Summary of Study ST001661
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 PR001066. The data can be accessed directly via it's Project DOI: 10.21228/M8X12C 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 | ST001661 |
| Study Title | Extension of Diagnostic Fragmentation Filtering for Automated Discovery in DNA Adductomics |
| Study Summary | Development of high resolution/accurate mass liquid chromatography-coupled tandem mass spectrometry (LC-MS/MS) methodology enables the characterization of covalently modified DNA induced by interaction with genotoxic agents in complex biological samples. Constant neutral loss monitoring of 2´-deoxyribose or the nucleobases using data-dependent acquisition represents a powerful approach for the unbiased detection of DNA modifications (adducts). The lack of available bioinformatics tools necessitates manual processing of acquired spectral data and hampers high throughput application of these techniques. To address this limitation, we present an automated workflow for the detection and curation of putative DNA adducts by using diagnostic fragmentation filtering of LC-MS/MS experiments within the open-source software MZmine. The workflow utilizes a new feature detection algorithm, DFBuilder, which employs diagnostic fragmentation filtering using a user-defined list of fragmentation patterns to reproducibly generate feature lists for precursor ions of interest. The DFBuilder feature detection approach readily fits into a complete small molecule discovery workflow and drastically reduces the processing time associated with analyzing DNA adductomics results. We validate our workflow using a mixture of authentic DNA adduct standards and demonstrate the effectiveness of our approach by reproducing and expanding the results of a previously published study of colibactin-induced DNA adducts. The reported workflow serves as a technique to assess the diagnostic potential of novel fragmentation pattern combinations for the unbiased detection of chemical classes of interest. |
| Institute | University of Minnesota |
| Department | School of Public Health, Division of Environmental Health Sciences |
| Laboratory | Balbo Research Group |
| Last Name | Murray |
| First Name | Kevin |
| Address | 2-210 CCRB, 2231 6th St SE, Minneapolis, MN 55455 |
| murra668@umn.edu | |
| Phone | 612-626-2182 |
| Submit Date | 2021-01-25 |
| Num Groups | 1 |
| Total Subjects | 3 |
| Study Comments | Synthetic samples of authentic standards for workflow testing and validation. |
| Publications | Murray K.J.; Carlson E.S.; Stornetta A.; Balskus E.P.; Villalta P.W.; Balbo S. Extension of Diagnostic Fragmentation Filtering for Automated Discovery in DNA Adductomics. Anal. Chem. 2021. (In Revision). |
| Raw Data Available | Yes |
| Raw Data File Type(s) | mzML |
| Analysis Type Detail | LC-MS |
| Release Date | 2021-04-25 |
| Release Version | 1 |
Select appropriate tab below to view additional metadata details:
Project:
| Project ID: | PR001066 |
| Project DOI: | doi: 10.21228/M8X12C |
| Project Title: | Extension of Diagnostic Fragmentation Filtering for Automated Discovery in DNA Adductomics |
| Project Summary: | Development of high resolution/accurate mass liquid chromatography-coupled tandem mass spectrometry (LC-MS/MS) methodology enables the characterization of covalently modified DNA induced by interaction with genotoxic agents in complex biological samples. Constant neutral loss monitoring of 2´-deoxyribose or the nucleobases using data-dependent acquisition represents a powerful approach for the unbiased detection of DNA modifications (adducts). The lack of available bioinformatics tools necessitates manual processing of acquired spectral data and hampers high throughput application of these techniques. To address this limitation, we present an automated workflow for the detection and curation of putative DNA adducts by using diagnostic frag-mentation filtering of LC-MS/MS experiments within the open-source software MZmine. The workflow utilizes a new feature detection algorithm, DFBuilder, which employs diagnostic fragmentation filtering using a user-defined list of fragmentation pat-terns to reproducibly generate feature lists for precursor ions of interest. The DFBuilder feature detection approach readily fits into a complete small molecule discovery workflow and drastically reduces the processing time associated with analyzing DNA adductomics results. We validate our workflow using a mixture of authentic DNA adduct standards and demonstrate the effectiveness of our approach by reproducing and expanding the results of a previously published study of colibactin-induced DNA adducts. The reported workflow serves as a technique to assess the diagnostic potential of novel fragmentation pattern combinations for the unbiased detection of chemical classes of interest. |
| Institute: | University of Minnesota |
| Department: | School of Public Health, Division of Environmental Health Sciences |
| Laboratory: | Balbo Research Group |
| Last Name: | Murray |
| First Name: | Kevin |
| Address: | 2-210 CCRB, 2231 6th St SE, Minneapolis, MN 55455 |
| Email: | murra668@umn.edu |
| Phone: | 612-625-2280 |
| Project Comments: | Experimental data for the reproduction and testing of the DFBuilder workflow for the automated detection of DNA adducts using diagnostic fragmentation filtering. |
| Publications: | Murray K.J.; Carlson E.S.; Stornetta A.; Balskus E.P.; Villalta P.W.; Balbo S. Extension of Diagnostic Fragmentation Filtering for Automated Discovery in DNA Adductomics. Anal. Chem. 2021. (In Revision). |
