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BMC Structural Biology - Latest Articles   [more] [xml]
 2014-11-05T12:00:00Z Structure and functional characterization of pyruvate decarboxylase from Gluconacetobacter diazotrophicus
Background: Bacterial pyruvate decarboxylases (PDC) are rare. Their role in ethanol production and in bacterially mediated ethanologenic processes has, however, ensured a continued and growing interest. PDCs from Zymomonas mobilis (ZmPDC), Zymobacter palmae (ZpPDC) and Sarcina ventriculi (SvPDC) have been characterized and ZmPDC has been produced successfully in a range of heterologous hosts. PDCs from the Acetobacteraceae and their role in metabolism have not been characterized to the same extent. Examples include Gluconobacter oxydans (GoPDC), G. diazotrophicus (GdPDC) and Acetobacter pasteutrianus (ApPDC). All of these organisms are of commercial importance. Results: This study reports the kinetic characterization and the crystal structure of a PDC from Gluconacetobacter diazotrophicus (GdPDC). Enzyme kinetic analysis indicates a high affinity for pyruvate (K M 0.06 mM at pH 5), high catalytic efficiencies (1.3 • 106 M−1•s−1 at pH 5), pHopt of 5.5 and Topt at 45°C. The enzyme is not thermostable (T½ of 18 minutes at 60°C) and the calculated number of bonds between monomers and dimers do not give clear indications for the relatively lower thermostability compared to other PDCs. The structure is highly similar to those described for Z. mobilis (ZmPDC) and A. pasteurianus PDC (ApPDC) with a rmsd value of 0.57 Å for Cα when comparing GdPDC to that of ApPDC. Indole-3-pyruvate does not serve as a substrate for the enzyme. Structural differences occur in two loci, involving the regions Thr341 to Thr352 and Asn499 to Asp503. Conclusions: This is the first study of the PDC from G. diazotrophicus (PAL5) and lays the groundwork for future research into its role in this endosymbiont. The crystal structure of GdPDC indicates the enzyme to be evolutionarily closely related to homologues from Z. mobilis and A. pasteurianus and suggests strong selective pressure to keep the enzyme characteristics in a narrow range. The pH optimum together with reduced thermostability likely reflect the host organisms niche and conditions under which these properties have been naturally selected for. The lack of activity on indole-3-pyruvate excludes this decarboxylase as the enzyme responsible for indole acetic acid production in G. diazotrophicus.


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BMC Bioinformatics - Latest Articles   [more] [xml]
 2014-11-21T00:00:00Z Computational approach for calculating the probability of eukaryotic translation initiation from ribo-seq data that takes into account leaky scanning
Background: Ribosome profiling (ribo-seq) provides experimental data on the density of elongating or initiating ribosomes at the whole transcriptome level that can be potentially used for estimating absolute levels of translation initiation at individual Translation Initiation Sites (TISs). These absolute levels depend on the mutual organisation of TISs within individual mRNAs. For example, according to the leaky scanning model of translation initiation in eukaryotes, a strong TIS downstream of another strong TIS is unlikely to be productive, since only a few scanning ribosomes would be able to reach the downstream TIS. In order to understand the dependence of translation initiation efficiency on the surrounding nucleotide context, it is important to estimate the strength of TISs independently of their mutual organisation, i.e. to estimate with what probability a ribosome would initiate at a particular TIS. Results: We designed a simple computational approach for estimating the probabilities of ribosomes initiating at individual start codons using ribosome profiling data. The method is based on the widely accepted leaky scanning model of translation initiation in eukaryotes which postulates that scanning ribosomes may skip a start codon if the initiation context is unfavourable and continue on scanning. We tested our approach on three independent ribo-seq datasets obtained in mammalian cultured cells. Conclusions: Our results suggested that the method successfully discriminates between weak and strong TISs and that the majority of numerous non-AUG TISs reported recently are very weak. Therefore the high frequency of non-AUG TISs observed in ribosome profiling experiments is due to their proximity to mRNA 5?-ends rather than their strength. Detectable translation initiation at non-AUG codons downstream of AUG codons is comparatively infrequent. The leaky scanning method will be useful for the characterization of differences in start codon selection between tissues, developmental stages and in response to stress conditions.
