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BMC Structural Biology - Latest Articles   [more] [xml]
 2014-04-15T00:00:00Z Designing and evaluating the MULTICOM protein local and global model quality prediction methods in the CASP10 experiment
Background: Protein model quality assessment is an essential component of generating and using protein structural models. During the Tenth Critical Assessment of Techniques for Protein Structure Prediction (CASP10), we developed and tested four automated methods (MULTICOM-REFINE, MULTICOM-CLUSTER, MULTICOM-NOVEL, and MULTICOM-CONSTRUCT) that predicted both local and global quality of protein structural models. Results: MULTICOM-REFINE was a clustering approach that used the average pairwise structural similarity between models to measure the global quality and the average Euclidean distance between a model and several top ranked models to measure the local quality. MULTICOM-CLUSTER and MULTICOM-NOVEL were two new support vector machine-based methods of predicting both the local and global quality of a single protein model. MULTICOM-CONSTRUCT was a new weighted pairwise model comparison (clustering) method that used the weighted average similarity between models in a pool to measure the global model quality. Our experiments showed that the pairwise model assessment methods worked better when a large portion of models in the pool were of good quality, whereas single-model quality assessment methods performed better on some hard targets when only a small portion of models in the pool were of reasonable quality. Conclusions: Since digging out a few good models from a large pool of low-quality models is a major challenge in protein structure prediction, single model quality assessment methods appear to be poised to make important contributions to protein structure modeling. The other interesting finding was that single-model quality assessment scores could be used to weight the models by the consensus pairwise model comparison method to improve its accuracy.


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BMC Bioinformatics - Latest Articles   [more] [xml]
 2014-04-17T00:00:00Z Probabilistic drug connectivity mapping
Background: The aim of connectivity mapping is to match drugs using drug-treatment gene expression profilesfrom multiple cell lines. This can be viewed as an information retrieval task, with the goal of findingthe most relevant profiles for a given query drug. We infer the relevance for retrieval by data-drivenprobabilistic modeling of the drug responses, resulting in probabilistic connectivity mapping, andfurther consider the available cell lines as different data sources. We use a special type of probabilisticmodel to separate what is shared and specific between the sources, in contrast to earlier connectivitymapping methods that have intentionally aggregated all available data, neglecting information aboutthe differences between the cell lines. Results: We show that the probabilistic multi-source connectivity mapping method is superior to alternativesin finding functionally and chemically similar drugs from the Connectivity Map data set. We alsodemonstrate that an extension of the method is capable of retrieving combinations of drugs that matchdifferent relevant parts of the query drug response profile. Conclusions: The probabilistic modeling-based connectivity mapping method provides a promising alternative toearlier methods. Principled integration of data from different cell lines helps to identify relevantresponses for specific drug repositioning applications.
 2014-04-17T00:00:00Z Structural genomics analysis of uncharacterized protein families overrepresented in human gut bacteria identifies a novel glycoside hydrolase
Background: Bacteroides spp. form a significant part of our gut microbiome and are well known for optimized metabolism of diverse polysaccharides. Initial analysis of the archetypal Bacteroides thetaiotaomicron genome identified 172 glycosyl hydrolases and a large number of uncharacterized proteins associated with polysaccharide metabolism. Results: BT_1012 from Bacteroides thetaiotaomicron VPI-5482 is a protein of unknown function and a member of a large protein family consisting entirely of uncharacterized proteins. Initial sequence analysis predicted that this protein has two domains, one on the N- and one on the C-terminal. A PSI-BLAST search found over 150 full length and over 90 half size homologs consisting only of the N-terminal domain. The experimentally determined three-dimensional structure of the BT_1012 protein confirms its two-domain architecture and structural analysis of both domains suggests their specific functions. The N-terminal domain is a putative catalytic domain with significant similarity to known glycoside hydrolases, the C-terminal domain has a beta-sandwich fold typical found in C-terminal domains of other glycosyl hydrolases, however these domains are typically involved in substrate binding. We describe the structure of the BT_1012 protein and discuss its sequence-structure relationship and their possible functional implications. Conclusions: Structural and sequence analyses of the BT_1012 protein identifies it as a glycosyl hydrolase, expanding an already impressive catalog of enzymes involved in polysaccharide metabolism in Bacteroides spp. Based on this we have renamed the Pfam families representing the two domains found in the BT_1012 protein, PF13204 and PF12904, as putative glycoside hydrolase and glycoside hydrolase-associated C-terminal domain respectively.
