DREAMS: deep read-level error model for sequencing data applied to
Por um escritor misterioso
Last updated 03 março 2025
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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.
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Cancers, Free Full-Text
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Jesper Berg Nors - Research - Aarhus University
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Applied Sciences, Free Full-Text
Reimagining our futures together: a new social contract for education
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PDF) DREAMS: deep read-level error model for sequencing data
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SequencErr: measuring and suppressing sequencer errors in next
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Stop Stealing Dreams. if you don't underestimate me, I…
Active Site Sequence Representations of Human Kinases Outperform
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DREAMS: deep read-level error model for sequencing data applied to
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