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.

Abstract: Systems and methods for processing sequencing data of ribonucleic acid (RNA) molecules from a test sample include obtaining a plurality of sequence reads each derived from a RNA molecule obtained from the test sample, filtering the plurality of sequence reads, identifying one or more candidate variants from the filtered plurality of sequence reads, determining a quality score for each of the identified one or more candidate variants, the quality score indicating a likelihood that the candidate variant is a false positive detection of a mutation in the RNA molecule, and outputting the one or more candidate variants having a quality score greater than a threshold quality score.

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.

Abstract: A processing system uses a Bayesian inference based model for targeted sequencing or variant calling. In an embodiment, the processing system determines first depths and first alternate depths of first sequence reads from a cell free nucleic acid sample of a subject. The processing system determines second depths and second alternate depths of second sequence reads from a genomic nucleic acid sample of the subject. The processing system determines likelihoods of true alternate frequency of the cell free nucleic acid sample and of the genomic nucleic acid sample. Using the first likelihood, the second likelihood, and one or more parameters, the processing system determines 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.