 2014-04-17T00:00:00Z A comprehensive study of small non-frameshift insertions/deletions in proteins and prediction of their phenotypic effects by a machine learning method (KD4i)
Background: Small insertion and deletion polymorphisms (Indels) are the second most common mutations in the human genome, after Single Nucleotide Polymorphisms (SNPs). Recent studies have shown that they have significant influence on genetic variation by altering human traits and can cause multiple human diseases. In particular, many Indels that occur in protein coding regions are known to impact the structure or function of the protein. A major challenge is to predict the effects of these Indels and to distinguish between deleterious and neutral variants. When an Indel occurs within a coding region, it can be either frameshifting (FS) or non-frameshifting (NFS). FS-Indels either modify the complete C-terminal region of the protein or result in premature termination of translation. NFS-Indels insert/delete multiples of three nucleotides leading to the insertion/deletion of one or more amino acids. Results: In order to study the relationships between NFS-Indels and Mendelian diseases, we characterized NFS-Indels according to numerous structural, functional and evolutionary parameters. We then used these parameters to identify specific characteristics of disease-causing and neutral NFS-Indels. Finally, we developed a new machine learning approach, KD4i, that can be used to predict the phenotypic effects of NFS-Indels. Conclusions: We demonstrate in a large-scale evaluation that the accuracy of KD4i is comparable to existing state-of-the-art methods. However, a major advantage of our approach is that we also provide the reasons for the predictions, in the form of a set of rules. The rules are interpretable by non-expert humans and they thus represent new knowledge about the relationships between the genotype and phenotypes of NFS-Indels and the causative molecular perturbations that result in the disease.
 2014-04-15T00:00:00Z On finding bicliques in bipartite graphs: a novel algorithm and its application to the integration of diverse biological data types
Background: Integrating and analyzing heterogeneous genome-scale data is a huge algorithmic challenge for modern systems biology. Bipartite graphs can be useful for representing relationships across pairs of disparate data types, with the interpretation of these relationships accomplished through an enumeration of maximal bicliques. Most previously-known techniques are generally ill-suited to this foundational task, because they are relatively inefficient and without effective scaling. In this paper, a powerful new algorithm is described that produces all maximal bicliques in a bipartite graph. Unlike most previous approaches, the new method neither places undue restrictions on its input nor inflates the problem size. Efficiency is achieved through an innovative exploitation of bipartite graph structure, and through computational reductions that rapidly eliminate non-maximal candidates from the search space. An iterative selection of vertices for consideration based on non-decreasing common neighborhood sizes boosts efficiency and leads to more balanced recursion trees. Results: The new technique is implemented and compared to previously published approaches from graph theory and data mining. Formal time and space bounds are derived. Experiments are performed on both random graphs and graphs constructed from functional genomics data. It is shown that the new method substantially outperforms the best previous alternatives. Conclusions: The new method is streamlined, efficient, and particularly well-suited to the study of huge and diverse biological data. A robust implementation has been incorporated into GeneWeaver, an online tool for integrating and analyzing functional genomics experiments, available at http://geneweaver.org. The enormous increase in scalability it provides empowers users to study complex and previously unassailable gene-set associations between genes and their biological functions in a hierarchical fashion and on a genome-wide scale. This practical computational resource is adaptable to almost any applications environment in which bipartite graphs can be used to model relationships between pairs of heterogeneous entities.
