DREAMS: deep read-level error model for sequencing data applied to low-frequency variant calling and circulating tumor DNA detection.
Mikkel H ChristensenSimon O DrueMads H RasmussenAmanda FrydendahlIben LyskjærChristina DemuthJesper NorsKåre A GotschalckLene H IversenClaus L AndersenJakob Skou PedersenPublished in: Genome biology (2023)
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.
Keyphrases
- circulating tumor
- cell free
- circulating tumor cells
- papillary thyroid
- loop mediated isothermal amplification
- single molecule
- electronic health record
- label free
- squamous cell
- real time pcr
- big data
- single cell
- squamous cell carcinoma
- lymph node metastasis
- patient safety
- drug delivery
- machine learning
- emergency department
- artificial intelligence
- gene expression
- adverse drug