Methods for Optimizing Direct Targeted Sequencing

Patent metadata

Field	Value
Publication number	US-2020082908-A1
Application number	US-201916558009-A
Country	US
Kind code	A1
Filing date	Aug 30, 2019
Priority date	Mar 3, 2017
Publication date	Mar 12, 2020
Grant date	—

Abstract

Described are methods for selecting an amount of a critical parameter (such as an amount of a sequencing library, amount of a capture probe library, or a number of amplification cycles) for direct targeted sequencing. The methods include hybridizing capture probes in a capture probe library to surface-bound oligonucleotides; extending the surface-bound oligonucleotides using the hybridized capture probes as a template; hybridizing nucleic acid molecules from a sequencing library to the surface-bound capture probes; extending the surface-bound capture probes using the hybridized nucleic acid molecules as a template; amplifying the surface-bound complements of the nucleic acid molecules by bridge amplification for a number of amplification cycles; sequencing the amplified surface-bound complements of the nucleic acid molecules to determine an average cluster density after a predetermined number of sequencing cycles; repeating these steps at a plurality of different amounts of the critical parameter; and selecting an amount of the critical parameter.

First claim

1 .- 53 . (canceled)

54 . A method of sequencing a test sequencing library, comprising:
(a) hybridizing capture probes in a capture probe library to surface-bound oligonucleotides, the capture probes comprising a first end comprising a sequence that hybridizes to surface-bound oligonucleotides and a second end comprising a portion of a region of interest, wherein the concentration of the capture probes is about 40 to about 70 nanomolar; (b) extending the surface-bound oligonucleotides using the hybridized capture probes as a template to produce surface-bound capture probes comprising a sequence that hybridizes to a portion of a region of interest; (c) removing the capture probes; (d) hybridizing nucleic acid molecules from about 1 μM to about 50 μM of a test sequencing library comprising the region of interest to the surface-bound capture probes, wherein the concentration of the nucleic acid molecules results in a cluster density of about 600 K/mm 2 to about 1500 K/mm 2 ; (e) extending the surface-bound capture probes using the hybridized nucleic acid molecules as a template to produce surface-bound complements of the nucleic acid molecules; (f) amplifying the surface-bound complements of the nucleic acid molecules by bridge amplification for at least 30 amplification cycles; (g) sequencing the amplified surface-bound complements of the nucleic acid molecules.

55 .- 59 . (canceled)

60 . A method for selecting an amount of a sequencing library for direct targeted sequencing, comprising sequencing a test sequencing library according to claim 54 , wherein step (g) comprises sequencing the amplified surface-bound complements of the nucleic acid molecules to determine an average cluster density after a predetermined number of sequencing cycles, and wherein the method further comprises:
(h) repeating steps (a)-(g) at a plurality of different amounts of the sequencing library; and (i) selecting an amount of the sequencing library that provides:
(1) the highest average cluster density, wherein the highest average cluster density is within a predetermined cluster density range;
(2) an average cluster density that overlaps with a variance of the highest average cluster density, wherein the highest average cluster density and the average cluster density provided by the selected amount of the sequencing library are within a predetermined cluster density range; or
(3) a cluster density variance that overlaps with the variance of the highest average cluster density, wherein the highest average cluster density and the average cluster density provided by the selected amount of the sequencing library are within a predetermined cluster density range.

61 . The method of claim 60 , comprising:
determining an average sequencing quality metric after the predetermined number of sequencing cycles; selecting a plurality of amounts of the sequencing library that provide an average cluster density that overlaps with a variance of the highest average cluster density, or a cluster density variance that overlaps with the variance of the highest average cluster density, wherein the highest average cluster density and the average cluster densities provided by the plurality of selected amounts of the sequencing library are within the predetermined cluster density range; and selecting the amount of the sequencing library that provides the highest average sequencing quality metric from the plurality of selected amounts of the sequencing library that provide an average cluster density that overlaps with a variance of the highest average cluster density or a cluster density variance that overlaps with the variance of the highest average cluster density.

62 . The method of claim 60 , further comprising:
determining an average cluster intensity and an average sequencing quality metric after the predetermined number of sequencing cycles; selecting a plurality of amounts of the sequencing library that provide an average cluster density that overlaps with a variance of the highest average cluster density, or a cluster density variance that overlaps with the variance of the highest average cluster density, wherein the highest average cluster density and the average cluster densities provided by the plurality of selected amounts of the sequencing library are within a predetermined cluster density range; selecting a plurality of amounts of the sequencing library that provide an average sequencing quality metric that overlaps with a variance of the highest average sequencing quality metric, or a sequencing quality metric variance that overlaps with the variance of the highest average sequencing quality metric, from the plurality of selected amounts of the sequencing library that provide an average cluster density that overlaps with a variance of the highest average cluster density or a cluster density variance that overlaps with the variance of the highest average cluster density; and selecting the amount of the sequencing library that provides the highest average cluster intensity from the plurality of selected amounts of the sequencing library that provide an average sequencing quality metric that overlaps with a variance of the highest average sequencing quality metric, or a sequencing quality metric variance that overlaps with the variance of the highest average sequencing quality metric.

63 . The method of claim 60 , comprising:
determining an average cluster intensity after the predetermined number of sequencing cycles; selecting a plurality of amounts of the sequencing library that provide an average cluster density that overlaps with a variance of the highest average cluster density, or a cluster density variance that overlaps with the variance of the highest average cluster density, wherein the highest average cluster density and the average cluster densities provided by the plurality of selected amounts of the sequencing library are within a predetermined cluster density range; and selecting an the amount of the sequencing library that provides the highest average cluster intensity from plurality of selected amounts of the sequencing library that provide an average cluster density that overlaps with a variance of the highest average cluster density or a cluster density variance that overlaps with the variance of the highest average cluster density.

64 . The method of claim 60 , further comprising repeating steps (a)-(g) at a plurality of amounts of the capture probe library; and selecting an amount of the capture probe library that provides:
(1) the highest average cluster density, wherein the highest average cluster density is within a predetermined cluster density range; (2) an average cluster density that overlaps with a variance of the highest average cluster density, wherein the highest average cluster density and the average cluster density provided by the selected amount of the capture probe library are within a predetermined cluster density range; or (3) a cluster density variance that overlaps with the variance of the highest average cluster density, wherein the highest average cluster density and the average cluster density provided by the selected amount of the capture probe library are within a predetermined cluster density range.

65 . The method of claim 64 , comprising:
determining an average sequencing quality metric after the predetermined number of sequencing cycles; selecting a plurality of amounts of the capture probe library that provide an average cluster density that overlaps with a variance of the highest average cluster density, or a cluster density variance that overlaps with the variance of the highest average cluster density, wherein the highest average cluster density and the average cluster densities provided by the

Assignees

• Myriad Womens Health Inc

Inventors

• Lai Henry
• Chu Clement S

Classifications

• C12N15/1089

  Design, preparation, screening or analysis of libraries using computer algorithms · CPC title

• G16B40/00

  ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding · CPC title

• C12Q1/6874

  involving nucleic acid arrays, e.g. sequencing by hybridisation · CPC title

• C12N15/1068

  Template (nucleic acid) mediated chemical library synthesis, e.g. chemical and enzymatical DNA-templated organic molecule synthesis, libraries prepared by non ribosomal polypeptide synthesis [NRPS], DNA/RNA-polymerase mediated polypeptide synthesis · CPC title

• G16B30/00 Primary

  ICT specially adapted for sequence analysis involving nucleotides or amino acids · CPC title