bluuebunny/crossref_metadata_2025 huggingface dataset:
Finding differentially expressed regions of arbitrary length in quantitative genomic data based on marked point process model
Authors: Hatsuda Hiroshi
Published: September 2012

Motivation: High-throughput nucleotide sequencing technologies provide large amounts of quantitative genomic data at nucleotide resolution, which are important for the present and future biomedical researches; for example differential analysis of base-level RNA expression data will improve our understanding of transcriptome, including both coding and non-coding genes. However, most studies of these data have relied on existing genome annotations and thus are limited to the analysis of known transcripts.

Results: In this article, we propose a novel method based on a marked point process model to find differentially expressed genomic regions of arbitrary length without using genome annotations. The presented method conducts a statistical test for differential analysis in regions of various lengths at each nucleotide and searches the optimal configuration of the regions by using a Monte Carlo simulation. We applied the proposed method to both synthetic and real genomic data, and their results demonstrate the effectiveness of our method.

Availability: The program used in this study is available at https://sites.google.com/site/hiroshihatsuda/.

Contact:  H.Hatsuda@warwick.ac.uk
Graphlet-based edge clustering reveals pathogen-interacting proteins
Authors: Solava R. W. , Michaels R. P. , Milenković T.
Published: September 2012

Motivation: Prediction of protein function from protein interaction networks has received attention in the post-genomic era. A popular strategy has been to cluster the network into functionally coherent groups of proteins and assign the entire cluster with a function based on functions of its annotated members. Traditionally, network research has focused on clustering of nodes. However, clustering of edges may be preferred: nodes belong to multiple functional groups, but clustering of nodes typically cannot capture the group overlap, while clustering of edges can. Clustering of adjacent edges that share many neighbors was proposed recently, outperforming different node clustering methods. However, since some biological processes can have characteristic ‘signatures’ throughout the network, not just locally, it may be of interest to consider edges that are not necessarily adjacent.

Results: We design a sensitive measure of the ‘topological similarity’ of edges that can deal with edges that are not necessarily adjacent. We cluster edges that are similar according to our measure in different baker's yeast protein interaction networks, outperforming existing node and edge clustering approaches. We apply our approach to the human network to predict new pathogen-interacting proteins. This is important, since these proteins represent drug target candidates.

Availability: Software executables are freely available upon request.

Contact:  tmilenko@nd.edu

Supplementary Information:  Supplementary data are available at Bioinformatics online.
Trajectory-oriented Bayesian experiment design versus Fisher A-optimal design: an in depth comparison study
Authors: Weber Patrick , Kramer Andrei , Dingler Clemens , Radde Nicole
Published: September 2012

Motivation: Experiment design strategies for biomedical models with the purpose of parameter estimation or model discrimination are in the focus of intense research. Experimental limitations such as sparse and noisy data result in unidentifiable parameters and render-related design tasks challenging problems. Often, the temporal resolution of data is a limiting factor and the amount of possible experimental interventions is finite. To address this issue, we propose a Bayesian experiment design algorithm to minimize the prediction uncertainty for a given set of experiments and compare it to traditional A-optimal design.

Results: In an in depth numerical study involving an ordinary differential equation model of the trans-Golgi network with 12 partly non-identifiable parameters, we minimized the prediction uncertainty efficiently for predefined scenarios. The introduced method results in twice the prediction precision as the same amount of A-optimal designed experiments while introducing a useful stopping criterion. The simulation intensity of the algorithm's major design step is thereby reasonably affordable. Besides smaller variances in the predicted trajectories compared with Fisher design, we could also achieve smaller parameter posterior distribution entropies, rendering this method superior to A-optimal Fisher design also in the parameter space.

Availability: Necessary software/toolbox information are available in the supplementary material. The project script including example data can be downloaded from http://www.ist.uni-stuttgart.de/%7eweber/BayesFisher2012.

Contact:  patrick.weber@ist.uni-stuttgart.de

Supplementary Information:  Supplementary data are available at Bioinformatics online.
Reveal—visual eQTL analytics
Authors: Jäger Günter , Battke Florian , Nieselt Kay
Published: September 2012

Motivation: The analysis of expression quantitative trait locus (eQTL) data is a challenging scientific endeavor, involving the processing of very large, heterogeneous and complex data. Typical eQTL analyses involve three types of data: sequence-based data reflecting the genotypic variations, gene expression data and meta-data describing the phenotype. Based on these, certain genotypes can be connected with specific phenotypic outcomes to infer causal associations of genetic variation, expression and disease.

To this end, statistical methods are used to find significant associations between single nucleotide polymorphisms (SNPs) or pairs of SNPs and gene expression. A major challenge lies in summarizing the large amount of data as well as statistical results and to generate informative, interactive visualizations.

