Supplemental Data and Datasets to Publication: Kraemer S, Busch W, Schuettler A (2019): A self-organizing map of the fathead minnow liver transcriptome to identify consistent toxicogenomic patterns across chemical fingerprints. Environmental Toxicology and Chemistry 0 7457

Projekt
POF3 - T42 - Exposome - ProxiesOfTheEcoexposome - FHMSOM
Beschreibung
Title: A self-organizing map of the fathead-minnow liver transcriptome to identify consistent toxicogenomic patterns across chemical fingerprints
Abstract: Lack of consistent findings in different experimental settings remains to be a major challenge in toxicogenomics. The present study investigated, whether consistency between findings of different microarray experiments can be improved when the analysis is based on a common reference frame (“toxicogenomic universe”), which can be generated using the machine learning-algorithm of the self-organizing map (SOM). This algorithm arranges and clusters genes on a two-dimensional grid according to their similarity in expression across all considered data. In the present study, nineteen data sets, comprising of 54 different adult fathead minnow liver exposure experiments, were retrieved from Gene Expression Omnibus and used to train a SOM. The resulting toxicogenomic universe aggregates 58,872 probes to 2,500 nodes and was used to project, visualize and compare the fingerprints of these 54 different experiments. For example, we could identify a common pattern, with 14% of significantly regulated nodes in common, in the data sets of an interlaboratory study of ethinylestradiol exposures, previously published by Feswick et al. (2017). Consistency could be improved compared to the 5% total overlap in regulated genes reported before. Furthermore, we could determine a specific and consistent estrogen-related pattern of differentially expressed nodes and clusters in the toxicogenomic universe applying additional clustering steps and comparing all obtained fingerprints. This study shows that the SOM-based approach is useful for generating comparable toxicogenomic fingerprints and improving consistency between results of different experiments.
Journal: Environmental Toxicology and Chemistry
Keywords: self-organizing map, estrogens, consistency, gene expression, toxicogenomics
Größe
89,6 MB
Lizenz- und Nutzungsbedingungen
LIZENZ- und NUTZUNGSBEDINGUNGEN
Datenqualität
qualitätsgesicherte Daten
(Pflicht)
(optional)
7 + 10 − 9  =
 
Falls Sie bezüglich des Datensatzes Kontakt zu uns aufnehmen wollen, füllen Sie bitte das nachfolgende Formular vollständig aus.
Ihre Nachricht wird automatisch an die richtigen Ansprechpartner weiter geleitet.
(maximal 100 Zeichen)
(maximal 4000 Zeichen)
78 − 5 + 6  =
 
Metadaten Kataloge
Dublin Core
URI
http://www.ufz.de/record/dmp/archive/7457
Thema
zip-file (containing csv-Tables, Figures (pdf), R-markdown, text-documents)
Geltungsbereich
Publication Kraemer S, Busch W, Schuettler A (2019): A self-organizing map of the fathead minnow liver transcriptome to identify consistent toxicogenomic patterns across chemical fingerprints. Environmental Toxicology and Chemistry (accepted)
Datenart
Sammlung verschiedener Daten
Datenqualität
qualitätsgesicherte Daten
Datenstatus
bearbeitete Daten
Datenformat
zip
csv
txt
pdf
rdata
docx
Urheber
Krämer, Stefan
Herausgeber
Helmholtz-Zentrum für Umweltforschung
Rechteinhaber
Helmholtz-Zentrum für Umweltforschung
Hinweis zur Zitation
Kraemer S, Busch W, Schuettler A (2019): A self-organizing map of the fathead minnow liver transcriptome to identify consistent toxicogenomic patterns across chemical fingerprints. Environmental Toxicology and Chemistry (accepted)
Kontakt
Stefan Kraemer (stefan.kraemer@ufz.de)
Zeitpunkt oder -bereich der Erzeugung der Daten
01.11.2017 - 05.12.2019
Sprache des Inhalts
Englisch
Zielgruppe
publicly available
Aktualisierungspolitik
Der Datenbestand wird nicht erweitert
Zugriffsrechte
freier Zugriff