DREAMS: deep read-level error model for sequencing data applied to
Por um escritor misterioso
Last updated 06 julho 2024
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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.
Top 10 Projects for Beginners in Computer Vision and Medical
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Using Nucleus and TensorFlow for DNA Sequencing Error Correction
![DREAMS: deep read-level error model for sequencing data applied to](https://www.biorxiv.org/content/biorxiv/early/2022/09/28/2022.09.27.509150/F4.large.jpg)
DREAMS: Deep Read-level Error Model for Sequencing data applied to
![DREAMS: deep read-level error model for sequencing data applied to](https://pure.au.dk/portal/files/207673513/Jesper_Nors_kopi.jpg)
Jesper Berg Nors - Research - Aarhus University
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Stop Stealing Dreams. if you don't underestimate me, I…
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DeSP: a systematic DNA storage error simulation pipeline
![DREAMS: deep read-level error model for sequencing data applied to](https://media.springernature.com/lw685/springer-static/image/art%3A10.1186%2Fs13059-023-02920-1/MediaObjects/13059_2023_2920_Fig3_HTML.png)
DREAMS: deep read-level error model for sequencing data applied to
![DREAMS: deep read-level error model for sequencing data applied to](https://www.biorxiv.org/content/biorxiv/early/2019/05/20/642843/F1.large.jpg)
Benchmarking of computational error-correction methods for next
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DREAMS: Deep Read-level Error Model for Sequencing data applied to
![DREAMS: deep read-level error model for sequencing data applied to](https://media.springernature.com/lw685/springer-static/image/art%3A10.1186%2Fs13059-019-1659-6/MediaObjects/13059_2019_1659_Fig6_HTML.png)
Analysis of error profiles in deep next-generation sequencing data
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Explain it to me like a 5-year-old: Deep Sequence Modeling
Reimagining our futures together: a new social contract for education
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Characterizing genetic intra-tumor heterogeneity across 2,658
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