DREAMS: deep read-level error model for sequencing data applied to low-frequency variant calling and circulating tumor DNA detection.
Cancer research
Circulating tumor DNA
Colorectal cancer
Machine learning
Next-generation sequencing
Journal
Genome biology
ISSN: 1474-760X
Titre abrégé: Genome Biol
Pays: England
ID NLM: 100960660
Informations de publication
Date de publication:
30 04 2023
30 04 2023
Historique:
received:
08
07
2022
accepted:
03
04
2023
medline:
2
5
2023
pubmed:
1
5
2023
entrez:
30
4
2023
Statut:
epublish
Résumé
Circulating tumor DNA detection using next-generation sequencing (NGS) data of plasma DNA is promising for cancer identification and characterization. However, the tumor signal in the blood is often low and difficult to distinguish from errors. We present DREAMS (Deep Read-level Modelling of Sequencing-errors) for estimating error rates of individual read positions. Using DREAMS, we develop statistical methods for variant calling (DREAMS-vc) and cancer detection (DREAMS-cc). For evaluation, we generate deep targeted NGS data of matching tumor and plasma DNA from 85 colorectal cancer patients. The DREAMS approach performs better than state-of-the-art methods for variant calling and cancer detection.
Identifiants
pubmed: 37121998
doi: 10.1186/s13059-023-02920-1
pii: 10.1186/s13059-023-02920-1
pmc: PMC10150536
doi:
Substances chimiques
Circulating Tumor DNA
0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
99Informations de copyright
© 2023. The Author(s).
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