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1.
Background
Data integration is a crucial task in the biomedical domain and integrating data sources is one approach to integrating data. Data elements (DEs) in particular play an important role in data integration. We combine schema- and instance-based approaches to mapping DEs to terminological resources in order to facilitate data sources integration.Methods
We extracted DEs from eleven disparate biomedical sources. We compared these DEs to concepts and/or terms in biomedical controlled vocabularies and to reference DEs. We also exploited DE values to disambiguate underspecified DEs and to identify additional mappings.Results
82.5% of the 474 DEs studied are mapped to entries of a terminological resource and 74.7% of the whole set can be associated with reference DEs. Only 6.6% of the DEs had values that could be semantically typed.Conclusion
Our study suggests that the integration of biomedical sources can be achieved automatically with limited precision and largely facilitated by mapping DEs to terminological resources.2.
Dorothea Lesche Roland Geyer Daniel Lienhard Christos T. Nakas Gaëlle Diserens Peter Vermathen Alexander B. Leichtle 《Metabolomics : Official journal of the Metabolomic Society》2016,12(10):159
Background
Centrifugation is an indispensable procedure for plasma sample preparation, but applied conditions can vary between labs.Aim
Determine whether routinely used plasma centrifugation protocols (1500×g 10 min; 3000×g 5 min) influence non-targeted metabolomic analyses.Methods
Nuclear magnetic resonance spectroscopy (NMR) and High Resolution Mass Spectrometry (HRMS) data were evaluated with sparse partial least squares discriminant analyses and compared with cell count measurements.Results
Besides significant differences in platelet count, we identified substantial alterations in NMR and HRMS data related to the different centrifugation protocols.Conclusion
Already minor differences in plasma centrifugation can significantly influence metabolomic patterns and potentially bias metabolomics studies.3.
4.
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.5.
6.
Ingo Meinshausen Peter Müller-Beilschmidt Tobias Viere 《The International Journal of Life Cycle Assessment》2016,21(9):1231-1235
Purpose
This paper introduces the new EcoSpold data format for life cycle inventory (LCI).Methods
A short historical retrospect on data formats in the life cycle assessment (LCA) field is given. The guiding principles for the revision and implementation are explained. Some technical basics of the data format are described, and changes to the previous data format are explained.Results
The EcoSpold 2 data format caters for new requirements that have arisen in the LCA field in recent years.Conclusions
The new data format is the basis for the Ecoinvent v3 database, but since it is an open data format, it is expected to be adopted by other LCI databases. Several new concepts used in the new EcoSpold 2 data format open the way for new possibilities for the LCA practitioners and to expand the application of the datasets in other fields beyond LCA (e.g., Material Flow Analysis, Energy Balancing).7.
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.8.
Background
One of the 3 tracks of iDASH Privacy & Security Workshop 2017 competition was to execute a whole genome variants search on private genomic data. Particularly, the search application was to find the top most significant SNPs (Single-Nucleotide Polymorphisms) in a database of genome records labeled with control or case. In this paper we discuss the solution submitted by our team to this competition.Methods
Privacy and confidentiality of genome data had to be ensured using Intel SGX enclaves. The typical use-case of this application is the multi-party computation (each party possessing one or several genome records) of the SNPs which statistically differentiate control and case genome datasets.Results
Our solution consists of two applications: (i) compress and encrypt genome files and (ii) perform genome processing (top most important SNPs search). We have opted for a horizontal treatment of genome records and heavily used parallel processing. Rust programming language was employed to develop both applications.Conclusions
Execution performance of the processing applications scales well and very good performance metrics are obtained. Contest organizers selected it as the best submission amongst other received competition entries and our team was awarded the first prize on this track.9.
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.10.
Background
In recent years the visualization of biomagnetic measurement data by so-called pseudo current density maps or Hosaka-Cohen (HC) transformations became popular.Methods
The physical basis of these intuitive maps is clarified by means of analytically solvable problems.Results
Examples in magnetocardiography, magnetoencephalography and magnetoneurography demonstrate the usefulness of this method.Conclusion
Hardware realizations of the HC-transformation and some similar transformations are discussed which could advantageously support cross-platform comparability of biomagnetic measurements.11.
A. B. Araújo J. M. Furlan G. D. Salton T. Schmalfuss L. M. Röhsig L. M. R. Silla E. P. Passos A. H. Paz 《Biotechnology letters》2018,40(6):989-998
Objective
To compare four enzymatic protocols for mesenchymal stem cells (MSCs) isolation from amniotic (A-MSC) and chorionic (C-MSC) membranes, umbilical cord (UC-MSC) and placental decidua (D-MSC) in order to define a robust, practical and low-cost protocol for each tissue.Results
A-MSCs and UC-MSCs could be isolated from all samples using trypsin/collagenase-based protocols; C-MSCs could be isolated from all samples with collagenase- and trypsin/collagenase-based protocols; D-MSCs were isolated from all samples exclusively with a collagenase-based protocol.Conclusions
The trypsin-only protocol was least efficient; the collagenase-only protocol was best for C-MSCs and D-MSCs; the combination of trypsin and collagenase was best for UC-MSCs and none of tested protocols was adequate for A-MSCs isolation.12.