 2014-04-14T00:00:00Z Copy number variation detection using next generation sequencing read counts
Background: A copy number variation (CNV) is a difference between genotypes in the number of copies of agenomic region. Next generation sequencing (NGS) technologies provide sensitive and accuratetools for detecting genomic variations that include CNVs. However, statistical approaches for CNVidentification using NGS are limited. We propose a new methodology for detecting CNVs usingNGS data. This method (henceforth denoted by m-HMM) is based on a hidden Markov modelwith emission probabilities that are governed by mixture distributions. We use the Expectation-Maximization (EM) algorithm to estimate the parameters in the model. Results: A simulation study demonstrates that our proposed m-HMMapproach has greater power for detectingcopy number gains and losses relative to existing methods. Furthermore, application of our m-HMMto DNA sequencing data from the two maize inbred lines B73 and Mo17 to identify CNVs that mayplay a role in creating phenotypic differences between these inbred lines provides results concordantwith previous array-based efforts to identify CNVs. Conclusions: The new m-HMM method is a powerful and practical approach for identifying CNVs from NGS data.
 2014-04-14T00:00:00Z A Two-Step Hierarchical Hypothesis Set Testing Framework, with Applications to Gene Expression Data on Ordered Categories
Background: In complex large-scale experiments, in addition to simultaneously considering a large number offeatures, multiple hypotheses are often being tested for each feature. This leads to a problem ofmulti-dimensional multiple testing. For example, in gene expression studies over ordered categories(such as time-course or dose-response experiments), interest is often in testing differential expressionacross several categories for each gene. In this paper, we consider a framework for testing multiplesets of hypothesis, which can be applied to a wide range of problems. Results: We adopt the concept of the overall false discovery rate (OFDR) for controlling false discoveries onthe hypothesis set level. Based on an existing procedure for identifying differentially expressed genesets, we discuss a general two-step hierarchical hypothesis set testing procedure, which controls theoverall false discovery rate under independence across hypothesis sets. In addition, we discuss theconcept of the mixed-directional false discovery rate (mdFDR), and extend the general procedure toenable directional decisions for two-sided alternatives. We applied the framework to the case ofmicroarray time-course/dose-response experiments, and proposed three procedures for testingdifferential expression and making multiple directional decisions for each gene. Simulation studiesconfirm the control of the OFDR and mdFDR by the proposed procedures under independence andpositive correlations across genes. Simulation results also show that two of our new proceduresachieve higher power than previous methods. Finally, the proposed methodology is applied to amicroarray dose-response study, to identify 17B-estradiol sensitive genes in breast cancer cells thatare induced at low concentrations. Conclusions: The framework we discuss provides a platform for multiple testing procedures covering situationsinvolving two (or potentially more) sources of multiplicity. The framework is easy to use andadaptable to various practical settings that frequently occur in large-scale experiments. Proceduresgenerated from the framework are shown to maintain control of the OFDR and mdFDR, quantitiesthat are especially relevant in the case of multiple hypothesis set testing. The procedures work wellin both simulations and real datasets, and are shown to have better power than existing methods.