Results: We present Reveal, our visual analytics approach to this challenge. We introduce a graph-based visualization of associations between SNPs and gene expression and a detailed genotype view relating summarized patient cohort genotypes with data from individual patients and statistical analyses.

Availability: Reveal is included in Mayday, our framework for visual exploration and analysis. It is available at http://it.inf.uni-tuebingen.de/software/reveal/.

Contact:  guenter.jaeger@uni-tuebingen.de
A novel approach for resolving differences in single-cell gene expression patterns from zygote to blastocyst
Authors: Buettner Florian , Theis Fabian J.
Published: September 2012

Motivation: Single-cell experiments of cells from the early mouse embryo yield gene expression data for different developmental stages from zygote to blastocyst. To better understand cell fate decisions during differentiation, it is desirable to analyse the high-dimensional gene expression data and assess differences in gene expression patterns between different developmental stages as well as within developmental stages. Conventional methods include univariate analyses of distributions of genes at different stages or multivariate linear methods such as principal component analysis (PCA). However, these approaches often fail to resolve important differences as each lineage has a unique gene expression pattern which changes gradually over time yielding different gene expressions both between different developmental stages as well as heterogeneous distributions at a specific stage. Furthermore, to date, no approach taking the temporal structure of the data into account has been presented.

Results: We present a novel framework based on Gaussian process latent variable models (GPLVMs) to analyse single-cell qPCR expression data of 48 genes from mouse zygote to blastocyst as presented by (Guo et al., 2010). We extend GPLVMs by introducing gene relevance maps and gradient plots to provide interpretability as in the linear case. Furthermore, we take the temporal group structure of the data into account and introduce a new factor in the GPLVM likelihood which ensures that small distances are preserved for cells from the same developmental stage. Using our novel framework, it is possible to resolve differences in gene expressions for all developmental stages. Furthermore, a new subpopulation of cells within the 16-cell stage is identified which is significantly more trophectoderm-like than the rest of the population. The trophectoderm-like subpopulation was characterized by considerable differences in the expression of Id2, Gata4 and, to a smaller extent, Klf4 and Hand1. The relevance of Id2 as early markers for TE cells is consistent with previously published results.

Availability: The mappings were implemented based on Prof. Neil Lawrence's FGPLVM toolbox1; extensions for relevance analysis and including the structure of the data can be obtained from one of the authors' homepage.2

Contact:  f.buettner@helmholtz-muenchen.de
Stitching gene fragments with a network matching algorithm improves gene assembly for metagenomics
Authors: Wu Yu-Wei , Rho Mina , Doak Thomas G. , Ye Yuzhen
Published: September 2012

Motivation: One of the difficulties in metagenomic assembly is that homologous genes from evolutionarily closely related species may behave like repeats and confuse assemblers. As a result, small contigs, each representing a short gene fragment, instead of complete genes, may be reported by an assembler. This further complicates annotation of metagenomic datasets, as annotation tools (such as gene predictors or similarity search tools) typically perform poorly on configs encoding short gene fragments.

Results: We present a novel way of using the de Bruijn graph assembly of metagenomes to improve the assembly of genes. A network matching algorithm is proposed for matching the de Bruijn graph of contigs against reference genes, to derive ‘gene paths’ in the graph (sequences of contigs containing gene fragments) that have the highest similarities to known genes, allowing gene fragments contained in multiple contigs to be connected to form more complete (or intact) genes. Tests on simulated and real datasets show that our approach (called GeneStitch) is able to significantly improve the assembly of genes from metagenomic sequences, by connecting contigs with the guidance of homologous genes—information that is orthogonal to the sequencing reads. We note that the improvement of gene assembly can be observed even when only distantly related genes are available as the reference. We further propose to use ‘gene graphs’ to represent the assembly of reads from homologous genes and discuss potential applications of gene graphs to improving functional annotation for metagenomics.

Availability: The tools are available as open source for download at http://omics.informatics.indiana.edu/GeneStitch

Contact:  yye@indiana.edu
EnrichNet: network-based gene set enrichment analysis
Authors: Glaab Enrico , Baudot Anaïs , Krasnogor Natalio , Schneider Reinhard , Valencia Alfonso
Published: September 2012

Motivation: Assessing functional associations between an experimentally derived gene or protein set of interest and a database of known gene/protein sets is a common task in the analysis of large-scale functional genomics data. For this purpose, a frequently used approach is to apply an over-representation-based enrichment analysis. However, this approach has four drawbacks: (i) it can only score functional associations of overlapping gene/proteins sets; (ii) it disregards genes with missing annotations; (iii) it does not take into account the network structure of physical interactions between the gene/protein sets of interest and (iv) tissue-specific gene/protein set associations cannot be recognized.

