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1.
Background
Mathematical modeling has achieved a broad interest in the field of biology. These models represent the associations among the metabolism of the biological phenomenon with some mathematical equations such that the observed time course profile of the biological data fits the model. However, the estimation of the unknown parameters of the model is a challenging task. Many algorithms have been developed for parameter estimation, but none of them is entirely capable of finding the best solution. The purpose of this paper is to develop a method for precise estimation of parameters of a biological model.Methods
In this paper, a novel particle swarm optimization algorithm based on a decomposition technique is developed. Then, its root mean square error is compared with simple particle swarm optimization, Iterative Unscented Kalman Filter and Simulated Annealing algorithms for two different simulation scenarios and a real data set related to the metabolism of CAD system.Results
Our proposed algorithm results in 54.39% and 26.72% average reduction in root mean square error when applied to the simulation and experimental data, respectively.Conclusion
The results show that the metaheuristic approaches such as the proposed method are very wise choices for finding the solution of nonlinear problems with many unknown parameters.2.
Xia Xiaoxuan Weng Haoyi Men Ruoting Sun Rui Zee Benny Chung Ying Chong Ka Chun Wang Maggie Haitian 《BMC genetics》2018,19(1):67-37
Background
Association studies using a single type of omics data have been successful in identifying disease-associated genetic markers, but the underlying mechanisms are unaddressed. To provide a possible explanation of how these genetic factors affect the disease phenotype, integration of multiple omics data is needed.Results
We propose a novel method, LIPID (likelihood inference proposal for indirect estimation), that uses both single nucleotide polymorphism (SNP) and DNA methylation data jointly to analyze the association between a trait and SNPs. The total effect of SNPs is decomposed into direct and indirect effects, where the indirect effects are the focus of our investigation. Simulation studies show that LIPID performs better in various scenarios than existing methods. Application to the GAW20 data also leads to encouraging results, as the genes identified appear to be biologically relevant to the phenotype studied.Conclusions
The proposed LIPID method is shown to be meritorious in extensive simulations and in real-data analyses.3.
Nadine Strehmel David Strunk Veronika Strehmel 《Metabolomics : Official journal of the Metabolomic Society》2017,13(11):135
Introduction
Aqueous–methanol mixtures have successfully been applied to extract a broad range of metabolites from plant tissue. However, a certain amount of material remains insoluble.Objectives
To enlarge the metabolic compendium, two ionic liquids were selected to extract the methanol insoluble part of trunk from Betula pendula.Methods
The extracted compounds were analyzed by LC/MS and GC/MS.Results
The results show that 1-butyl-3-methylimidazolium acetate (IL-Ac) predominantly resulted in fatty acids, whereas 1-ethyl-3-methylimidazolium tosylate (IL-Tos) mostly yielded phenolic structures. Interestingly, bark yielded more ionic liquid soluble metabolites compared to interior wood.Conclusion
From this one can conclude that the application of ionic liquids may expand the metabolic snapshot.4.
Background
Time course measurement of single molecules on a cell surface provides detailed information about the dynamics of the molecules that would otherwise be inaccessible. To extract the quantitative information, single particle tracking (SPT) is typically performed. However, trajectories extracted by SPT inevitably have linking errors when the diffusion speed of single molecules is high compared to the scale of the particle density.Methods
To circumvent this problem, we develop an algorithm to estimate diffusion constants without relying on SPT. The proposed algorithm is based on a probabilistic model of the distance to the nearest point in subsequent frames. This probabilistic model generalizes the model of single particle Brownian motion under an isolated environment into the one surrounded by indistinguishable multiple particles, with a mean field approximation.Results
We demonstrate that the proposed algorithm provides reasonable estimation of diffusion constants, even when other methods suffer due to high particle density or inhomogeneous particle distribution. In addition, our algorithm can be used for visualization of time course data from single molecular measurements.Conclusions
The proposed algorithm based on the probabilistic model of indistinguishable Brownian particles provide accurate estimation of diffusion constants even in the regime where the traditional SPT methods underestimate them due to linking errors.5.
