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  • Articles  (81,203)
  • Oxford University Press  (79,222)
  • Berkeley Electronic Press (now: De Gruyter)
  • Medicine  (59,190)
  • Economics  (22,071)
  • Political Science  (519)
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  • 1
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    The Power of Word-Frequency Based Alignment-Free Functions: a Comprehensive Large-Scale Experimental Analysis (2021)
    Oxford University Press
    Details
    Publication Date: 2021-10-30
    Description: Motivation Alignment-free (AF) distance/similarity functions are a key tool for sequence analysis. Experimental studies on real datasets abound and, to some extent, there are also studies regarding their control of false positive rate (Type I error). However, assessment of their power, i.e., their ability to identify true similarity, has been limited to some members of the D2 family. The corresponding experimental studies have concentrated on short sequences, a scenario no longer adequate for current applications, where sequence lengths may vary considerably. Such a State of the Art is methodologically problematic, since information regarding a key feature such as power is either missing or limited. Results By concentrating on a representative set of word-frequency based AF functions, we perform the first coherent and uniform evaluation of the power, involving also Type I error for completeness. Two Alternative models of important genomic features (CIS Regulatory Modules and Horizontal Gene Transfer), a wide range of sequence lengths from a few thousand to millions, and different values of k have been used. As a result, we provide a characterization of those AF functions that is novel and informative. Indeed, we identify weak and strong points of each function considered, which may be used as a guide to choose one for analysis tasks. Remarkably, of the fifteen functions that we have considered, only four stand out, with small differences between small and short sequence length scenarios. Finally, in order to encourage the use of our methodology for validation of future AF functions, the Big Data platform supporting it is public. Availability The software is available at: https://github.com/pipp8/power_statistics Supplementary information Supplementary data are available at Bioinformatics online.
    Print ISSN: 1367-4803
    Electronic ISSN: 1460-2059
    Topics: Biology , Computer Science , Medicine
    Published by Oxford University Press
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  • 2
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    CoCoPRED: coiled-coil protein structural feature prediction from amino acid sequence using deep neural networks (2021)
    Oxford University Press
    Details
    Publication Date: 2021-10-30
    Description: Motivation Coiled-coil is composed of two or more helices that are wound around each other. It widely exists in proteins and has been discovered to play a variety of critical roles in biology processes. Generally, there are three types of structural features in coiled-coil: coiled-coil domain (CCD), oligomeric state, and register. However, most of the existing computational tools only focus on one of them. Results Here, we describe a new deep learning model, CoCoPRED, which is based on convolutional layers, bidirectional long short-term memory, and attention mechanism. It has three networks, i.e., CCD network, oligomeric state network, and register network, corresponding to the three types of structural features in coiled-coil. This means CoCoPRED has the ability of fulfilling comprehensive prediction for coiled-coil proteins. Through the 5-fold cross-validation experiment, we demonstrate that CoCoPRED can achieve better performance than the state-of-the-art models on both CCD prediction and oligomeric state prediction. Further analysis suggests the CCD prediction may be a performance indicator of the oligomeric state prediction in CoCoPRED. The attention heads in CoCoPRED indicate that registers a, b, and e are more crucial for the oligomeric state prediction. Availability CoCoPRED is available at http://www.csbio.sjtu.edu.cn/bioinf/CoCoPRED. Supplementary information Supplementary data are available at Bioinformatics online.
