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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.4.
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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.6.
Mang Ching Lai Anne-Laure Bechy Franziska Denk Emma Collins Maria Gavriliouk Judith B. Zaugg Brent J. Ryan Richard Wade-Martins Tara M. Caffrey 《Molecular neurodegeneration》2017,12(1):79
Background
Genome wide association studies have identified microtubule associated protein tau (MAPT) H1 haplotype single nucleotide polymorphisms (SNPs) as leading common risk variants for Parkinson’s disease, progressive supranuclear palsy and corticobasal degeneration. The MAPT risk variants fall within a large 1.8 Mb region of high linkage disequilibrium, making it difficult to discern the functionally important risk variants. Here, we leverage the strong haplotype-specific expression of MAPT exon 3 to investigate the functionality of SNPs that fall within this H1 haplotype region of linkage disequilibrium.Methods
In this study, we dissect the molecular mechanisms by which haplotype-specific SNPs confer allele-specific effects on the alternative splicing of MAPT exon 3. Firstly, we use haplotype-hybrid whole-locus genomic MAPT vectors studies to identify functional SNPs. Next, we characterise the RNA-protein interactions at two loci by mass spectrometry. Lastly, we knockdown candidate splice factors to determine their effect on MAPT exon 3 using a novel allele-specific qPCR assay.Results
Using whole-locus genomic DNA expression vectors to express MAPT haplotype variants, we demonstrate that rs17651213 regulates exon 3 inclusion in a haplotype-specific manner. We further investigated the functionality of this region using RNA-electrophoretic mobility shift assays to show differential RNA-protein complex formation at the H1 and H2 sequence variants of SNP rs17651213 and rs1800547 and subsequently identified candidate trans-acting splicing factors interacting with these functional SNPs sequences by RNA-protein pull-down experiment and mass spectrometry. Finally, gene knockdown of candidate splice factors identified by mass spectrometry demonstrate a role for hnRNP F and hnRNP Q in the haplotype-specific regulation of exon 3 inclusion.Conclusions
We identified common splice factors hnRNP F and hnRNP Q regulating the haplotype-specific splicing of MAPT exon 3 through intronic variants rs1800547 and rs17651213. This work demonstrates an integrated approach to characterise the functionality of risk variants in large regions of linkage disequilibrium.7.
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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.11.
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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.15.
Antonio Rosato Leonardo Tenori Marta Cascante Pedro Ramon De Atauri Carulla Vitor A. P. Martins dos Santos Edoardo Saccenti 《Metabolomics : Official journal of the Metabolomic Society》2018,14(4):37
Introduction
Metabolomics is a well-established tool in systems biology, especially in the top–down approach. Metabolomics experiments often results in discovery studies that provide intriguing biological hypotheses but rarely offer mechanistic explanation of such findings. In this light, the interpretation of metabolomics data can be boosted by deploying systems biology approaches.Objectives
This review aims to provide an overview of systems biology approaches that are relevant to metabolomics and to discuss some successful applications of these methods.Methods
We review the most recent applications of systems biology tools in the field of metabolomics, such as network inference and analysis, metabolic modelling and pathways analysis.Results
We offer an ample overview of systems biology tools that can be applied to address metabolomics problems. The characteristics and application results of these tools are discussed also in a comparative manner.Conclusions
Systems biology-enhanced analysis of metabolomics data can provide insights into the molecular mechanisms originating the observed metabolic profiles and enhance the scientific impact of metabolomics studies.16.
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.17.
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Background
DNA methylation has been identified to be widely associated to complex diseases. Among biological platforms to profile DNA methylation in human, the Illumina Infinium HumanMethylation450 BeadChip (450K) has been accepted as one of the most efficient technologies. However, challenges exist in analysis of DNA methylation data generated by this technology due to widespread biases.Results
Here we proposed a generalized framework for evaluating data analysis methods for Illumina 450K array. This framework considers the following steps towards a successful analysis: importing data, quality control, within-array normalization, correcting type bias, detecting differentially methylated probes or regions and biological interpretation.Conclusions
We evaluated five methods using three real datasets, and proposed outperform methods for the Illumina 450K array data analysis. Minfi and methylumi are optimal choice when analyzing small dataset. BMIQ and RCP are proper to correcting type bias and the normalized result of them can be used to discover DMPs. R package missMethyl is suitable for GO term enrichment analysis and biological interpretation.19.
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Ryoko Sawamoto Takehiro Nozaki Tomoe Nishihara Tomokazu Furukawa Tomokazu Hata Gen Komaki Nobuyuki Sudo 《BioPsychoSocial medicine》2017,11(1):14