific sequence profiles for Basic Local Alignment Search Tool (BLAST) and compared the new tool (CS-BLASTdis) to BLAST and the previous context-specific version (CS-BLASTgen). On a dataset filtered to 20% maximum sequence identity, CS-BLASTdisis was 51% more sensitive than BLAST and 17% more sensitive than CS-BLASTgenin, detecting remote homologues at 10% false discovery rate. At 30% maximum sequence identity, its alignments contain 21 and 12% more correct residue pairs than those of BLAST and CS-BLASTgen, respectively. Clear improvements are also seen when the approach is combined with PSI-BLAST and HHblits. We believe the context-specific approach should replace substitution matrices wherever sensitivity and alignment quality are critical. Availability: Source code (GNU General Public License, version 3) and benchmark data are available at ftp://toolkit.genzentrum.lmu.de/pub/csblast/ . Contact: soeding@genzentrum.lmu.de Supplementary information: Supplementary data are available at Bioinformatics online.

Description: Motivation: Statistical methods for comparing relative rates of synonymous and non - synonymous substitutions maintain a central role in detecting positive selection. To identify selection, researchers often estimate the ratio of these relative rates ( ) at individual alignment sites. Fitting a codon substitution model that captures heterogeneity in across sites provides a reliable way to perform such estimation, but it remains computationally prohibitive for massive datasets. By using crude estimates of the numbers of synonymous and non - synonymous substitutions at each site, counting approaches scale well to large datasets, but they fail to account for ancestral state reconstruction uncertainty and to provide site-specific estimates. Results: We propose a hybrid solution that borrows the computational strength of counting methods, but augments these methods with empirical Bayes modeling to produce a relatively fast and reliable method capable of estimating site-specific values in large datasets. Importantly, our hybrid approach, set in a Bayesian framework, integrates over the posterior distribution of phylogenies and ancestral reconstructions to quantify uncertainty about site-specific estimates. Simulations demonstrate that this method competes well with more - principled statistical procedures and , in some cases , even outperforms them. We illustrate the utility of our method using human immunodeficiency virus, feline panleukopenia and canine parvovirus evolution examples. Availability: Renaissance counting is implemented in the development branch of BEAST, freely available at http://code.google.com/p/beast-mcmc/ . The method will be made available in the next public release of the package, including support to set up analyses in BEAUti. Contact: philippe.lemey@rega.kuleuven.be or msuchard@ucla.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Description: Motivation: Metagenomes are often characterized by high levels of unknown sequences. Reads derived from known microorganisms can easily be identified and analyzed using fast homology search algorithms and a suitable reference database, but the unknown sequences are often ignored in further analyses, biasing conclusions. Nevertheless, it is possible to use more data in a comparative metagenomic analysis by creating a cross-assembly of all reads, i.e. a single assembly of reads from different samples. Comparative metagenomics studies the interrelationships between metagenomes from different samples. Using an assembly algorithm is a fast and intuitive way to link (partially) homologous reads without requiring a database of reference sequences. Results: Here, we introduce crAss, a novel bioinformatic tool that enables fast simple analysis of cross-assembly files, yielding distances between all metagenomic sample pairs and an insightful image displaying the similarities. Availability and implementation: crAss is available as a web server at http://edwards.sdsu.edu/crass/ , and the Perl source code can be downloaded to run as a stand-alone command line tool. Contact: dutilh@cmbi.ru.nl Supplementary information: Supplementary data are available at Bioinformatics online.

Description: Motivation: The discovery of novel gene fusions can lead to a better comprehension of cancer progression and development. The emergence of deep sequencing of trancriptome, known as RNA-seq, has opened many opportunities for the identification of this class of genomic alterations, leading to the discovery of novel chimeric transcripts in melanomas, breast cancers and lymphomas. Nowadays, few computational approaches have been developed for the detection of chimeric transcripts. Although all of these computational methods show good sensitivity, much work remains to reduce the huge number of false-positive calls that arises from this analysis. Results: We proposed a novel computational framework, named chimEric tranScript detection algorithm (EricScript), for the identification of gene fusion products in paired-end RNA-seq data. Our simulation study on synthetic data demonstrates that EricScript enables to achieve higher sensitivity and specificity than existing methods with noticeably lower running times. We also applied our method to publicly available RNA-seq tumour datasets, and we showed its capability in rediscovering known gene fusions. Availability: The EricScript package is freely available under GPL v3 license at http://ericscript.sourceforge.net . Contact: matteo.benelli@gmail.com Supplementary information: Supplementary data are available at Bioinformatics online.

Description: Motivation: The integration of multiple datasets remains a key challenge in systems biology and genomic medicine. Modern high-throughput technologies generate a broad array of different data types, providing distinct—but often complementary—information. We present a Bayesian method for the unsupervised integrative modelling of multiple datasets, which we refer to as MDI (Multiple Dataset Integration). MDI can integrate information from a wide range of different datasets and data types simultaneously (including the ability to model time series data explicitly using Gaussian processes). Each dataset is modelled using a Dirichlet-multinomial allocation (DMA) mixture model, with dependencies between these models captured through parameters that describe the agreement among the datasets. Results: Using a set of six artificially constructed time series datasets, we show that MDI is able to integrate a significant number of datasets simultaneously, and that it successfully captures the underlying structural similarity between the datasets. We also analyse a variety of real Saccharomyces cerevisiae datasets. In the two-dataset case, we show that MDI’s performance is comparable with the present state-of-the-art. We then move beyond the capabilities of current approaches and integrate gene expression, chromatin immunoprecipitation–chip and protein–protein interaction data, to identify a set of protein complexes for which genes are co-regulated during the cell cycle. Comparisons to other unsupervised data integration techniques—as well as to non-integrative approaches—demonstrate that MDI is competitive, while also providing information that would be difficult or impossible to extract using other methods. Availability: A Matlab implementation of MDI is available from http://www2.warwick.ac.uk/fac/sci/systemsbiology/research/software/ . Contact: D.L.Wild@warwick.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online.

Description: Motivation: Gene selection for cancer classification is one of the most important topics in the biomedical field. However, microarray data pose a severe challenge for computational techniques. We need dimension reduction techniques that identify a small set of genes to achieve better learning performance. From the perspective of machine learning, the selection of genes can be considered to be a feature selection problem that aims to find a small subset of features that has the most discriminative information for the target. Results: In this article, we proposed an Ensemble Correlation-Based Gene Selection algorithm based on symmetrical uncertainty and Support Vector Machine. In our method, symmetrical uncertainty was used to analyze the relevance of the genes, the different starting points of the relevant subset were used to generate the gene subsets and the Support Vector Machine was used as an evaluation criterion of the wrapper. The efficiency and effectiveness of our method were demonstrated through comparisons with other feature selection techniques, and the results show that our method outperformed other methods published in the literature. Availability: By request from the author. Contact: pyz@dblab.chungbuk.ac.kr ; khryu@dblab.cbnu.ac.kr