    Print ISSN: 1367-4803
    Electronic ISSN: 1460-2059
    Topics: Biology , Computer Science , Medicine
    Published by Oxford University Press
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  • 3
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    AOP-helpFinder webserver: a tool for comprehensive analysis of the literature to support adverse outcome pathways development (2021)
    Oxford University Press
    Details
    Publication Date: 2021-10-30
    Description: Motivation Adverse Outcome Pathways (AOPs) are a conceptual framework developed to support the use of alternative toxicology approaches in the risk assessment. AOPs are structured linear organizations of existing knowledge illustrating causal pathways from the initial molecular perturbation triggered by various stressors, through key events (KEs) at different levels of biology, to the ultimate health or ecotoxicological adverse outcome. Results Artificial intelligence can be used to systematically explore available toxicological data that can be parsed in the scientific literature. Recently a tool called AOP-helpFinder was developed to identify associations between stressors and KEs supporting thus documentation of AOPs. To facilitate the utilization of this advanced bioinformatics tool by the scientific and the regulatory community, a webserver was created. The proposed AOP-helpFinder webserver uses better performing version of the tool which reduces the need for manual curation of the obtained results. As an example, the server was successfully applied to explore relationships of a set of endocrine disruptors with metabolic-related events. The AOP-helpFinder webserver assists in a rapid evaluation of existing knowledge stored in the PubMed database, a global resource of scientific information, to build AOPs and Adverse Outcome Networks (AONs) supporting the chemical risk assessment. Availability and implementation AOP-helpFinder is available at http://aop-helpfinder.u-paris-sciences.fr/index.php
    Print ISSN: 1367-4803
    Electronic ISSN: 1460-2059
    Topics: Biology , Computer Science , Medicine
    Published by Oxford University Press
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  • 4
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    Importance-Penalized Joint Graphical Lasso (IPJGL): differential network inference via GGMs (2021)
    Oxford University Press
    Details
    Publication Date: 2021-10-30
    Description: Motivation Differential network inference is a fundamental and challenging problem to reveal gene interactions and regulation relationships under different conditions. Many algorithms have been developed for this problem; however, they do not consider the differences between the importance of genes, which may not fit the real-world situation. Different genes have different mutation probabilities, and the vital genes associated with basic life activities have less fault tolerance to mutation. Equally treating all genes may bias the results of differential network inference. Thus, it is necessary to consider the importance of genes in the models of differential network inference. Results Based on the Gaussian graphical model with adaptive gene importance regularization, we develop a novel importance-penalized joint graphical Lasso method, IPJGL, for differential network inference. The presented method is validated by the simulation experiments as well as the real datasets. Furthermore, to precisely evaluate the results of differential network inference, we propose a new metric named APC2 for the differential levels of gene pairs. We apply IPJGL to analyze the TCGA colorectal and breast cancer datasets and find some candidate cancer genes with significant survival analysis results, including SOST for colorectal cancer and RBBP8 for breast cancer. We also conduct further analysis based on the interactions in the Reactome database and confirm the utility of our method. Availability R source code of importance-penalized joint graphical lasso is freely available at https://github.com/Wu-Lab/IPJGL. Supplementary information Supplementary materials are available at Bioinformatics online.
    Print ISSN: 1367-4803
    Electronic ISSN: 1460-2059
    Topics: Biology , Computer Science , Medicine
    Published by Oxford University Press
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  • 5
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    Predictors of Pain Reduction in Trials of Interventions for Aromatase Inhibitor-Associated Musculoskeletal Symptoms (2021)
    Oxford University Press
    Details
    Publication Date: 2021-10-30
    Description: Background Almost half of aromatase inhibitor (AI)-treated breast cancer patients experience AI-associated musculoskeletal symptoms (AIMSS); 20-30% discontinue treatment because of severe symptoms. We hypothesized that we could identify predictors of pain reduction in AIMSS intervention trials by combining data from previously conducted trials. Methods We pooled patient-level data from 3 randomized trials testing interventions (omega-3 fatty acids, acupuncture, and duloxetine) for AIMSS that had similar eligibility criteria and the same patient-reported outcome measures. Only patients with baseline Brief Pain Inventory (BPI) average pain score of ≥ 4 of 10 were included. The primary outcome examined was 2-point reduction in average pain from baseline to week 12. Variable cut-point selection and logistic regression were used. Risk models were built by summing the number of factors statistically significantly associated with pain reduction. Analyses were stratified by study and adjusted for treatment arm. Results For the 583 analyzed patients, the four factors statistically significantly associated with pain reduction were FACT Functional Well-Being 〉24 and Physical Well-Being 〉14 (higher scores reflect better function), and WOMAC
    Electronic ISSN: 2515-5091
    Topics: Chemistry and Pharmacology , Medicine
    Published by Oxford University Press on behalf of National Cancer Institute.
