DREAMS: deep read-level error model for sequencing data applied to low-frequency variant calling and circulating tumor DNA detection, Genome Biology
Por um escritor misterioso
Last updated 30 julho 2024
![DREAMS: deep read-level error model for sequencing data applied to low-frequency variant calling and circulating tumor DNA detection, Genome Biology](https://media.springernature.com/lw685/springer-static/image/art%3A10.1186%2Fs13059-023-02920-1/MediaObjects/13059_2023_2920_Fig4_HTML.png)
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.
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Additional file 1 of DREAMS: deep read-level error model for
![DREAMS: deep read-level error model for sequencing data applied to low-frequency variant calling and circulating tumor DNA detection, Genome Biology](https://www.researchgate.net/publication/370417420/figure/fig1/AS:11431281154579375@1682906276276/Error-generation-in-next-generation-sequencing-data-Normal-cells-gray-and-cancer-cells_Q320.jpg)
PDF) DREAMS: deep read-level error model for sequencing data
![DREAMS: deep read-level error model for sequencing data applied to low-frequency variant calling and circulating tumor DNA detection, Genome Biology](https://www.science.org/cms/10.1126/sciadv.abe3722/asset/a324d52b-dc13-4780-ae11-962fb7e08ea1/assets/graphic/abe3722-f1.jpeg)
Integration of intra-sample contextual error modeling for improved
![DREAMS: deep read-level error model for sequencing data applied to low-frequency variant calling and circulating tumor DNA detection, Genome Biology](https://media.springernature.com/full/springer-static/image/art%3A10.1038%2Fs41591-020-0915-3/MediaObjects/41591_2020_915_Fig1_HTML.png)
Genome-wide cell-free DNA mutational integration enables ultra
![DREAMS: deep read-level error model for sequencing data applied to low-frequency variant calling and circulating tumor DNA detection, Genome Biology](https://dfzljdn9uc3pi.cloudfront.net/2021/10897/1/fig-1-2x.jpg)
Bioinformatic strategies for the analysis of genomic aberrations
![DREAMS: deep read-level error model for sequencing data applied to low-frequency variant calling and circulating tumor DNA detection, Genome Biology](https://media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41586-022-04975-9/MediaObjects/41586_2022_4975_Fig1_HTML.png)
Deep whole-genome ctDNA chronology of treatment-resistant prostate
![DREAMS: deep read-level error model for sequencing data applied to low-frequency variant calling and circulating tumor DNA detection, Genome Biology](https://www.biorxiv.org/content/biorxiv/early/2022/09/28/2022.09.27.509150/F5.large.jpg?download=true)
DREAMS: Deep Read-level Error Model for Sequencing data applied to
![DREAMS: deep read-level error model for sequencing data applied to low-frequency variant calling and circulating tumor DNA detection, Genome Biology](https://media.springernature.com/m685/springer-static/image/art%3A10.1038%2Fs41598-017-10269-2/MediaObjects/41598_2017_10269_Fig1_HTML.jpg)
Targeted error-suppressed quantification of circulating tumor DNA
![DREAMS: deep read-level error model for sequencing data applied to low-frequency variant calling and circulating tumor DNA detection, Genome Biology](https://www.future-science.com/cms/10.2144/btn-2020-0045/asset/images/medium/figure1.gif)
Analytical validation of an error-corrected ultra-sensitive ctDNA
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