Description: Motivation: The existence of families with many individuals affected by the same complex disease has long suggested the possibility of rare alleles of high penetrance. In contrast to Mendelian diseases, however, linkage studies have identified very few reproducibly linked loci in diseases such as diabetes and autism. Genome-wide association studies have had greater success with such diseases, but these results explain neither the extreme disease load nor the within-family linkage peaks, of some large pedigrees. Combining linkage information with exome or genome sequencing from large complex disease pedigrees might finally identify family-specific, high-penetrance mutations. Results: Olorin is a tool , which integrates gene flow within families with next generation sequencing data to enable the analysis of complex disease pedigrees. Users can interactively filter and prioritize variants based on haplotype sharing across selected individuals and other measures of importance, including predicted functional consequence and population frequency. Availability: http://www.sanger.ac.uk/resources/software/olorin Contact: olorin@sanger.ac.uk

Description: Motivation: Structural characterization of protein interactions is necessary for understanding and modulating biological processes. On one hand, X-ray crystallography or NMR spectroscopy provide atomic resolution structures but the data collection process is typically long and the success rate is low. On the other hand, computational methods for modeling assembly structures from individual components frequently suffer from high false-positive rate, rarely resulting in a unique solution. Results: Here, we present a combined approach that computationally integrates data from a variety of fast and accessible experimental techniques for rapid and accurate structure determination of protein–protein complexes. The integrative method uses atomistic models of two interacting proteins and one or more datasets from five accessible experimental techniques: a small-angle X-ray scattering (SAXS) profile, 2D class average images from negative-stain electron microscopy micrographs (EM), a 3D density map from single-particle negative-stain EM, residue type content of the protein–protein interface from NMR spectroscopy and chemical cross-linking detected by mass spectrometry. The method is tested on a docking benchmark consisting of 176 known complex structures and simulated experimental data. The near-native model is the top scoring one for up to 61% of benchmark cases depending on the included experimental datasets; in comparison to 10% for standard computational docking. We also collected SAXS, 2D class average images and 3D density map from negative-stain EM to model the PCSK9 antigen–J16 Fab antibody complex, followed by validation of the model by a subsequently available X-ray crystallographic structure. Availability: http://salilab.org/idock Contact: dina@salilab.org or sali@salilab.org Supplementary information: Supplementary data are available at Bioinformatics online.

Description: : MicroRNA (miRNA) target prediction is an important problem. Given an miRNA sequence the task is to determine the identity of the messenger RNAs targeted by it, the locations within them where the interactions happen and the specifics of the formed heteroduplexes. Here, we describe a web-based application, RNA22-GUI, which we have designed and implemented for the interactive exploration and in-context visualization of predictions by RNA22, one of the popular miRNA target prediction algorithms. Central to our design has been the requirement to provide informative and comprehensive visualization that is integrated with interactive search capabilities and permits one to selectively isolate and focus on relevant information that is distilled on-the-fly from a large repository of pre-compiled predictions. RNA22-GUI is currently available for Homo sapiens , Mus musculus , Drosophila melanogaster and Caenorhabditis elegans . Availability: http://cm.jefferson.edu/rna22v1.0/ . Contact: Isidore.Rigoutsos@jefferson.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Description: Purpose   To analyse, in older community-dwelling people living in Italy’s Lombardy region, 8-year trends in new users of spironolactone co-prescribed with angiotensin-converting-enzyme inhibitors (ACE-Is) and/or angiotensin receptor blockers (ARBs); blood test monitoring; and independent predictors of appropriate blood test monitoring. Methods   The region’s administrative health database from 2001 to 2008 was used to retrieve yearly frequencies of subjects aged 65+ who started this co-prescription. Multivariate analyses were adjusted for age, sex, local health unit, treatment with beta-blockers, drugs for diabetes, and polypharmacy (i.e., exposure to five or more different drugs). Results   Only new users of spironolactone co-prescribed with ARBs increased from 2001 to 2008 ( P  〈 0.001). In the 6 months before starting the co-prescriptions 96 to 100% of patients measured serum creatinine (mean 99.3%), sodium (97.3%) and potassium (98.6%). Within 3 months of starting the co-prescriptions 96 to 99% of patients measured serum sodium (mean 97.3%) and potassium (98.6%), but on average only 48% of them (range 43 to 53%) measured serum creatinine, with an increase over time (odds ratio [change in regression per year] = 1.03, 95% CI 1.02–1.05, P  〈 0.001). At multivariate analysis polypharmacy was found to be the only independent predictor of such creatinine monitoring ( P  〈 0.001). Conclusions   Our results support the need for greater awareness within the medical community of the potential renal toxicity of the association of spironolactone with ACE-Is and/or ARBs. Adequate short-term monitoring of serum creatinine in all older community-dwelling people who receive such co-prescription is necessary in order to ensure safe usage of these medications. Content Type Journal Article Category Pharmacoepidemiology and Prescription Pages 1-9 DOI 10.1007/s00228-012-1401-8 Authors Claudio Bilotta, Geriatric Medicine Outpatient Service, Department of Urban Outpatient Services, Istituti Clinici di Perfezionamento Hospital, viale Doria 52, 20124 Milan, Italy Carlotta Franchi, Laboratory for Quality Assessment of Geriatric Therapies and Services, Mario Negri Institute for Pharmacological Research, via La Masa 19, 20156 Milan, Italy Alessandro Nobili, Laboratory for Quality Assessment of Geriatric Therapies and Services, Mario Negri Institute for Pharmacological Research, via La Masa 19, 20156 Milan, Italy Paola Nicolini, Thoraco-Pulmonary and Cardiocirculatory Department, University of Milan, via F. Sforza 35, 20122 Milan, Italy Codjo Djignefa Djade, Laboratory for Quality Assessment of Geriatric Therapies and Services, Mario Negri Institute for Pharmacological Research, via La Masa 19, 20156 Milan, Italy Mauro Tettamanti, Laboratory for Quality Assessment of Geriatric Therapies and Services, Mario Negri Institute for Pharmacological Research, via La Masa 19, 20156 Milan, Italy Ida Fortino, Regional Health Ministry Lombardy Region, Piazza Città di Lombardia 1, 20124 Milan, Italy Angela Bortolotti, Regional Health Ministry Lombardy Region, Piazza Città di Lombardia 1, 20124 Milan, Italy Luca Merlino, Regional Health Ministry Lombardy Region, Piazza Città di Lombardia 1, 20124 Milan, Italy Carlo Vergani, Department of Medical Sciences, Geriatric Medicine, University of Milan, via Pace 9, 20122 Milan, Italy Journal European Journal of Clinical Pharmacology Online ISSN 1432-1041 Print ISSN 0031-6970

