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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]
 2014-07-22T00:00:00Z Dynamic Probabilistic Threshold Networks to Infer Signaling Pathways from Time-Course Perturbation Data
Background: Network inference deals with the reconstruction of molecular networks from experimental data. Given N molecular species, the challenge is to find the underlying network. Due to data limitations, this typically is an ill-posed problem, and requires the integration of prior biological knowledge or strong regularization. We here focus on the situation when time-resolved measurements of a system's response after systematic perturbations are available. Results: We present a novel method to infer signaling networks from time-course perturbation data. We utilize dynamic Bayesian networks with probabilistic Boolean threshold functions to describe protein activation. The model posterior distribution is analyzed using evolutionary MCMC sampling and subsequent clustering, resulting in probability distributions over alternative networks. We evaluate our method on simulated data, and study its performance with respect to data set size and levels of noise. We then use our method to study EGF-mediated signaling in the ERBB pathway. Conclusions: Dynamic Probabilistic Threshold Networks is a new method to infer signaling networks from time-series perturbation data. It exploits the dynamic response of a system after external perturbation for network reconstruction. On simulated data, we show that the approach outperforms current state of the art methods. On the ERBB data, our approach recovers a significant fraction of the known interactions, and predicts novel mechanisms in the ERBB pathway.
 2014-07-21T00:00:00Z Lineage grammars: describing, simulating and analyzing population dynamics
Background: Precise description of the dynamics of biological processes would enable the mathematical analysis and computational simulation of complex biological phenomena. Languages such as Chemical Reaction Networks and Process Algebras cater for the detailed description of interactions among individuals and for the simulation and analysis of ensuing behaviors of populations. However, often knowledge of such interactions is lacking or not available. Yet complete oblivion to the environment would make the description of any biological process vacuous. Here we present a language for describing population dynamics that abstracts away detailed interaction among individuals, yet captures in broad terms the effect of the changing environment, based on environment-dependent Stochastic Tree Grammars (eSTG). It is comprised of a set of stochastic tree grammar transition rules, which are context-free and as such abstract away specific interactions among individuals. Transition rule probabilities and rates, however, can depend on global parameters such as population size, generation count, and elapsed time. Results: We show that eSTGs conveniently describe population dynamics at multiple levels including cellular dynamics, tissue development and niches of organisms. Notably, we show the utilization of eSTG for cases in which the dynamics is regulated by environmental factors, which affect the fate and rate of decisions of the different species. eSTGs are lineage grammars, in the sense that execution of an eSTG program generates the corresponding lineage trees, which can be used to analyze the evolutionary and developmental history of the biological system under investigation. These lineage trees contain a representation of the entire events history of the system, including the dynamics that led to the existing as well as to the extinct individuals. Conclusions: We conclude that our suggested formalism can be used to easily specify, simulate and analyze complex biological systems, and supports modular description of local biological dynamics that can be later used as "black boxes" in a larger scope, thus enabling a gradual and hierarchical definition and simulation of complex biological systems. The simple, yet robust formalism enables to target a broad class of stochastic dynamic behaviors, especially those that can be modeled using global environmental feedback regulation rather than direct interaction between individuals.
