Correction to “Pregnancy-Induced Metabolic Phenotype Variations in Maternal Plasma”

Luan, Hemi; Meng, Nan; Liu, Ping; Feng, Qi; Lin, Shuhai; Fu, Jin; Davidson, R. J. L.; Chen, Xiaomin; Rao, Weiqiao; Jiang, Hui; Xu, Xun; Cai, Zongwei; Wang, Jun

doi:10.1021/acs.jproteome.5b00430

Cited by 3 publications

(3 citation statements)

References 2 publications

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“…To assess the reproducibility and reliability of the LC/ESI-Q-TOF-MS system, quality control (QC) samples were also prepared by mixing equal volumes (10 ml from each sample of root exudate. The pooled QC sample was injected six times at the beginning of the run to ensure system equilibrium and then after every 6 samples to further monitor the stability of the analysis (Luan et al, 2014;Zhou et al, 2016).…”

Section: Rir =mentioning

confidence: 99%

Root Exudates Metabolic Profiling Suggests Distinct Defense Mechanisms Between Resistant and Susceptible Tobacco Cultivars Against Black Shank Disease

et al. 2020

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Section: Rir =mentioning

confidence: 99%

Root Exudates Metabolic Profiling Suggests Distinct Defense Mechanisms Between Resistant and Susceptible Tobacco Cultivars Against Black Shank Disease

et al. 2020

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“…In contrast to prior studies [25, 27–29, 52, 59, 61, 67], the larger sample size of this study, and the co-randomization of pregnant and non-pregnant women (which is not always done [26]) across runs of a more sensitive high-throughput mass-spectrometry metabolomic platform, enabled us to identify a larger number of metabolites associated with pregnancy.…”

Section: Discussionmentioning

confidence: 98%

The plasma metabolome of women in early pregnancy differs from that of non-pregnant women

et al. 2019

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Background In comparison to the non-pregnant state, the first trimester of pregnancy is characterized by systemic adaptation of the mother. The extent to which these adaptive processes are reflected in the maternal blood metabolome is not well characterized. Objective To determine the differences between the plasma metabolome of non-pregnant and pregnant women before 16 weeks gestation. Study design This study included plasma samples from 21 non-pregnant women and 50 women with a normal pregnancy (8-16 weeks of gestation). Combined measurements by ultrahigh performance liquid chromatography/tandem mass spectrometry and by gas chromatography/ mass spectrometry generated molecular abundance measurements for each sample. Molecular species detected in at least 10 samples were included in the analysis. Differential

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“…For example, in reviewing a Data Note presenting metabolomics and lipidomics data [47] discrepancies in the previously reported methods came to light. This leads to an Erratum being published in the J Proteome Res explaining care should be taken when interpreting some of the results [48]. Availability and scrutiny of code is just as important as data, and our Min-ION™ sequenced reference bacterial genome Data Note [65] had to be corrected after an error in a script had been reported due to a fix supplied by an anonymous online contributor on Github [66].…”

Section: Lessons Learned In Reproducible Data Publishingmentioning

confidence: 99%

Experiences in integrated data and research object publishing using GigaDB

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et al. 2016

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In the era of computation and data-driven research, traditional methods of disseminating research are no longer fit-for-purpose. New approaches for disseminating data, methods and results are required to maximize knowledge discovery. The "long tail" of small, unstructured datasets is well catered for by a number of general-purpose repositories, but there has been less support for "big data". Outlined here are our experiences in attempting to tackle the gaps in publishing large-scale, computationally intensive research.GigaScience is an open-access, open-data journal aiming to revolutionize large-scale biological data dissemination, organization and re-use. Through use of the data handling infrastructure of the genomics centre BGI, GigaScience links standard manuscript publication with an integrated database (GigaDB) that hosts all associated data, and provides additional data analysis tools and computing resources. Furthermore, the supporting workflows and methods are also integrated to make published articles more transparent and open. GigaDB has released many new and previously unpublished datasets and data types, including as urgently needed data to tackle infectious disease outbreaks, cancer and the growing food crisis. Other "executable" research objects, such as workflows, virtual machines and software from several GigaScience articles have been archived and shared in reproducible, transparent and usable formats. With data citation producing evidence Office for National Statistics, Duffryn, Government Buildings, Cardiff Rd, Newport NP10 8XG, UK of, and credit for, its use in the wider research community, GigaScience demonstrates a move towards more executable publications. Here data analyses can be reproduced and built upon by users without coding backgrounds or heavy computational infrastructure in a more democratized manner.

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Correction to “Pregnancy-Induced Metabolic Phenotype Variations in Maternal Plasma”

Cited by 3 publications

References 2 publications

Root Exudates Metabolic Profiling Suggests Distinct Defense Mechanisms Between Resistant and Susceptible Tobacco Cultivars Against Black Shank Disease

Root Exudates Metabolic Profiling Suggests Distinct Defense Mechanisms Between Resistant and Susceptible Tobacco Cultivars Against Black Shank Disease

The plasma metabolome of women in early pregnancy differs from that of non-pregnant women

Experiences in integrated data and research object publishing using GigaDB

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