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  • Computational Methods, Genomics  (140)
  • Synthetic Biology and Assembly Cloning  (70)
  • Pathogens & Pathogenicity  (51)
  • Oxford University Press  (261)
  • American Chemical Society
  • American Institute of Physics (AIP)
  • MDPI Publishing
  • Wiley
Collection
Keywords
Publisher
  • Oxford University Press  (261)
  • American Chemical Society
  • American Institute of Physics (AIP)
  • MDPI Publishing
  • Wiley
Years
  • 1
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    A low-latency, big database system and browser for storage, querying and visualization of 3D genomic data (2015)
    Oxford University Press
    Details
    Publication Date: 2015-09-19
    Description: Recent releases of genome three-dimensional (3D) structures have the potential to transform our understanding of genomes. Nonetheless, the storage technology and visualization tools need to evolve to offer to the scientific community fast and convenient access to these data. We introduce simultaneously a database system to store and query 3D genomic data ( 3DBG ), and a 3D genome browser to visualize and explore 3D genome structures ( 3DGB ). We benchmark 3DBG against state-of-the-art systems and demonstrate that it is faster than previous solutions, and importantly gracefully scales with the size of data. We also illustrate the usefulness of our 3D genome Web browser to explore human genome structures. The 3D genome browser is available at http://3dgb.cs.mcgill.ca/ .
    Keywords: Computational Methods, Genomics
    Print ISSN: 0305-1048
    Electronic ISSN: 1362-4962
    Topics: Biology
    Published by Oxford University Press
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  • 2
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    Analysis of strand-specific RNA-seq data using machine learning reveals the structures of transcription units in Clostridium thermocellum (2015)
    Oxford University Press
    Details
    Publication Date: 2015-05-29
    Description: Identification of transcription units (TUs) encoded in a bacterial genome is essential to elucidation of transcriptional regulation of the organism. To gain a detailed understanding of the dynamically composed TU structures, we have used four strand-specific RNA-seq (ssRNA-seq) datasets collected under two experimental conditions to derive the genomic TU organization of Clostridium thermocellum using a machine-learning approach. Our method accurately predicted the genomic boundaries of individual TUs based on two sets of parameters measuring the RNA-seq expression patterns across the genome: expression-level continuity and variance. A total of 2590 distinct TUs are predicted based on the four RNA-seq datasets. Among the predicted TUs, 44% have multiple genes. We assessed our prediction method on an independent set of RNA-seq data with longer reads. The evaluation confirmed the high quality of the predicted TUs. Functional enrichment analyses on a selected subset of the predicted TUs revealed interesting biology. To demonstrate the generality of the prediction method, we have also applied the method to RNA-seq data collected on Escherichia coli and achieved high prediction accuracies. The TU prediction program named SeqTU is publicly available at https://code.google.com/p/seqtu/ . We expect that the predicted TUs can serve as the baseline information for studying transcriptional and post-transcriptional regulation in C. thermocellum and other bacteria.
    Keywords: Computational Methods, Genomics
    Print ISSN: 0305-1048
    Electronic ISSN: 1362-4962
    Topics: Biology
    Published by Oxford University Press
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  • 3
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    A thesaurus of genetic variation for interrogation of repetitive genomic regions (2015)
    Oxford University Press
    Details
    Publication Date: 2015-05-29
    Description: Detecting genetic variation is one of the main applications of high-throughput sequencing, but is still challenging wherever aligning short reads poses ambiguities. Current state-of-the-art variant calling approaches avoid such regions, arguing that it is necessary to sacrifice detection sensitivity to limit false discovery. We developed a method that links candidate variant positions within repetitive genomic regions into clusters. The technique relies on a resource, a thesaurus of genetic variation, that enumerates genomic regions with similar sequence. The resource is computationally intensive to generate, but once compiled can be applied efficiently to annotate and prioritize variants in repetitive regions. We show that thesaurus annotation can reduce the rate of false variant calls due to mappability by up to three orders of magnitude. We apply the technique to whole genome datasets and establish that called variants in low mappability regions annotated using the thesaurus can be experimentally validated. We then extend the analysis to a large panel of exomes to show that the annotation technique opens possibilities to study variation in hereto hidden and under-studied parts of the genome.