 2014-07-21T00:00:00Z Clinical phenotype-based gene prioritization: an initial study using semantic similarity and the human phenotype ontology
Background: Exome sequencing is a promising method for diagnosing patients with a complex phenotype. However, variant interpretation relative to patient phenotype can be challenging in some scenarios, particularly clinical assessment of rare complex phenotypes. Each patient's sequence reveals many possibly damaging variants that must be individually assessed to establish clear association with patient phenotype. To assist interpretation, we implemented an algorithm that ranks a given set of genes relative to patient phenotype. The algorithm orders genes by the semantic similarity computed between phenotypic descriptors associated with each gene and those describing the patient. Phenotypic descriptor terms are taken from the Human Phenotype Ontology (HPO) and semantic similarity is derived from each term's information content. Results: Model validation was performed via simulation and with clinical data. We simulated 33 Mendelian diseases with 100 patients per disease. We modeled clinical conditions by adding noise and imprecision, i.e. phenotypic terms unrelated to the disease and terms less specific than the actual disease terms. We ranked the causative gene against all 2488 HPO annotated genes. The median causative gene rank was 1 for the optimal and noise cases, 12 for the imprecision case, and 60 for the imprecision with noise case. Additionally, we examined a clinical cohort of subjects with hearing impairment. The disease gene median rank was 22. However, when also considering the patient's exome data and filtering non-exomic and common variants, the median rank improved to 3. Conclusions: Semantic similarity can rank a causative gene highly within a gene list relative to patient phenotype characteristics, provided that imprecision is mitigated. The clinical case results suggest that phenotype rank combined with variant analysis provides significant improvement over the individual approaches. We expect that this combined prioritization approach may increase accuracy and decrease effort for clinical genetic diagnosis.
 2014-07-19T00:00:00Z Variant detection sensitivity and biases in whole genome and exome sequencing
Background: Less than two percent of the human genome is protein coding, yet that small fraction harbours themajority of known disease causing mutations. Despite rapidly falling whole genome sequencing(WGS) costs, much research and increasingly the clinical use of sequence data is likely to remainfocused on the protein coding exome. We set out to quantify and understand howWGS compares withthe targeted capture and sequencing of the exome (exome-seq), for the specific purpose of identifyingsingle nucleotide polymorphisms (SNPs) in exome targeted regions. Results: We have compared polymorphism detection sensitivity and systematic biases using a set of tissuesamples that have been subject to both deep exome and whole genome sequencing. The scoringof detection sensitivity was based on sequence down sampling and reference to a set of goldstandardSNP calls for each sample. Despite evidence of incremental improvements in exome capturetechnology over time, whole genome sequencing has greater uniformity of sequence read coverageand reduced biases in the detection of non-reference alleles than exome-seq. Exome-seq achieves95% SNP detection sensitivity at a mean on-target depth of 40 reads, whereas WGS only requires amean of 14 reads. Known disease causing mutations are not biased towards easy or hard to sequenceareas of the genome for either exome-seq or WGS. Conclusions: From an economic perspective, WGS is at parity with exome-seq for variant detection in the targetedcoding regions. WGS offers benefits in uniformity of read coverage and more balanced alleleratio calls, both of which can in most cases be offset by deeper exome-seq, with the caveat thatsome exome-seq targets will never achieve sufficient mapped read depth for variant detection due to technical difficulties or probe failures. AsWGS is intrinsically richer data that can provide insight intopolymorphisms outside coding regions and reveal genomic rearrangements, it is likely to progressivelyreplace exome-seq for many applications.
 2014-07-19T00:00:00Z Rapid screening for phenotype-genotype associations by linear transformations of genomic evaluations
Background: Currently, association studies are analysed using statistical mixed models, with marker effects estimated by a linear transformation of genomic breeding values. The variances of marker effects are needed when performing the tests of association. However, approaches used to estimate the parameters rely on a prior variance or on a constant estimate of the additive variance. Alternatively, we propose a standardized test of association using the variance of each marker effect, which generally differ among each other. Random breeding values from a mixed model including fixed effects and a genomic covariance matrix are linearly transformed to estimate the marker effects. Results: The standardized test was neither conservative nor liberal with respect to type I error rate (false-positives), compared to a similar test using Predictor Error Variance, a method that was too conservative. Furthermore, genomic predictions are solved efficiently by the procedure, and the p-values are virtually identical to those calculated from tests for one marker effect at a time. Moreover, the standardized test reduces computing time and memory requirements.The following steps are used to locate genome segments displaying strong association. The marker with the highest − log(p-value) in each chromosome is selected, and the segment is expanded one Mb upstream and one Mb downstream of the marker. A genomic matrix is calculated using the information from those markers only, which is used as the variance-covariance of the segment effects in a model that also includes fixed effects and random genomic breeding values. The likelihood ratio is then calculated to test for the effect in every chromosome against a reduced model with fixed effects and genomic breeding values. In a case study with pigs, a significant segment from chromosome 6 explained 11% of total genetic variance. Conclusions: The standardized test of marker effects using their own variance helps in detecting specific genomic regions involved in the additive variance, and in reducing false positives. Moreover, genome scanning of candidate segments can be used in meta-analyses of genome-wide association studies, as it enables the detection of specific genome regions that affect an economically relevant trait when using multiple populations.
