bluuebunny/crossref_metadata_2025_split huggingface dataset:
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

Abstract
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

AbstractMotivation: 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.esSupplementary 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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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.