Description: Purpose   Citalopram is a selective serotonin reuptake inhibitor (SSRI) antidepressant that is widely used in clinical practice. Recent data have indicated that high therapeutic citalopram doses may cause electrocardiographic abnormalities, and the regulatory authorities have amended its licenced dosage. The present manuscript reviews the available data concerning citalopram and cardiac toxicity. Methods   Published data concerning the cardiac effects of citalopram were ascertained, and clinical data were considered separately between adverse effects arising from therapeutic use versus toxicity in the setting of intentional overdose. Results   The occurrence of electrocardiographic abnormalities has long been recognised as a complication of acute citalopram overdose; a dose-effect relationship for QT prolongation has been described in a number of large case series, including several cases of torsades de pointes. In contrast, few data indicate the occurrence of QT prolongation and arrhythmia after therapeutic doses, and a dose-effect relationship within the therapeutic range has only recently been established. Citalopram is more likely to cause QT prolongation in patients with metabolic disturbance or pre-existing cardiac disease. Conclusions   A dose-effect relationship for QT prolongation exists across a broad range of citalopram doses, such that caution must be exercised when prescribing high doses or if there are co-existent risk factors for QT effects. The available data illustrate how clinical toxicity data may offer an earlier signal of cardiac effects than ascertained from conventional pharmacovigilance methods. Content Type Journal Article Category Review Article Pages 1-6 DOI 10.1007/s00228-012-1408-1 Authors M. J. Cooke, Pharmacy Department, York Teaching Hospital NHS Foundation Trust, Wigginton Road, York, YO31 8HE UK W. S. Waring, Acute Medical Unit, York Teaching Hospital NHS Foundation Trust, Wigginton Road, York, YO31 8HE UK Journal European Journal of Clinical Pharmacology Online ISSN 1432-1041 Print ISSN 0031-6970

Description: Objective   Our aim was to characterize Adverse Drug Reactions (ADRs) related to drug–drug interactions (DDIs) related to involvement of cytochrome P450 (CYP450) isoenzymes in a pharmacovigilance database. Methods   ADRs recorded by Midi-Pyrénées PharmacoVigilance center (France) between 1 January and 31 August 2008 were extracted from the French PharmacoVigilance Database (FPVD). Results   Among the 1,205 reported ADRs, 16 (1.3 %), can be explained by involvement of CYP450 isoenzymes (including 4 “serious”). All interactions involved CYP inhibitors, mainly for CYP3A4/5. Conclusion   The percentage of ADRs reported in the pharmacovigilance database and related to CYP450-induced DDIs appears to be relatively low (~ 1–2 %). Content Type Journal Article Category Pharmacoepidemiology and Prescription Pages 1-4 DOI 10.1007/s00228-012-1394-3 Authors Anne Charlotte Danton, Service de Pharmacologie Clinique, Centre Midi-Pyrénées de Pharmacovigilance, de Pharmacoépidémiologie et d’Informations sur le Médicament, Centre Hospitalier Universitaire de Toulouse, Toulouse, France François Montastruc, Service de Pharmacologie Clinique, Centre Midi-Pyrénées de Pharmacovigilance, de Pharmacoépidémiologie et d’Informations sur le Médicament, Centre Hospitalier Universitaire de Toulouse, Toulouse, France Agnès Sommet, Service de Pharmacologie Clinique, Centre Midi-Pyrénées de Pharmacovigilance, de Pharmacoépidémiologie et d’Informations sur le Médicament, Centre Hospitalier Universitaire de Toulouse, Toulouse, France Geneviève Durrieu, Service de Pharmacologie Clinique, Centre Midi-Pyrénées de Pharmacovigilance, de Pharmacoépidémiologie et d’Informations sur le Médicament, Centre Hospitalier Universitaire de Toulouse, Toulouse, France Haleh Bagheri, Service de Pharmacologie Clinique, Centre Midi-Pyrénées de Pharmacovigilance, de Pharmacoépidémiologie et d’Informations sur le Médicament, Centre Hospitalier Universitaire de Toulouse, Toulouse, France Emmanuelle Bondon-Guitton, Service de Pharmacologie Clinique, Centre Midi-Pyrénées de Pharmacovigilance, de Pharmacoépidémiologie et d’Informations sur le Médicament, Centre Hospitalier Universitaire de Toulouse, Toulouse, France Maryse Lapeyre-Mestre, Service de Pharmacologie Clinique, Centre Midi-Pyrénées de Pharmacovigilance, de Pharmacoépidémiologie et d’Informations sur le Médicament, Centre Hospitalier Universitaire de Toulouse, Toulouse, France Jean-Louis Montastruc, Service de Pharmacologie Clinique, Centre Midi-Pyrénées de Pharmacovigilance, de Pharmacoépidémiologie et d’Informations sur le Médicament, Centre Hospitalier Universitaire de Toulouse, Toulouse, France Journal European Journal of Clinical Pharmacology Online ISSN 1432-1041 Print ISSN 0031-6970

Description: Purpose   We previously reported that chronic heart failure (CHF) treatments reduce the duration of hospitalisation, even in elderly patients. The present study aimed to determine whether CHF treatment also provides long-term benefits in terms of reduced mortality at 8 years. Methods   A cohort of 281 patients who were admitted to a French teaching hospital with a main diagnosis of CHF were followed through the health insurance databases for 1 year and through the national mortality database for 8 years. Results   Diuretics (236 patients, 84 %) and angiotensin-converting enzyme (ACE) inhibitors (193 patients, 69 %) were the most-frequently prescribed medications. The median duration of survival was 46 months. Mortality rates were significantly lower for patients administered beta-blockers (59 %) and statins (56 %) than for patients not exposed to these drugs (82 %, p  〈 0.001 and 78 %, p  = 0.001 respectively). No significant differences in mortality were observed for spironolactone, diuretics or ACE inhibitors. After adjustment, beta-blocker treatment remained associated with a significantly lower risk of mortality (hazard ratio, HR = 0.54 [0.34–0.84]). After adjustment, the use of two or three CHF drugs was associated with longer survival (HR = 0.53 [0.36–0.77]) than the use of zero or one CHF drug. Statins were also associated with longer survival after adjustment (HR = 0.53 [0.31–0.89]). In patients 75 years of age or older ( n  = 73), only beta-blocker treatment was associated with a significantly lower risk of mortality (HR = 0.31 [0.16–0.63]) in multivariate analysis. Conclusions   The use of beta-blockers was associated with better survival rates. The use of statins was also associated with better survival at 8 years. Randomised controlled trials are required to confirm these observations. Content Type Journal Article Category Pharmacoepidemiology and Prescription Pages 1-8 DOI 10.1007/s00228-012-1400-9 Authors Patrick Maison, Service de Pharmacologie Clinique, AP-HP, Hôpital H. Mondor—A. Chenevier, Créteil, 94000 France Gaelle Desamericq, Service de Pharmacologie Clinique, AP-HP, Hôpital H. Mondor—A. Chenevier, Créteil, 94000 France François Hemery, Département d’information médicale, AP-HP, Hôpital H. Mondor—A. Chenevier, Créteil, 94000 France Nicole Elie, National Health Insurance Fund, Créteil, France Aldo Del’Volgo, National Health Insurance Fund, Créteil, France Jean Luc Dubois-Randé, Fédération de Cardiologie, AP-HP, Hôpital H. Mondor—A. Chenevier, Créteil, 94000 France Luc Hittinger, Fédération de Cardiologie, AP-HP, Hôpital H. Mondor—A. Chenevier, Créteil, 94000 France Isabelle Macquin-Mavier, Service de Pharmacologie Clinique, AP-HP, Hôpital H. Mondor—A. Chenevier, Créteil, 94000 France Journal European Journal of Clinical Pharmacology Online ISSN 1432-1041 Print ISSN 0031-6970