 2014-07-18T00:00:00Z Computational reconstruction of proteome-wide protein interaction networks between HTLV retroviruses and Homo sapiens
Background: Human T-cell leukemia viruses (HTLV) tend to induce some fatal human diseases like Adult T-cell Leukemia (ATL) by targeting human T lymphocytes. To indentify the protein-protein interactions (PPI) between HTLV viruses and Homo sapiens is one of the significant approaches to reveal the underlying mechanism of HTLV infection and host defence. At present, as biological experiments are labor-intensive and expensive, the identified part of the HTLV-human PPI networks is rather small. Although recent years have witnessed much progress in computational modeling for reconstructing pathogen-host PPI networks, data scarcity and data unavailability are two major challenges to be effectively addressed. To our knowledge, no computational method for proteome-wide HTLV-human PPI networks reconstruction has been reported. Results: In this work we develop Multi-instance Adaboost method to conduct homolog knowledge transfer for computationally reconstructing proteome-wide HTLV-human PPI networks. In this method, the homolog knowledge in the form of gene ontology (GO) is treated as auxiliary homolog instance to address the problems of data scarcity and data unavailability, while the potential negative knowledge transfer is automatically attenuated by AdaBoost instance reweighting. The cross validation experiments show that the homolog knowledge transfer in the form of independent homolog instances can effectively enrich the feature information and substitute for the missing GO information. Moreover, the independent tests show that the method can validate 70.3% of the recently curated interactions, significantly exceeding the 2.1% recognition rate by the HT-Y2H experiment. We have used the method to reconstruct the proteome-wide HTLV-human PPI networks and further conducted gene ontology based clustering of the predicted networks for further biomedical research. The gene ontology based clustering analysis of the predictions provides much biological insight into the pathogenesis of HTLV retroviruses. Conclusions: The Multi-instance AdaBoost method can effectively address the problems of data scarcity and data unavailability for the proteome-wide HTLV-human PPI interaction networks reconstruction. The gene ontology based clustering analysis of the predictions reveals some important signaling pathways and biological modules that HTLV retroviruses are likely to target.
 2014-07-16T00:00:00Z DBSecSys: a database of Burkholderia mallei secretion systems
Background: Bacterial pathogenicity represents a major public health concern worldwide. Secretion systems are a key component of bacterial pathogenicity, as they provide the means for bacterial proteins to penetrate host-cell membranes and insert themselves directly into the host cells' cytosol. Burkholderia mallei is a Gram-negative bacterium that uses multiple secretion systems during its host infection life cycle. To date, the identities of secretion system proteins for B. mallei are not well known, and their pathogenic mechanisms of action and host factors are largely uncharacterized.Description: We present the Database of Burkholderia mallei Secretion Systems (DBSecSys), a compilation of manually curated and computationally predicted bacterial secretion system proteins and their host factors. Currently, DBSecSys contains comprehensive experimentally and computationally derived information about B. mallei strain ATCC 23344. The database includes 143 B. mallei proteins associated with five secretion systems, their 1,635 human and murine interacting targets, and the corresponding 2,400 host-B. mallei interactions. The database also includes information about 10 pathogenic mechanisms of action for B. mallei secretion system proteins inferred from the available literature. Additionally, DBSecSys provides details about 42 virulence attenuation experiments for 27 B. mallei secretion system proteins. Users interact with DBSecSys through a Web interface that allows for data browsing, querying, visualizing, and downloading. Conclusions: DBSecSys provides a comprehensive, systematically organized resource of experimental and computational data associated with B. mallei secretion systems. It provides the unique ability to study secretion systems not only through characterization of their corresponding pathogen proteins, but also through characterization of their host-interacting partners.The database is available at https://applications.bhsai.org/dbsecsys.
