Dataset Information

RElative QUantitation Inferred by Evaluating Mixtures (REQUIEM).

ABSTRACT: Motivated by the lack of easily implementable and generally applicable strategies to increase and assess data accuracy, we devised a novel label-free approach, termed REQUIEM, to address challenges in relative quantitation. For comparing the relative amounts of analytes in two samples, a mixture is prepared from aliquots of the samples, and the samples and the mixture are analyzed in parallel according to the intended workflow. Processing of the resulting data using the REQUIEM algorithm yields unbiased analyte fold-changes and associated statistics, allowing several types of errors to be diagnosed or eliminated. Extensive simulations and analysis of carefully prepared standard samples demonstrated the rigorous foundations of REQUIEM. We applied REQUIEM to several real-world analytical techniques and workflows, notably to tandem mass spectrometry analysis by using isomeric oligosaccharides as test analytes. We conclude that REQUIEM can reveal inaccuracies in the data that are difficult to identify by using traditional approaches.

SUBMITTER: Tuomivaara ST

PROVIDER: S-EPMC5735846 | biostudies-literature | 2017 Nov

REPOSITORIES: biostudies-literature

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RElative QUantitation Inferred by Evaluating Mixtures (REQUIEM).

Tuomivaara Sami T ST Schliekelman Paul P Nairn Alison V AV Moremen Kelley W KW York William S WS

Analytica chimica acta 20171004

Motivated by the lack of easily implementable and generally applicable strategies to increase and assess data accuracy, we devised a novel label-free approach, termed REQUIEM, to address challenges in relative quantitation. For comparing the relative amounts of analytes in two samples, a mixture is prepared from aliquots of the samples, and the samples and the mixture are analyzed in parallel according to the intended workflow. Processing of the resulting data using the REQUIEM algorithm yields ...[more]

PMID: 29078952

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Project description:Environmental protection is critical to maintain ecosystem services essential for human well-being. It is important to be able to rank countries by their environmental impact so that poor performers as well as policy 'models' can be identified. We provide novel metrics of country-specific environmental impact ranks - one proportional to total resource availability per country and an absolute (total) measure of impact - that explicitly avoid incorporating confounding human health or economic indicators. Our rankings are based on natural forest loss, habitat conversion, marine captures, fertilizer use, water pollution, carbon emissions and species threat, although many other variables were excluded due to a lack of country-specific data. Of 228 countries considered, 179 (proportional) and 171 (absolute) had sufficient data for correlations. The proportional index ranked Singapore, Korea, Qatar, Kuwait, Japan, Thailand, Bahrain, Malaysia, Philippines and Netherlands as having the highest proportional environmental impact, whereas Brazil, USA, China, Indonesia, Japan, Mexico, India, Russia, Australia and Peru had the highest absolute impact (i.e., total resource use, emissions and species threatened). Proportional and absolute environmental impact ranks were correlated, with mainly Asian countries having both high proportional and absolute impact. Despite weak concordance among the drivers of environmental impact, countries often perform poorly for different reasons. We found no evidence to support the environmental Kuznets curve hypothesis of a non-linear relationship between impact and per capita wealth, although there was a weak reduction in environmental impact as per capita wealth increases. Using structural equation models to account for cross-correlation, we found that increasing wealth was the most important driver of environmental impact. Our results show that the global community not only has to encourage better environmental performance in less-developed countries, especially those in Asia, there is also a requirement to focus on the development of environmentally friendly practices in wealthier countries.

Project description:BackgroundAlthough fold change is a commonly used criterion in quantitative proteomics for differentiating regulated proteins, it does not provide an estimation of false positive and false negative rates that is often desirable in a large-scale quantitative proteomic analysis. We explore the possibility of applying the Significance Analysis of Microarray (SAM) method (PNAS 98:5116-5121) to a differential proteomics problem of two samples with replicates. The quantitative proteomic analysis was carried out with nanoliquid chromatography/linear iron trap-Fourier transform mass spectrometry. The biological sample model included two Mycobacterium smegmatis unlabeled cell cultures grown at pH 5 and pH 7. The objective was to compare the protein relative abundance between the two unlabeled cell cultures, with an emphasis on significance analysis of protein differential expression using the SAM method. Results using the SAM method are compared with those obtained by fold change and the conventional t-test.ResultsWe have applied the SAM method to solve the two-sample significance analysis problem in liquid chromatography/mass spectrometry (LC/MS) based quantitative proteomics. We grew the pH5 and pH7 unlabelled cell cultures in triplicate resulting in 6 biological replicates. Each biological replicate was mixed with a common 15N-labeled reference culture cells for normalization prior to SDS/PAGE fractionation and LC/MS analysis. For each biological replicate, one center SDS/PAGE gel fraction was selected for triplicate LC/MS analysis. There were 121 proteins quantified in at least 5 of the 6 biological replicates. Of these 121 proteins, 106 were significant in differential expression by the t-test (p < 0.05) based on peptide-level replicates, 54 were significant in differential expression by SAM with Delta = 0.68 cutoff and false positive rate at 5%, and 29 were significant in differential expression by the t-test (p < 0.05) based on protein-level replicates. The results indicate that SAM appears to overcome the false positives one encounters using the peptide-based t-test while allowing for identification of a greater number of differentially expressed proteins than the protein-based t-test.ConclusionWe demonstrate that the SAM method can be adapted for effective significance analysis of proteomic data. It provides much richer information about the protein differential expression profiles and is particularly useful in the estimation of false discovery rates and miss rates.

Dataset Information

RElative QUantitation Inferred by Evaluating Mixtures (REQUIEM).

Publications

RElative QUantitation Inferred by Evaluating Mixtures (REQUIEM).

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