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BMC Structural Biology - Latest Articles   [more] [xml]
 2014-07-19T00:00:00Z A simple method for finding a protein¿s ligand-binding pockets
Background: This paper provides a simple and rapid method for a protein-clustering strategy. The basic idea implemented here is to use computational geometry methods to predict and characterize ligand-binding pockets of a given protein structure. In addition to geometrical characteristics of the protein structure, we consider some simple biochemical properties that help recognize the best candidates for pockets in a protein’s active site. Results: Our results are shown to produce good agreement with known empirical results. Conclusions: The method presented in this paper is a low-cost rapid computational method that could be used to classify proteins and other biomolecules, and furthermore could be useful in reducing the cost and time of drug discovery.


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BMC Bioinformatics - Latest Articles   [more] [xml]
 2014-08-30T00:00:00Z AliGROOVE -- visualization of heterogeneous sequence divergence within multiple sequence alignments and detection of inflated branch support
Background: Masking of multiple sequence alignment blocks has become a powerful method to enhance thetree-likeness of the underlying data. However, existing masking approaches are insensitive toheterogeneous sequence divergence which can mislead tree reconstructions. We presentAliGROOVE, a new method based on a sliding window and a Monte Carlo resampling approach,that visualizes heterogeneous sequence divergence or alignment ambiguity related to single taxa orsubsets of taxa within a multiple sequence alignment and tags suspicious branches on a given tree. Results: We used simulated multiple sequence alignments to show that the extent of alignment ambiguity inpairwise sequence comparison is correlated with the frequency of misplaced taxa in treereconstructions. The approach implemented in AliGROOVE allows to detect nodes within a tree thatare supported despite the absence of phylogenetic signal in the underlying multiple sequencealignment. We show that AliGROOVE equally well detects heterogeneous sequence divergence in acase study based on an empirical data set of mitochondrial DNA sequences of chelicerates. Conclusions: The AliGROOVE approach has the potential to identify single taxa or subsets of taxa which showpredominantly randomized sequence similarity in comparison with other taxa in a multiple sequencealignment. It further allows to evaluate the reliability of node support in a novel way.
 2014-08-29T00:00:00Z jvenn: an interactive Venn diagram viewer
Background: Venn diagrams are commonly used to display list comparison. In biology, they are widely used to show the differences between gene lists originating from different differential analyses, for instance. They thus allow the comparison between different experimental conditions or between different methods. However, when the number of input lists exceeds four, the diagram becomes difficult to read. Alternative layouts and dynamic display features can improve its use and its readability. Results: jvenn is a new JavaScript library. It processes lists and produces Venn diagrams. It handles up to six input lists and presents results using classical or Edwards-Venn layouts. User interactions can be controlled and customized. Finally, jvenn can easily be embeded in a web page, allowing to have dynamic Venn diagrams. Conclusions: jvenn is an open source component for web environments helping scientists to analyze their data. The library package, which comes with full documentation and an example, is freely available at http://bioinfo.genotoul.fr/jvenn.
 2014-08-29T00:00:00Z Native structure-based modeling and simulation of biomolecular systems per mouse click
Background: Molecular dynamics (MD) simulations provide valuable insight into biomolecular systems at the atomic level. Notwithstanding the ever-increasing power of high performance computers current MD simulations face several challenges: the fastest atomic movements require time steps of a few femtoseconds which are small compared to biomolecular relevant timescales of milliseconds or even seconds for large conformational motions. At the same time, scalability to a large number of cores is limited mostly due to long-range interactions. An appealing alternative to atomic-level simulations is coarse-graining the resolution of the system or reducing the complexity of the Hamiltonian to improve sampling while decreasing computational costs. Native structure-based models, also called G¿-type models, are based on energy landscape theory and the principle of minimal frustration. They have been tremendously successful in explaining fundamental questions of, e.g., protein folding, RNA folding or protein function. At the same time, they are computationally sufficiently inexpensive to run complex simulations on smaller computing systems or even commodity hardware. Still, their setup and evaluation is quite complex even though sophisticated software packages support their realization. Results: Here, we establish an efficient infrastructure for native structure-based models to support the community and enable high-throughput simulations on remote computing resources via GridBeans and UNICORE middleware. This infrastructure organizes the setup of such simulations resulting in increased comparability of simulation results. At the same time, complete workflows for advanced simulation protocols can be established and managed on remote resources by a graphical interface which increases reusability of protocols and additionally lowers the entry barrier into such simulations for, e.g., experimental scientists who want to compare their results against simulations. We demonstrate the power of this approach by illustrating it for protein folding simulations for a range of proteins. Conclusions: We present software enhancing the entire workflow for native structure-based simulations including exception-handling and evaluations. Extending the capability and improving the accessibility of existing simulation packages the software goes beyond the state of the art in the domain of biomolecular simulations. Thus we expect that it will stimulate more individuals from the community to employ more confidently modeling in their research.
 2014-08-27T00:00:00Z Substituting random forest for multiple linear regression improves binding affinity prediction of scoring functions: cyscore as a case study