Background
Studies of intrinsically disordered proteins that lack a stable tertiary structure but still have important biological functions critically rely on computational methods that predict this property based on sequence information. Although a number of fairly successful models for prediction of protein disorder have been developed over the last decade, the quality of their predictions is limited by available cases of confirmed disorders.Results
To more reliably estimate protein disorder from protein sequences, an iterative algorithm is proposed that integrates predictions of multiple disorder models without relying on any protein sequences with confirmed disorder annotation. The iterative method alternately provides the maximum a posterior (MAP) estimation of disorder prediction and the maximum-likelihood (ML) estimation of quality of multiple disorder predictors. Experiments on data used at CASP7, CASP8, and CASP9 have shown the effectiveness of the proposed algorithm.Conclusions
The proposed algorithm can potentially be used to predict protein disorder and provide helpful suggestions on choosing suitable disorder predictors for unknown protein sequences.6.
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.7.
N. Cesbron A.-L. Royer Y. Guitton A. Sydor B. Le Bizec G. Dervilly-Pinel 《Metabolomics : Official journal of the Metabolomic Society》2017,13(8):99
Introduction
Collecting feces is easy. It offers direct outcome to endogenous and microbial metabolites.Objectives
In a context of lack of consensus about fecal sample preparation, especially in animal species, we developed a robust protocol allowing untargeted LC-HRMS fingerprinting.Methods
The conditions of extraction (quantity, preparation, solvents, dilutions) were investigated in bovine feces.Results
A rapid and simple protocol involving feces extraction with methanol (1/3, M/V) followed by centrifugation and a step filtration (10 kDa) was developed.Conclusion
The workflow generated repeatable and informative fingerprints for robust metabolome characterization.8.
Background
Integrative analysis on multi-omics data has gained much attention recently. To investigate the interactive effect of gene expression and DNA methylation on cancer, we propose a directed random walk-based approach on an integrated gene-gene graph that is guided by pathway information.Methods
Our approach first extracts a single pathway profile matrix out of the gene expression and DNA methylation data by performing the random walk over the integrated graph. We then apply a denoising autoencoder to the pathway profile to further identify important pathway features and genes. The extracted features are validated in the survival prediction task for breast cancer patients.Results
The results show that the proposed method substantially improves the survival prediction performance compared to that of other pathway-based prediction methods, revealing that the combined effect of gene expression and methylation data is well reflected in the integrated gene-gene graph combined with pathway information. Furthermore, we show that our joint analysis on the methylation features and gene expression profile identifies cancer-specific pathways with genes related to breast cancer.Conclusions
In this study, we proposed a DRW-based method on an integrated gene-gene graph with expression and methylation profiles in order to utilize the interactions between them. The results showed that the constructed integrated gene-gene graph can successfully reflect the combined effect of methylation features on gene expression profiles. We also found that the selected features by DA can effectively extract topologically important pathways and genes specifically related to breast cancer.9.
10.
A streamlined method for analysing genome-wide DNA methylation patterns from low amounts of FFPE DNA
Jackie L. Ludgate James Wright Peter A. Stockwell Ian M. Morison Michael R. Eccles Aniruddha Chatterjee 《BMC medical genomics》2017,10(1):54
Background
Formalin fixed paraffin embedded (FFPE) tumor samples are a major source of DNA from patients in cancer research. However, FFPE is a challenging material to work with due to macromolecular fragmentation and nucleic acid crosslinking. FFPE tissue particularly possesses challenges for methylation analysis and for preparing sequencing-based libraries relying on bisulfite conversion. Successful bisulfite conversion is a key requirement for sequencing-based methylation analysis.Methods
Here we describe a complete and streamlined workflow for preparing next generation sequencing libraries for methylation analysis from FFPE tissues. This includes, counting cells from FFPE blocks and extracting DNA from FFPE slides, testing bisulfite conversion efficiency with a polymerase chain reaction (PCR) based test, preparing reduced representation bisulfite sequencing libraries and massively parallel sequencing.Results
The main features and advantages of this protocol are:
- An optimized method for extracting good quality DNA from FFPE tissues.
- An efficient bisulfite conversion and next generation sequencing library preparation protocol that uses 50 ng DNA from FFPE tissue.
- Incorporation of a PCR-based test to assess bisulfite conversion efficiency prior to sequencing.
Conclusions
We provide a complete workflow and an integrated protocol for performing DNA methylation analysis at the genome-scale and we believe this will facilitate clinical epigenetic research that involves the use of FFPE tissue.11.
