Models for targeted sequencing

A computer-product comprising a non-transitory computer readable medium storing a plurality of instructions for controlling a computer system to:

• generate a plurality of candidate variants of a cell free nucleic acid sample of a subject;
• for each position of a plurality of positions of a reference allele:
• determine a first depth and a first alternate depth of first sequence reads from the cell free nucleic acid sample, wherein the first sequence reads are obtained from a sample of blood, whole blood, plasma, serum, urine, cerebrospinal fluid, feces, saliva, tears, a tissue biopsy, pleural fluid, pericardial fluid, or peritoneal fluid of the subject, and wherein the first depth represents a total number of the first sequence reads at the position, and wherein the first alternate depth represents a number of the first sequence reads having a mutation at the position based on the reference allele;
• and
• determine a second depth and a second alternate depth of second sequence reads from a genomic nucleic acid sample of the subject, wherein the second sequence reads are obtained from a sample of white blood cells or tumor cells of a tumor biopsy of the subject, and wherein the second depth represents a total number of the second sequence reads at the position, and wherein the second alternate depth represents a number of the second sequence reads having a mutation at the position based on the reference allele;
• determine a first likelihood of true alternate frequency of the cell free nucleic acid sample by applying a Bayesian hierarchical model to model the first alternate depths using aa first function parameterized by the first depths and the true alternate frequency of the cell free nucleic acid sample and (ii) a first noise level of mutations with respect to healthy cell free nucleic acid samples wherein the first noise level describes expected noise rates per position of the first sequence reads;
• determine a second likelihood of true alternate frequency of the genomic nucleic acid sample by applying the Bayesian hierarchical model to model the second alternate depths using aa second function parameterized by the second depths and the true alternate frequency of the genomic nucleic acid sample and (ii) a second noise level of mutations with respect to healthy genomic nucleic acid samples, wherein the second noise level describes expected noise rates per position of the second sequence reads;
• filter the plurality of candidate variants at least by a machine learning model using the first likelihood and the second likelihood to determine a probability that the true alternate frequency of the cell free nucleic acid sample is greater than a function of the true alternate frequency of the genomic nucleic acid sample;
• and
• output the filtered candidate variants.

A system comprising a computer processor and a memory, the memory storing computer program instructions that when executed by the computer processor cause the processor to perform steps comprising:

• generating a plurality of candidate variants of a cell free nucleic acid sample of a subject;
• for each position of a plurality of positions of a reference allele:
• determining a first depth and a first alternate depth of first sequence reads from the cell free nucleic acid sample, wherein the first sequence reads are obtained from a sample of blood, whole blood, plasma, serum, urine, cerebrospinal fluid, feces, saliva, tears, a tissue biopsy, pleural fluid, pericardial fluid, or peritoneal fluid of the subject, and wherein the first depth represents a total number of the first sequence reads at the position, and wherein the first alternate depth represents a number of the first sequence reads having a mutation at the position based on the reference allele;
• and
• determining a second depth and a second alternate depth of second sequence reads from a genomic nucleic acid sample of the subject, wherein the second sequence reads are obtained from a sample of white blood cells or tumor cells of a tumor biopsy of the subject, and wherein the second depth represents a total number of the second sequence reads at the position, and wherein the second alternate depth represents a number of the second sequence reads having a mutation at the position based on the reference allele;
• determining a first likelihood of true alternate frequency of the cell free nucleic acid sample by applying a Bayesian hierarchical model to model the first alternate depths using (i) a first function parameterized by the first depths and the true alternate frequency of the cell free nucleic acid sample and (ii) a first noise level of mutations with respect to healthy cell free nucleic acid samples, wherein the first noise level describes expected noise rates per position of the first sequence reads;
• determining a second likelihood of true alternate frequency of the genomic nucleic acid sample by applying the Bayesian hierarchical model to model the second alternate depths using aa second function parameterized by the second depths and the true alternate frequency of the genomic nucleic acid sample and (ii) a second noise level of mutations with respect to healthy genomic nucleic acid samples, wherein the second noise level describes expected noise rates per position of the second sequence reads;
• filtering the plurality of candidate variants at least by a machine learning model using the first likelihood and the second likelihood to determine a probability that the true alternate frequency of the cell free nucleic acid sample is greater than a function of the true alternate frequency of the genomic nucleic acid sample;
• and
• outputting the filtered candidate variants.