Background: State-of-the-art protein-ligand docking methods are generally limited by the traditionally low accuracy of their scoring functions, which are used to predict binding affinity and thus vital for discriminating between active and inactive compounds. Despite intensive research over the years, classical scoring functions have reached a plateau in their predictive performance. These assume a predetermined additive functional form for some sophisticated numerical features, and use standard multivariate linear regression (MLR) on experimental data to derive the coefficients. Results: In this study we show that such a simple functional form is detrimental for the prediction performance of a scoring function, and replacing linear regression by machine learning techniques like random forest (RF) can improve prediction performance. We investigate the conditions of applying RF under various contexts and find that given sufficient training samples RF manages to comprehensively capture the non-linearity between structural features and measured binding affinities. Incorporating more structural features and training with more samples can both boost RF performance. In addition, we analyze the importance of structural features to binding affinity prediction using the RF variable importance tool. Lastly, we use Cyscore, a top performing empirical scoring function, as a baseline for comparison study. Conclusions: Machine-learning scoring functions are fundamentally different from classical scoring functions because the former circumvents the fixed functional form relating structural features with binding affinities. RF, but not MLR, can effectively exploit more structural features and more training samples, leading to higher prediction performance. The future availability of more X-ray crystal structures will further widen the performance gap between RF-based and MLR-based scoring functions. This further stresses the importance of substituting RF for MLR in scoring function development.
 2014-08-27T00:00:00Z MendeLIMS: a web-based laboratory information management system for clinical genome sequencing
Background: Large clinical genomics studies using next generation DNA sequencing requires the ability to select and track samples from a large population of patients through many experimental steps. With the number of clinical genome sequencing studies increasing, it is critical to maintain adequate laboratory information managements systems to manage the thousands of patient samples that are subject to this type of genetic analysis. Results: To meet the needs of clinical population studies using genome sequencing, we developed a web-based laboratory information management system (LIMS) with a flexible configuration that is adaptable to continuously evolving experimental protocols of next generation DNA sequencing technologies. Our system is referred to as MendeLIMS, is easily implemented with open source tools and is also highly configurable and extensible. MendeLIMS has been invaluable in the management of our clinical genome sequencing studies. Conclusions: We maintain a publicly available demonstration version of the application for evaluation purposes at http://mendelims.stanford.edu. MendeLIMS is programmed in Ruby on Rails (RoR) and accesses data stored in SQL-compliant relational databases. Software is freely available for non-commercial use at http://dna-discovery.stanford.edu/software/mendelims/.
 2014-08-27T00:00:00Z Quality versus accuracy: result of a reanalysis of protein-binding microarrays from the DREAM5 challenge by using BayesPI2 including dinucleotide interdependence
Background: Computational modeling transcription factor (TF) sequence specificity is an important research topic in regulatory genomics. A systematic comparison of 26 algorithms to learn TF-DNA binding specificity in in vitro protein-binding microarray (PBM) data was published recently, but the quality of those examined PBMs was not evaluated completely. Results: Here, new quality-control parameters such as principal component analysis (PCA) ellipse is proposed to assess the data quality for either single or paired PBMs. Additionally, a biophysical model of TF-DNA interactions including adjacent dinucleotide interdependence was implemented in a new program - BayesPI2, where sparse Bayesian learning and relevance vector machine are used to predict unknown model parameters. Then, 66 mouse TFs from the DREAM5 challenge were classified into two groups (i.e. good vs. bad) based on the paired PBM quality-control parameters. Subsequently, computational methods to model TF sequence specificity were evaluated between the two groups. Conclusion: Results indicate that both the algorithm performance and the predicted TF-binding energy-level of a motif are significantly influenced by PBM data quality, where poor PBM data quality is linked to specific protein domains (e.g. C2H2 DNA-binding domain). Especially, the new dinucleotide energy-dependent model (BayesPI2) offers great improvement in testing prediction accuracy over the simple energy-independent model, for at least 21% of analyzed the TFs.
 2014-08-26T00:00:00Z APTE: identification of indirect read-out A-DNA promoter elements in genomes
Background: Transcriptional regulation is normally based on the recognition by a transcription factor of a defined base sequence in a process of direct read-out. However, the nucleic acid secondary and tertiary structure can also act as a recognition site for the transcription factor in a process known as indirect read-out, although this is much less understood. We have previously identified such a transcriptional control mechanism in early Xenopus development where the interaction of the transcription factor ilf3 and the gata2 promoter requires the presence of both an unusual A-form DNA structure and a CCAAT sequence. Rapid identification of such promoters elsewhere in the Xenopus and other genomes would provide insight into a less studied area of gene regulation, although currently there are few tools to analyse genomes in such ways. Results: In this paper we report the implementation of a novel bioinformatics approach that has identified 86 such putative promoters in the Xenopus genome. We have shown that five of these sites are A-form in solution, bind to transcription factors and fully validated one of these newly identified promoters as interacting with the ilf3 containing complex CBTF. This interaction regulates the transcription of a previously uncharacterised downstream gene that is active in early development. Conclusions: A Perl program (APTE) has located a number of potential A-form DNA promotor elements in the Xenopus genome, five of these putative targets have been experimentally validated as A-form and as targets for specific DNA binding proteins; one has also been shown to interact with the A-form binding transcription factor ilf3. APTE is available from http://www.port.ac.uk/research/cmd/software/ under the terms of the GNU General Public License.
