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Generation and quality control of maternal plasma lipidomics data associated with preterm birth

By ZhanLong Mei, Lingfei Ye, Kang Huang, Xi Yang, Xiaomin Chen, Miaolan Cen, Yuan Chen, Sujun Zhu, Juan Zeng, Bhaskar Roy, Hui Jiang, Wen-Jing Wang

Posted 26 Jul 2019
bioRxiv DOI: 10.1101/714790

Preterm birth is not only one of the most common causes of infant deaths but also a great risk for them to have severe subsequent health problems. The causes of preterm birth may be due to a combination of genetic and environmental factors, however, it remains largely unknown. Here we report an untargeted lipidomics dataset of plasma specimens from 258 pregnant women at the stage of twelve to twenty-five gestational weeks. Among them, 44 had extremely to very preterm births, 54 had moderate preterm births, 71 had late preterm births and 89 had full-term deliveries. The metabolomic profiling was generated with an UPLC-MS in both the positive and negative mode, and putative identification of all the metabolites was provided by searching against online databases. The quality assessment performed on quality control samples showed that the data is reproducible, robust and reliable. Both the raw data files, the raw and processed data matrix were available on MetaboLights, which may be used as a valuable validation dataset for new findings and a test dataset for novel algorithms.

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