Reconstructing complex regions of genomes using long-read sequencing technology

Cataloged from PDF version of article.Obtaining high-quality sequence continuity of complex regions of recent segmental duplication remains one of the major challenges of finishing genome assemblies. In the human and mouse genomes, this was achieved by targeting large-insert clones using costly and laborious capillary-based sequencing approaches. Sanger shotgun sequencing of clone inserts, however, has now been largely abandoned, leaving most of these regions unresolved in newer genome assemblies generated primarily by next-generation sequencing hybrid approaches. Here we show that it is possible to resolve regions that are complex in a genome-wide context but simple in isolation for a fraction of the time and cost of traditional methods using long-read single molecule, real-time (SMRT) sequencing and assembly technology from Pacific Biosciences (PacBio). We sequenced and assembled BAC clones corresponding to a 1.3-Mbp complex region of chromosome 17q21.31, demonstrating 99.994% identity to Sanger assemblies of the same clones. We targeted 44 differences using Illumina sequencing and find that PacBio and Sanger assemblies share a comparable number of validated variants, albeit with different sequence context biases. Finally, we targeted a poorly assembled 766-kbp duplicated region of the chimpanzee genome and resolved the structure and organization for a fraction of the cost and time of traditional finishing approaches. Our data suggest a straightforward path for upgrading genomes to a higher quality finished state

Hypofractionated Stereotactic Ablative Radiotherapy for Recurrent or Oligometastatic Tumours in Children and Young Adults

AIMS Cancer remains a leading cause of death in children and adolescents in the developed world. Despite advances in oncological management, rates of primary treatment failure remain significant. Radiation of recurrent or metastatic disease improves survival in adults but there is little data to support clinical decision making in the paediatric/teenage and young adult population. Materials And Methods We present a retrospective case series of 14 patients treated with stereotactic ablative body radiotherapy or stereotactic radiosurgery at The Royal Marsden Hospital from September 2011 to December 2015. Eligible patients were aged <25 years, with Lansky/Karnofsky performance status ≥60 with confirmed relapsed or metastatic tumour in fewer than three sites. Follow-up was in accordance with standard clinical care and included regular outpatient review and radiological surveillance. Local control, progression-free survival and overall survival are presented. RESULTS Data for 14 patients with 18 treated lesions were included. The median patient age was 15 years (range 5–20 years). Nine patients were treated for local recurrence and five for metastatic lesions. All patients had already undergone multiple previous treatments. Eleven patients had undergone previous radiotherapy. The median interval between the completion of initial radiotherapy and reirradiation was 29.0 months (range 0.2–49.5 months). The median follow-up was 3.4 years (range 0.28–6.4 years). The 1-year local control rate was 78.6% and the 2-year local control rate was 57.1%. Overall median survival was 58.4 months (95% confidence interval 33.8–82.9 months). Cumulative biologically effective doses (BED) over 200 Gy were associated with late toxicity (P = 0.04). CONCLUSION Radical doses of short-course hypofractionated radiotherapy can achieve excellent local control and may contribute to the prolongation of overall survival. There is a need for prospective trials exploring the use of ablative radiotherapy in metastatic disease in paediatric/teenage and young adult patients in order to establish safe and effective treatment schedules

Sceptical Employees as CSR Ambassadors in Times of Financial Uncertainty

This chapter offers new insights into the understanding of internal (employee) perceptions of organizational corporate social responsibility (CSR) policies and strategies. This study explores the significance of employees’ involvement and scepticism upon CSR initiatives and focuses on the effects it may have upon word of mouth (WOM) and the development of employee–organisation relationships. Desk research introduces the research questions. Data for the research questions were gathered through a self-completion questionnaire distributed in a hardcopy form to the sample. An individual’s level of scepticism and involvement appears to affect the development of a positive effect on employees’ WOM. Involvement with the domain of the investment may be a central factor affecting relationship building within the organization, and upon generation of positive WOM. The chapter offers a conceptual framework to public relations (PR) and corporate communications practitioners, which may enrich their views and understanding of the use and value of CSR for communication strategies and practices. For-profit organisations are major institutions in today’s society. CSR is proffered as presenting advantages for (at macro level) society and (micro level) the organization and its employees. Concepts, such as involvement and scepticism, which have not been rigorously examined in PR and corporate communication literature, are addressed. By examining employee perceptions, managers and academic researchers gain insights into the acceptance, appreciation and effectiveness of CSR policies and activities upon the employee stakeholder group. This will affect current and future CSR communication strategies. The knowledge acquired from this chapter may be transferable outside the for-profit sector

Fetal pH and postnatal adaptation in preterm vaginal deliveries

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Recursive SVM Feature Selection and Sample Classification for Mass-Spectrometry and Microarray Data

Background: Like microarray-based investigations, high-throughput proteomics techniques require machine learning algorithms to identify biomarkers that are informative for biological classification problems. Feature selection and classification algorithms need to be robust to noise and outliers in the data. Results: We developed a recursive support vector machine (R-SVM) algorithm to select important genes/biomarkers for the classification of noisy data. We compared its performance to a similar, state-of-the-art method (SVM recursive feature elimination or SVM-RFE), paying special attention to the ability of recovering the true informative genes/biomarkers and the robustness to outliers in the data. Simulation experiments show that a 5 %-~20 % improvement over SVM-RFE can be achieved regard to these properties. The SVM-based methods are also compared with a conventional univariate method and their respective strengths and weaknesses are discussed. R-SVM was applied to two sets of SELDI-TOF-MS proteomics data, one from a human breast cancer study and the other from a study on rat liver cirrhosis. Important biomarkers found by the algorithm were validated by follow-up biological experiments. Conclusion: The proposed R-SVM method is suitable for analyzing noisy high-throughput proteomics and microarray data and it outperforms SVM-RFE in the robustness to noise and in the ability to recover informative features. The multivariate SVM-based method outperforms the univariate method in the classification performance, but univariate methods can reveal more of the differentially expressed features especially when there are correlations between the features.Statistic

