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  • Articles  (336)
  • Oxford University Press  (336)
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  • Journals
  • Articles  (336)
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  • Oxford University Press  (336)
  • American Chemical Society (ACS)
  • Frontiers Media
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  • 2015-2019  (336)
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  • 1
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    Resistance of non-typeable Haemophilus influenzae biofilms is independent of biofilm size (2017)
    Oxford University Press
    Details
    Publication Date: 2017-01-08
    Description: The inflammatory middle ear disease known as otitis media can become chronic or recurrent in some cases due to failure of the antibiotic treatment to clear the bacterial etiological agent. Biofilms are known culprits of antibiotic-resistant infections; however, the mechanisms of resistance for non-typeable Haemophilus influenzae biofilms have not been completely elucidated. In this study, we utilized in vitro static biofilm assays to characterize clinical strain biofilms and addressed the hypothesis that biofilms with greater biomass and/or thickness would be more resistant to antimicrobial-mediated eradication than thinner and/or lower biomass biofilms. Consistent with previous studies, antibiotic concentrations required to eliminate biofilm bacteria tended to be drastically higher than concentrations required to kill planktonic bacteria. The size characterizations of the biofilms formed by the clinical isolates were compared to their minimum biofilm eradication concentrations for four antibiotics. This revealed no correlation between biofilm thickness or biomass and the ability to resist eradication by antibiotics. Therefore, we concluded that biofilm size does not play a role in antibiotic resistance, suggesting that reduction of antibiotic penetration may not be a significant mechanism for antibiotic resistance for this bacterial opportunist.
    Print ISSN: 0928-8244
    Topics: Biology , Medicine
    Published by Oxford University Press
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  • 2
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    Hfq and three Hfq-dependent small regulatory RNAs--MgrR, RyhB and McaS--coregulate the locus of enterocyte effacement in enteropathogenic Escherichia coli (2017)
    Oxford University Press
    Details
    Publication Date: 2017-01-08
    Description: Enteropathogenic Escherichia coli (EPEC) is a significant cause of infantile diarrhea and death in developing countries. The pathogenicity island l ocus of e nterocyte e ffacement (LEE) is essential for EPEC to cause diarrhea. Besides EPEC, the LEE is also present in other gastrointestinal pathogens, most notably enterohemorrhagic E. coli (EHEC). Whereas transcriptional control of the LEE has been meticulously examined, posttranscriptional regulation, including the role of Hfq-dependent small RNAs, remains undercharacterized. However, the past few years have witnessed a surge in the identification of riboregulators of the LEE in EHEC. Contrastingly, the posttranscriptional regulatory landscape of EPEC remains cryptic. Here we demonstrate that the RNA-chaperone Hfq represses the LEE of EPEC by targeting the 5' untranslated leader region of grlR in the grlRA mRNA. Three conserved small regulatory RNAs (sRNAs)—MgrR, RyhB and McaS—are involved in the Hfq-dependent regulation of grlRA . MgrR and RyhB exert their effects by directly base-pairing to the 5' region of grlR . Whereas MgrR selectively represses grlR but activates grlA , RyhB represses gene expression from the entire grlRA transcript. Meanwhile, McaS appears to target the grlRA mRNA indirectly. Thus, our results provide the first definitive evidence that implicates multiple sRNAs in regulating the LEE and the resulting virulence of EPEC.
    Print ISSN: 0928-8244
    Topics: Biology , Medicine
    Published by Oxford University Press
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  • 3
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    Iterative error correction of long sequencing reads maximizes accuracy and improves contig assembly (2017)
    Oxford University Press
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    Publication Date: 2017-01-10
    Description: Next-generation sequencers such as Illumina can now produce reads up to 300 bp with high throughput, which is attractive for genome assembly. A first step in genome assembly is to computationally correct sequencing errors. However, correcting all errors in these longer reads is challenging. Here, we show that reads with remaining errors after correction often overlap repeats, where short erroneous k -mers occur in other copies of the repeat. We developed an iterative error correction pipeline that runs the previously published String Graph Assembler (SGA) in multiple rounds of k -mer-based correction with an increasing k -mer size, followed by a final round of overlap-based correction. By combining the advantages of small and large k -mers, this approach corrects more errors in repeats and minimizes the total amount of erroneous reads. We show that higher read accuracy increases contig lengths two to three times. We provide SGA-Iteratively Correcting Errors ( https://github.com/hillerlab/IterativeErrorCorrection/ ) that implements iterative error correction by using modules from SGA.
