Abstract: This disclosure describes methods, non-transitory-computer readable media, and systems that can simultaneously determine estimated methylation-level values for cytosine bases and genotype calls for a target genomic sample. The disclosed system can utilize a Bayesian method on a target genomic sample's nucleotide-read data to generate estimated methylation-level values that indicate genomic coordinates at which the target genomic sample comprises a reference cytosine base or a nucleobase that could be called as a cytosine. The disclosed system can estimate methylation-level values based on prior genotype probabilities and observed nucleobases at a genomic coordinate from a read pileup of a target genomic sample. Based on the estimated methylation-level values and base-call-quality metrics, the disclosed system may generate posterior genotype probabilities for the genomic sample at the genomic coordinate.

Abstract: The disclosure describes methods, non-transitory computer-readable media, and systems that can (a) compare variants of a tumor profile generated from a subject's tumor sample with variants detected in reads of the subject's subsequent sample and (b) determine a likelihood that the subject's subsequent sample comprises residual tumor material based on the comparison of variants. For example, the disclosed system identifies a tumor profile for a sample with a subset of variants making up the profile. By later sequencing a subsequent sample from the subject and counting biological observables—such as reads supporting the subset of variants in the tumor profile—the system can use a minimal residual disease (MRD) model to compare the variants from the biological observables and the tumor profile's variants to determine a likelihood that the subsequent sample comprises residual tumor material.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for classification of a single cell from a biological sample of an entity. In one aspect, the method can include obtaining data indicating a plurality of reference positions where a known variant sequence exists for the entity in respective reference positions of the plurality of reference positions, obtaining a plurality of reads for the single cell from the biological sample of the entity, determining, for respective reads of the obtained plurality of reads, a score indicating whether a variant sequence in the respective reads of the biological sample of the entity matches the plurality of reference positions where the known variant sequence exists, and classifying the single cell as a tumor cell or normal cell based on an aggregation of the score determined for the respective reads of the obtained plurality of reads.