Results: To address these limitations, we introduce an integrative analysis approach and web-application called EnrichNet. It combines a novel graph-based statistic with an interactive sub-network visualization to accomplish two complementary goals: improving the prioritization of putative functional gene/protein set associations by exploiting information from molecular interaction networks and tissue-specific gene expression data and enabling a direct biological interpretation of the results. By using the approach to analyse sets of genes with known involvement in human diseases, new pathway associations are identified, reflecting a dense sub-network of interactions between their corresponding proteins.

Availability: EnrichNet is freely available at http://www.enrichnet.org.

Contact:  Natalio.Krasnogor@nottingham.ac.uk, reinhard.schneider@uni.lu or avalencia@cnio.es

Supplementary Information:  Supplementary data are available at Bioinformatics Online.
Comprehensive estimation of input signals and dynamics in biochemical reaction networks
Authors: Schelker M. , Raue A. , Timmer J. , Kreutz C.
Published: September 2012

Motivation: Cellular information processing can be described mathematically using differential equations. Often, external stimulation of cells by compounds such as drugs or hormones leading to activation has to be considered. Mathematically, the stimulus is represented by a time-dependent input function.

Parameters such as rate constants of the molecular interactions are often unknown and need to be estimated from experimental data, e.g. by maximum likelihood estimation. For this purpose, the input function has to be defined for all times of the integration interval. This is usually achieved by approximating the input by interpolation or smoothing of the measured data. This procedure is suboptimal since the input uncertainties are not considered in the estimation process which often leads to overoptimistic confidence intervals of the inferred parameters and the model dynamics.

Results: This article presents a new approach which includes the input estimation into the estimation process of the dynamical model parameters by minimizing an objective function containing all parameters simultaneously. We applied this comprehensive approach to an illustrative model with simulated data and compared it to alternative methods. Statistical analyses revealed that our method improves the prediction of the model dynamics and the confidence intervals leading to a proper coverage of the confidence intervals of the dynamic parameters. The method was applied to the JAK-STAT signaling pathway.

Availability: MATLAB code is available on the authors' website http://www.fdmold.uni-freiburg.de/~schelker/.

Contact:  max.schelker@fdm.uni-freiburg.de

Supplementary Information: Additional information is available at Bioinformatics Online.
MetaCluster 5.0: a two-round binning approach for metagenomic data for low-abundance species in a noisy sample
Authors: Wang Yi , Leung Henry C.M. , Yiu S.M. , Chin Francis Y.L.
Published: September 2012

Motivation: Metagenomic binning remains an important topic in metagenomic analysis. Existing unsupervised binning methods for next-generation sequencing (NGS) reads do not perform well on (i) samples with low-abundance species or (ii) samples (even with high abundance) when there are many extremely low-abundance species. These two problems are common for real metagenomic datasets. Binning methods that can solve these problems are desirable.

Results: We proposed a two-round binning method (MetaCluster 5.0) that aims at identifying both low-abundance and high-abundance species in the presence of a large amount of noise due to many extremely low-abundance species. In summary, MetaCluster 5.0 uses a filtering strategy to remove noise from the extremely low-abundance species. It separate reads of high-abundance species from those of low-abundance species in two different rounds. To overcome the issue of low coverage for low-abundance species, multiple w values are used to group reads with overlapping w-mers, whereas reads from high-abundance species are grouped with high confidence based on a large w and then binning expands to low-abundance species using a relaxed (shorter) w. Compared to the recent tools, TOSS and MetaCluster 4.0, MetaCluster 5.0 can find more species (especially those with low abundance of say 6× to 10×) and can achieve better sensitivity and specificity using less memory and running time.

Availability:  http://i.cs.hku.hk/~alse/MetaCluster/

Contact:  chin@cs.hku.hk
Protein domain recurrence and order can enhance prediction of protein functions
Authors: Messih Mario Abdel , Chitale Meghana , Bajic Vladimir B. , Kihara Daisuke , Gao Xin
Published: September 2012

Motivation: Burgeoning sequencing technologies have generated massive amounts of genomic and proteomic data. Annotating the functions of proteins identified in this data has become a big and crucial problem. Various computational methods have been developed to infer the protein functions based on either the sequences or domains of proteins. The existing methods, however, ignore the recurrence and the order of the protein domains in this function inference.

Results: We developed two new methods to infer protein functions based on protein domain recurrence and domain order. Our first method, DRDO, calculates the posterior probability of the Gene Ontology terms based on domain recurrence and domain order information, whereas our second method, DRDO-NB, relies on the naïve Bayes methodology using the same domain architecture information. Our large-scale benchmark comparisons show strong improvements in the accuracy of the protein function inference achieved by our new methods, demonstrating that domain recurrence and order can provide important information for inference of protein functions.

