共查询到20条相似文献,搜索用时 15 毫秒
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Nicholas J. Bond Albert Koulman Julian L. Griffin Zoe Hall 《Metabolomics : Official journal of the Metabolomic Society》2017,13(11):128
Introduction
Mass spectrometry imaging (MSI) experiments result in complex multi-dimensional datasets, which require specialist data analysis tools.Objectives
We have developed massPix—an R package for analysing and interpreting data from MSI of lipids in tissue.Methods
massPix produces single ion images, performs multivariate statistics and provides putative lipid annotations based on accurate mass matching against generated lipid libraries.Results
Classification of tissue regions with high spectral similarly can be carried out by principal components analysis (PCA) or k-means clustering.Conclusion
massPix is an open-source tool for the analysis and statistical interpretation of MSI data, and is particularly useful for lipidomics applications.4.
Alexey А. Moskalev Anna V. Kudryavtseva Alexander S. Graphodatsky Violetta R. Beklemisheva Natalya A. Serdyukova Konstantin V. Krutovsky Ivan V. Kulakovskiy Andrey S. Lando Artem S. Kasianov Dmitry A. Kuzmin Yuliya A. Putintseva Sergey I. Feranchuk Mikhail V. Shaposhnikov Vadim E. Fraifeld Dmitri Toren Anastasia V. Snezhkina Vasily V. Sitnik 《BMC evolutionary biology》2017,17(2):258
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Background
Inference of gene networks from expression data is an important problem in computational biology. Many algorithms have been proposed for solving the problem efficiently. However, many of the available implementations are programming libraries that require users to write code, which limits their accessibility.Results
We have developed a tool called CyNetworkBMA for inferring gene networks from expression data that integrates with Cytoscape. Our application offers a graphical user interface for networkBMA, an efficient implementation of Bayesian Model Averaging methods for network construction. The client-server architecture of CyNetworkBMA makes it possible to distribute or centralize computation depending on user needs.Conclusions
CyNetworkBMA is an easy-to-use tool that makes network inference accessible to non-programmers through seamless integration with Cytoscape. CyNetworkBMA is available on the Cytoscape App Store at http://apps.cytoscape.org/apps/cynetworkbma.6.
Thijs Welle Anna T. Hoekstra Ineke A. J. J. M. Daemen Celia R. Berkers Matheus O. Costa 《Metabolomics : Official journal of the Metabolomic Society》2017,13(7):83
Introduction
Swine dysentery caused by Brachyspira hyodysenteriae is a production limiting disease in pig farming. Currently antimicrobial therapy is the only treatment and control method available.Objective
The aim of this study was to characterize the metabolic response of porcine colon explants to infection by B. hyodysenteriae.Methods
Porcine colon explants exposed to B. hyodysenteriae were analyzed for histopathological, metabolic and pro-inflammatory gene expression changes.Results
Significant epithelial necrosis, increased levels of l-citrulline and IL-1α were observed on explants infected with B. hyodysenteriae.Conclusions
The spirochete induces necrosis in vitro likely through an inflammatory process mediated by IL-1α and NO.7.
Lili Wei Leixi Cao Yanyan Miao Shuju Wu Shumei Xu Ruisheng Wang Jun Du Aihua Liang Yuejun Fu 《Biotechnology letters》2017,39(8):1129-1139
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Rachel A. Spicer Christoph Steinbeck 《Metabolomics : Official journal of the Metabolomic Society》2018,14(1):16
Introduction
Data sharing is being increasingly required by journals and has been heralded as a solution to the ‘replication crisis’.Objectives
(i) Review data sharing policies of journals publishing the most metabolomics papers associated with open data and (ii) compare these journals’ policies to those that publish the most metabolomics papers.Methods
A PubMed search was used to identify metabolomics papers. Metabolomics data repositories were manually searched for linked publications.Results
Journals that support data sharing are not necessarily those with the most papers associated to open metabolomics data.Conclusion
Further efforts are required to improve data sharing in metabolomics.10.
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Justin Y. Lee Mark P. Styczynski 《Metabolomics : Official journal of the Metabolomic Society》2018,14(12):153
Introduction
A common problem in metabolomics data analysis is the existence of a substantial number of missing values, which can complicate, bias, or even prevent certain downstream analyses. One of the most widely-used solutions to this problem is imputation of missing values using a k-nearest neighbors (kNN) algorithm to estimate missing metabolite abundances. kNN implicitly assumes that missing values are uniformly distributed at random in the dataset, but this is typically not true in metabolomics, where many values are missing because they are below the limit of detection of the analytical instrumentation.Objectives
Here, we explore the impact of nonuniformly distributed missing values (missing not at random, or MNAR) on imputation performance. We present a new model for generating synthetic missing data and a new algorithm, No-Skip kNN (NS-kNN), that accounts for MNAR values to provide more accurate imputations.Methods
We compare the imputation errors of the original kNN algorithm using two distance metrics, NS-kNN, and a recently developed algorithm KNN-TN, when applied to multiple experimental datasets with different types and levels of missing data.Results
Our results show that NS-kNN typically outperforms kNN when at least 20–30% of missing values in a dataset are MNAR. NS-kNN also has lower imputation errors than KNN-TN on realistic datasets when at least 50% of missing values are MNAR.Conclusion
Accounting for the nonuniform distribution of missing values in metabolomics data can significantly improve the results of imputation algorithms. The NS-kNN method imputes missing metabolomics data more accurately than existing kNN-based approaches when used on realistic datasets.12.