 2014-08-26T00:00:00Z Content-based histopathology image retrieval using CometCloud
Background: The development of digital imaging technology is creating extraordinary levels of accuracy that provide support for improved reliability in different aspects of the image analysis, such as content-based image retrieval, image segmentation, and classification. This has dramatically increased the volume and rate at which data are generated. Together these facts make querying and sharing non-trivial and render centralized solutions unfeasible. Moreover, in many cases this data is often distributed and must be shared across multiple institutions requiring decentralized solutions. In this context, a new generation of data/information driven applications must be developed to take advantage of the national advanced cyber-infrastructure (ACI) which enable investigators to seamlessly and securely interact with information/data which is distributed across geographically disparate resources. This paper presents the development and evaluation of a novel content-based image retrieval (CBIR) framework. The methods were tested extensively using both peripheral blood smears and renal glomeruli specimens. The datasets and performance were evaluated by two pathologists to determine the concordance. Results: The CBIR algorithms that were developed can reliably retrieve the candidate image patches exhibiting intensity and morphological characteristics that are most similar to a given query image. The methods described in this paper are able to reliably discriminate among subtle staining differences and spatial pattern distributions. By integrating a newly developed dual-similarity relevance feedback module into the CBIR framework, the CBIR results were improved substantially. By aggregating the computational power of high performance computing (HPC) and cloud resources, we demonstrated that the method can be successfully executed in minutes on the Cloud compared to weeks using standard computers. Conclusions: In this paper, we present a set of newly developed CBIR algorithms and validate them using two different pathology applications, which are regularly evaluated in the practice of pathology. Comparative experimental results demonstrate excellent performance throughout the course of a set of systematic studies. Additionally, we present and evaluate a framework to enable the execution of these algorithms across distributed resources. We show how parallel searching of content-wise similar images in the dataset significantly reduces the overall computational time to ensure the practical utility of the proposed CBIR algorithms.
 2014-08-25T00:00:00Z Identification of highly related references about gene-disease association
Background: Curation of gene-disease associations published in literature should be based on careful and frequent survey of the references that are highly related to specific gene-disease associations. Retrieval of the references is thus essential for timely and complete curation. Results: We present a technique CRFref (Conclusive, Rich, and Focused References) that, given a gene-disease pair < g, d>, ranks high those biomedical references that are likely to provide conclusive, rich, and focused results about g and d. Such references are expected to be highly related to the association between g and d. CRFref ranks candidate references based on their scores. To estimate the score of a reference r, CRFref estimates and integrates three measures: degree of conclusiveness, degree of richness, and degree of focus of r with respect to < g, d>. To evaluate CRFref, experiments are conducted on over one hundred thousand references for over one thousand gene-disease pairs. Experimental results show that CRFref performs significantly better than several typical types of baselines in ranking high those references that expert curators select to develop the summaries for specific gene-disease associations. Conclusion: CRFref is a good technique to rank high those references that are highly related to specific gene-disease associations. It can be incorporated into existing search engines to prioritize biomedical references for curators and researchers, as well as those text mining systems that aim at the study of gene-disease associations.
 2014-08-23T00:00:00Z A Generalizable NLP Framework for Fast Development of Pattern-based Biomedical Relation Extraction Systems
Background: Text mining is increasingly used in the biomedical domain because of its ability to automatically gather information from large amount of scientific articles. One important task in biomedical text mining is relation extraction, which aims to identify designated relations among biological entities reported in literature. A relation extraction system achieving high performance is expensive to develop because of the substantial time and effort required for its design and implementation. Here, we report a novel framework to facilitate the development of a pattern-based biomedical relation extraction system. It has several unique design features: (1) leveraging syntactic variations possible in a language and automatically generating extraction patterns in a systematic manner, (2) applying sentence simplification to improve the coverage of extraction patterns, and (3) identifying referential relations between a syntactic argument of a predicate and the actual target expected in the relation extraction task. Results: A relation extraction system derived using the proposed framework achieved overall F-scores of 72.66% for the Simple events and 55.57% for the Binding events on the BioNLP-ST 2011 GE test set, comparing favorably with the top performing systems that participated in the BioNLP-ST 2011 GE task. We obtained similar results on the BioNLP-ST 2013 GE test set (80.07% and 60.58%, respectively). We conducted additional experiments on the training and development sets to provide a more detailed analysis of the system and its individual modules. This analysis indicates that without increasing the number of patterns, simplification and referential relation linking play a key role in the effective extraction of biomedical relations. Conclusions: In this paper, we present a novel framework for fast development of relation extraction systems. The framework requires only a list of triggers as input, and does not need information from an annotated corpus. Thus, we reduce the involvement of domain experts, who would otherwise have to provide manual annotations and help with the design of hand crafted patterns. We demonstrate how our framework is used to develop a system which achieves state-of-the-art performance on a public benchmark corpus.