Description: Purpose   The objective of this study was to compare different methods of adjusted indirect comparisons that can be used to investigate the relative bioavailability of different generic products. To achieve this goal, generic artemether/lumefantrine 20/120 mg tablets that have been prequalified by the World Health Organization (WHO) were selected as model products for study. Methods   Data from three bioequivalence studies conducted independently that compared three generics with the same reference product were used to indirectly determine the relative bioavailability between the generics themselves. Results   The different methods of indirect comparison examined in this study provide consistent results. Methods based on the assumption of a large sample size give slightly narrower 90 % confidence intervals. Therefore, the use of methods based on the t test is recommended. Given the precision of the area under the time–concentration curve (AUC) data, it is possible to conclude that the extent of exposure of artemether and lumefantrine is bioequivalent between the different generics studied. However, given the precision of the drug peak concentration (C max ) data, it is not possible to demonstrate equivalence within the conventional acceptance range for all comparisons; it is possible to conclude bioequivalence within the widened acceptance range 75–133 %. Conclusions   From a clinical viewpoint, not only are these prequalified generics bioequivalent and interchangeable with the reference product (Coartem, Novartis), but also the existing indirect evidence makes it possible to conclude that these WHO prequalified products are bioequivalent between themselves with respect to the AUC. The lack of the necessary precision to demonstrate bioequivalence between generics with respect to the C max within the conventional acceptance range does not preclude considering them as interchangeable, if necessary, since C max is considered to be of less clinical relevance for the relevant therapy. Content Type Journal Article Category Pharmacokinetics and Disposition Pages 1-8 DOI 10.1007/s00228-012-1396-1 Authors Luther Gwaza, Evaluation and Registration Unit, Medicines Control Authority of Zimbabwe, Harare, Zimbabwe John Gordon, Division of Biopharmaceutics Evaluation, Bureau of Pharmaceutical Sciences, Therapeutic Products Directorate, Health Canada, Ottawa, Canada Jan Welink, Medicines Evaluation Board, Utrecht, The Netherlands Henrike Potthast, Subdepartment of Biostatistics and Pharmacokinetics, Federal Insitute for Drugs and Medical Devices, Bonn, Germany Henrik Hansson, Efficacy and Safety Department 1, Medical Products Agency, Uppsala, Sweden Matthias Stahl, The Prequalification of Medicines Programme Quality Assurance & Safety: Medicines Essential Medicines and Health Products, World Health Organization, Geneva, Switzerland Alfredo García-Arieta, División de Farmacología y Evaluación Clínica, Subdirección de Medicamentos de Uso Humano, Agencia Española de Medicamentos y Productos Sanitarios, C/ Campezo 1. Edificio 8, Planta 2 Oeste, 28022 Madrid, Spain Journal European Journal of Clinical Pharmacology Online ISSN 1432-1041 Print ISSN 0031-6970

Description: Purpose   To present concept, methods and use of a knowledge database providing assessments of potential fetal risks for all drugs on the Swedish market. Methods   Assessments of fetal risks are made primarily by analyzing prospective epidemiological data from the Swedish Medical Birth Register on drug intake in relation to birth outcome. This is complemented by evaluation of the scientific literature. Following standardized working procedures, a statement is compiled for each substance, which is also classified into one of three categories depending on the estimated risk level. The final documents include drug product names on the market, via linkage to a medicinal products register. The information is free and published on the website www.janusinfo.se . It can also be used as an integrated part of electronic health records. Results   The database covers assessments of fetal risks for close to 1,250 medicinal drug substances on the Swedish market. Each year, 96,000 searches are made, which might be compared to the around 100,000 children born in Sweden yearly. Apart from the Swedish Physicians’ Desk Reference (Fass), the database is the most commonly used resource among specialists within gynaecology and perinatal medicine for information on drugs during pregnancy. Conclusions   A non-commercial knowledge base with assessments of fetal risk of different drugs is valued by health care professionals and is used extensively in Sweden. Based on analyses of national health registers, the database provides unique information on teratogenic drug risks. Content Type Journal Article Category Pharmacoepidemiology and Prescription Pages 1-11 DOI 10.1007/s00228-012-1399-y Authors Ulrika Nörby, E-health and Strategic IT, Public Healthcare Administration, Stockholm County Council, P.O. Box 17533, SE-118 91 Stockholm, Sweden Karin Källén, Tornblad Institute, University of Lund, Lund, Sweden Birgit Eiermann, Division of Clinical Pharmacology, Department of Laboratory Medicine, Karolinska Institutet, Huddinge, Sweden Seher Korkmaz, E-health and Strategic IT, Public Healthcare Administration, Stockholm County Council, P.O. Box 17533, SE-118 91 Stockholm, Sweden Birger Winbladh, Department of Clinical Sciences and Education Södersjukhuset, Karolinska Institutet, Stockholm, Sweden Lars L. Gustafsson, Division of Clinical Pharmacology, Department of Laboratory Medicine, Karolinska Institutet, Huddinge, Sweden Journal European Journal of Clinical Pharmacology Online ISSN 1432-1041 Print ISSN 0031-6970

Description: : We developed MolBioLib to address the need for adaptable next-generation sequencing analysis tools. The result is a compact, portable and extensively tested C++11 software framework and set of applications tailored to the demands of next-generation sequencing data and applicable to many other applications. MolBioLib is designed to work with common file formats and data types used both in genomic analysis and general data analysis. A central relational-database-like Table class is a flexible and powerful object to intuitively represent and work with a wide variety of tabular datasets, ranging from alignment data to annotations. MolBioLib has been used to identify causative single-nucleotide polymorphisms in whole genome sequencing, detect balanced chromosomal rearrangements and compute enrichment of messenger RNAs (mRNAs) on microtubules, typically requiring applications of under 200 lines of code. MolBioLib includes programs to perform a wide variety of analysis tasks, such as computing read coverage, annotating genomic intervals and novel peak calling with a wavelet algorithm. Although MolBioLib was designed primarily for bioinformatics purposes, much of its functionality is applicable to a wide range of problems. Complete documentation and an extensive automated test suite are provided. Availability: MolBioLib is available for download at: http://sourceforge.net/projects/molbiolib Contact : ohsumit@molbio.mgh.harvard.edu

Description: Motivation: With improved short-read assembly algorithms and the recent development of long-read sequencers, split mapping will soon be the preferred method for structural variant (SV) detection. Yet, current alignment tools are not well suited for this. Results: We present YAHA, a fast and flexible hash-based aligner. YAHA is as fast and accurate as BWA-SW at finding the single best alignment per query and is dramatically faster and more sensitive than both SSAHA2 and MegaBLAST at finding all possible alignments. Unlike other aligners that report all, or one, alignment per query, or that use simple heuristics to select alignments, YAHA uses a directed acyclic graph to find the optimal set of alignments that cover a query using a biologically relevant breakpoint penalty. YAHA can also report multiple mappings per defined segment of the query. We show that YAHA detects more breakpoints in less time than BWA-SW across all SV classes, and especially excels at complex SVs comprising multiple breakpoints. Availability: YAHA is currently supported on 64-bit Linux systems. Binaries and sample data are freely available for download from http://faculty.virginia.edu/irahall/YAHA . Contact: imh4y@virginia.edu