Availability: The new models are provided as open source programs at http://sfb.kaust.edu.sa/Pages/Software.aspx.

Contact:  dkihara@cs.purdue.edu, xin.gao@kaust.edu.sa

Supplementary information:  Supplementary data are available at Bioinformatics Online.
Random sampling of elementary flux modes in large-scale metabolic networks
Authors: Machado Daniel , Soons Zita , Patil Kiran Raosaheb , Ferreira Eugénio C. , Rocha Isabel
Published: September 2012

Motivation: The description of a metabolic network in terms of elementary (flux) modes (EMs) provides an important framework for metabolic pathway analysis. However, their application to large networks has been hampered by the combinatorial explosion in the number of modes. In this work, we develop a method for generating random samples of EMs without computing the whole set.

Results: Our algorithm is an adaptation of the canonical basis approach, where we add an additional filtering step which, at each iteration, selects a random subset of the new combinations of modes. In order to obtain an unbiased sample, all candidates are assigned the same probability of getting selected. This approach avoids the exponential growth of the number of modes during computation, thus generating a random sample of the complete set of EMs within reasonable time. We generated samples of different sizes for a metabolic network of Escherichia coli, and observed that they preserve several properties of the full EM set. It is also shown that EM sampling can be used for rational strain design. A well distributed sample, that is representative of the complete set of EMs, should be suitable to most EM-based methods for analysis and optimization of metabolic networks.

Availability: Source code for a cross-platform implementation in Python is freely available at http://code.google.com/p/emsampler.

Contact:  dmachado@deb.uminho.pt

Supplementary information:  Supplementary data are available at Bioinformatics online.
PARADIGM-SHIFT predicts the function of mutations in multiple cancers using pathway impact analysis
Authors: Ng Sam , Collisson Eric A. , Sokolov Artem , Goldstein Theodore , Gonzalez-Perez Abel , Lopez-Bigas Nuria , Benz Christopher , Haussler David , Stuart Joshua M.
Published: September 2012

Motivation: A current challenge in understanding cancer processes is to pinpoint which mutations influence the onset and progression of disease. Toward this goal, we describe a method called PARADIGM-SHIFT that can predict whether a mutational event is neutral, gain-or loss-of-function in a tumor sample. The method uses a belief-propagation algorithm to infer gene activity from gene expression and copy number data in the context of a set of pathway interactions.

Results: The method was found to be both sensitive and specific on a set of positive and negative controls for multiple cancers for which pathway information was available. Application to the Cancer Genome Atlas glioblastoma, ovarian and lung squamous cancer datasets revealed several novel mutations with predicted high impact including several genes mutated at low frequency suggesting the approach will be complementary to current approaches that rely on the prevalence of events to reach statistical significance.

Availability: All source code is available at the github repository http:github.org/paradigmshift.

Contact:  jstuart@soe.ucsc.edu

Supplementary information:  Supplementary data are available at Bioinformatics online.
Imaging, quantification and visualization of spatio-temporal patterning in mESC colonies under different culture conditions
Authors: Scherf N. , Herberg M. , Thierbach K. , Zerjatke T. , Kalkan T. , Humphreys P. , Smith A. , Glauche I. , Roeder I.
Published: September 2012

Motivation: Mouse embryonic stem cells (mESCs) have developed into a prime system to study the regulation of pluripotency in stable cell lines. It is well recognized that different, established protocols for the maintenance of mESC pluripotency support morphologically and functionally different cell cultures. However, it is unclear how characteristic properties of cell colonies develop over time and how they are re-established after cell passage depending on the culture conditions. Furthermore, it appears that cell colonies have an internal structure with respect to cell size, marker expression or biomechanical properties, which is not sufficiently understood. The analysis of these phenotypic properties is essential for a comprehensive understanding of mESC development and ultimately requires a bioinformatics approach to guarantee reproducibility and high-throughput data analysis.

Results: We developed an automated image analysis and colony tracking framework to obtain an objective and reproducible quantification of structural properties of cell colonies as they evolve in space and time. In particular, we established a method that quantifies changes in colony shape and (internal) motion using fluid image registration and image segmentation. The methodology also allows to robustly track motion, splitting and merging of colonies over a sequence of images. Our results provide a first quantitative assessment of temporal mESC colony formation and estimates of structural differences between colony growth under different culture conditions. Furthermore, we provide a stream-based visualization of structural features of individual colonies over time for the whole experiment, facilitating visual comprehension of differences between experimental conditions. Thus, the presented method establishes the basis for the model-based analysis of mESC colony development. It can be easily extended to integrate further functional information using fluorescence signals and differentiation markers.