 2014-11-21T00:00:00Z Bayesian neural networks for detecting epistasis in genetic association studies
Background: Discovering causal genetic variants from large genetic association studies poses many difficult challenges. Assessing which genetic markers are involved in determining trait status is a computationally demanding task, especially in the presence of gene-gene interactions. Results: A non-parametric Bayesian approach in the form of a Bayesian neural network is proposed for use in analyzing genetic association studies. Demonstrations on synthetic and real data reveal they are able to efficiently and accurately determine which variants are involved in determining case-control status. By using graphics processing units (GPUs) the time needed to build these models is decreased by several orders of magnitude. In comparison with commonly used approaches for detecting interactions, Bayesian neural networks perform very well across a broad spectrum of possible genetic relationships. Conclusions: The proposed framework is shown to be a powerful method for detecting causal SNPs while being computationally efficient enough to handle large datasets.
 2014-11-21T00:00:00Z A fast algorithm for determining bounds and accurate approximate p -values of the rank product statistic for replicate experiments
Background: The rank product method is a powerful statistical technique for identifying differentially expressed molecules in replicated experiments. A critical issue in molecule selection is accurate calculation of the p-value of the rank product statistic to adequately address multiple testing. Both exact calculation and permutation and gamma approximations have been proposed to determine molecule-level significance. These current approaches have serious drawbacks as they are either computationally burdensome or provide inaccurate estimates in the tail of the p-value distribution. Results: We derive strict lower and upper bounds to the exact p-value along with an accurate approximation that can be used to assess the significance of the rank product statistic in a computationally fast manner. The bounds and the proposed approximation are shown to provide far better accuracy over existing approximate methods in determining tail probabilities, with the slightly conservative upper bound protecting against false positives. We illustrate the proposed method in the context of a recently published analysis on transcriptomic profiling performed in blood. Conclusions: We provide a method to determine upper bounds and accurate approximate p-values of the rank product statistic. The proposed algorithm provides an order of magnitude increase in throughput as compared with current approaches and offers the opportunity to explore new application domains with even larger multiple testing issue. The R code is published in one of the Additional files and is available athttp://www.ru.nl/publish/pages/726696/rankprodbounds.zip.
 2014-11-21T00:00:00Z A framework for generalized subspace pattern mining in high-dimensional datasets
Background: A generalized notion of biclustering involves the identification of patterns across subspaces within a data matrix. This approach is particularly well-suited to analysis of heterogeneous molecular biology datasets, such as those collected from populations of cancer patients. Different definitions of biclusters will offer different opportunities to discover information from datasets, making it pertinent to tailor the desired patterns to the intended application. This paper introduces `GABi?, a customizable framework for subspace pattern mining suited to large heterogeneous datasets. Most existing biclustering algorithms discover biclusters of only a few distinct structures. However, by enabling definition of arbitrary bicluster models, the GABi framework enables the application of biclustering to tasks for which no existing algorithm could be used. Results: First, a series of artificial datasets were constructed to represent three clearly distinct scenarios for applying biclustering. With a bicluster model created for each distinct scenario, GABi is shown to recover the correct solutions more effectively than a panel of alternative approaches, where the bicluster model may not reflect the structure of the desired solution. Secondly, the GABi framework is used to integrate clinical outcome data with an ovarian cancer DNA methylation dataset, leading to the discovery that widespread dysregulation of DNA methylation associates with poor patient prognosis, a result that has not previously been reported. This illustrates a further benefit of the flexible bicluster definition of GABi, which is that it enables incorporation of multiple sources of data, with each data source treated in a specific manner, leading to a means of intelligent integrated subspace pattern mining across multiple datasets. Conclusions: The GABi framework enables discovery of biologically relevant patterns of any specified structure from large collections of genomic data. An R implementation of the GABi framework is available through CRAN (http://cran.r-project.org/web/packages/GABi/index.html).
 2014-11-20T00:00:00Z Sequence-based identification of recombination spots using pseudo nucleic acid representation and recursive feature extraction by linear kernel SVM
Background: Identification of the recombination hot/cold spots is critical for understanding the mechanism of recombination as well as the genome evolution process. However, experimental identification of recombination spots is both time-consuming and costly. Developing an accurate and automated method for reliably and quickly identifying recombination spots is thus urgently needed. Results: Here we proposed a novel approach by fusing features from pseudo nucleic acid composition (PseNAC), including NAC, n-tier NAC and pseudo dinucleotide composition (PseDNC). A recursive feature extraction by linear kernel support vector machine (SVM) was then used to rank the integrated feature vectors and extract optimal features. SVM was adopted for identifying recombination spots based on these optimal features. To evaluate the performance of the proposed method, jackknife cross-validation test was employed on a benchmark dataset. The overall accuracy of this approach was 84.09%, which was higher (from 0.37% to 3.79%) than those of state-of-the-art tools. Conclusions: Comparison results suggested that linear kernel SVM is a useful vehicle for identifying recombination hot/cold spots.