    Keywords: Computational Methods, Genomics
    Print ISSN: 0305-1048
    Electronic ISSN: 1362-4962
    Topics: Biology
    Published by Oxford University Press
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  • 4
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    A human pathogenic bacterial infection model using the two-spotted cricket, Gryllus bimaculatus (2016)
    Oxford University Press
    Details
    Publication Date: 2016-07-15
    Description: Invertebrate animal species that can withstand temperatures as high as 37°C, the human body temperature, are limited. In the present study, we utilized the two-spotted cricket, Gryllus bimaculatus , which lives in tropical and subtropical regions, as an animal model of human pathogenic bacterial infection. Injection of Pseudomonas aeruginosa or Staphylococcus aureus into the hemolymph killed crickets. Injected P. aeruginosa or S. aureus proliferated in the hemolymph until the cricket died. The ability of these pathogenic bacteria to kill the crickets was blocked by the administration of antibiotics. S. aureus gene-knockout mutants of virulence factors, including cvfA, agr and srtA , exhibited decreased killing ability compared with the parent strain. The dose at which 50% of crickets were killed by P. aeruginosa or S. aureus was not decreased at 37°C compared with that at 27°C. Injection of Listeria monocytogenes , which upregulates toxin expression at 37°C, killed crickets, and the dose at which 50% of crickets were killed was decreased at 37°C compared with that at 27°C. These findings suggest that the two-spotted cricket is a useful model animal for evaluating the virulence properties of various human pathogenic bacteria at variable temperature including 37°C.
    Keywords: Pathogens & Pathogenicity
    Print ISSN: 0378-1097
    Electronic ISSN: 1574-6968
    Topics: Biology
    Published by Oxford University Press
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  • 5
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    A conserved OmpA-like protein in Legionella pneumophila required for efficient intracellular replication (2016)
    Oxford University Press
    Details
    Publication Date: 2016-07-31
    Description: The OmpA-like protein domain has been associated with peptidoglycan-binding proteins, and is often found in virulence factors of bacterial pathogens. The intracellular pathogen Legionella pneumophila encodes for six proteins that contain the OmpA-like domain, among them the highly conserved uncharacterized protein we named CmpA. Here we set out to characterize the CmpA protein and determine its contribution to intracellular survival of L. pneumophila . Secondary structure analysis suggests that CmpA is an inner membrane protein with a peptidoglycan-binding domain at the C-teminus. A cmpA mutant was able to replicate normally in broth, but failed to compete with an isogenic wild-type strain in an intracellular growth competition assay. The cmpA mutant also displayed significant intracellular growth defects in both the protozoan host Acanthamoeba castellanii and in primary bone marrow-derived macrophages, where uptake into the cells was also impaired. The cmpA phenotypes were completely restored upon expression of CmpA in trans . The data presented here establish CmpA as a novel virulence factor of L. pneumophila that is required for efficient intracellular replication in both mammalian and protozoan hosts.
    Keywords: Pathogens & Pathogenicity
    Print ISSN: 0378-1097
    Electronic ISSN: 1574-6968
    Topics: Biology
    Published by Oxford University Press
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  • 6
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    A secretome view of colonisation factors in Shiga toxin-encoding Escherichia coli (STEC): from enterohaemorrhagic E. coli (EHEC) to related enteropathotypes (2016)
    Oxford University Press
    Details
    Publication Date: 2016-08-05
    Description: Shiga toxin-encoding Escherichia coli (STEC) regroup strains that carry genes encoding Shiga toxin (Stx). Among intestinal pathogenic E. coli , enterohaemorrhagic E. coli (EHEC) constitute the major subgroup of virulent STEC. EHEC cause serious human disease such as haemorrhagic colitis and haemolytic-uremic syndrome. While EHEC have evolved from enteropathogenic E. coli , hybrids with enteroaggregative E. coli have recently emerged. Of note, some enteroinvasive E. coli also belong to the STEC group. While the LEE (locus of enterocyte effacement) is a key and prominent molecular determinant in the pathogenicity, neither all EHEC nor STEC contain the LEE, suggesting that they possess additional virulence and colonisation factors. Currently, nine protein secretion systems have been described in diderm-lipopolysaccharide bacteria (archetypal Gram-negative) and can be involved in the secretion of extracellular effectors, cell-surface proteins or assembly of cell-surface organelles, such as flagella or pili. In this review, we focus on the secretome of STEC and related enteropathotypes, which are relevant to the colonisation of biotic and abiotic surfaces. Considering the wealth of potential protein trafficking mechanisms, the different combinations of colonisation factors and modulation of their expression is further emphasised with regard to the ecophysiology of STEC.
    Keywords: Pathogens & Pathogenicity
    Print ISSN: 0378-1097
    Electronic ISSN: 1574-6968
    Topics: Biology
    Published by Oxford University Press
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  • 7
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    Post-translational control of genetic circuits using Potyvirus proteases (2016)
    Oxford University Press
    Details
    Publication Date: 2016-07-28
    Description: Genetic engineering projects often require control over when a protein is degraded. To this end, we use a fusion between a degron and an inactivating peptide that can be added to the N-terminus of a protein. When the corresponding protease is expressed, it cleaves the peptide and the protein is degraded. Three protease:cleavage site pairs from Potyvirus are shown to be orthogonal and active in exposing degrons, releasing inhibitory domains and cleaving polyproteins. This toolbox is applied to the design of genetic circuits as a means to control regulator activity and degradation. First, we demonstrate that a gate can be constructed by constitutively expressing an inactivated repressor and having an input promoter drive the expression of the protease. It is also shown that the proteolytic release of an inhibitory domain can improve the dynamic range of a transcriptional gate (200-fold repression). Next, we design polyproteins containing multiple repressors and show that their cleavage can be used to control multiple outputs. Finally, we demonstrate that the dynamic range of an output can be improved (8-fold to 190-fold) with the addition of a protease-cleaved degron. Thus, controllable proteolysis offers a powerful tool for modulating and expanding the function of synthetic gene circuits.