Availability: The analysis tool is implemented C++ and Mathematica 8.0 (Wolfram Research Inc., Champaign, IL, USA). The tool is freely available from the authors. We will also provide the source code upon request.

Contact:  nico.scherf@tu-dresden.de
Bayesian assignment of gene ontology terms to gene expression experiments
Authors: Sykacek P.
Published: September 2012

Motivation: Gene expression assays allow for genome scale analyses of molecular biological mechanisms. State-of-the-art data analysis provides lists of involved genes, either by calculating significance levels of mRNA abundance or by Bayesian assessments of gene activity. A common problem of such approaches is the difficulty of interpreting the biological implication of the resulting gene lists. This lead to an increased interest in methods for inferring high-level biological information. A common approach for representing high level information is by inferring gene ontology (GO) terms which may be attributed to the expression data experiment.

Results: This article proposes a probabilistic model for GO term inference. Modelling assumes that gene annotations to GO terms are available and gene involvement in an experiment is represented by a posterior probabilities over gene-specific indicator variables. Such probability measures result from many Bayesian approaches for expression data analysis. The proposed model combines these indicator probabilities in a probabilistic fashion and provides a probabilistic GO term assignment as a result. Experiments on synthetic and microarray data suggest that advantages of the proposed probabilistic GO term inference over statistical test-based approaches are in particular evident for sparsely annotated GO terms and in situations of large uncertainty about gene activity. Provided that appropriate annotations exist, the proposed approach is easily applied to inferring other high level assignments like pathways.

Availability: Source code under GPL license is available from the author.

Contact:  peter.sykacek@boku.ac.at
Nonlinear dimension reduction with Wright–Fisher kernel for genotype aggregation and association mapping
Authors: Zhu Hongjie , Li Lexin , Zhou Hua
Published: September 2012

Motivation: Association tests based on next-generation sequencing data are often under-powered due to the presence of rare variants and large amount of neutral or protective variants. A successful strategy is to aggregate genetic information within meaningful single-nucleotide polymorphism (SNP) sets, e.g. genes or pathways, and test association on SNP sets. Many existing methods for group-wise tests require specific assumptions about the direction of individual SNP effects and/or perform poorly in the presence of interactions.

Results: We propose a joint association test strategy based on two key components: a nonlinear supervised dimension reduction approach for effective SNP information aggregation and a novel kernel specially designed for qualitative genotype data. The new test demonstrates superior performance in identifying causal genes over existing methods across a large variety of disease models simulated from sequence data of real genes. In general, the proposed method provides an association test strategy that can (i) detect both rare and common causal variants, (ii) deal with both additive and interaction effect, (iii) handle both quantitative traits and disease dichotomies and (iv) incorporate non-genetic covariates. In addition, the new kernel can potentially boost the power of the entire family of kernel-based methods for genetic data analysis.

Availability: The method is implemented in MATLAB. Source code is available upon request.

Contact:  hongjie.zhu@duke.edu
A structure-based protocol for learning the family-specific mechanisms of membrane-binding domains
Authors: Källberg Morten , Bhardwaj Nitin , Langlois Robert , Lu Hui
Published: September 2012

Motivation: Peripheral membrane-targeting domain (MTD) families, such as C1-, C2- and PH domains, play a key role in signal transduction and membrane trafficking by dynamically translocating their parent proteins to specific plasma membranes when changes in lipid composition occur. It is, however, difficult to determine the subset of domains within families displaying this property, as sequence motifs signifying the membrane binding properties are not well defined. For this reason, procedures based on sequence similarity alone are often insufficient in computational identification of MTDs within families (yielding less than 65% accuracy even with a sequence identity of 70%).

Results: We present a machine learning protocol for determining membrane-targeting properties achieving 85–90% accuracy in separating binding and non-binding domains within families. Our model is based on features from both sequence and structure, thereby incorporation statistics obtained from the entire domain family and domain-specific physical quantities such as surface electrostatics. In addition, by using the enriched rules in alternating decision tree classifiers, we are able to determine the meaning of the assigned function labels in terms of biological mechanisms.

Conclusions: The high accuracy of the learned models and good agreement between the rules discovered using the ADtree classifier and mechanisms reported in the literature reflect the value of machine learning protocols in both prediction and biological knowledge discovery. Our protocol can thus potentially be used as a general function annotation and knowledge mining tool for other protein domains.