Description: Purpose   To explore the impact of UDP-glucuronosyltransferase polymorphisms ( UGT1A9-118(dT) 9/10 , UGT1A9 CI399T , UGT1A9 C-440T and UGT2B7 G211T ) on the pharmacokinetics of mycophenolic acid (MPA) in healthy Chinese volunteers. Methods   We recruited ten healthy volunteers with no polymorphisms (control group), 11 homozygotes of mutants UGT1A9 CI399T and UGT1A9-118(dT) 9/10 , ten heterozygotes of UGT1A9 C440T and seven carriers of UGT2B7 211T from a total of 518 healthy Chinese volunteers. All the volunteers were orally administered a single dose of 1.5 g mycophenolate mofetil (MMF) after an overnight fast. Plasma was then collected 72 h after MMF administration. MPA, MPA-7-O-glucuronide (MPAG) and its acylglucuronide (AcMPAG) were detected by ultra-pressure liquid chromatography with UV detection. Results   Compared with the control group, the UGT1A9 CI399T and UGT1A9-118(dT) 9/10 mutant homozygotes had higher MPAG plasma concentrations. Subjects with UGT1A9-440TC had enhanced MPA exposure while carriers of UGT2B7 211T had higher concentrations of the toxic metabolite, AcMPAG. Conclusions   The current results indicate that UGT1A9 and UGT2B7 genotypes could significantly alter MPA pharmacokinetics in healthy Chinese volunteers after a single oral dose of MMF. Content Type Journal Article Category Pharmacokinetics and Disposition Pages 1-7 DOI 10.1007/s00228-012-1409-0 Authors Dong Guo, Pharmacogenetics Research group, Institute of Clinical Pharmacology, Central South University, 110 Xiangya Road, Changsha, Hunan 410078, China Liang-Fang Pang, Pharmacy Department, Donghu Hospital, Wuhan, 430079 China Yang Han, Pharmacogenetics Research group, Institute of Clinical Pharmacology, Central South University, 110 Xiangya Road, Changsha, Hunan 410078, China Hong Yang, Pharmacogenetics Research group, Institute of Clinical Pharmacology, Central South University, 110 Xiangya Road, Changsha, Hunan 410078, China Guo Wang, Pharmacogenetics Research group, Institute of Clinical Pharmacology, Central South University, 110 Xiangya Road, Changsha, Hunan 410078, China Zhi-rong Tan, Pharmacogenetics Research group, Institute of Clinical Pharmacology, Central South University, 110 Xiangya Road, Changsha, Hunan 410078, China Wei Zhang, Pharmacogenetics Research group, Institute of Clinical Pharmacology, Central South University, 110 Xiangya Road, Changsha, Hunan 410078, China Hong-Hao Zhou, Pharmacogenetics Research group, Institute of Clinical Pharmacology, Central South University, 110 Xiangya Road, Changsha, Hunan 410078, China Journal European Journal of Clinical Pharmacology Online ISSN 1432-1041 Print ISSN 0031-6970

Description: ACE inhibitors and ribavirin-associated cough: a common undefined predisposing factor? Content Type Journal Article Category Letter to the Editors Pages 1-3 DOI 10.1007/s00228-012-1397-0 Authors Laura Milazzo, Department of Infectious Diseases, Luigi Sacco University Hospital, Via GB Grassi 74, 20157 Milan, Italy Dario Cattaneo, Unit of Clinical Pharmacology, Luigi Sacco University Hospital, Milan, Italy Stefania Cheli, Unit of Clinical Pharmacology, Luigi Sacco University Hospital, Milan, Italy Laurenzia Ferraris, Department of Infectious Diseases, Luigi Sacco University Hospital, Via GB Grassi 74, 20157 Milan, Italy Elisa Colella, Department of Infectious Diseases, Luigi Sacco University Hospital, Via GB Grassi 74, 20157 Milan, Italy Emilio Clementi, Unit of Clinical Pharmacology, Luigi Sacco University Hospital, Milan, Italy Cristina Gervasoni, Department of Infectious Diseases, Luigi Sacco University Hospital, Via GB Grassi 74, 20157 Milan, Italy Journal European Journal of Clinical Pharmacology Online ISSN 1432-1041 Print ISSN 0031-6970

Description: Objectives   The main aim of this study was to determine whether CYP2C9 and VKORC1 polymorphisms influence warfarin dose variability during initial dose-finding phase and during maintenance treatment after 360 days. Methods   Two hundred and six consecutive patients who were beginning warfarin therapy were selected. They were assessed for general and clinical characteristics; prescribed warfarin dose; response to therapy on days 7–10, 30, 60, 180, and 360; adverse events; and CYP2C9 *2, *3, *5, *6, *8, *11, and VKORC1 1639G 〉A assays. Results   During the first 30 days of anticoagulation, the relative variability of warfarin dose was significantly associated with CYP2C9*2 and CYP2C9*3 polymorphisms ( p  = 0.02) and with VKORC1 1639G 〉A genotypes ( p  = 0.04). Warfarin variability was also statistically different according to predicted metabolic phenotype and to VKORC1 genotypes after 360 days of treatment, and in the phase between 180 and 360 days (long-term dose variability). Both CYP2C9 and VKORC1 polymorphisms were associated with the international normalized ratio (INR) made between 7 and 10 days/initial dose ratio, adjusted for covariates ( p  〈 0.01 and p  = 0.02, respectively). Patients carrying VKORC1 and CYP2C9 variants presented lower required dose (at the end of follow-up of 360 days) compared to patients carrying wild-type genotypes ( p  = 0.04 and p  = 0.03, respectively). Conclusions   Genetic information on CYP2C9 and VKORC1 is important both for the initial dose-finding phase and during maintenance treatment with warfarin. Content Type Journal Article Category Pharmacogenetics Pages 1-9 DOI 10.1007/s00228-012-1404-5 Authors Paulo Caleb Junior Lima Santos, Laboratory of Genetics and Molecular Cardiology, Heart Institute (InCor), University of Sao Paulo Medical School, Av. Dr. Eneas de Carvalho Aguiar, 44 Cerqueira Cesar, Sao Paulo, SP CEP 05403-000, Brazil Carla Luana Dinardo, Laboratory of Genetics and Molecular Cardiology, Heart Institute (InCor), University of Sao Paulo Medical School, Av. Dr. Eneas de Carvalho Aguiar, 44 Cerqueira Cesar, Sao Paulo, SP CEP 05403-000, Brazil Isolmar Tadeu Schettert, Laboratory of Genetics and Molecular Cardiology, Heart Institute (InCor), University of Sao Paulo Medical School, Av. Dr. Eneas de Carvalho Aguiar, 44 Cerqueira Cesar, Sao Paulo, SP CEP 05403-000, Brazil Renata Alonso Gadi Soares, Laboratory of Genetics and Molecular Cardiology, Heart Institute (InCor), University of Sao Paulo Medical School, Av. Dr. Eneas de Carvalho Aguiar, 44 Cerqueira Cesar, Sao Paulo, SP CEP 05403-000, Brazil Liz Kawabata-Yoshihara, University Hospital of the University of Sao Paulo, Internal Medicine Division, Sao Paulo, Brazil Isabela Martins Bensenor, University Hospital of the University of Sao Paulo, Internal Medicine Division, Sao Paulo, Brazil José Eduardo Krieger, Laboratory of Genetics and Molecular Cardiology, Heart Institute (InCor), University of Sao Paulo Medical School, Av. Dr. Eneas de Carvalho Aguiar, 44 Cerqueira Cesar, Sao Paulo, SP CEP 05403-000, Brazil Paulo Andrade Lotufo, University Hospital of the University of Sao Paulo, Internal Medicine Division, Sao Paulo, Brazil Alexandre Costa Pereira, Laboratory of Genetics and Molecular Cardiology, Heart Institute (InCor), University of Sao Paulo Medical School, Av. Dr. Eneas de Carvalho Aguiar, 44 Cerqueira Cesar, Sao Paulo, SP CEP 05403-000, Brazil Journal European Journal of Clinical Pharmacology Online ISSN 1432-1041 Print ISSN 0031-6970

