公开(公告)号：US20220284984A1

公开(公告)日：2022-09-08

申请号：US17735906

申请日：2022-05-03

Applicant: Personalis, Inc.

Inventor： Patrick Jongeneel ,  Nicholas Phillips ,  Jason Harris

IPC: G16B20/20 ,  G16B40/20

Abstract: Methods for somatic variant calling from an unmatched biological samples is provided. The method can include obtaining nucleic acid sequence data corresponding to a biological sample of a subject. The method can also include aligning the nucleic acid sequence data to a reference genome. The method can also include identifying, based on the aligned nucleic acid sequence data, a set of candidate variants in said nucleic acid sequence data. The set of candidate variants may include one or more somatic variants and one or more germline variants. The method can also include, without using a nucleic acid sequencing data from a matching biological sample of the subject, processing the set of candidate variants using a trained machine-learning model to identify the somatic variants. The method can also include outputting a report that identifies the somatic variants.

公开(公告)号：US12217830B2

公开(公告)日：2025-02-04

申请号：US17735904

申请日：2022-05-03

Applicant: Personalis, Inc.

Inventor： Nicholas Phillips ,  Jason Harris

IPC: G16B40/20 ,  C12Q1/6886 ,  G16B20/20 ,  C12Q1/686 ,  C12Q1/6874

Abstract: The disclosure provides methods for estimating tumor purity from tumor samples without use of matched-normal controls. A set of genomic regions are identified based on a nucleic acid sequence data that is aligned to a reference genome. Each genomic region of the set of genomic regions includes one or more nucleotide-sequence variants relative to a corresponding genomic region of the reference genome. A B-allele frequency distribution for the biological sample is determined based on a B-allele frequency determined for each genomic region of the set of genomic regions. The B-allele frequency distribution is processed using a trained machine-learning model to estimate a metric identifying tumor purity in the biological sample.