Summary of Study ST001162
This data is available at the NIH Common Fund's National Metabolomics Data Repository (NMDR) website, the Metabolomics Workbench, https://www.metabolomicsworkbench.org, where it has been assigned Project ID PR000777. The data can be accessed directly via it's Project DOI: 10.21228/M87T23 This work is supported by NIH grant, U2C- DK119886.
See: https://www.metabolomicsworkbench.org/about/howtocite.php
This study contains a large results data set and is not available in the mwTab file. It is only available for download via FTP as data file(s) here.
| Study ID | ST001162 |
| Study Title | Evaluation of computational tools using serial mixtures of human plasma and vegetable juice (part - II) |
| Study Summary | Mass spectrometry-based metabolomics is developed rapidly in the past few decades. There are few major vendors for LC-MS platform instruments, for example, Thermo ScientificTM LTQ Orbitrap Velos and Agilent 6510 Q-TOF mass spectrometer were used for metabolomics research. The data acquired cross different platform are rarely compared other than the comparison of the instrument itself on resolution, mass accuracy, sensitivity, dynamic range, scan speed etc., which is largely due to the foundation and principle of the instrument design. Other than this, there are many choice for data preprocessing, i.e., the data acquired from the same platform may have been processed with different feature extraction software tools. The discrepancy for the feature detections with different software will lead to the variation of the down-stream statistics analysis and metabolomics pathway interpretation. In addition, the impact of the LC-MS platform and data preprocessing software tools on the quantitative capabilities is also an interesting topic. In this research, XCMS, mzMine 2.37 and apLCMS are three tools used for the feature extraction of data acquired with Thermo ScientificTM LTQ Orbitrap Velos and Agilent 6510 Q-TOF LC-MS platform by serial dilution experiment. The quantification capability is estimated at the same time based on the linearity, accuracy, and precision. |
| Institute | Emory University |
| Last Name | Wang |
| First Name | Yating |
| Address | 615 Michael St. Ste 225, Atlanta, GA, 30322, USA |
| yating.wang@emory.edu | |
| Phone | 4047275091 |
| Submit Date | 2019-03-29 |
| Raw Data Available | Yes |
| Raw Data File Type(s) | d |
| Analysis Type Detail | LC-MS |
| Release Date | 2019-05-15 |
| Release Version | 1 |
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Collection:
| Collection ID: | CO001221 |
| Collection Summary: | Commercially available |
| Sample Type: | Blood (plasma) |