    Print ISSN: 1467-5463
    Electronic ISSN: 1477-4054
    Topics: Biology , Computer Science
    Published by Oxford University Press
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  • 4
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    Computational methods to identify metabolic sub-networks based on metabolomic profiles (2017)
    Oxford University Press
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    Publication Date: 2017-01-10
    Description: Untargeted metabolomics makes it possible to identify compounds that undergo significant changes in concentration in different experimental conditions. The resulting metabolomic profile characterizes the perturbation concerned, but does not explain the underlying biochemical mechanisms. Bioinformatics methods make it possible to interpret results in light of the whole metabolism. This knowledge is modelled into a network, which can be mined using algorithms that originate in graph theory. These algorithms can extract sub-networks related to the compounds identified. Several attempts have been made to adapt them to obtain more biologically meaningful results. However, there is still no consensus on this kind of analysis of metabolic networks. This review presents the main graph approaches used to interpret metabolomic data using metabolic networks. Their advantages and drawbacks are discussed, and the impacts of their parameters are emphasized. We also provide some guidelines for relevant sub-network extraction and also suggest a range of applications for most methods.
    Print ISSN: 1467-5463
    Electronic ISSN: 1477-4054
    Topics: Biology , Computer Science
    Published by Oxford University Press
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  • 5
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    Computational recognition for long non-coding RNA (lncRNA): Software and databases (2017)
    Oxford University Press
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    Publication Date: 2017-01-10
    Description: Since the completion of the Human Genome Project, it has been widely established that most DNA is not transcribed into proteins. These non-protein-coding regions are believed to be moderators within transcriptional and post-transcriptional processes, which play key roles in the onset of diseases. Long non-coding RNAs (lncRNAs) are generally lacking in conserved motifs typically used for detection and thus hard to identify, but nonetheless present certain characteristic features that can be exploited by bioinformatics methods. By combining lncRNA detection with known miRNA, RNA-binding protein and chromatin interaction, current tools are able to recognize and functionally annotate large number of lncRNAs. This review discusses databases and platforms dedicated to cataloging and annotating lncRNAs, as well as tools geared at discovering novel sequences. We emphasize the issues posed by the diversity of lncRNAs and their complex interaction mechanisms, as well as technical issues such as lack of unified nomenclature. We hope that this wide overview of existing platforms and databases might help guide biologists toward the tools they need to analyze their experimental data, while our discussion of limitations and of current lncRNA-related methods may assist in the development of new computational tools.
    Print ISSN: 1467-5463
    Electronic ISSN: 1477-4054
    Topics: Biology , Computer Science
    Published by Oxford University Press
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  • 6
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    MTD: a mammalian transcriptomic database to explore gene expression and regulation (2017)
    Oxford University Press
    Details
    Publication Date: 2017-01-10
    Description: A systematic transcriptome survey is essential for the characterization and comprehension of the molecular basis underlying phenotypic variations. Recently developed RNA-seq methodology has facilitated efficient data acquisition and information mining of transcriptomes in multiple tissues/cell lines. Current mammalian transcriptomic databases are either tissue-specific or species-specific, and they lack in-depth comparative features across tissues and species. Here, we present a mammalian transcriptomic database (MTD) that is focused on mammalian transcriptomes, and the current version contains data from humans, mice, rats and pigs. Regarding the core features, the MTD browses genes based on their neighboring genomic coordinates or joint KEGG pathway and provides expression information on exons, transcripts and genes by integrating them into a genome browser. We developed a novel nomenclature for each transcript that considers its genomic position and transcriptional features. The MTD allows a flexible search of genes or isoforms with user-defined transcriptional characteristics and provides both table-based descriptions and associated visualizations. To elucidate the dynamics of gene expression regulation, the MTD also enables comparative transcriptomic analysis in both intraspecies and interspecies manner. The MTD thus constitutes a valuable resource for transcriptomic and evolutionary studies. The MTD is freely accessible at http://mtd.cbi.ac.cn .
    Print ISSN: 1467-5463
    Electronic ISSN: 1477-4054
    Topics: Biology , Computer Science
    Published by Oxford University Press
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  • 7
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    Model parameters of molecular evolution explain genomic correlations (2017)
    Oxford University Press
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    Publication Date: 2017-01-10
    Description: One long-standing research focus in evolutionary genomics is trying to resolve how biological variables (expression, essentiality, protein–protein interaction, structural stability, etc.) determine the rate of protein evolution. While these studies have considerably deepened our understanding of molecular evolution, many issues remain unsolved. In this opinion article, after having a brief survey of literatures, we establish relationships between model parameters of molecular evolution and genomic variables, based on which, most-observed genomic correlations and confounds can be explained by model parameter combinations under different conditions, which include the strength of stabilizing selection, mutational variance, expression sufficiency, gene pleiotropy, as well as the effective population size. We suggest that the problem to discern biological variable(s) that may determine the rate of protein evolution can be tackled at two levels. The first level, as discussed here, is to demonstrate how the model of molecular evolution can predict potential genomic correlations under various conditions. And the second level is to estimate genome-wide variations of model parameters (or combinations) that help to identify canonical biological variables that may underlie the rate variation among genes that ranges up to at least three magnitudes.