 2014-07-16T00:00:00Z In silico single strand melting curve: a new approach to identify nucleic acid polymorphisms in Totiviridae
Background: The PCR technique and its variations have been increasingly used in the clinical laboratory and recent advances in this field generated new higher resolution techniques based on nucleic acid denaturation dynamics. The principle of these new molecular tools is based on the comparison of melting profiles, after denaturation of a DNA double strand. Until now, the secondary structure of single-stranded nucleic acids has not been exploited to develop identification systems based on PCR. To test the potential of single-strand RNA denaturation as a new alternative to detect specific nucleic acid variations, sequences from viruses of the Totiviridae family were compared using a new in silico melting curve approach. This family comprises double-stranded RNA virus, with a genome constituted by two ORFs, ORF1 and ORF2, which encodes the capsid/RNA binding proteins and an RNA-dependent RNA polymerase (RdRp), respectively. Results: A phylogenetic tree based on RdRp amino acid sequences was constructed, and eight monophyletic groups were defined. Alignments of RdRp RNA sequences from each group were screened to identify RNA regions with conserved secondary structure. One region in the second half of ORF2 was identified and individually modeled using the RNAfold tool. Afterwards, each DNA or RNA sequence was denatured in silico using the softwares MELTSIM and RNAheat that generate melting curves considering the denaturation of a double stranded DNA and single stranded RNA, respectively. The same groups identified in the RdRp phylogenetic tree were retrieved by a clustering analysis of the melting curves data obtained from RNAheat. Moreover, the same approach was used to successfully discriminate different variants of Trichomonas vaginalis virus, which was not possible by the visual comparison of the double stranded melting curves generated by MELTSIM. Conclusion: In silico analysis indicate that ssRNA melting curves are more informative than dsDNA melting curves. Furthermore, conserved RNA structures may be determined from analysis of individuals that are phylogenetically related, and these regions may be used to support the reconstitution of their phylogenetic groups. These findings are a robust basis for the development of in vitro systems to ssRNA melting curves detection.
 2014-07-16T00:00:00Z Accurate genome relative abundance estimation for closely related species in a metagenomic sample
Background: Metagenomics has a great potential to discover previously unattainable information about microbialcommunities. An important prerequisite for such discoveries is to accurately estimate the compositionof microbial communities. Most of prevalent homology-based approaches utilize solely the results ofan alignment tool such as BLAST, limiting their estimation accuracy to high ranks of the taxonomytree. Results: We developed a new homology-based approach called Taxonomic Analysis by Elimination and Correction(TAEC), which utilizes the similarity in the genomic sequence in addition to the result of analignment tool. The proposed method is comprehensively tested on various simulated benchmarkdatasets of diverse complexity of microbial structure. Compared with other available methods designedfor estimating taxonomic composition at a relatively low taxonomic rank, TAEC demonstratesgreater accuracy in quantification of genomes in a given microbial sample. We also applied TAECon two real metagenomic datasets, oral cavity dataset and Crohn's disease dataset. Our results, whileagreeing with previous findings at higher ranks of the taxonomy tree, provide accurate estimation oftaxonomic compositions at the species/strain level, narrowing down which species/strains need moreattention in the study of oral cavity and the Crohn's disease. Conclusions: By taking account of the similarity in the genomic sequence TAEC outperforms other available tools inestimating taxonomic composition at a very low rank, especially when closely related species/strainsexist in a metagenomic sample.