Description: Purpose   Numerous studies have documented suboptimal adherence to guideline recommendations in secondary prevention of coronary heart disease (CHD SP ). Clinical practice guidelines (CPGs) are continuously developed to define appropriate patient care, aiming to reduce risk of morbidity and death. The Medication Assessment Tool for CHD SP (MAT-CHD SP ) was developed to assess adherence to CPGs concerning medication therapy and follow-up of patients with CHD SP . The aim of this study was to explore whether the MAT-CHD SP could be applied retrospectively to assess guideline adherence and therapy goal achievement in secondary prevention of CHD. Methods   We collected data from electronic medical records of all patients who underwent percutaneous coronary intervention with stent implantation from January to March 2008 ( n  = 300) and applied the MAT-CHD SP . We measured time for data collection and MAT application and tested reproducibility by calculating Cohen’s kappa (κ) value for inter and intraobserver agreement. Results   A total of 247 MAT applications were analyzed, showing overall applicability of 66 % of the 4,446 MAT-CHD SP criteria and a high reproducibility of MAT-CHD SP application (κ values 0.93 and 0.95 for intra- and interobserver agreement, respectively). Mean time for data collection and MAT-CHD SP application was 11 min. Adherence to criteria concerning prescription was high (〉75 %), but achievement of therapy goals for cholesterol and blood pressure was low (〈50 %). Documentation of lifestyle advice achieved intermediate (50–75 %) or low adherence, as did therapy amendments in patients in whom therapy goals were unachieved at hospital admission. Conclusions   The MAT-CHD SP offers a means to identify both adherence and nonadherence to CPGs concerning CHD SP is applicable in retrospective assessment of CHD SP , and identifies potentials for improved patient care. Content Type Journal Article Category Pharmacoepidemiology and Prescription Pages 1-7 DOI 10.1007/s00228-012-1402-7 Authors Beate Hennie Garcia, Hospital Pharmacy of North Norway Trust, Tromsoe, Norway Lars Småbrekke, Department of Pharmacy, University of Tromsoe, Tromsoe, Norway Thor Trovik, Department of Cardiology, University Hospital of Tromsoe, Tromsoe, Norway Trude Giverhaug, Regional Drug Information Centre of North Norway, University Hospital of Tromsoe, Tromsoe, Norway Journal European Journal of Clinical Pharmacology Online ISSN 1432-1041 Print ISSN 0031-6970

Description: Purpose   Pre-clinical experiments have shown that almorexant, a dual orexin receptor antagonist, is able to inhibit cytochrome P450 3A4 (CYP3A4). Therefore, a study was conducted to investigate the effects of multiple-dose almorexant on the pharmacokinetics of midazolam and simvastatin, two CYP3A4 model substrates. Methods   Fourteen healthy male subjects were enrolled in an open-label, randomized, two-way crossover study. Treatment period A consisted of a single oral dose of 2 mg midazolam on day 1 and 40 mg simvastatin on day 3. In treatment period B, subjects received 200 mg almorexant once daily for 9 days together with a single oral dose of midazolam on day 7 and simvastatin on day 9. Results   Concomitant administration of midazolam with almorexant at steady-state levels, achieved within 4–5 days, resulted in an increase of 1.2-fold [90 % confidence interval (CI) 1.0–1.4], 1.4-fold (90 % CI 1.2–1.6), and 1.3-fold (90 % CI 1.2–1.4) in the maximum plasma concentration (C max ), area under the concentration–time curve from time 0 to infinity (AUC 0-∞ ), and terminal half-life (t 1/2 ), respectively, of midazolam; the time to peak plasma concentration (t max ) was unchanged. Whereas C max and t max were not influenced by almorexant, the AUC 0-∞ of hydroxy-midazolam increased by 1.2-fold (90 % CI 1.1–1.4) and the t 1/2 by 1.3-fold (90 % CI 1.0–1.5). Concomitant administration of simvastatin with almorexant at steady-state resulted in an increase of 2.7-fold (90 % CI 2.0–3.7) and 3.4-fold (90 % CI 2.6–4.4) in C max and AUC 0-∞ , respectively, for simvastatin; the t 1/2 and t max were unchanged. The C max and AUC 0-∞ of hydroxyacid simvastatin both increased by 2.8-fold, with 90 % CIs of 2.3–3.5 and 2.2–3.5, respectively; the t max increased by 2 h and the t 1/2 was unchanged. The urinary 6-β-hydroxycortisol/cortisol ratio was unaffected by almorexant. Conclusions   Our results suggest that the observed interaction was caused by the inhibition of CYP3A4 activity, most probably at the gut level. Content Type Journal Article Category Pharmacokinetics and Disposition Pages 1-10 DOI 10.1007/s00228-012-1403-6 Authors Matthias Hoch, Clinical Pharmacology, Actelion Pharmaceuticals Ltd, Gewerbestrasse 16, 4123 Allschwil, Switzerland Petra Hoever, Clinical Pharmacology, Actelion Pharmaceuticals Ltd, Gewerbestrasse 16, 4123 Allschwil, Switzerland Federica Alessi, Biostatistics, Actelion Pharmaceuticals Italia Srl, Imperia, Italy Rudolf Theodor, PHAROS GmbH, Ulm, Germany Jasper Dingemanse, Clinical Pharmacology, Actelion Pharmaceuticals Ltd, Gewerbestrasse 16, 4123 Allschwil, Switzerland Journal European Journal of Clinical Pharmacology Online ISSN 1432-1041 Print ISSN 0031-6970

