Methods and Processes for Assessment of Genetic Variations

Abstract

Technology provided herein relates in part to non-invasive classification of one or more genetic copy number variations (CNVs) for a test sample. Technology provided herein is useful for classifying a genetic CNV for a sample as part of non-invasive pre-natal (NIPT) testing and oncology testing, for example.

1 - 55 . (canceled) 
     
     
         1 . A computer-implemented method, comprising:
 obtaining nucleic acid from a biological sample that was obtained from a subject;   sequencing the nucleic acid from the biological sample or derivatives thereof to obtain thousands to millions of sequence reads; and   determining a presence or absence of a copy number variation based on an analysis of the thousands to millions of sequence reads, wherein the analysis comprises determining a plurality of sequence read quantifications corresponding to a plurality of segments.   
     
     
         2 . The computer-implemented method of claim  56 , wherein the biological sample is a liquid sample, and the nucleic acid is cell-free nucleic acid. 
     
     
         3 . The computer-implemented method of claim  56 , wherein the biological sample comprises a majority nucleic acid species and a minority nucleic acid species. 
     
     
         4 . The computer-implemented method of claim  56 , further comprising:
 capturing a set of fragments in the nucleic acid from the biological sample using a hybridization-based technique;   ligating an adapter oligonucleotide to each fragment, wherein each adapter oligonucleotide comprises a unique barcode polynucleotide; and   enriching the set of ligated fragments to obtain a library of nucleic acid, wherein the library of nucleic acid is the derivatives of the nucleic acid from the biological sample.   
     
     
         5 . The computer-implemented method of claim  59 , further comprising:
 data-mining disease databases to determine a set of genomic regions;   aligning the thousands to millions of sequence reads to the set of genomic regions of a reference genome; and   retaining sequence reads aligned to a subset of the set of genomic regions by filtering out one or more portions of the set of genomic regions based on a predetermined criterion.   
     
     
         6 . The computer-implemented method of claim  60 , further comprising eliminating non-uniquely mapped sequence reads from the analysis. 
     
     
         7 . The computer-implemented method of claim  59 , wherein the determining the presence or absence of the copy number variation comprises determining the presence or absence of the copy number variation in a fetal fraction of the biological sample according to a set of consensus sequences generated from the sequence reads using the unique barcode polynucleotides. 
     
     
         8 . A system comprising:
 one or more data processors; and   a non-transitory computer readable storage medium containing instructions which, when executed on the one or more data processors, cause the one or more data processors to perform:
 obtaining nucleic acid from a biological sample that was obtained from a subject; 
 sequencing the nucleic acid from the biological sample or derivatives thereof to obtain thousands to millions of sequence reads; and 
 determining a presence or absence of a copy number variation based on an analysis of the thousands to millions of sequence reads, wherein the analysis comprises determining a plurality of sequence read quantifications corresponding to a plurality of segments. 
   
     
     
         9 . The system of claim  63 , wherein the biological sample is a liquid sample, and the nucleic acid is cell-free nucleic acid. 
     
     
         10 . The system of claim  63 , wherein the biological sample comprises a majority nucleic acid species and a minority nucleic acid species. 
     
     
         11 . The system of claim  63 , wherein the one or more data processors is further configured to perform:
 capturing a set of fragments in the nucleic acid from the biological sample using a hybridization-based technique;   ligating an adapter oligonucleotide to each fragment, wherein each adapter oligonucleotide comprises a unique barcode polynucleotide; and   enriching the set of ligated fragments to obtain a library of nucleic acid, wherein the library of nucleic acid is the derivatives of the nucleic acid from the biological sample.   
     
     
         12 . The system of claim  66 , wherein the one or more data processors is further configured to perform:
 data-mining disease databases to determine a set of genomic regions;   aligning the thousands to millions of sequence reads to the set of genomic regions of a reference genome; and   retaining sequence reads aligned to a subset of the set of genomic regions by filtering out one or more portions of the set of genomic regions based on a predetermined criterion.   
     
     
         13 . The system of claim  67 , wherein the one or more data processors is further configured to perform eliminating non-uniquely mapped sequence reads from the analysis. 
     
     
         14 . The system of claim  66 , wherein the determining the presence or absence of the copy number variation comprises determining the presence or absence of the copy number variation in a fetal fraction of the biological sample according to a set of consensus sequences generated from the sequence reads using the unique barcode polynucleotides. 
     
     
         15 . A non-transitory computer readable storage medium storing instructions that, when executed by one or more processors of a computing system, cause the computing system to perform:
 obtaining nucleic acid from a biological sample that was obtained from a subject;   sequencing the nucleic acid from the biological sample or derivatives thereof to obtain thousands to millions of sequence reads; and   determining a presence or absence of a copy number variation based on an analysis of the thousands to millions of sequence reads, wherein the analysis comprises determining a plurality of sequence read quantifications corresponding to a plurality of segments.   
     
     
         16 . The non-transitory computer readable storage medium of claim  70 , wherein the biological sample is a liquid sample, and the nucleic acid is cell-free nucleic acid. 
     
     
         17 . The non-transitory computer readable storage medium of claim  70 , wherein the instructions are further configured to perform:
 capturing a set of fragments in the nucleic acid from the biological sample using a hybridization-based technique;   ligating an adapter oligonucleotide to each fragment, wherein each adapter oligonucleotide comprises a unique barcode polynucleotide; and   enriching the set of ligated fragments to obtain a library of nucleic acid, wherein the library of nucleic acid is the derivatives of the nucleic acid from the biological sample.   
     
     
         18 . The non-transitory computer readable storage medium of claim  72 , wherein the instructions are further configured to perform:
 data-mining disease databases to determine a set of genomic regions;   aligning the thousands to millions of sequence reads to the set of genomic regions of a reference genome; and   retaining sequence reads aligned to a subset of the set of genomic regions by filtering out one or more portions of the set of genomic regions based on a predetermined criterion.   
     
     
         19 . The non-transitory computer readable storage medium of claim  73 , wherein the instructions are further configured to perform eliminating non-uniquely mapped sequence reads from the analysis. 
     
     
         20 . The non-transitory computer readable storage medium of claim  72 , wherein the determining the presence or absence of the copy number variation comprises determining the presence or absence of the copy number variation in a fetal fraction of the biological sample according to a set of consensus sequences generated from the sequence reads using the unique barcode polynucleotides.