 2014-04-14T00:00:00Z MUBII-TB-DB: a database of mutations associated with antibiotic resistance in Mycobacterium tuberculosis
Background: Tuberculosis is an infectious bacterial disease caused by Mycobacterium tuberculosis. It remains a major health threat, killing over one million people every year worldwide. An early antibiotic therapy is the basis of the treatment, and the emergence and spread of multidrug and extensively drug-resistant mutant strains raise significant challenges. As these bacteria grow very slowly, drug resistance mutations are currently detected using molecular biology techniques. Resistance mutations are identified by sequencing the resistance-linked genes followed by a comparison with the literature data. The only online database is the TB Drug Resistance Mutation database (TBDReaM database); however, it requires mutation detection before use, and its interrogation is complex due to its loose syntax and grammar.Description: The MUBII-TB-DB database is a simple, highly structured text-based database that contains a set of Mycobacterium tuberculosis mutations (DNA and proteins) occurring at seven loci: rpoB, pncA, katG; mabA(fabG1)-inhA, gyrA, gyrB, and rrs. Resistance mutation data were extracted after the systematic review of MEDLINE referenced publications before March 2013. MUBII analyzes the query sequence obtained by PCR-sequencing using two parallel strategies: i) a BLAST search against a set of previously reconstructed mutated sequences and ii) the alignment of the query sequences (DNA and its protein translation) with the wild-type sequences. The post-treatment includes the extraction of the aligned sequences together with their descriptors (position and nature of mutations). The whole procedure is performed using the internet. The results are graphs (alignments) and text (description of the mutation, therapeutic significance). The system is quick and easy to use, even for technicians without bioinformatics training. Conclusion: MUBII-TB-DB is a structured database of the mutations occurring at seven loci of major therapeutic value in tuberculosis management. Moreover, the system provides interpretation of the mutations in biological and therapeutic terms and can evolve by the addition of newly described mutations. Its goal is to provide easy and comprehensive access through a client-server model over the Web to an up-to-date database of mutations that lead to the resistance of M. tuberculosis to antibiotics.
 2014-04-12T00:00:00Z dRiskKB: a large-scale disease-disease risk relationship knowledge base constructed from biomedical text
Background: Discerning the genetic contributions to complex human diseases is a challenging mandate that demands new types of data and calls for new avenues for advancing the state-of-the-art in computational approaches to uncovering disease etiology. Systems approaches to studying observable phenotypic relationships among diseases are emerging as an active area of research for both novel disease gene discovery and drug repositioning. Currently, systematic study of disease relationships on a phenomewide scale is limited due to the lack of large-scale machine understandable disease phenotype relationship knowledge bases. Our study innovates a semi-supervised iterative pattern learning approach that is used to build an precise, large-scale disease-disease risk relationship (D1¿D2) knowledge base (dRiskKB) from a vast corpus of free-text published biomedical literature. Results: 21,354,075 MEDLINE records comprised the text corpus under study. First, we used one typical disease risk-specific syntactic pattern (i.e. "D1 due to D2") as a seed to automatically discover other patterns specifying similar semantic relationships among diseases. We then extracted D1¿D2 risk pairs fromMEDLINE using the learned patterns. We manually evaluated the precisions of the learned patterns and extracted pairs. Finally, we analyzed the correlations between disease-disease risk pairs and their associated genes and drugs. The newly created dRiskKB consists of a total of 34,448 unique D1¿D2 pairs, representing the risk-specific semantic relationships among 12,981 diseases with each disease linked to its associated genes and drugs. The identified patterns are highly precise (average precision of 0.99) in specifying the risk-specific relationships among diseases. The precisions of extracted pairs are 0.919 for those that are exactly matched and 0.988 for those that are partially matched. By comparing the iterative pattern approach starting from different seeds, we demonstrated that our algorithm is robust in terms of seed choice. We show that diseases and their risk diseases as well as diseases with similar risk profiles tend to share both genes and drugs. Conclusions: This unique dRiskKB, when combined with existing phenotypic, genetic, and genomic datasets, can have profound implications in our deeper understanding of disease etiology and in drug repositioning.