Description: Background   Risk factors for stroke are well known in atrial fibrillation (AF) patients, while less is known on the effect of these factors on total mortality. Objective   Our aim was to study the impact of cardiovascular drug classes on mortality in AF patients treated in primary care. Methods   The study population was chosen based on patient data from 75 primary care centres in Sweden compiled in a database. Individuals diagnosed with AF who were older than 45 years were enrolled ( n  = 12,302, of whom 6,660 were men). Cox regression analysis with mortality (years to death) as outcome was conducted in the men and women separately, as well in the age categories 〈80 and ≥80 years, with cardiovascular drugs as independent factors, and age, cardiovascular diagnoses and educational level as covariates. Results   Lower mortality was shown for anticoagulant treatment among men, both younger (〈80 years) [adjusted hazard ratio (HR) 0.43, 95 % confidence interval (CI) 0.31–0.61] and older (≥80 years) (adjusted HR 0.47, 95 % CI 0.32–0.69), and among younger women (adjusted HR 0.46, 95 % CI 0.29–0.74), and for antiplatelet treatment in older men (adjusted HR 0.51, 95 % CI 0.35–0.74). Treatment with thiazides was associated with lower mortality among younger men (adjusted HR 0.68, 95 % CI 0.48–0.96), older men (adjusted HR 0.67, 95 % CI 0.46–0.98) and older women (adjusted HR 0.70, 95 % CI 0.52–0.94). Statins were associated with lower mortality among younger patients, in both men (adjusted HR 0.47, 95 % CI 0.32–0.68) and women (adjusted HR 0.54, 95 % CI 0.35–0.82). Conclusions   The differences in age and gender patterns need further exploration. Content Type Journal Article Category Pharmacoepidemiology and Prescription Pages 1-9 DOI 10.1007/s00228-012-1395-2 Authors Per Wändell, Centre for Family Medicine, Karolinska Institutet, Alfred Nobels Allé 12, 141 83 Huddinge, Sweden Axel C. Carlsson, Centre for Family Medicine, Karolinska Institutet, Alfred Nobels Allé 12, 141 83 Huddinge, Sweden Kristina Sundquist, Center for Primary Health Care Research, Lund University, Malmö, Sweden Sven-Erik Johansson, Center for Primary Health Care Research, Lund University, Malmö, Sweden Jan Sundquist, Center for Primary Health Care Research, Lund University, Malmö, Sweden Journal European Journal of Clinical Pharmacology Online ISSN 1432-1041 Print ISSN 0031-6970

Description: : Identification of metabolites using high-resolution multi-stage mass spectrometry (MS n ) data is a significant challenge demanding access to all sorts of computational infrastructures. MetiTree is a user-friendly, web application dedicated to organize, process, share, visualize and compare MS n data. It integrates several features to export and visualize complex MS n data, facilitating the exploration and interpretation of metabolomics experiments. A dedicated spectral tree viewer allows the simultaneous presentation of three related types of MS n data, namely, the spectral data, the fragmentation tree and the fragmentation reactions. MetiTree stores the data in an internal database to enable searching for similar fragmentation trees and matching against other MS n data. As such MetiTree contains much functionality that will make the difficult task of identifying unknown metabolites much easier. Availability: MetiTree is accessible at http://www.MetiTree.nl . The source code is available at https://github.com/NetherlandsMetabolomicsCentre/metitree/wiki . Contact: m.rojas@lacdr.leidenuniv.nl or t.reijmers@lacdr.leidenuniv.nl

Description: Motivation: Currently, there is great interest in detecting complex trait rare variant associations using next-generation sequence data. On a monthly basis, new rare variant association methods are published. It is difficult to evaluate these methods because there is no standard to generate data and often comparisons are biased. In order to fairly compare rare variant association methods, it is necessary to generate data using realistic population demographic and phenotypic models. Result: SimRare is an interactive program that integrates generation of rare variant genotype/phenotype data and evaluation of association methods using a unified platform. Variant data are generated for gene regions using forward-time simulation that incorporates realistic population demographic and evolutionary scenarios. Phenotype data can be obtained for both case–control and quantitative traits. SimRare has a user-friendly interface that allows for easy entry of genetic and phenotypic parameters. Novel rare variant association methods implemented in R can also be imported into SimRare, to evaluate their performance and compare results, e.g. power and Type I error, with other currently available methods both numerically and graphically. Availability: http://code.google.com/p/simrare/ Contact: sleal@bcm.edu

Description: Purpose   Several strategies have been proposed for the prevention of vancomycin-induced nephrotoxicity. Here, we review available evidence supporting the respective strategies. Method   Data were collected by searching the Scopus, PubMed, and Medline databases and the Cochrane database of systematic reviews. The key words used as search terms were “vancomycin,” “nephrotoxicity”, “renal failure,” “renal damage,” “nephroprotective,” “renoprotective”, and “prevention.” Prospective or retrospective observational animal studies that evaluated the effects of a modality for the prevention of vancomycin-induced nephrotoxicity was included. Results and conclusion   Animal studies show beneficial effects of various antioxidants, such as erdosteine, vitamin E, vitamin C, N -acetylcysteine, caffeic acid phenethyl ester, and erythropoietin, in the prevention of vancomycin-induced nephrotoxicity. However, before these agents can be used in clinical practice, their potential benefits must be confirmed in future randomized controlled human studies. Content Type Journal Article Category Review Article Pages 1-8 DOI 10.1007/s00228-012-1406-3 Authors Sepideh Elyasi, Department of Clinical Pharmacy, Faculty of Pharmacy, Tehran University of Medical Sciences, P.O.Box: 14155/6451, 1417614411 Tehran, Iran Hossein Khalili, Department of Clinical Pharmacy, Faculty of Pharmacy, Tehran University of Medical Sciences, P.O.Box: 14155/6451, 1417614411 Tehran, Iran Shima Hatamkhani, Department of Clinical Pharmacy, Faculty of Pharmacy, Tehran University of Medical Sciences, P.O.Box: 14155/6451, 1417614411 Tehran, Iran Simin Dashti-Khavidaki, Department of Clinical Pharmacy, Faculty of Pharmacy, Tehran University of Medical Sciences, P.O.Box: 14155/6451, 1417614411 Tehran, Iran Journal European Journal of Clinical Pharmacology Online ISSN 1432-1041 Print ISSN 0031-6970

