Evidence map›Paper›PMID 31549505›Full record

ArticleChemical research in toxicology2019

Methods and Challenges for Computational Data Analysis for DNA Adductomics.

Scott J Walmsley, Jingshu Guo, Jinhua Wang, Peter W Villalta, Robert J Turesky

Open access · greenAbstract read
In one paragraph

Article in Chemical research in toxicology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
0.9field-weighted citation impact, top 25% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

16 citing papers in PubMed, 25 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Review
  5. Article
  6. Mass Spectral Library for DNA Adductomics.Chemical research in toxicology · 2024
    Article
  7. A Novel Adductomics Workflow IncorporatingEnvironmental science & technology · 2024
    Article
  8. Article
  9. Article
  10. Review
  11. Review
  12. Article
  13. Review
  14. Article
  15. Article
  16. Applying Tobacco, Environmental, and Dietary-Related Biomarkers to Understand Cancer Etiology and Evaluate Prevention Strategies.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2020
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 1 institution in 1 country.

Scott J WalmsleyMasonic Cancer Center , University of Minnesota , Minneapolis , Minnesota 55455 , United States.ORCID 0000-0003-2108-1121
Jingshu GuoMasonic Cancer Center , University of Minnesota , Minneapolis , Minnesota 55455 , United States.ORCID 0000-0001-8987-6911
Jinhua WangMasonic Cancer Center , University of Minnesota , Minneapolis , Minnesota 55455 , United States.
Peter W VillaltaMasonic Cancer Center , University of Minnesota , Minneapolis , Minnesota 55455 , United States.ORCID 0000-0002-0067-3083
Robert J TureskyMasonic Cancer Center , University of Minnesota , Minneapolis , Minnesota 55455 , United States.ORCID 0000-0001-7355-9903
University of Minnesota · US

Funding

Women's CancerP30CA077598 · NCI · UNIVERSITY OF MINNESOTA TWIN CITIES · PI Jeffrey S. Miller · 1998 to 2026
$100.4M
University of Minnesota Clinical and Translational Science Institute (UMN CTSI)UL1TR000114 · NCATS · UNIVERSITY OF MINNESOTA · PI BLAZAR, BRUCE R · 2012 to 2015
$35.0M
Chemical markers of heterocyclic aromatic amines for human biomonitoringR01CA122320 · NCI · WADSWORTH CENTER · PI TURESKY, ROBERT J. · 2007 to 2018
$4.8M
DNA adductomics of the urinary systemR01CA220367 · NCI · UNIVERSITY OF MINNESOTA · PI ROSENQUIST, THOMAS A, TURESKY, ROBERT J. · 2017 to 2022
$3.0M
New biomonitoring methodologies to measure DNA adducts in human tissuesR01ES019564 · NIEHS · WADSWORTH CENTER · PI TURESKY, ROBERT J. · 2011 to 2020
$2.8M
Mass Spectrometric Analytical Collaborations with Members of the Masonic Cancer Center of the University of MinnesotaR50CA211256 · NCI · UNIVERSITY OF MINNESOTA · PI Peter William Villalta · 2016 to 2026
$2.1M
NCATS NIH HHS UL1 TR000114NCI NIH HHS P30 CA077598NCI NIH HHS R01 CA122320NCI NIH HHS R01 CA220367NCI NIH HHS R50 CA211256NIEHS NIH HHS R01 ES019564
6 · The paper itself

Abstract

Frequent exposure to chemicals in the environment, diet, and endogenous electrophiles leads to chemical modification of DNA and the formation of DNA adducts. Some DNA adducts can induce mutations during cell division and, when occurring in critical regions of the genome, can lead to the onset of disease, including cancer. The targeted analysis of DNA adducts over the past 30 years has revealed that the human genome contains many types of DNA damages. However, a long-standing limitation in conducting DNA adduct measurements has been the inability to screen for the total complement of DNA adducts derived from a wide range of chemicals in a single assay. With the advancement of high-resolution mass spectrometry (MS) instrumentation and new scanning technologies, nontargeted "omics" approaches employing data-dependent acquisition and data-independent acquisition methods have been established to simultaneously screen for multiple DNA adducts, a technique known as DNA adductomics. However, notable challenges in data processing must be overcome for DNA adductomics to become a mature technology. DNA adducts occur at low abundance in humans, and current softwares do not reliably detect them when using common MS data acquisition methods. In this perspective, we discuss contemporary computational tools developed for feature finding of MS data widely utilized in the disciplines of proteomics and metabolomics and highlight their limitations for conducting nontargeted DNA-adduct biomarker discovery. Improvements to existing MS data processing software and new algorithms for adduct detection are needed to develop DNA adductomics into a powerful tool for the nontargeted identification of potential cancer-causing agents.

Indexed as

DNA AdductsBiomarkersComputational BiologyData AnalysisHumansMass SpectrometryWorkflowXenobioticsBiomarkersDNA AdductsXenobiotics

Identifiers

PMID31549505
PMCPMC7127864
OpenAlexW2977129830

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.