 2014-11-20T00:00:00Z A ratiometric-based measure of gene co-expression
Background: Gene co-expression analysis has previously been based on measures that include correlation coefficients and mutual information, as well as newcomers such as MIC. These measures depend primarily on the degree of association between the RNA levels of two genes and to a lesser extent on their variability. They focus on the similarity of expression value trajectories that change in like manner across samples. However there are relationships of biological interest for which these classical measures are expected to be insensitive. These include genes whose expression levels are ratiometrically stable and genes whose variance is tightly constrained. Large-scale studies of relatively homogeneous samples, including single cell RNA-seq, are experimental settings in which such relationships might be especially pertinent. Results: We develop and implement a ratiometric approach for detecting gene associations (abbreviated RA). It is based on the coefficient of variation of the measured expression ratio of each pair of genes. We apply it to a collection of lymphoblastoid RNA-seq data from the 1000 Genomes Project Consortium, a typical sample set with high overall homogeneity. RA is a selective method, reporting in this case ~1/4 of all possible gene pairs, yet these relationships include a distilled picture of biological relationships previously found by other methods. In addition, RA reveals expression relationships that are not detected by traditional correlation and mutual information methods. We also analyze data from individual lymphoblastoid cells and show that desirable properties of the RA method extend to single-cell RNA-seq. Conclusion: We show that our ratiometric method identifies biologically significant relationships that are often missed or low-ranked by conventional association-based methods when applied to a relatively homogenous dataset. The results open new questions about the regulatory mechanisms that produce strong RA relationships. RA is scalable and potentially well suited for the analysis of thousands of bulk-RNA or single-cell transcriptomes.
 2014-11-19T12:00:00Z TE-Tracker: systematic identification of transposition events through whole-genome resequencing
Background: Transposable elements (TEs) are DNA sequences that are able to move from their location in the genome by cutting or copying themselves to another locus. As such, they are increasingly recognized as impacting all aspects of genome function. With the dramatic reduction in cost of DNA sequencing, it is now possible to resequence whole genomes in order to systematically characterize novel TE mobilization in a particular individual. However, this task is made difficult by the inherently repetitive nature of TE sequences, which in some eukaryotes compose over half of the genome sequence. Currently, only a few software tools dedicated to the detection of TE mobilization using next-generation-sequencing are described in the literature. They often target specific TEs for which annotation is available, and are only able to identify families of closely related TEs, rather than individual elements. Results: We present TE-Tracker, a general and accurate computational method for the de-novo detection of germ line TE mobilization from re-sequenced genomes, as well as the identification of both their source and destination sequences. We compare our method with the two classes of existing software: specialized TE-detection tools and generic structural variant (SV) detection tools. We show that TE-Tracker, while working independently of any prior annotation, bridges the gap between these two approaches in terms of detection power. Indeed, its positive predictive value (PPV) is comparable to that of dedicated TE software while its sensitivity is typical of a generic SV detection tool. TE-Tracker demonstrates the benefit of adopting an annotation-independent, de novo approach for the detection of TE mobilization events. We use TE-Tracker to provide a comprehensive view of transposition events induced by loss of DNA methylation in Arabidopsis. TE-Tracker is freely available at http://www.genoscope.cns.fr/TE-Tracker. Conclusions: We show that TE-Tracker accurately detects both the source and destination of novel transposition events in re-sequenced genomes. Moreover, TE-Tracker is able to detect all potential donor sequences for a given insertion, and can identify the correct one among them. Furthermore, TE-Tracker produces significantly fewer false positives than common SV detection programs, thus greatly facilitating the detection and analysis of TE mobilization events.
 2014-11-19T12:00:00Z Vindel: a simple pipeline for checking indel redundancy
Background: With the advance of next generation sequencing (NGS) technologies, a large number of insertion and deletion (indel) variants have been identified in human populations. Despite much research into variant calling, it has been found that a non-negligible proportion of the identified indel variants might be false positives due to sequencing errors, artifacts caused by ambiguous alignments, and annotation errors. Results: In this paper, we examine indel redundancy in dbSNP, one of the central databases for indel variants, and develop a standalone computational pipeline, dubbed Vindel, to detect redundant indels. The pipeline first applies indel position information to form candidate redundant groups, then performs indel mutations to the reference genome to generate corresponding indel variant substrings. Finally the indel variant substrings in the same candidate redundant groups are compared in a pairwise fashion to identify redundant indels. We applied our pipeline to check for redundancy in the human indels in dbSNP. Our pipeline identified approximately 8% redundancy in insertion type indels, 12% in deletion type indels, and overall 10% for insertions and deletions combined. These numbers are largely consistent across all human autosomes. We also investigated indel size distribution and adjacent indel distance distribution for a better understanding of the mechanisms generating indel variants. Conclusions: Vindel, a simple yet effective computational pipeline, can be used to check whether a set of indels are redundant with respect to those already in the database of interest such as NCBI?s dbSNP. Of the approximately 5.9 million indels we examined, nearly 0.6 million are redundant, revealing a serious limitation in the current indel annotation. Statistics results prove the consistency of the pipeline on indel redundancy detection for all 22 chromosomes. Apart from the standalone Vindel pipeline, the indel redundancy check algorithm is also implemented in the web server http://bioinformatics.cs.vt.edu/zhanglab/indelRedundant.php.