 2014-04-12T00:00:00Z BAYSIC: a Bayesian method for combining sets of genome variants with improved specificity and sensitivity
Background: Accurate genomic variant detection is an essential step in gleaning medically useful information from genome data. However, low concordance among variant-calling methods reduces confidence in the clinical validity of whole genome and exome sequence data, and confounds downstream analysis for applications in genome medicine.Here we describe BAYSIC (BAYeSian Integrated Caller), which combines SNP variant calls produced by different methods (e.g. GATK, FreeBayes, Atlas, SamTools, etc.) into a more accurate set of variant calls. BAYSIC differs from majority voting, consensus or other ad hoc intersection-based schemes for combining sets of genome variant calls. Unlike other classification methods, the underlying BAYSIC model does not require training using a "gold standard" of true positives. Rather, with each new dataset, BAYSIC performs an unsupervised, fully Bayesian latent class analysis to estimate false positive and false negative error rates for each input method. The user specifies a posterior probability threshold according to the user's tolerance for false positive and false negative errors; lowering the posterior probability threshold allows the user to trade specificity for sensitivity while raising the threshold increases specificity in exchange for sensitivity. Results: We assessed the performance of BAYSIC in comparison to other variant detection methods using ten low coverage (~5X) samples from The 1000 Genomes Project, a tumor/normal exome pair (40X), and exome sequences (40X) from positive control samples previously identified to contain clinically relevant SNPs. We demonstrated BAYSIC's superior variant-calling accuracy, both for somatic mutation detection and germline variant detection. Conclusions: BAYSIC provides a method for combining sets of SNP variant calls produced by different variant calling programs. The integrated set of SNP variant calls produced by BAYSIC improves the sensitivity and specificity of the variant calls used as input. In addition to combining sets of germline variants, BAYSIC can also be used to combine sets of somatic mutations detected in the context of tumor/normal sequencing experiments.
 2014-04-12T00:00:00Z Improved multi-level protein¿protein interaction prediction with semantic-based regularization
Background: Protein¿protein interactions can be seen as a hierarchical process occurring at three related levels: {\em proteins} bind by means of specific {\em domains}, which in turn form interfaces through patches of {\em residues}. Detailed knowledge about which domains and residues are involved in a given interaction has extensive applications to biology, including better understanding of the binding process and more efficient drug/enzyme design. Alas, most current interaction prediction methods do not identify which parts of a protein actually instantiate an interaction. Furthermore, they also fail to leverage the hierarchical nature of the problem, ignoring otherwise useful information available at the lower levels; when they do, they do not generate predictions that are guaranteed to be consistent between levels. Results: Inspired by earlier ideas of Yip {\em et al.} (BMC Bioinformatics 10:241, 2009), in the present paper we view the problem as a multi-level learning task, with one task per level (proteins, domains and residues), and propose a machine learning method that collectively infers the binding state of all object pairs. Our method is based on Semantic Based Regularization (SBR), a flexible and theoretically sound machine learning framework that uses First Order Logic constraints to tie the learning tasks together. We introduce a set of biologically motivated rules that enforce consistent predictions between the hierarchy levels. Conclusions: We study the empirical performance of our method using a standard validation procedure, and compare its performance against the only other existing multi-level prediction technique. We present results showing that our method substantially outperforms the competitor in several experimental settings, indicating that exploiting the hierarchical nature of the problem can lead to better predictions. In addition, our method is also guaranteed to produce interactions that are consistent with respect to the protein¿domain¿residue hierarchy.


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BMC Genomics - Latest Articles   [more] [xml]
 2014-04-17T10:26:48Z Deciphering gamma-decalactone biosynthesis in strawberry fruit using a combination of genetic mapping, RNA-Seq and eQTL analyses
Background: Understanding the basis for volatile organic compound (VOC) biosynthesis and regulation is of great importance for the genetic improvement of fruit flavor. Lactones constitute an essential group of fatty acid-derived VOCs conferring peach-like aroma to a number of fruits including peach, plum, pineapple and strawberry. Early studies on lactone biosynthesis suggest that several enzymatic pathways could be responsible for the diversity of lactones, but detailed information on them remained elusive. In this study, we have integrated genetic mapping and genome-wide transcriptome analysis to investigate the molecular basis of natural variation in γ-decalactone content in strawberry fruit. Results: As a result, the fatty acid desaturase FaFAD1 was identified as the gene underlying the locus at LGIII-2 that controls γ-decalactone production in ripening fruit. The FaFAD1 gene is specifically expressed in ripe fruits and its expression fully correlates with the presence of γ-decalactone in all 95 individuals of the mapping population. In addition, we show that the level of expression of FaFAH1, with similarity to cytochrome p450 hydroxylases, significantly correlates with the content of γ-decalactone in the mapping population. The analysis of expression quantitative trait loci (eQTL) suggests that the product of this gene also has a regulatory role in the biosynthetic pathway of lactones. Conclusions: Altogether, this study provides mechanistic information of how the production of γ-decalactone is naturally controlled in strawberry, and proposes enzymatic activities necessary for the formation of this VOC in plants.