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  • 6
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    Eureka! A Petroleum Transactions Guide that Delivers (2021)
    Oxford University Press
    Details
    Publication Date: 2021-10-30
    Print ISSN: 1754-9957
    Electronic ISSN: 1754-9965
    Topics: Energy, Environment Protection, Nuclear Power Engineering , Law , Economics
    Published by Oxford University Press
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  • 7
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    Local border reforms and economic activity (2021)
    Oxford University Press
    Details
    Publication Date: 2021-10-30
    Description: In this article, we make use of large-scale municipal border changes in Germany to provide the first evidence on the effect of local border changes on the distribution of activity in space. To allow for a comparison of economic activity within unique geographical units over time, we use geo-coded light data as well as local land-use data. Applying a difference-in-differences approach, we find evidence that municipalities absorbing their merger partners and hosting the new administrative center experience a significant increase in local activity, while the municipalities that are being absorbed and are losing the administrative center experience a decrease in such activity. The difference between the gains in activity from absorbing municipalities and the losses from absorbed ones is positive. These previously undocumented results point to the importance of distance to the administrative center as a determinant of the spatial distribution of economic activity.
    Print ISSN: 1468-2702
    Electronic ISSN: 1468-2710
    Topics: Geography , Economics
    Published by Oxford University Press
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  • 8
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    Rival guests or defiant hosts? The local economic impact of hosting refugees (2021)
    Oxford University Press
    Details
    Publication Date: 2021-10-28
    Description: This article investigates the local economic cost of hosting refugees. Using administrative data in France, we show that the opening of small housing centers for refugees decreases the economic activity in hosting municipalities. We demonstrate that this downturn is related to a decline in the population by around 2% due to fewer people moving to hosting municipalities. We show that this avoidance behavior of natives results from prejudices, and is unlikely to be driven by a labor market supply shock from the arrival of refugees. We also estimate the aggregate cost of hosting refugees.
    Print ISSN: 1468-2702
    Electronic ISSN: 1468-2710
    Topics: Geography , Economics
    Published by Oxford University Press
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  • 9
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    Translation of MT-ATP6 pathogenic variants reveals distinct regulatory consequences from the co-translational quality control of mitochondrial protein synthesis (2021)
    Oxford University Press
    Details
    Publication Date: 2021-10-28
    Description: Pathogenic variants that disrupt human mitochondrial protein synthesis are associated with a clinically heterogenous group of diseases. Despite an impairment in oxidative phosphorylation being a common phenotype, the underlying molecular pathogenesis is more complex than simply a bioenergetic deficiency. Currently, we have limited mechanistic understanding on the scope by which a primary defect in mitochondrial protein synthesis contributes to organelle dysfunction. Since the proteins encoded in the mitochondrial genome are hydrophobic and need co-translational insertion into a lipid bilayer, responsive quality control mechanisms are required to resolve aberrations that arise with the synthesis of truncated and misfolded proteins. Here, we show that defects in the OXA1L-mediated insertion of MT-ATP6 nascent chains into the mitochondrial inner membrane are rapidly resolved by the AFG3L2 protease complex. Using pathogenic MT-ATP6 variants, we then reveal discrete steps in this quality control mechanism and the differential functional consequences to mitochondrial gene expression. The inherent ability of a given cell type to recognize and resolve impairments in mitochondrial protein synthesis may in part contribute at the molecular level to the wide clinical spectrum of these disorders.
    Print ISSN: 0964-6906
    Electronic ISSN: 1460-2083
    Topics: Biology , Medicine
    Published by Oxford University Press
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  • 10
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    LocalNgsRelate: a software tool for inferring IBD sharing along the genome between pairs of individuals from low-depth NGS data (2021)
    Oxford University Press
    Details
    Publication Date: 2021-10-28
    Description: Motivation Inference of Identity-by-descent (IBD) sharing along the genome between pairs of individuals has important uses. But all existing inference methods are based on genotypes, which is not ideal for low-depth Next Generation Sequencing (NGS) data from which genotypes can only be called with high uncertainty. Results We present a new probabilistic software tool, LocalNgsRelate, for inferring IBD sharing along the genome between pairs of individuals from low-depth NGS data. Its inference is based on genotype likelihoods instead of genotypes, and thereby it takes the uncertainty of the genotype calling into account. Using real data from the 1000 Genomes project, we show that LocalNgsRelate provides more accurate IBD inference for low-depth NGS data than two state-of-the-art genotype based methods, Albrechtsen et al. (2009) and hap-IBD. We also show that the method works well for NGS data down to a depth of 2X. Availability LocalNgsRelate is freely available at https://github.com/idamoltke/LocalNgsRelate Supplementary Data Supplementary data are available at Bioinformatics online.
    Print ISSN: 1367-4803
    Electronic ISSN: 1460-2059
    Topics: Biology , Computer Science , Medicine
    Published by Oxford University Press
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