 2014-11-19T12:00:00Z NeatFreq: reference-free data reduction and coverage normalization for De Novo sequence assembly
Background: Deep shotgun sequencing on next generation sequencing (NGS) platforms has contributed significant amounts of data to enrich our understanding of genomes, transcriptomes, amplified single-cell genomes, and metagenomes. However, deep coverage variations in short-read data sets and high sequencing error rates of modern sequencers present new computational challenges in data interpretation, including mapping and de novo assembly. New lab techniques such as multiple displacement amplification (MDA) of single cells and sequence independent single primer amplification (SISPA) allow for sequencing of organisms that cannot be cultured, but generate highly variable coverage due to amplification biases. Results: Here we introduce NeatFreq, a software tool that reduces a data set to more uniform coverage by clustering and selecting from reads binned by their median kmer frequency (RMKF) and uniqueness. Previous algorithms normalize read coverage based on RMKF, but do not include methods for the preferred selection of (1) extremely low coverage regions produced by extremely variable sequencing of random-primed products and (2) 2-sided paired-end sequences. The algorithm increases the incorporation of the most unique, lowest coverage, segments of a genome using an error-corrected data set. NeatFreq was applied to bacterial, viral plaque, and single-cell sequencing data. The algorithm showed an increase in the rate at which the most unique reads in a genome were included in the assembled consensus while also reducing the count of duplicative and erroneous contigs (strings of high confidence overlaps) in the deliverable consensus. The results obtained from conventional Overlap-Layout-Consensus (OLC) were compared to simulated multi-de Bruijn graph assembly alternatives trained for variable coverage input using sequence before and after normalization of coverage. Coverage reduction was shown to increase processing speed and reduce memory requirements when using conventional bacterial assembly algorithms. Conclusions: The normalization of deep coverage spikes, which would otherwise inhibit consensus resolution, enables High Throughput Sequencing (HTS) assembly projects to consistently run to completion with existing assembly software. The NeatFreq software package is free, open source and available at https://github.com/bioh4x/NeatFreq.
 2014-11-19T12:00:00Z Using phylogenetically-informed annotation (PIA) to search for light-interacting genes in transcriptomes from non-model organisms
Background: Tools for high throughput sequencing and de novo assembly make the analysis of transcriptomes (i.e. the suite of genes expressed in a tissue) feasible for almost any organism. Yet a challenge for biologists is that it can be difficult to assign identities to gene sequences, especially from non-model organisms. Phylogenetic analyses are one useful method for assigning identities to these sequences, but such methods tend to be time-consuming because of the need to re-calculate trees for every gene of interest and each time a new data set is analyzed. In response, we employed existing tools for phylogenetic analysis to produce a computationally efficient, tree-based approach for annotating transcriptomes or new genomes that we term Phylogenetically-Informed Annotation (PIA), which places uncharacterized genes into pre-calculated phylogenies of gene families. Results: We generated maximum likelihood trees for 109 genes from a Light Interaction Toolkit (LIT), a collection of genes that underlie the function or development of light-interacting structures in metazoans. To do so, we searched protein sequences predicted from 30 fully-sequenced genomes and built trees using tools for phylogenetic analysis in the Osiris package of Galaxy (an open-source workflow management system). Next, to rapidly annotate transcriptomes from organisms that lack sequenced genomes, we repurposed a maximum likelihood-based Evolutionary Placement Algorithm (implemented in RAxML) to place sequences of potential LIT genes on to our pre-calculated gene trees. Finally, we implemented PIA in Galaxy and used it to search for LIT genes in 28 newly-sequenced transcriptomes from the light-interacting tissues of a range of cephalopod mollusks, arthropods, and cubozoan cnidarians. Our new trees for LIT genes are available on the Bitbucket public repository (http://bitbucket.org/osiris_phylogenetics/pia/) and we demonstrate PIA on a publicly-accessible web server (http://galaxy-dev.cnsi.ucsb.edu/pia/). Conclusions: Our new trees for LIT genes will be a valuable resource for researchers studying the evolution of eyes or other light-interacting structures. We also introduce PIA, a high throughput method for using phylogenetic relationships to identify LIT genes in transcriptomes from non-model organisms. With simple modifications, our methods may be used to search for different sets of genes or to annotate data sets from taxa outside of Metazoa.