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BMC Genomics - Latest Articles   [more] [xml]
 2014-08-30T00:00:00Z Characterization of Paenibacillus larvae bacteriophages and their genomic relationships to firmicute bacteriophages
Background: Paenibacillus larvae is a Firmicute bacterium that causes American Foulbrood, a lethal disease in honeybees and is a major source of global agricultural losses. Although P. larvae phages were isolated prior to 2013, no full genome sequences of P. larvae bacteriophages were published or analyzed. This report includes an in-depth analysis of the structure, genomes, and relatedness of P. larvae myoviruses Abouo, Davis, Emery, Jimmer1, Jimmer2, and siphovirus phiIBB_Pl23 to each other and to other known phages. Results: P. larvae phages Abouo, Davies, Emery, Jimmer1, and Jimmer2 are myoviruses with ~50 kbp genomes. The six P. larvae phages form three distinct groups by dotplot analysis. An annotated linear genome map of these six phages displays important identifiable genes and demonstrates the relationship between phages. Sixty phage assembly or structural protein genes and 133 regulatory or other non-structural protein genes were identifiable among the six P. larvae phages. Jimmer1, Jimmer2, and Davies formed stable lysogens resistant to superinfection by genetically similar phages. The correlation between tape measure protein gene length and phage tail length allowed identification of co-isolated phages Emery and Abouo in electron micrographs. A Phamerator database was assembled with the P. larvae phage genomes and 107 genomes of Firmicute-infecting phages, including 71 Bacillus phages. Phamerator identified conserved domains in 1,501 of 6,181 phamilies (only 24.3%) encoded by genes in the database and revealed that P. larvae phage genomes shared at least one phamily with 72 of the 107 other phages. The phamily relationship of large terminase proteins was used to indicate putative DNA packaging strategies. Analyses from CoreGenes, Phamerator, and electron micrograph measurements indicated Jimmer1, Jimmer2, Abouo and Davies were related to phages phiC2, EJ-1, KC5a, and AQ113, which are small-genome myoviruses that infect Streptococcus, Lactobacillus, and Clostridium, respectively. Conclusions: This paper represents the first comparison of phage genomes in the Paenibacillus genus and the first organization of P. larvae phages based on sequence and structure. This analysis provides an important contribution to the field of bacteriophage genomics by serving as a foundation on which to build an understanding of the natural predators of P. larvae.