    Print ISSN: 1467-5463
    Electronic ISSN: 1477-4054
    Topics: Biology , Computer Science
    Published by Oxford University Press
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  • 8
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    Translational bioinformatics in the era of real-time biomedical, health care and wellness data streams (2017)
    Oxford University Press
    Details
    Publication Date: 2017-01-10
    Description: Monitoring and modeling biomedical, health care and wellness data from individuals and converging data on a population scale have tremendous potential to improve understanding of the transition to the healthy state of human physiology to disease setting. Wellness monitoring devices and companion software applications capable of generating alerts and sharing data with health care providers or social networks are now available. The accessibility and clinical utility of such data for disease or wellness research are currently limited. Designing methods for streaming data capture, real-time data aggregation, machine learning, predictive analytics and visualization solutions to integrate wellness or health monitoring data elements with the electronic medical records (EMRs) maintained by health care providers permits better utilization. Integration of population-scale biomedical, health care and wellness data would help to stratify patients for active health management and to understand clinically asymptomatic patients and underlying illness trajectories. In this article, we discuss various health-monitoring devices, their ability to capture the unique state of health represented in a patient and their application in individualized diagnostics, prognosis, clinical or wellness intervention. We also discuss examples of translational bioinformatics approaches to integrating patient-generated data with existing EMRs, personal health records, patient portals and clinical data repositories. Briefly, translational bioinformatics methods, tools and resources are at the center of these advances in implementing real-time biomedical and health care analytics in the clinical setting. Furthermore, these advances are poised to play a significant role in clinical decision-making and implementation of data-driven medicine and wellness care.
    Print ISSN: 1467-5463
    Electronic ISSN: 1477-4054
    Topics: Biology , Computer Science
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  • 9
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    The recent progress in proteochemometric modelling: focusing on target descriptors, cross-term descriptors and application scope (2017)
    Oxford University Press
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    Publication Date: 2017-01-10
    Description: As an extension of the conventional quantitative structure activity relationship models, proteochemometric (PCM) modelling is a computational method that can predict the bioactivity relations between multiple ligands and multiple targets. Traditional PCM modelling includes three essential elements: descriptors (including target descriptors, ligand descriptors and cross-term descriptors), bioactivity data and appropriate learning functions that link the descriptors to the bioactivity data. Since its appearance, PCM modelling has developed rapidly over the past decade by taking advantage of the progress of different descriptors and machine learning techniques, along with the increasing amounts of available bioactivity data. Specifically, the new emerging target descriptors and cross-term descriptors not only significantly increased the performance of PCM modelling but also expanded its application scope from traditional protein–ligand interaction to more abundant interactions, including protein–peptide, protein–DNA and even protein–protein interactions. In this review, target descriptors and cross-term descriptors, as well as the corresponding application scope, are intensively summarized. Additionally, we look forward to seeing PCM modelling extend into new application scopes, such as Target-Catalyst-Ligand systems, with the further development of descriptors, machine learning techniques and increasing amounts of available bioactivity data.
    Print ISSN: 1467-5463
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    Topics: Biology , Computer Science
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  • 10
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    Multi-hierarchical profiling: an emerging and quantitative approach to characterizing diverse biological networks (2017)
    Oxford University Press
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    Publication Date: 2017-01-10
    Description: Multi-hierarchical profiling may offer valuable insights into the structural stability and functional direction of biological networks in cellular development, pathological process and disease variation. Owing to the emergence of several new techniques, such as bioinformatics for omics data, structural biology and structural bioinformatics, the pace of network hierarchical research has accelerated a lot in recent years. Here, we discuss and compare the techniques available for quantifying multilevel hierarchies, with a focus on their features, capabilities and drawbacks when used for different applications. Then, we classify these methods into three types: topological spatial-scales, multilevel hierarchical control and feature ordering. We observe that challenges and limitations do exist in functional hierarchical identification. And, we also provide useful suggestions on how to analyze the dynamic data of complex network studies.
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    Topics: Biology , Computer Science
    Published by Oxford University Press
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