Models for targeted sequencing

Abstract

A processing system uses a Bayesian inference based model for targeted sequencing or variant calling. In an embodiment, the processing system generates candidate variants of a cell free nucleic acid sample. The processing system determines likelihoods of true alternate frequencies for each of the candidate variants in the cell free nucleic acid sample and in a corresponding genomic nucleic acid sample. The processing system filters or scores the candidate variants by the model using at least the likelihoods of true alternate frequencies. The processing system outputs the filtered candidate variants, which may be used to generate features for a predictive cancer or disease model.

Description

technical field [0001] The present disclosure generally relates to models for targeted sequencing, utilization of models in variant calling and quality control, and statistical analysis of results of physical assays performed on test samples. Background technique [0002] Computational techniques can be used on DNA sequencing data to identify mutations or variants in the DNA that may correspond to various types of cancer or other diseases. Thus, cancer diagnosis or prediction can be made by analyzing a biological sample such as a tissue biopsy or blood drawn from a subject. Detecting tumor (tumor) cell-derived DNA from blood samples is difficult because circulating tumor DNA (ctDNA) is usually present at lower levels relative to other molecules in cell-free DNA (cfDNA) extracted from blood. Existing methods do not have the ability to identify true positive results (e.g., indicative of cancer in a subject) from signal noise, impairing the ability of known and future systems ...

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Problems solved by technology

In other words, this damage can lead to systematic errors and can lead to mutations being incorrectly identified, such as false positives

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