Availability: metador.bioengr.uic.edu

Contact:  huilu@uic.edu
Boolean approach to signalling pathway modelling in HGF-induced keratinocyte migration
Authors: Singh Amit , Nascimento Juliana M. , Kowar Silke , Busch Hauke , Boerries Melanie
Published: September 2012

Motivation: Cell migration is a complex process that is controlled through the time-sequential feedback regulation of protein signalling and gene regulation. Based on prior knowledge and own experimental data, we developed a large-scale dynamic network describing the onset and maintenance of hepatocyte growth factor-induced migration of primary human keratinocytes. We applied Boolean logic to capture the qualitative behaviour as well as short-and long-term dynamics of the complex signalling network involved in this process, comprising protein signalling, gene regulation and autocrine feedback.

Results: A Boolean model has been compiled from time-resolved transcriptome data and literature mining, incorporating the main pathways involved in migration from initial stimulation to phenotype progress. Steady-state analysis under different inhibition and stimulation conditions of known key molecules reproduces existing data and predicts novel interactions based on our own experiments. Model simulations highlight for the first time the necessity of a temporal sequence of initial, transient MET receptor (met proto-oncogene, hepatocyte growth factor receptor) and subsequent, continuous epidermal growth factor/integrin signalling to trigger and sustain migration by autocrine signalling that is integrated through the Focal adhesion kinase protein. We predicted in silico and verified in vitro that long-term cell migration is stopped if any of the two feedback loops are inhibited.

Availability: The network file for analysis with the R BoolNet library is available in the Supplementary Information.

Contact:  melanie.boerries@frias.uni-freiburg.de or hauke.busch@frias.uni-freiburg.de

Supplementary information:  Supplementary data are available at Bioinformatics online.
Long read alignment based on maximal exact match seeds
Authors: Liu Yongchao , Schmidt Bertil
Published: September 2012

Motivation: The explosive growth of next-generation sequencing datasets poses a challenge to the mapping of reads to reference genomes in terms of alignment quality and execution speed. With the continuing progress of high-throughput sequencing technologies, read length is constantly increasing and many existing aligners are becoming inefficient as generated reads grow larger.

Results: We present CUSHAW2, a parallelized, accurate, and memory-efficient long read aligner. Our aligner is based on the seed-and-extend approach and uses maximal exact matches as seeds to find gapped alignments. We have evaluated and compared CUSHAW2 to the three other long read aligners BWA-SW, Bowtie2 and GASSST, by aligning simulated and real datasets to the human genome. The performance evaluation shows that CUSHAW2 is consistently among the highest-ranked aligners in terms of alignment quality for both single-end and paired-end alignment, while demonstrating highly competitive speed. Furthermore, our aligner shows good parallel scalability with respect to the number of CPU threads.

Availability: CUSHAW2, written in C++, and all simulated datasets are available at http://cushaw2.sourceforge.net

Contact:  liuy@uni-mainz.de; bertil.schmidt@uni-mainz.de

Supplementary information:  Supplementary data are available at Bioinformatics online.
Gene–gene interaction analysis for the survival phenotype based on the Cox model
Authors: Lee Seungyeoun , Kwon Min-Seok , Oh Jung Mi , Park Taesung
Published: September 2012

Motivation: For the past few decades, many statistical methods in genome-wide association studies (GWAS) have been developed to identify SNP–SNP interactions for case-control studies. However, there has been less work for prospective cohort studies, involving the survival time. Recently, Gui et al. (2011) proposed a novel method, called Surv-MDR, for detecting gene–gene interactions associated with survival time. Surv-MDR is an extension of the multifactor dimensionality reduction (MDR) method to the survival phenotype by using the log-rank test for defining a binary attribute. However, the Surv-MDR method has some drawbacks in the sense that it needs more intensive computations and does not allow for a covariate adjustment. In this article, we propose a new approach, called Cox-MDR, which is an extension of the generalized multifactor dimensionality reduction (GMDR) to the survival phenotype by using a martingale residual as a score to classify multi-level genotypes as high- and low-risk groups. The advantages of Cox-MDR over Surv-MDR are to allow for the effects of discrete and quantitative covariates in the frame of Cox regression model and to require less computation than Surv-MDR.

Results: Through simulation studies, we compared the power of Cox-MDR with those of Surv-MDR and Cox regression model for various heritability and minor allele frequency combinations without and with adjusting for covariate. We found that Cox-MDR and Cox regression model perform better than Surv-MDR for low minor allele frequency of 0.2, but Surv-MDR has high power for minor allele frequency of 0.4. However, when the effect of covariate is adjusted for, Cox-MDR and Cox regression model perform much better than Surv-MDR. We also compared the performance of Cox-MDR and Surv-MDR for a real data of leukemia patients to detect the gene–gene interactions with the survival time.

