Evidence map›Paper›PMID 33102660›Full record

ArticleData in brief2020

Data on chemical-gene interactions and biological categories enriched with genes sensitive to chemical exposures.

Alexander Suvorov, Victoria Salemme, Joseph McGaunn, Anthony Poluyanoff, Saira Amir

Open access · goldAbstract read
In one paragraph

Article in Data in brief, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.6field-weighted citation impact, top 26% 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

3 citing papers in PubMed, 6 citations in OpenAlex.

  1. Article
  2. Article
  3. Chemical Exposures Affect Innate Immune Response to SARS-CoV-2.International journal of molecular sciences · 2021
    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 2 countries.

Alexander SuvorovDepartment of Environmental Health Sciences, School of Public Health and Health Sciences, University of Massachusetts, 173B-Goessmann, 686 North Pleasant Street, Amherst, MA 01003, USA.
Victoria SalemmeDepartment of Environmental Health Sciences, School of Public Health and Health Sciences, University of Massachusetts, 173B-Goessmann, 686 North Pleasant Street, Amherst, MA 01003, USA.
Joseph McGaunnDepartment of Environmental Health Sciences, School of Public Health and Health Sciences, University of Massachusetts, 173B-Goessmann, 686 North Pleasant Street, Amherst, MA 01003, USA.
Anthony PoluyanoffDepartment of Environmental Health Sciences, School of Public Health and Health Sciences, University of Massachusetts, 173B-Goessmann, 686 North Pleasant Street, Amherst, MA 01003, USA.
Saira AmirDepartment of Environmental Health Sciences, School of Public Health and Health Sciences, University of Massachusetts, 173B-Goessmann, 686 North Pleasant Street, Amherst, MA 01003, USA.
University of Massachusetts Amherst · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A dataset of chemical-gene interactions was created by extracting data from the Comparative Toxicogenomics Database (CTD) with the following filtering criteria: data was extracted only from experiments that used human, rat, or mouse cells/tissues and used high-throughput approaches for gene expression analysis. Genes not present in genomes of all three species were filtered out. The resulting dataset included 591,084 chemical-gene interaction. All chemical compounds in the database were annotated for their major uses. For every gene in the database number of chemical-gene interactions was calculated and used as a metric of gene sensitivity to a variety of chemical exposures. The lists of genes with corresponding numbers of chemical-gene interactions were used in gene-set enrichment analysis (GSEA) to identify potential sensitivity to chemical exposures of molecular pathways in Hallmark, KEGG and Reactome collections. Thus, data presented here represent unbiased and searchable datasets of sensitivity of genes and molecular pathways to a broad range of chemical exposures. As such the data can be used for a diverse range of toxicological and regulatory applications. Approach for the identification of molecular mechanisms sensitive to chemical exposures may inform regulatory toxicology about best toxicity testing strategies. Analysis of sensitivity of genes and molecular pathways to chemical exposures based on these datasets was published in Chemosphere (Suvorov et al., 2021) [1].

Indexed as

Adverse outcome pathwayComputational toxicologyToxicity pathwayToxicogenomics

Identifiers

PMID33102660
PMCPMC7578666
OpenAlexW3092104022

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.