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BMC Biochemistry - Latest Articles   [more] [xml]
 2014-08-25T00:00:00Z Chemical-genetic induction of Malonyl-CoA decarboxylase in skeletal muscle
Background: Defects in skeletal muscle fatty acid oxidation have been implicated in the etiology of insulin resistance. Malonyl-CoA decarboxylase (MCD) has been a target of investigation because it reduces the concentration of malonyl-CoA, a metabolite that inhibits fatty acid oxidation. The in vivo role of muscle MCD expression in the development of insulin resistance remains unclear. Results: To determine the role of MCD in skeletal muscle of diet induced obese and insulin resistant mouse models we generated mice expressing a muscle specific transgene for MCD (Tg-fMCDSkel) stabilized posttranslationally by the small molecule, Shield-1. Tg-fMCDSkel and control mice were placed on either a high fat or low fat diet for 3.5 months. Obese and glucose intolerant as well as lean control Tg-fMCDSkel and nontransgenic control mice were treated with Shield-1 and changes in their body weight and insulin sensitivity were determined upon induction of MCD. Inducing MCD activity >5-fold in skeletal muscle over two weeks did not alter body weight or glucose intolerance of obese mice. MCD induction further potentiated the defects in insulin signaling of obese mice. In addition, key enzymes in fatty acid oxidation were suppressed following MCD induction. Conclusion: Acute induction of MCD in the skeletal muscle of obese and glucose intolerant mice did not improve body weight and decreased insulin sensitivity compared to obese nontransgenic controls. Induction of MCD in skeletal muscle resulted in a suppression of mitochondrial oxidative genes suggesting a redundant and metabolite driven regulation of gene expression.


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Nature   [more] [xml]
 2005-01-19 Einstein is dead
Until its next revolution, much of the glory of physics will be in engineering. It is a shame that the physicists who do so much of it keep so quiet about it.

Einstein is dead

Nature 433, 179 (2005). doi:10.1038/433179a

Until its next revolution, much of the glory of physics will be in engineering. It is a shame that the physicists who do so much of it keep so quiet about it.



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Science: Current Issue   [more] [xml]
 2014-08-29 [Editorial] Australia needs a strategy
Australians are constantly told that our economy is ‘“in transition.”’ We need to move up the global value chain, build knowledge-based industries, prepare for the Asian Century, and be the “food bowl of the world.” These are grand ambitions that we share with many other nations, in our region and beyond. The question for all of us is: How? Author: Ian Chubb

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