Approximation of Stochastic Partial Differential Equations by a Kernel-based Collocation Method

In this paper we present the theoretical framework needed to justify the use of a kernel-based collocation method (meshfree approximation method) to estimate the solution of high-dimensional stochastic partial differential equations (SPDEs). Using an implicit time stepping scheme, we transform stochastic parabolic equations into stochastic elliptic equations. Our main attention is concentrated on the numerical solution of the elliptic equations at each time step. The estimator of the solution of the elliptic equations is given as a linear combination of reproducing kernels derived from the differential and boundary operators of the SPDE centered at collocation points to be chosen by the user. The random expansion coefficients are computed by solving a random system of linear equations. Numerical experiments demonstrate the feasibility of the method.Comment: Updated Version in International Journal of Computer Mathematics, Closed to Ye's Doctoral Thesi

Tandem application of C-C bond-forming reactions with reductive ozonolysis

Several variants of reductive ozonolysis, defined here as the in situ generation of aldehydes or ketones during ozonolytic cleavage of alkenes, are demonstrated to work effectively in tandem with a number of C-C bond-forming reactions. For reactions involving basic nucleophiles (1,2- addition of Grignard reagents, Wittig or Horner-Emmons olefinations, and directed Aldol reactions of lithium enolates) the one-pot process offers a rapid and high-yielding alternative to traditional two-step protocols

Structure-guided design and optimization of small molecules targeting the protein-protein interaction between the von hippel-lindau (VHL) E3 ubiquitin ligase and the hypoxia inducible factor (HIF) alpha subunit with in vitro nanomolar affinities

E3 ubiquitin ligases are attractive targets in the ubiquitin-proteasome system, however, the development of small-molecule ligands has been rewarded with limited success. The von Hippel-Lindau protein (pVHL) is the substrate recognition subunit of the VHL E3 ligase that targets HIF-1α for degradation. We recently reported inhibitors of the pVHL:HIF-1α interaction, however they exhibited moderate potency. Herein, we report the design and optimization, guided by X-ray crystal structures, of a ligand series with nanomolar binding affinities

MCL-CAw: A refinement of MCL for detecting yeast complexes from weighted PPI networks by incorporating core-attachment structure

Abstract Background The reconstruction of protein complexes from the physical interactome of organisms serves as a building block towards understanding the higher level organization of the cell. Over the past few years, several independent high-throughput experiments have helped to catalogue enormous amount of physical protein interaction data from organisms such as yeast. However, these individual datasets show lack of correlation with each other and also contain substantial number of false positives (noise). Over these years, several affinity scoring schemes have also been devised to improve the qualities of these datasets. Therefore, the challenge now is to detect meaningful as well as novel complexes from protein interaction (PPI) networks derived by combining datasets from multiple sources and by making use of these affinity scoring schemes. In the attempt towards tackling this challenge, the Markov Clustering algorithm (MCL) has proved to be a popular and reasonably successful method, mainly due to its scalability, robustness, and ability to work on scored (weighted) networks. However, MCL produces many noisy clusters, which either do not match known complexes or have additional proteins that reduce the accuracies of correctly predicted complexes. Results Inspired by recent experimental observations by Gavin and colleagues on the modularity structure in yeast complexes and the distinctive properties of "core" and "attachment" proteins, we develop a core-attachment based refinement method coupled to MCL for reconstruction of yeast complexes from scored (weighted) PPI networks. We combine physical interactions from two recent "pull-down" experiments to generate an unscored PPI network. We then score this network using available affinity scoring schemes to generate multiple scored PPI networks. The evaluation of our method (called MCL-CAw) on these networks shows that: (i) MCL-CAw derives larger number of yeast complexes and with better accuracies than MCL, particularly in the presence of natural noise; (ii) Affinity scoring can effectively reduce the impact of noise on MCL-CAw and thereby improve the quality (precision and recall) of its predicted complexes; (iii) MCL-CAw responds well to most available scoring schemes. We discuss several instances where MCL-CAw was successful in deriving meaningful complexes, and where it missed a few proteins or whole complexes due to affinity scoring of the networks. We compare MCL-CAw with several recent complex detection algorithms on unscored and scored networks, and assess the relative performance of the algorithms on these networks. Further, we study the impact of augmenting physical datasets with computationally inferred interactions for complex detection. Finally, we analyse the essentiality of proteins within predicted complexes to understand a possible correlation between protein essentiality and their ability to form complexes. Conclusions We demonstrate that core-attachment based refinement in MCL-CAw improves the predictions of MCL on yeast PPI networks. We show that affinity scoring improves the performance of MCL-CAw.http://deepblue.lib.umich.edu/bitstream/2027.42/78256/1/1471-2105-11-504.xmlhttp://deepblue.lib.umich.edu/bitstream/2027.42/78256/2/1471-2105-11-504-S1.PDFhttp://deepblue.lib.umich.edu/bitstream/2027.42/78256/3/1471-2105-11-504-S2.ZIPhttp://deepblue.lib.umich.edu/bitstream/2027.42/78256/4/1471-2105-11-504.pdfPeer Reviewe