Chia-Chi Wang Yuan-Chung Lin Syu-Ruei Jhang Chun-Wei Tung 《Biomedical engineering online》2017,16(1):66
Background
The immunotoxicity of engine exhausts is of high concern to human health due to the increasing prevalence of immune-related diseases. However, the evaluation of immunotoxicity of engine exhausts is currently based on expensive and time-consuming experiments. It is desirable to develop efficient methods for immunotoxicity assessment.Methods
To accelerate the development of safe alternative fuels, this study proposed a computational method for identifying informative features for predicting proinflammatory potentials of engine exhausts. A principal component regression (PCR) algorithm was applied to develop prediction models. The informative features were identified by a sequential backward feature elimination (SBFE) algorithm.Results
A total of 19 informative chemical and biological features were successfully identified by SBFE algorithm. The informative features were utilized to develop a computational method named FS-CBM for predicting proinflammatory potentials of engine exhausts. FS-CBM model achieved a high performance with correlation coefficient values of 0.997 and 0.943 obtained from training and independent test sets, respectively.Conclusions
The FS-CBM model was developed for predicting proinflammatory potentials of engine exhausts with a large improvement on prediction performance compared with our previous CBM model. The proposed method could be further applied to construct models for bioactivities of mixtures.12.
Objectives
Biomass subpopulations in mammalian cell culture processes cause impurities and influence productivity, which requires this critical process parameter to be monitored in real-time.Results
For this reason, a novel soft sensor concept for estimating viable, dead and lysed cell concentration was developed, based on the robust and cheap in situ measurements of permittivity and turbidity in combination with a simple model. It could be shown that the turbidity measurements contain information about all investigated biomass subpopulations. The novelty of the developed soft sensor is the real-time estimation of lysed cell concentration, which is directly correlated to process-related impurities such as DNA and host cell protein in the supernatant. Based on data generated by two fed-batch processes the developed soft sensor is described and discussed.Conclusions
The presented soft sensor concept provides a tool for viable, dead and lysed cell concentration estimation in real-time with adequate accuracy and enables further applications with respect to process optimization and control.13.
J. C. Martínez-Ávila A. García Bartolomé I. García I. Dapía Hoi Y. Tong L. Díaz P. Guerra J. Frías A. J. Carcás Sansuan A. M. Borobia 《Metabolomics : Official journal of the Metabolomic Society》2018,14(5):70
Introduction
Zonisamide is a new-generation anticonvulsant antiepileptic drug metabolized primarily in the liver, with subsequent elimination via the renal route.Objectives
Our objective was to evaluate the utility of pharmacometabolomics in the detection of zonisamide metabolites that could be related to its disposition and therefore, to its efficacy and toxicity.Methods
This study was nested to a bioequivalence clinical trial with 28 healthy volunteers. Each participant received a single dose of zonisamide on two separate occasions (period 1 and period 2), with a washout period between them. Blood samples of zonisamide were obtained from all patients at baseline for each period, before volunteers were administered any medication, for metabolomics analysis.Results
After a Lasso regression was applied, age, height, branched-chain amino acids, steroids, triacylglycerols, diacyl glycerophosphoethanolamine, glycerophospholipids susceptible to methylation, phosphatidylcholines with 20:4 FA (arachidonic acid) and cholesterol ester and lysophosphatidylcholine were obtained in both periods.Conclusion
To our knowledge, this is the only research study to date that has attempted to link basal metabolomic status with pharmacokinetic parameters of zonisamide.14.
Patrick J. C. Tardivel Cécile Canlet Gaëlle Lefort Marie Tremblay-Franco Laurent Debrauwer Didier Concordet Rémi Servien 《Metabolomics : Official journal of the Metabolomic Society》2017,13(10):109
Introduction
Experiments in metabolomics rely on the identification and quantification of metabolites in complex biological mixtures. This remains one of the major challenges in NMR/mass spectrometry analysis of metabolic profiles. These features are mandatory to make metabolomics asserting a general approach to test a priori formulated hypotheses on the basis of exhaustive metabolome characterization rather than an exploratory tool dealing with unknown metabolic features.Objectives
In this article we propose a method, named ASICS, based on a strong statistical theory that handles automatically the metabolites identification and quantification in proton NMR spectra.Methods
A statistical linear model is built to explain a complex spectrum using a library containing pure metabolite spectra. This model can handle local or global chemical shift variations due to experimental conditions using a warping function. A statistical lasso-type estimator identifies and quantifies the metabolites in the complex spectrum. This estimator shows good statistical properties and handles peak overlapping issues.Results
The performances of the method were investigated on known mixtures (such as synthetic urine) and on plasma datasets from duck and human. Results show noteworthy performances, outperforming current existing methods.Conclusion
ASICS is a completely automated procedure to identify and quantify metabolites in 1H NMR spectra of biological mixtures. It will enable empowering NMR-based metabolomics by quickly and accurately helping experts to obtain metabolic profiles.15.