Contact:  leesy@sejong.ac.kr; tspark@snu.ac.kr
Context-specific transcriptional regulatory network inference from global gene expression maps using double two-way t-tests
Authors: Qi Jianlong , Michoel Tom
Published: September 2012

Motivation: Transcriptional regulatory network inference methods have been studied for years. Most of them rely on complex mathematical and algorithmic concepts, making them hard to adapt, re-implement or integrate with other methods. To address this problem, we introduce a novel method based on a minimal statistical model for observing transcriptional regulatory interactions in noisy expression data, which is conceptually simple, easy to implement and integrate in any statistical software environment and equally well performing as existing methods.

Results: We developed a method to infer regulatory interactions based on a model where transcription factors (TFs) and their targets are both differentially expressed in a gene-specific, critical sample contrast, as measured by repeated two-way t-tests. Benchmarking on standard Escherichia coli and yeast reference datasets showed that this method performs equally well as the best existing methods. Analysis of the predicted interactions suggested that it works best to infer context-specific TF-target interactions which only co-express locally. We confirmed this hypothesis on a dataset of >1000 normal human tissue samples, where we found that our method predicts highly tissue-specific and functionally relevant interactions, whereas a global co-expression method only associates general TFs to non-specific biological processes.

Availability: A software tool called TwixTrix is available from http://twixtrix.googlecode.com.

Supplementary information:  Supplementary Material is available from http://www.roslin.ed.ac.uk/tom-michoel/supplementary-data.

Contact:  tom.michoel@roslin.ed.ac.uk
Performance reproducibility index for classification
Authors: Yousefi Mohammadmahdi R. , Dougherty Edward R.
Published: September 2012

Motivation: A common practice in biomarker discovery is to decide whether a large laboratory experiment should be carried out based on the results of a preliminary study on a small set of specimens. Consideration of the efficacy of this approach motivates the introduction of a probabilistic measure, for whether a classifier showing promising results in a small-sample preliminary study will perform similarly on a large independent sample. Given the error estimate from the preliminary study, if the probability of reproducible error is low, then there is really no purpose in substantially allocating more resources to a large follow-on study. Indeed, if the probability of the preliminary study providing likely reproducible results is small, then why even perform the preliminary study?

Results: This article introduces a reproducibility index for classification, measuring the probability that a sufficiently small error estimate on a small sample will motivate a large follow-on study. We provide a simulation study based on synthetic distribution models that possess known intrinsic classification difficulties and emulate real-world scenarios. We also set up similar simulations on four real datasets to show the consistency of results. The reproducibility indices for different distributional models, real datasets and classification schemes are empirically calculated. The effects of reporting and multiple-rule biases on the reproducibility index are also analyzed.

Availability: We have implemented in C code the synthetic data distribution model, classification rules, feature selection routine and error estimation methods. The source code is available at http://gsp.tamu.edu/Publications/supplementary/yousefi12a/. Supplementary simulation results are also included.

Contact:  edward@ece.tamu.edu

Supplementary Information:  Supplementary data are available at Bioinformatics online.
Mendel-GPU: haplotyping and genotype imputation on graphics processing units
Authors: Chen Gary K. , Wang Kai , Stram Alex H. , Sobel Eric M. , Lange Kenneth
Published: September 2012

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.
SAPIN: A framework for the structural analysis of protein interaction networks
Authors: Yang Jae-Seong , Campagna Anne , Delgado Javier , Vanhee Peter , Serrano Luis , Kiel Christina
Published: September 2012

Summary: 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.
Diversity Among Ralstonia solanacearum Strains Isolated from the Southeastern United States
Authors: Hong Jason C. , Norman David J. , Reed David L. , Momol M. Timur , Jones Jeffrey B.
Published: September 2012

This is the first comprehensive study of a collection of Ralstonia solanacearum strains from the southeastern United States to be characterized based on biovar, pathogenicity, hypersensitive reaction on tobacco, and phylogenetic analyses of the egl sequence. Rigorous phylogenetic analysis of the commonly used egl gene produced robust phylogenies that differed significantly from a neighbor-joining tree differed from and previously published phylogenies for R. solanacearum strains. These robust trees placed phylotype IV within the phylotype I clade, which may suggest that phylogenies based solely on egl may be misleading. As a result of phylogenetic analyses in this study, we determined that U.S. strains from Georgia, North Carolina, South Carolina, and older Florida strains isolated from solanaceous crops all belong to phylotype II sequevar 7. However, many strains recently isolated in Florida from tomato and other crops were more diverse than the southeastern United States population. These unique Florida strains grouped with strains mostly originating from the Caribbean and Central America. One of the exotic strains, which in a previous study was determined to be established in northern Florida, was characterized more extensively. Upon using Musa-specific multiplex polymerase chain reaction, this strain produced a unique banding pattern, which has not previously been reported. Inoculation of this strain into Musa spp. did not result in wilt symptoms; however, the plants were stunted and root masses were significantly reduced. Furthermore, following root inoculation, the bacterium, unlike a typical Florida race 1 biovar 1 strain, was recovered from the roots and stems, indicating systemic movement. This is the first report of an R. solanacearum strain isolated in the United States that is deleterious to the growth of Musa plants.
Combined action of type I and type III interferon restricts initial replication of severe acute respiratory syndrome coronavirus in the lung but fails to inhibit systemic virus spread
Authors: Mahlakõiv Tanel , Ritz Daniel , Mordstein Markus , DeDiego Marta L. , Enjuanes Luis , Müller Marcel A. , Drosten Christian , Staeheli Peter
Published: September 2012