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Background
Structural variations (SVs) are wide-spread in human genomes and may have important implications in disease-related and evolutionary studies. High-throughput sequencing (HTS) has become a major platform for SV detection and simulation serves as a powerful and cost-effective approach for benchmarking SV detection algorithms. Accurate performance assessment by simulation requires the simulator capable of generating simulation data with all important features of real data, such GC biases in HTS data and various complexities in tumor data. However, no available package has systematically addressed all issues in data simulation for SV benchmarking.Results
Pysim-sv is a package for simulating HTS data to evaluate performance of SV detection algorithms. Pysim-sv can introduce a wide spectrum of germline and somatic genomic variations. The package contains functionalities to simulate tumor data with aneuploidy and heterogeneous subclones, which is very useful in assessing algorithm performance in tumor studies. Furthermore, Pysim-sv can introduce GC-bias, the most important and prevalent bias in HTS data, in the simulated HTS data.Conclusions
Pysim-sv provides an unbiased toolkit for evaluating HTS-based SV detection algorithms.14.
Introduction
Untargeted metabolomics is a powerful tool for biological discoveries. To analyze the complex raw data, significant advances in computational approaches have been made, yet it is not clear how exhaustive and reliable the data analysis results are.Objectives
Assessment of the quality of raw data processing in untargeted metabolomics.Methods
Five published untargeted metabolomics studies, were reanalyzed.Results
Omissions of at least 50 relevant compounds from the original results as well as examples of representative mistakes were reported for each study.Conclusion
Incomplete raw data processing shows unexplored potential of current and legacy data.15.
Background
One of the recent challenges of computational biology is development of new algorithms, tools and software to facilitate predictive modeling of big data generated by high-throughput technologies in biomedical research.Results
To meet these demands we developed PROPER - a package for visual evaluation of ranking classifiers for biological big data mining studies in the MATLAB environment.Conclusion
PROPER is an efficient tool for optimization and comparison of ranking classifiers, providing over 20 different two- and three-dimensional performance curves.16.
Korey J. Brownstein Mahmoud Gargouri William R. Folk David R. Gang 《Metabolomics : Official journal of the Metabolomic Society》2017,13(11):133
Introduction
Botanicals containing iridoid and phenylethanoid/phenylpropanoid glycosides are used worldwide for the treatment of inflammatory musculoskeletal conditions that are primary causes of human years lived with disability, such as arthritis and lower back pain.Objectives
We report the analysis of candidate anti-inflammatory metabolites of several endemic Scrophularia species and Verbascum thapsus used medicinally by peoples of North America.Methods
Leaves, stems, and roots were analyzed by ultra-performance liquid chromatography-mass spectrometry (UPLC-MS) and partial least squares-discriminant analysis (PLS-DA) was performed in MetaboAnalyst 3.0 after processing the datasets in Progenesis QI.Results
Comparison of the datasets revealed significant and differential accumulation of iridoid and phenylethanoid/phenylpropanoid glycosides in the tissues of the endemic Scrophularia species and Verbascum thapsus.Conclusions
Our investigation identified several species of pharmacological interest as good sources for harpagoside and other important anti-inflammatory metabolites.17.
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Renato de Souza Pinto Lemgruber Kaspar Valgepea Mark P. Hodson Ryan Tappel Sean D. Simpson Michael Köpke Lars K. Nielsen Esteban Marcellin 《Metabolomics : Official journal of the Metabolomic Society》2018,14(3):35
Introduction
Quantification of tetrahydrofolates (THFs), important metabolites in the Wood–Ljungdahl pathway (WLP) of acetogens, is challenging given their sensitivity to oxygen.Objective
To develop a simple anaerobic protocol to enable reliable THFs quantification from bioreactors.Methods
Anaerobic cultures were mixed with anaerobic acetonitrile for extraction. Targeted LC–MS/MS was used for quantification.Results
Tetrahydrofolates can only be quantified if sampled anaerobically. THF levels showed a strong correlation to acetyl-CoA, the end product of the WLP.Conclusion
Our method is useful for relative quantification of THFs across different growth conditions. Absolute quantification of THFs requires the use of labelled standards.19.
Lisa?M?Connor Jacob?E?Kohlmeier Lynn?Ryan Alan?D?Roberts Tres?Cookenham Marcia?A?Blackman David?L?Woodland
Background
Virus-specific memory CD8+ T cells persist long after infection is resolved and are important for mediating recall responses to secondary infection. Although the number of memory T cells remains relatively constant over time, little is known about the overall stability of the memory T cell pool, particularly with respect to T cell clonal diversity. In this study we developed a novel assay to measure the composition of the memory T cell pool in large cohorts of mice over time following respiratory virus infection.Results
We find that the clonal composition of the virus-specific memory CD8+ T cell pool begins to change within months of the initial infection. These early clonal perturbations eventually result in large clonal expansions that have been associated with ageing.Conclusions
Maintenance of clonal diversity is important for effective long-term memory responses and dysregulation of the memory response begins early after infection.20.
Arianna Filntisi Charalambos Fotakis Pantelis Asvestas George K. Matsopoulos Panagiotis Zoumpoulakis Dionisis Cavouras 《Metabolomics : Official journal of the Metabolomic Society》2017,13(12):146