 2014-07-14T00:00:00Z Dataset size and composition impact the reliability of performance benchmarks for peptide-MHC binding predictions
Background: It is important to accurately determine the performance of peptide:MHC binding predictions, as this enables users to compare and choose between different prediction methods and provides estimates of the expected error rate. Two common approaches to determine prediction performance are cross-validation, in which all available data are iteratively split into training and testing data, and the use of blind sets generated separately from the data used to construct the predictive method. In the present study, we have compared cross-validated prediction performances generated on our last benchmark dataset from 2009 with prediction performances generated on data subsequently added to the Immune Epitope Database (IEDB) which served as a blind set. Results: We found that cross-validated performances systematically overestimated performance on the blind set. This was found not to be due to the presence of similar peptides in the cross-validation dataset. Rather, we found that small size and low sequence/affinity diversity of either training or blind datasets were associated with large differences in cross-validated vs. blind prediction performances. We use these findings to derive quantitative rules of how large and diverse datasets need to be to provide generalizable performance estimates. Conclusion: It has long been known that cross-validated prediction performance estimates often overestimate performance on independently generated blind set data. We here identify and quantify the specific factors contributing to this effect for MHC-I binding predictions. An increasing number of peptides for which MHC binding affinities are measured experimentally have been selected based on binding predictions and thus are less diverse than historic datasets sampling the entire sequence and affinity space, making them more difficult benchmark data sets. This has to be taken into account when comparing performance metrics between different benchmarks, and when deriving error estimates for predictions based on benchmark performance.


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BMC Genomics - Latest Articles   [more] [xml]
 2014-07-24T00:00:00Z Genome sequence and virulence variation-related transcriptome profiles of Curvularia lunata, an important maize pathogenic fungus
Background: Curvularia lunata is an important maize foliar fungal pathogen that distributes widely in maize growing area in China. Genome sequencing of the pathogen will provide important information for globally understanding its virulence mechanism. Results: We report the genome sequences of a highly virulent C. lunata strain. Phylogenomic analysis indicates that C. lunata was evolved from Bipolaris maydis (Cochliobolus heterostrophus). The highly virulent strain has a high potential to evolve into other pathogenic stains based on analyses on transposases and repeat-induced point mutations. C. lunata has a smaller proportion of secreted proteins as well as B. maydis than entomopathogenic fungi. C. lunata and B. maydis have a similar proportion of protein-encoding genes highly homologous to experimentally proven pathogenic genes from pathogen-host interaction database. However, relative to B. maydis, C. lunata possesses not only many expanded protein families including MFS transporters, G-protein coupled receptors, protein kinases and proteases for transport, signal transduction or degradation, but also many contracted families including cytochrome P450, lipases, glycoside hydrolases and polyketide synthases for detoxification, hydrolysis or secondary metabolites biosynthesis, which are expected to be crucial for the fungal survival in varied stress environments. Comparative transcriptome analysis between a lowly virulent C. lunata strain and its virulence-increased variant induced by resistant host selection reveals that the virulence increase of the pathogen is related to pathways of toxin and melanin biosynthesis in stress environments, and that the two pathways probably have some overlaps. Conclusions: The data will facilitate a full revelation of pathogenic mechanism and a better understanding of virulence differentiation of C. lunata.


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BMC Biochemistry - Latest Articles   [more] [xml]
 2014-07-09T00:00:00Z Application of Gaussia luciferase in bicistronic and non-conventional secretion reporter constructs
Background: Secreted luciferases are highly useful bioluminescent reporters for cell-based assays and drug discovery. A variety of secreted luciferases from marine organisms have been described that harbor an N-terminal signal peptide for release along the classical secretory pathway. Here, we have characterized the secretion of Gaussia luciferase in more detail. Results: We describe three basic mechanisms by which GLUC can be released from cells: first, classical secretion by virtue of the N-terminal signal peptide; second, internal signal peptide-mediated secretion and third, non-conventional secretion in the absence of an N-terminal signal peptide. Non-conventional release of dNGLUC is not stress-induced, does not require autophagy and can be enhanced by growth factor stimulation. Furthermore, we have identified the golgi-associated, gamma adaptin ear containing, ARF binding protein 1 (GGA1) as a suppressor of release of dNGLUC. Conclusions: Due to its secretion via multiple secretion pathways GLUC can find multiple applications as a research tool to study classical and non-conventional secretion. As GLUC can also be released from a reporter construct by internal signal peptide-mediated secretion it can be incorporated in a novel bicistronic secretion system.