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.13.
Alexandre Seyer Samia Boudah Simon Broudin Christophe Junot Benoit Colsch 《Metabolomics : Official journal of the Metabolomic Society》2016,12(5):91
Introduction
Due to its proximity with the brain, cerebrospinal fluid (CSF) could be a medium of choice for the discovery of biomarkers of neurological and psychiatric diseases using untargeted analytical approaches.Objectives
This study explored the CSF lipidome in order to generate a robust mass spectral database using an untargeted lipidomic approach.Methods
Cerebrospinal fluid samples from 45 individuals were analyzed by liquid chromatography coupled to high-resolution mass spectrometry method (LC-HRMS). A dedicated data processing workflow was implemented using XCMS software and adapted filters to select reliable features. In addition, an automatic annotation using an in silico lipid database and several MS/MS experiments were performed to identify CSF lipid species.Results
Using this complete workflow, 771 analytically relevant monoisotopic lipid species corresponding to 550 unique lipids which represent five major lipid families (i.e., free fatty acids, sphingolipids, glycerophospholipids, glycerolipids, and sterol lipids) were detected and annotated. In addition, MS/MS experiments enabled to improve the annotation of 304 lipid species. Thanks to LC-HRMS, it was possible to discriminate between isobaric and also isomeric lipid species; and interestingly, our study showed that isobaric ions represent about 50 % of the total annotated lipid species in the human CSF.Conclusion
This work provides an extensive LC/HRMS database of the human CSF lipidome which constitutes a relevant foundation for future studies aimed at finding biomarkers of neurological disorders.14.
Lívia do Vale Teixeira da Costa Fernanda Bertuccez Cordeiro Raquel Cellin Rochetti Michael Murgu Daniel Suslik Zylbersztejn Agnaldo Pereira Cedenho Edson Guimarães Lo Turco Renato Fraietta 《Metabolomics : Official journal of the Metabolomic Society》2017,13(6):70
Introduction
Ovulation induction protocols are key components for performing assisted reproduction treatments successfully. The importance of adding exogenous LH to the controlled ovarian stimulation protocols in young women is still a matter of discussion in the clinical practice.Objective
To estimate how LH addition to controlled ovarian stimulation protocols may affect the follicular fluid lipid profile of women undergoing in vitro fertilization treatment.Methods
We conducted the study using 28 self-paired samples, 14 per group. The patients received FSH during their first cycle of ovarian stimulation (FSH group). If the treatment did not result in pregnancy, the same patients returned for a new cycle and received stimulus with the addition of LH to the previous protocol (Low-dose-LH group). Lipidomics analysis was performed by UPLC-MSE mass spectrometry. The software Progenesis QI was used to identify potential lipid biomarkers. Statistical analysis was performed using the SPSS 18.0 and MetaboAnalyst 2.0 software.Results
The analysis of clinical data showed no statistically significant differences between groups, in contrast to lipidomic analysis. Concerning lipidomic profile, the lipids differentially expressed in FSH group belong to the following subclasses:triacylglycerols; branched fatty acids and diacylglycerophosphoethanolamines; while in Low-dose-LH group the subclasses are gangliosides; acylglycerophosphoethanolamines; triacylglycerols; acylglycerophosphoserines; GalNAcβ1-3Galα1-4Galβ1-4Glc-(Globo series); amino fatty acids; triacylglycerols and prostaglandins.Conclusion
The differences found between the groups may contribute to the establishment of potential therapeutical targets based on LH-associated lipid biomarkers aiming to individualize treatments and obtain reproductive success.15.
Bo-Young Lee Aimee E Howe Matthew A Conte Helena D'Cotta Elodie Pepey Jean-Francois Baroiller Federica di Palma Karen L Carleton Thomas D Kocher 《BMC genomics》2010,11(1):278
Background
Large collections of expressed sequence tags (ESTs) are a fundamental resource for analysis of gene expression and annotation of genome sequences. We generated 116,899 ESTs from 17 normalized and two non-normalized cDNA libraries representing 16 tissues from tilapia, a cichlid fish widely used in aquaculture and biological research.Results
The ESTs were assembled into 20,190 contigs and 36,028 singletons for a total of 56,218 unique sequences and a total assembled length of 35,168,415 bp. Over the whole project, a unique sequence was discovered for every 2.079 sequence reads. 17,722 (31.5%) of these unique sequences had significant BLAST hits (e-value < 10-10) to the UniProt database.Conclusion
Normalization of the cDNA pools with double-stranded nuclease allowed us to efficiently sequence a large collection of ESTs. These sequences are an important resource for studies of gene expression, comparative mapping and annotation of the forthcoming tilapia genome sequence.16.