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.16.
Background
Methylation analysis of cell-free DNA is a encouraging tool for tumor diagnosis, monitoring and prognosis. Sensitivity of methylation analysis is a very important matter due to the tiny amounts of cell-free DNA available in plasma. Most current methods of DNA methylation analysis are based on the difference of bisulfite-mediated deamination of cytosine between cytosine and 5-methylcytosine. However, the recovery of bisulfite-converted DNA based on current methods is very poor for the methylation analysis of cell-free DNA.Results
We optimized a rapid method for the crucial steps of bisulfite conversion with high recovery of cell-free DNA. A rapid deamination step and alkaline desulfonation was combined with the purification of DNA on a silica column. The conversion efficiency and recovery of bisulfite-treated DNA was investigated by the droplet digital PCR. The optimization of the reaction results in complete cytosine conversion in 30 min at 70 °C and about 65% of recovery of bisulfite-treated cell-free DNA, which is higher than current methods.Conclusions
The method allows high recovery from low levels of bisulfite-treated cell-free DNA, enhancing the analysis sensitivity of methylation detection from cell-free DNA.17.
Ferran Casbas Pinto Srinivarao Ravipati David A. Barrett T. Charles Hodgman 《Metabolomics : Official journal of the Metabolomic Society》2017,13(7):81
Introduction
It is difficult to elucidate the metabolic and regulatory factors causing lipidome perturbations.Objectives
This work simplifies this process.Methods
A method has been developed to query an online holistic lipid metabolic network (of 7923 metabolites) to extract the pathways that connect the input list of lipids.Results
The output enables pathway visualisation and the querying of other databases to identify potential regulators. When used to a study a plasma lipidome dataset of polycystic ovary syndrome, 14 enzymes were identified, of which 3 are linked to ELAVL1—an mRNA stabiliser.Conclusion
This method provides a simplified approach to identifying potential regulators causing lipid-profile perturbations.18.
Sonia Liggi Christine Hinz Zoe Hall Maria Laura Santoru Simone Poddighe John Fjeldsted Luigi Atzori Julian L. Griffin 《Metabolomics : Official journal of the Metabolomic Society》2018,14(4):52
Introduction
Data processing is one of the biggest problems in metabolomics, given the high number of samples analyzed and the need of multiple software packages for each step of the processing workflow.Objectives
Merge in the same platform the steps required for metabolomics data processing.Methods
KniMet is a workflow for the processing of mass spectrometry-metabolomics data based on the KNIME Analytics platform.Results
The approach includes key steps to follow in metabolomics data processing: feature filtering, missing value imputation, normalization, batch correction and annotation.Conclusion
KniMet provides the user with a local, modular and customizable workflow for the processing of both GC–MS and LC–MS open profiling data.19.
Background
Random forest (RF) is a machine-learning method that generally works well with high-dimensional problems and allows for nonlinear relationships between predictors; however, the presence of correlated predictors has been shown to impact its ability to identify strong predictors. The Random Forest-Recursive Feature Elimination algorithm (RF-RFE) mitigates this problem in smaller data sets, but this approach has not been tested in high-dimensional omics data sets.Results
We integrated 202,919 genotypes and 153,422 methylation sites in 680 individuals, and compared the abilities of RF and RF-RFE to detect simulated causal associations, which included simulated genotype–methylation interactions, between these variables and triglyceride levels. Results show that RF was able to identify strong causal variables with a few highly correlated variables, but it did not detect other causal variables.Conclusions
Although RF-RFE decreased the importance of correlated variables, in the presence of many correlated variables, it also decreased the importance of causal variables, making both hard to detect. These findings suggest that RF-RFE may not scale to high-dimensional data.20.