    Keywords: Synthetic Biology and Assembly Cloning
    Print ISSN: 0305-1048
    Electronic ISSN: 1362-4962
    Topics: Biology
    Published by Oxford University Press
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  • 8
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    A new molecular signature method for prediction of driver cancer pathways from transcriptional data (2016)
    Oxford University Press
    Details
    Publication Date: 2016-06-21
    Description: Assigning cancer patients to the most effective treatments requires an understanding of the molecular basis of their disease. While DNA-based molecular profiling approaches have flourished over the past several years to transform our understanding of driver pathways across a broad range of tumors, a systematic characterization of key driver pathways based on RNA data has not been undertaken. Here we introduce a new approach for predicting the status of driver cancer pathways based on signature functions derived from RNA sequencing data. To identify the driver cancer pathways of interest, we mined DNA variant data from TCGA and nominated driver alterations in seven major cancer pathways in breast, ovarian and colon cancer tumors. The activation status of these driver pathways were then characterized using RNA sequencing data by constructing classification signature functions in training datasets and then testing the accuracy of the signatures in test datasets. The signature functions differentiate well tumors with nominated pathway activation from tumors with no signs of activation: average AUC equals to 0.83. Our results confirm that driver genomic alterations are distinctively displayed at the transcriptional level and that the transcriptional signatures can generally provide an alternative to DNA sequencing methods in detecting specific driver pathways.
    Keywords: Computational Methods, Genomics
    Print ISSN: 0305-1048
    Electronic ISSN: 1362-4962
    Topics: Biology
    Published by Oxford University Press
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  • 9
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    DanQ: a hybrid convolutional and recurrent deep neural network for quantifying the function of DNA sequences (2016)
    Oxford University Press
    Details
    Publication Date: 2016-06-21
    Description: Modeling the properties and functions of DNA sequences is an important, but challenging task in the broad field of genomics. This task is particularly difficult for non-coding DNA, the vast majority of which is still poorly understood in terms of function. A powerful predictive model for the function of non-coding DNA can have enormous benefit for both basic science and translational research because over 98% of the human genome is non-coding and 93% of disease-associated variants lie in these regions. To address this need, we propose DanQ, a novel hybrid convolutional and bi-directional long short-term memory recurrent neural network framework for predicting non-coding function de novo from sequence. In the DanQ model, the convolution layer captures regulatory motifs, while the recurrent layer captures long-term dependencies between the motifs in order to learn a regulatory ‘grammar’ to improve predictions. DanQ improves considerably upon other models across several metrics. For some regulatory markers, DanQ can achieve over a 50% relative improvement in the area under the precision-recall curve metric compared to related models. We have made the source code available at the github repository http://github.com/uci-cbcl/DanQ .
    Keywords: Computational Methods, Genomics
    Print ISSN: 0305-1048
    Electronic ISSN: 1362-4962
    Topics: Biology
    Published by Oxford University Press
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  • 10
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    Phylogeny-aware identification and correction of taxonomically mislabeled sequences (2016)
    Oxford University Press
    Details
    Publication Date: 2016-06-21
    Description: Molecular sequences in public databases are mostly annotated by the submitting authors without further validation. This procedure can generate erroneous taxonomic sequence labels. Mislabeled sequences are hard to identify, and they can induce downstream errors because new sequences are typically annotated using existing ones. Furthermore, taxonomic mislabelings in reference sequence databases can bias metagenetic studies which rely on the taxonomy. Despite significant efforts to improve the quality of taxonomic annotations, the curation rate is low because of the labor-intensive manual curation process. Here, we present SATIVA, a phylogeny-aware method to automatically identify taxonomically mislabeled sequences (‘mislabels’) using statistical models of evolution. We use the Evolutionary Placement Algorithm (EPA) to detect and score sequences whose taxonomic annotation is not supported by the underlying phylogenetic signal, and automatically propose a corrected taxonomic classification for those. Using simulated data, we show that our method attains high accuracy for identification (96.9% sensitivity/91.7% precision) as well as correction (94.9% sensitivity/89.9% precision) of mislabels. Furthermore, an analysis of four widely used microbial 16S reference databases (Greengenes, LTP, RDP and SILVA) indicates that they currently contain between 0.2% and 2.5% mislabels. Finally, we use SATIVA to perform an in-depth evaluation of alternative taxonomies for Cyanobacteria. SATIVA is freely available at https://github.com/amkozlov/sativa .
    Keywords: Computational Methods, Genomics
    Print ISSN: 0305-1048
    Electronic ISSN: 1362-4962
    Topics: Biology
    Published by Oxford University Press
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