Kai Yang Fan Zhang Peng Han Zhuo-zhong Wang Kui Deng Yuan-yuan Zhang Wei-wei Zhao Wei Song Yu-qing Cai Kang Li Bin-bin Cui Zheng-Jiang Zhu 《Metabolomics : Official journal of the Metabolomic Society》2018,14(9):110
Introduction
Colorectal cancer (CRC) is a clinically heterogeneous disease, which necessitates a variety of treatments and leads to different outcomes. Only some CRC patients will benefit from neoadjuvant chemotherapy (NACT).Objectives
An accurate prediction of response to NACT in CRC patients would greatly facilitate optimal personalized management, which could improve their long-term survival and clinical outcomes.Methods
In this study, plasma metabolite profiling was performed to identify potential biomarker candidates that can predict response to NACT for CRC. Metabolic profiles of plasma from non-response (n?=?30) and response (n?=?27) patients to NACT were studied using UHPLC–quadruple time-of-flight)/mass spectrometry analyses and statistical analysis methods.Results
The concentrations of nine metabolites were significantly different when comparing response to NACT. The area under the receiver operating characteristic curve value of the potential biomarkers was up to 0.83 discriminating the non-response and response group to NACT, superior to the clinical parameters (carcinoembryonic antigen and carbohydrate antigen 199).Conclusion
These results show promise for larger studies that could result in more personalized treatment protocols for CRC patients.17.
Background
Protein-protein interaction (PPI) plays a core role in cellular functions. Massively parallel supercomputing systems have been actively developed over the past few years, which enable large-scale biological problems to be solved, such as PPI network prediction based on tertiary structures.Results
We have developed a high throughput and ultra-fast PPI prediction system based on rigid docking, “MEGADOCK”, by employing a hybrid parallelization (MPI/OpenMP) technique assuming usages on massively parallel supercomputing systems. MEGADOCK displays significantly faster processing speed in the rigid-body docking process that leads to full utilization of protein tertiary structural data for large-scale and network-level problems in systems biology. Moreover, the system was scalable as shown by measurements carried out on two supercomputing environments. We then conducted prediction of biological PPI networks using the post-docking analysis.Conclusions
We present a new protein-protein docking engine aimed at exhaustive docking of mega-order numbers of protein pairs. The system was shown to be scalable by running on thousands of nodes. The software package is available at: http://www.bi.cs.titech.ac.jp/megadock/k/.18.
Arianna Filntisi Charalambos Fotakis Pantelis Asvestas George K. Matsopoulos Panagiotis Zoumpoulakis Dionisis Cavouras 《Metabolomics : Official journal of the Metabolomic Society》2017,13(12):146
Introduction
Metabolite identification in biological samples using Nuclear Magnetic Resonance (NMR) spectra is a challenging task due to the complexity of the biological matrices.Objectives
This paper introduces a new, automated computational scheme for the identification of metabolites in 1D 1H NMR spectra based on the Human Metabolome Database.Methods
The methodological scheme comprises of the sequential application of preprocessing, data reduction, metabolite screening and combination selection.Results
The proposed scheme has been tested on the 1D 1H NMR spectra of: (a) an amino acid mixture, (b) a serum sample spiked with the amino acid mixture, (c) 20 blood serum, (d) 20 human amniotic fluid samples, (e) 160 serum samples from publicly available database. The methodological scheme was compared against widely used software tools, exhibiting good performance in terms of correct assignment of the metabolites.Conclusions
This new robust scheme accomplishes to automatically identify peak resonances in 1H-NMR spectra with high accuracy and less human intervention with a wide range of applications in metabolic profiling.19.
Hongchao Ji Zhimin Zhang Hongmei Lu 《Metabolomics : Official journal of the Metabolomic Society》2018,14(5):68
Introduction
Untargeted and targeted analyses are two classes of metabolic study. Both strategies have been advanced by high resolution mass spectrometers coupled with chromatography, which have the advantages of high mass sensitivity and accuracy. State-of-art methods for mass spectrometric data sets do not always quantify metabolites of interest in a targeted assay efficiently and accurately.Objectives
TarMet can quantify targeted metabolites as well as their isotopologues through a reactive and user-friendly graphical user interface.Methods
TarMet accepts vendor-neutral data files (NetCDF, mzXML and mzML) as inputs. Then it extracts ion chromatograms, detects peak position and bounds and confirms the metabolites via the isotope patterns. It can integrate peak areas for all isotopologues automatically.Results
TarMet detects more isotopologues and quantify them better than state-of-art methods, and it can process isotope tracer assay well.Conclusion
TarMet is a better tool for targeted metabolic and stable isotope tracer analyses.20.
Ryoko Sawamoto Takehiro Nozaki Tomoe Nishihara Tomokazu Furukawa Tomokazu Hata Gen Komaki Nobuyuki Sudo 《BioPsychoSocial medicine》2017,11(1):14