STAT1-deficient mice are more susceptible to infection with severe acute respiratory syndrome coronavirus (SARS-CoV) than type I interferon (IFN) receptor-deficient mice. We used mice lacking functional receptors for both type I and type III IFN (double knockout, dKO) to evaluate the possibility that type III IFN plays a decisive role in SARS-CoV protection. We found that viral peak titres in lungs of dKO and STAT1-deficient mice were similar, but significantly higher than in wild-type mice. The kinetics of viral clearance from the lung were also comparable in dKO and STAT1-deficient mice. Surprisingly, however, infected dKO mice remained healthy, whereas infected STAT1-deficient mice developed liver pathology and eventually succumbed to neurological disease. Our data suggest that the failure of STAT1-deficient mice to control initial SARS-CoV replication efficiently in the lung is due to impaired type I and type III IFN signalling, whereas the failure to control subsequent systemic viral spread is due to unrelated defects in STAT1-deficient mice.
Dynamic landscape of tandem 3′ UTRs during zebrafish development
Authors: Li Yuxin , Sun Yu , Fu Yonggui , Li Mengzhen , Huang Guangrui , Zhang Chenxu , Liang Jiahui , Huang Shengfeng , Shen Gaoyang , Yuan Shaochun , Chen Liangfu , Chen Shangwu , Xu Anlong
Published: September 2012

Tandem 3′ untranslated regions (UTRs), produced by alternative polyadenylation (APA) in the terminal exon of a gene, could have critical roles in regulating gene networks. Here we profiled tandem poly(A) events on a genome-wide scale during the embryonic development of zebrafish (Danio rerio) using a recently developed SAPAS method. We showed that 43% of the expressed protein-coding genes have tandem 3′ UTRs. The average 3′ UTR length follows a V-shaped dynamic pattern during early embryogenesis, in which the 3′ UTRs are first shortened at zygotic genome activation, and then quickly lengthened during gastrulation. Over 4000 genes are found to switch tandem APA sites, and the distinct functional roles of these genes are indicated by Gene Ontology analysis. Three families ofcis-elements, including miR-430 seed, U-rich element, and canonical poly(A) signal, are enriched in 3′ UTR-shortened/lengthened genes in a stage-specific manner, suggesting temporal regulation coordinated by APA andtrans-acting factors. Our results highlight the regulatory role of tandem 3′ UTR control in early embryogenesis and suggest that APA may represent a new epigenetic paradigm of physiological regulations.
Natural genetic variation in yeast longevity
Authors: Stumpferl Stefan W. , Brand Sue E. , Jiang James C. , Korona Boguslawa , Tiwari Anurag , Dai Jianliang , Seo Jae-Gu , Jazwinski S. Michal
Published: September 2012

The genetics of aging in the yeast Saccharomyces cerevisiae has involved the manipulation of individual genes in laboratory strains. We have instituted a quantitative genetic analysis of the yeast replicative lifespan by sampling the natural genetic variation in a wild yeast isolate. Haploid segregants from a cross between a common laboratory strain (S288c) and a clinically derived strain (YJM145) were subjected to quantitative trait locus (QTL) analysis, using 3048 molecular markers across the genome. Five significant, replicative lifespan QTL were identified. Among them, QTL 1 on chromosome IV has the largest effect and contains SIR2, whose product differs by five amino acids in the parental strains. Reciprocal gene swap experiments showed that this gene is responsible for the majority of the effect of this QTL on lifespan. The QTL with the second-largest effect on longevity was QTL 5 on chromosome XII, and the bulk of the underlying genomic sequence contains multiple copies (100–150) of the rDNA. Substitution of the rDNA clusters of the parental strains indicated that they play a predominant role in the effect of this QTL on longevity. This effect does not appear to simply be a function of extrachromosomal ribosomal DNA circle production. The results support an interaction between SIR2 and the rDNA locus, which does not completely explain the effect of these loci on longevity. This study provides a glimpse of the complex genetic architecture of replicative lifespan in yeast and of the potential role of genetic variation hitherto unsampled in the laboratory.