Analysis of measurements of a polymer

Patent metadata

Field	Value
Publication number	US-11959906-B2
Application number	US-201916245306-A
Country	US
Kind code	B2
Filing date	Jan 11, 2019
Priority date	Feb 16, 2012
Publication date	Apr 16, 2024
Grant date	Apr 16, 2024

Abstract

A time-ordered series of measurements of a polymer made during translocation of the polymer through a Nanopore are analysed. The measurements are dependent on the identity of k-mers in the Nanopore, a k-mer bring k polymer units of the polymer, where k is a positive integer. The method involves deriving, from the series of measurements, a feature vector of time-ordered features representing characteristics of the measurements; and determining similarity between the derived feature vector and at least one other feature vector.

First claim

The invention claimed is:

1. A method of estimating an identity of a target polymer, the method comprising:
making electrical measurements of a polymer during translocation of the polymer through a nanopore to generate a time-ordered series of electrical measurements, wherein the polymer is a polynucleotide comprising at least 1,000 nucleotide pairs, wherein the translocation is controlled by an enzyme molecular motor and wherein each of the electrical measurements is an electrical measurement of respective k polymer units of the polymer, where k is 3 or greater;
deriving, from the time-ordered series of electrical measurements and using at least one processor, a feature vector of time-ordered features representing characteristics of the time-ordered series of electrical measurements, wherein the features comprise: an average of the electrical measurements, a period of the electrical measurements, a variance of the electrical measurements, asymmetry information, confidence information of the electrical measurements, and a distribution of the electrical measurements, wherein the feature vector comprises at least 1000 sets of time-ordered features;
analyzing, using the at least one processor, the feature vector that comprises the at least 1000 sets of time-ordered features and at least one other feature vector indicative of the target polymer by using an alignment algorithm or a machine learning algorithm; and
estimating, using the at least one processor, the identity of the target polymer using results of analyzing the feature vector comprising the at least 1000 sets of time-ordered features and the at least one other feature vector indicative of the target polymer.

2. The method according to claim 1 , wherein the at least one other feature vector is at least one other feature vector stored in a memory in respect of at least one class.

3. The method according to claim 2 , further comprising selecting the at least one other feature vector stored in the memory based on the polymer.

4. The method according to claim 2 , wherein the at least one other feature vector stored in the memory comprises an overall feature vector of a common polymer constructed from the feature vectors of fragments.

5. The method according to claim 2 , wherein the analyzing comprises determining similarity between an entirety or part of the derived feature vector and an entirety of the at least one other feature vector stored in the memory.

6. The method according to claim 2 , wherein the analyzing comprises determining similarity between an entirety or part of the derived feature vector between the derived feature vector and a part of the at least one other feature vector stored in the memory.

7. The method according to claim 2 , further comprising classifying the polymer from which the derived feature vector is derived as belonging to a said class on a basis of the results of the analyzing.

8. The method according to claim 7 , further comprising counting numbers of feature vectors belonging to different classes.

9. The method according to claim 7 , further comprising identifying localized regions where the derived feature vector is dissimilar to a feature vector in respect of the class in which the polymer is classified as belonging.

10. The method according to claim 1 , wherein the at least one other feature vector is a feature vector derived using a same method.

11. The method according to claim 10 , wherein the at least one other feature vector is plural other feature vectors derived using the same method, and the method further comprises identifying features vectors that are derived from polymers that are fragments of a common polymer on basis of similarity in overlapping parts of the feature vectors.

12. The method according to claim 10 , further comprising constructing an overall feature vector of a common polymer from feature vectors of identified fragments.

13. The method according to claim 10 , wherein the at least one other feature vector is plural other feature vectors derived using the same method, and the method further comprises identifying clusters of similar feature vectors as a class and classifying the polymers from which the feature vectors are derived as belonging to an identified class.

14. The method according to claim 1 , wherein the at least one other feature vector comprises a feature vector stored in a memory and the analyzing comprises determining localized regions where the feature vector is dissimilar to the at least one other feature vector stored in the memory.

15. The method according to claim 1 , wherein groups of consecutive measurements are dependent on a respective k-mer that is different for each group, and deriving the feature vector comprises identifying groups of consecutive measurements, and, in respect of each group, deriving values of one or more features that represent characteristics of the measurements of the group.

16. The method according to claim 1 , wherein the polymer units are nucleotides.

17. The method according to claim 1 , wherein the nanopore is a biological pore.

18. The method according to claim 1 , wherein said translocation of the polymer through the nanopore is performed in a ratcheted manner in which successive k-mers are registered with the nanopore.

19. The method according to claim 1 , wherein the translocation of the polymer is controlled by a molecular ratchet that is a polymer binding protein.

20. At least one non-transitory computer readable medium storing instructions that, when executed by at least one processor, perform the method according to claim 1 .

21. The method according to claim 1 , wherein the machine learning algorithm utilizes a Hidden Markov Model (HMM).

22. The method of claim 1 , wherein making the electrical measurements of the polymer during translocation of the polymer through the nanopore comprises generating the time-ordered series of measurements of the polymer during cis to trans translocation of the polymer through the nanopore.

23. An analysis device, comprising:
means to make electrical measurements of a polymer during translocation of a polymer through a nanopore to generate a time-ordered series of electrical measurements, wherein the polymer is a polynucleotide comprising at least 1,000 nucleotide pairs, wherein the translocation is controlled by an enzyme molecular motor and wherein each electrical measurement is an electrical measurement of respective k polymer units of the polymer, where k is 3 or greater;
means for deriving, from the time-ordered series of electrical measurements, a feature vector of time-ordered features representing characteristics of the time-ordered series of electrical measurements, wherein the features comprise: an average of the electrical measurements, a period of the electrical measurements, a variance of the electrical measurements, asymmetry information, confidence information of the electrical measurements, and a distribution of the electrical measurements, wherein the feature vector comprises at least 1000 sets of time-ordered features; and
means for analyzing, using at least one processor, the feature vector that comprises the at least 1000 sets of time-ordered features and at least one other feature vector indicative of the target polymer by using an alignment algorithm or a machine learning algorithm;
means for estimating an identity of the target polymer using results of analyzing the feature vector comprising the at least 1000 sets of time-ordered features an

Assignees

• Oxford Nanopore Tech Plc

Inventors

• Reid Stuart William
• Clarke James Anthony
• White James
• Harper Gavin

Classifications

• G01N33/48792 Primary

  Data management, e.g. communication with processing unit (for in vivo diagnostics A61B5/0002; transmission systems for measured values G08C) · CPC title

• B82Y15/00

  Nanotechnology for interacting, sensing or actuating, e.g. quantum dots as markers in protein assays or molecular motors · CPC title

• C12Q1/6858 Primary

  Allele-specific amplification · CPC title

• C12Q1/6869

  Methods for sequencing · CPC title

• G01N27/02

  by investigating impedance · CPC title