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BMC Biochemistry - Latest Articles   [more] [xml]
 2014-04-03T00:00:00Z Plasmodium falciparum UvrD activities are downregulated by DNA-interacting compounds and its dsRNA inhibits malaria parasite growth
Background: Human malaria parasite infection and its control is a global challenge which is responsible for ~0.65 million deaths every year globally. The emergence of drug resistant malaria parasite is another challenge to fight with malaria. Enormous efforts are being made to identify suitable drug targets in order to develop newer classes of drug. Helicases play crucial roles in DNA metabolism and have been proposed as therapeutic targets for cancer therapy as well as viral and parasitic infections. Genome wide analysis revealed that Plasmodium falciparum possesses UvrD helicase, which is absent in the human host. Results: Recently the biochemical characterization of P. falciparum UvrD helicase revealed that N-terminal UvrD (PfUDN) hydrolyses ATP, translocates in 3’ to 5’ direction and interacts with MLH to modulate each other’s activity. In this follow up study, further characterization of P. falciparum UvrD helicase is presented. Here, we screened the effect of various DNA interacting compounds on the ATPase and helicase activity of PfUDN. This study resulted into the identification of daunorubicin (daunomycin), netropsin, nogalamycin, and ethidium bromide as the potential inhibitor molecules for the biochemical activities of PfUDN with IC50 values ranging from ~3.0 to ~5.0 μM. Interestingly etoposide did not inhibit the ATPase activity but considerable inhibition of unwinding activity was observed at 20 μM. Further study for analyzing the importance of PfUvrD enzyme in parasite growth revealed that PfUvrD is crucial/important for its growth ex-vivo. Conclusions: As PfUvrD is absent in human hence on the basis of this study we propose PfUvrD as suitable drug target to control malaria. Some of the PfUvrD inhibitors identified in the present study can be utilized to further design novel and specific inhibitor molecules.


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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]
 2014-04-18 [Errata] Erratum for the Research Article: “Total Synthesis of a Functional Designer Eukaryotic Chromosome” by N. Annaluru, H. Muller, L. A. Mitchell, S. Ramalingam, G. Stracquadanio, S. M. Richardson, J. S. Dymond, Z. Kuang, L. Z. Scheifele, E. M. Cooper, Y. Cai, K. Zeller, N. Agmon, J. S. Han, M. Hadjithomas, J. Tullman, K. Caravelli, K. Cirelli, Z. Guo, V. London, A. Yeluru, S. Murugan, K. Kandavelou, N. Agier, G. Fischer, K. Yang, J. A. Martin, M. Bilgel, P. Bohutskyi, K. M. Boulier, B. J. Capaldo, J. Chang, K. Charoen, W. J. Choi, P. Deng, J. E. DiCarlo, J. Doong, J. Dunn, J. I. Feinberg, C. Fernandez, C. E. Floria, D. Gladowski, P. Hadidi, I. Ishizuka, J. Jabbari, C. Y. L. Lau, P. A. Lee, S. Li, D. Lin, M. E. Linder, J. Ling, J. Liu, J. Liu, M. London, H. Ma, J. Mao, J. E. McDade, A. McMillan, A. M. Moore, W. C. Oh, Y. Ouyang, R. Patel, M. Paul, L. C. Paulsen, J. Qiu, A. Rhee, M. G. Rubashkin, I. Y. Soh, N. E. Sotuyo, V. Srinivas, A. Suarez, A. Wong, R. Wong, W. R. Xie, Y. Xu, A. T. Yu, R. Koszul, J. S. Bader, J. D. Boeke, S. Chandrasegaran

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