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BMC Genomics - Latest Articles   [more] [xml]
 2014-11-23T00:00:00Z Functional chromatin features are associated with structural mutations in cancer
Background: Structural mutations (SMs) play a major role in cancer development. In some cancers, such as breast and ovarian, DNA double-strand breaks (DSBs) occur more frequently in transcribed regions, while in other cancer types such as prostate, there is a consistent depletion of breakpoints in transcribed regions. Despite such regularity, little is understood about the mechanisms driving these effects. A few works have suggested that protein binding may be relevant, e.g. in studies of androgen receptor binding and active chromatin in specific cell types. We hypothesized that this behavior might be general, i.e. that correlation between protein-DNA binding (and open chromatin) and breakpoint locations is common across divergent cancers. Results: We investigated this hypothesis by comprehensively analyzing the relationship among 457 ENCODE protein binding ChIP-seq experiments, 125 DnaseI and 24 FAIRE experiments, and 14,600 SMs from 8 diverse cancer datasets covering 147 samples. In most cancers, including breast and ovarian, we found enrichment of protein binding and open chromatin in the vicinity of SM breakpoints at distances up to 200 kb. Furthermore, for all cancer types we observed an enhanced enrichment in regions distant from genes when compared to regions proximal to genes, suggesting that the SM-induction mechanism is independent from the bias of DSBs to occur near transcribed regions. We also observed a stronger effect for sites with more than one protein bound. Conclusions: Protein binding and open chromatin state are associated with nearby SM breakpoints in many cancer datasets. These observations suggest a consistent mechanism underlying SM locations across different cancers.


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BMC Biochemistry - Latest Articles   [more] [xml]
 2014-11-06T12:00:00Z Phosphorylation in intrinsically disordered regions regulates the activity of Neurogenin2
Background: Neuronal differentiation is largely under the control of basic Helix-Loop-Helix (bHLH) proneural transcription factors that play key roles during development of the embryonic nervous system. In addition to well-characterised regulation of their expression, increasing evidence is emerging for additional post-translational regulation of proneural protein activity. Of particular interest is the bHLH proneural factor Neurogenin2 (Ngn2), which orchestrates progression from neural progenitor to differentiated neuron in several regions of the central nervous system. Previous studies have demonstrated a key role for cell cycle-dependent multi-site phosphorylation of Ngn2 protein at Serine-Proline (SP) sites for regulation of its neuronal differentiation activity, although the potential structural and functional consequences of phosphorylation at different regions of the protein are unclear. Results: Here we characterise the role of phosphorylation of specific regions of Ngn2 on the stability of Ngn2 protein and on its neuronal differentiation activity in vivo in the developing embryo, demonstrating clearly that the location of SP sites is less important than the number of SP sites available for control of Ngn2 activity in vivo. We also provide structural evidence that Ngn2 contains large, intrinsically disordered regions that undergo phosphorylation by cyclin-dependent kinases (cdks). Conclusions: Phosphorylation of Ngn2 occurs in both the N- and C-terminal regions, either side of the conserved basic Helix-Loop-Helix domain. While these phosphorylation events do not change the intrinsic stability of Ngn2, phosphorylation on multiple sites acts to limit its ability to drive neuronal differentiation in vivo. Phosphorylated regions of Ngn2 are predicted to be intrinsically disordered and cdk-dependent phosphorylation of these intrinsically disordered regions contributes to Ngn2 regulation.


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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]
 2014-11-21 [Editorial] More Science in the Classroom
About a year ago, Bruce Alberts and I announced the launch of Science in the Classroom (scienceintheclassroom.org), an online resource of annotated research papers published in Science, with associated teaching materials designed to help pre-college and college students understand how science moves forward as a structured way of revealing the laws of nature. Since its fledgling beginning last year, the project has expanded its subject diversity and continues to add articles at the rate of two per month. These articles have reached about 3000 users per month. But now it is time to take this project to the next level—and you can help, by annotating new papers and designing creative activities to accompany them. Author: Marcia McNutt

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