Description: Background and objectives   A large number of clinical studies have well demonstrated that the loss-of-function variant allele CYP2C19 *2 is associated with attenuated response to clopidogrel and increased risk of developing stent thrombosis (ST) in white or black patients with stenting. However, similar association studies on the effect of the CYP2C19 *2 and *3 variants on maximum platelet aggregation (MPA) and the risk of cardiovascular events are currently unavailable for the Chinese patients. This work was aimed at assessing the impact of the CYP2C19 *2 and *3 variants on the antiplatelet effects and adverse cardiovascular events in clopidogrel-treated Chinese patients undergoing percutaneous coronary intervention (PCI). Methods   The study population consisted of 617 patients undergoing PCI. Genotypes were determined using MALDI/TOF-MS. MPA was measured by light transmittance aggregometry. The clinical end-point was the 1-year incidence of adverse cardiovascular events, including ST. Results   Carriers of CYP2C19 heterozygous (*1/*2, or *1/*3; n  = 278) and mutant homozygous (*2/*2, *2/*3, or *3/*3, n  = 80) genotypes had significantly higher MPA values than noncarriers (*1/*1; n  = 259; P  = 0.036 and 0.007 respectively). Moreover, the presence of the CYP2C19 *2 or *3 mutant allele was significantly associated with an increased risk of developing ST, with the higher risk of ST being seen in patients homozygous for the CYP2C19 *2 or *3 variant allele than in noncarriers (OR, 13.58; 95% CI, 1.49–123.31; P  = 0.012). Furthermore, multivariate analysis revealed an independent association of the presence of CYP2C19 *2 and/or *3 variant alleles with greater MPA values ( P  = 0.001) and increased risk of ST (OR, 11.67; 95% CI, 1.21–78.83; P  = 0.022). However, there was no significant influence of CYP2C19 *2 and *3 on the risk of developing other adverse cardiovascular events. Conclusions   Carriage of the loss-of-function genetic variants CYP2C19 *2 and *3 is significantly associated with attenuated platelet response to clopidogrel and an increased risk of ST in Chinese patients treated with stenting. Content Type Journal Article Category Clinical Trial Pages 1-7 DOI 10.1007/s00228-012-1392-5 Authors Jian-Jun Zou, Key Laboratory of Drug Metabolism and Pharmacokinetics, China Pharmaceutical University, Nanjing, 210009 China Hong-Guang Xie, General Clinical Research Center and Division of Clinical Pharmacology, Nanjing Hospital Affiliated to Nanjing Medical University, Nanjing, 210006 China Shao-Liang Chen, Division of Cardiology, Nanjing Hospital Affiliated to Nanjing Medical University, Nanjing, 210006 China Jie Tan, Division of Clinical Pharmacology, Nanjing Hospital Affiliated to Nanjing Medical University, Nanjing, 210006 China Ling Lin, Division of Cardiology, Nanjing Hospital Affiliated to Nanjing Medical University, Nanjing, 210006 China Ying-Ying Zhao, Division of Cardiology, Nanjing Hospital Affiliated to Nanjing Medical University, Nanjing, 210006 China Hai-Mei Xu, Division of Cardiology, Nanjing Hospital Affiliated to Nanjing Medical University, Nanjing, 210006 China Song Lin, Division of Cardiology, Nanjing Hospital Affiliated to Nanjing Medical University, Nanjing, 210006 China Juan Zhang, Division of Cardiology, Nanjing Hospital Affiliated to Nanjing Medical University, Nanjing, 210006 China Guang-Ji Wang, Key Laboratory of Drug Metabolism and Pharmacokinetics, China Pharmaceutical University, Nanjing, 210009 China Journal European Journal of Clinical Pharmacology Online ISSN 1432-1041 Print ISSN 0031-6970

Description: Motivation: We previously reported the development of a highly accurate statistical algorithm for identifying β-barrel outer membrane proteins or transmembrane β-barrels (TMBBs), from genomic sequence data of Gram-negative bacteria (Freeman,T.C. and Wimley,W.C. (2010) Bioinformatics , 26 , 1965–1974). We have now applied this identification algorithm to all available Gram-negative bacterial genomes (over 600 chromosomes) and have constructed a publicly available, searchable, up-to-date, database of all proteins in these genomes. Results: For each protein in the database, there is information on (i) β-barrel membrane protein probability for identification of β-barrels, (ii) β-strand and β-hairpin propensity for structure and topology prediction, (iii) signal sequence score because most TMBBs are secreted through the inner membrane translocon and, thus, have a signal sequence, and (iv) transmembrane α-helix predictions, for reducing false positive predictions. This information is sufficient for the accurate identification of most β-barrel membrane proteins in these genomes. In the database there are nearly 50 000 predicted TMBBs (out of 1.9 million total putative proteins). Of those, more than 15 000 are ‘hypothetical’ or ‘putative’ proteins, not previously identified as TMBBs. This wealth of genomic information is not available anywhere else. Availability: The TMBB genomic database is available at http://beta-barrel.tulane.edu/ . Contact: wwimley@tulane.edu

Description: Motivation: Meta-analysis of genomics data seeks to identify genes associated with a biological phenotype across multiple datasets; however, merging data from different platforms by their features (genes) is challenging. Meta-analysis using functionally or biologically characterized gene sets simplifies data integration is biologically intuitive and is seen as having great potential, but is an emerging field with few established statistical methods. Results: We transform gene expression profiles into binary gene set profiles by discretizing results of gene set enrichment analyses and apply a new iterative bi-clustering algorithm (iBBiG) to identify groups of gene sets that are coordinately associated with groups of phenotypes across multiple studies. iBBiG is optimized for meta-analysis of large numbers of diverse genomics data that may have unmatched samples. It does not require prior knowledge of the number or size of clusters. When applied to simulated data, it outperforms commonly used clustering methods, discovers overlapping clusters of diverse sizes and is robust in the presence of noise. We apply it to meta-analysis of breast cancer studies, where iBBiG extracted novel gene set—phenotype association that predicted tumor metastases within tumor subtypes. Availability: Implemented in the Bioconductor package iBBiG Contact: aedin@jimmy.harvard.edu

Description: Motivation: The prediction of a protein’s contact map has become in recent years, a crucial stepping stone for the prediction of the complete 3D structure of a protein. In this article, we describe a methodology for this problem that was shown to be successful in CASP8 and CASP9. The methodology is based on (i) the fusion of the prediction of a variety of structural aspects of protein residues, (ii) an ensemble strategy used to facilitate the training process and (iii) a rule-based machine learning system from which we can extract human-readable explanations of the predictor and derive useful information about the contact map representation. Results: The main part of the evaluation is the comparison against the sequence-based contact prediction methods from CASP9, where our method presented the best rank in five out of the six evaluated metrics. We also assess the impact of the size of the ensemble used in our predictor to show the trade-off between performance and training time of our method. Finally, we also study the rule sets generated by our machine learning system. From this analysis, we are able to estimate the contribution of the attributes in our representation and how these interact to derive contact predictions. Availability: http://icos.cs.nott.ac.uk/servers/psp.html . Contact: natalio.krasnogor@nottingham.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online.

Description: : Gibberellic acids (GAs) are key plant hormones, regulating various aspects of growth and development, which have been at the center of the ‘green revolution’. GRAS family proteins, the primary players in GA signaling pathways, remain poorly understood. Using sequence-profile searches, structural comparisons and phylogenetic analysis, we establish that the GRAS family first emerged in bacteria and belongs to the Rossmann fold methyltransferase superfamily. All bacterial and a subset of plant GRAS proteins are likely to function as small-molecule methylases. The remaining plant versions have lost one or more AdoMet (SAM)-binding residues while preserving their substrate-binding residues. We predict that GRAS proteins might either modify or bind small molecules such as GAs or their derivatives. Contact: aravind@ncbi.nlm.nih.gov Supplementary Information: Supplementary Material for this article is available at Bioinformatics online.