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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]
 2014-07-18 [Special Issue Research Article] A chromosome-based draft sequence of the hexaploid bread wheat (Triticum aestivum) genome
An ordered draft sequence of the 17-gigabase hexaploid bread wheat (Triticum aestivum) genome has been produced by sequencing isolated chromosome arms. We have annotated 124,201 gene loci distributed nearly evenly across the homeologous chromosomes and subgenomes. Comparative gene analysis of wheat subgenomes and extant diploid and tetraploid wheat relatives showed that high sequence similarity and structural conservation are retained, with limited gene loss, after polyploidization. However, across the genomes there was evidence of dynamic gene gain, loss, and duplication since the divergence of the wheat lineages. A high degree of transcriptional autonomy and no global dominance was found for the subgenomes. These insights into the genome biology of a polyploid crop provide a springboard for faster gene isolation, rapid genetic marker development, and precise breeding to meet the needs of increasing food demand worldwide. Authors: , Klaus F. X. Mayer, Jane Rogers, Jaroslav Doležel, Curtis Pozniak, Kellye Eversole, Catherine Feuillet, Bikram Gill, Bernd Friebe, Adam J. Lukaszewski, Pierre Sourdille, Takashi R. Endo, Marie Kubaláková, Jarmila Číhalíková, Zdeňka Dubská, Jan Vrána, Romana Šperková, Hana Šimková, Melanie Febrer, Leah Clissold, Kirsten McLay, Kuldeep Singh, Parveen Chhuneja, Nagendra K. Singh, Jitendra Khurana, Eduard Akhunov, Frédéric Choulet, Adriana Alberti, Valérie Barbe, Patrick Wincker, Hiroyuki Kanamori, Fuminori Kobayashi, Takeshi Itoh, Takashi Matsumoto, Hiroaki Sakai, Tsuyoshi Tanaka, Jianzhong Wu, Yasunari Ogihara, Hirokazu Handa, P. Ron Maclachlan, Andrew Sharpe, Darrin Klassen, David Edwards, Jacqueline Batley, Odd-Arne Olsen, Simen Rød Sandve, Sigbjørn Lien, Burkhard Steuernagel, Brande Wulff, Mario Caccamo, Sarah Ayling, Ricardo H. Ramirez-Gonzalez, Bernardo J. Clavijo, Jonathan Wright, Matthias Pfeifer, Manuel Spannagl, Mihaela M. Martis, Martin Mascher, Jarrod Chapman, Jesse A. Poland, Uwe Scholz, Kerrie Barry, Robbie Waugh, Daniel S. Rokhsar, Gary J. Muehlbauer, Nils Stein, Heidrun Gundlach, Matthias Zytnicki, Véronique Jamilloux, Hadi Quesneville, Thomas Wicker, Primetta Faccioli, Moreno Colaiacovo, Antonio Michele Stanca, Hikmet Budak, Luigi Cattivelli, Natasha Glover, Lise Pingault, Etienne Paux, Sapna Sharma, Rudi Appels, Matthew Bellgard, Brett Chapman, Thomas Nussbaumer, Kai Christian Bader, Hélène Rimbert, Shichen Wang, Ron Knox, Andrzej Kilian, Michael Alaux, Françoise Alfama, Loïc Couderc, Nicolas Guilhot, Claire Viseux, Mikaël Loaec, Beat Keller, Sebastien Praud

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