METHOD FOR DETERMINING A POLYMER SEQUENCE
The invention resides in a method of determining a sequence of a target polymer, or part thereof, comprising polymer units comprising canonical and non-canonical polymer units. The method comprises taking a series of measurements of a signal relating to the target polymer wherein a measurement of the signal is dependent upon a plurality of polymer units, and wherein the polymer units of the target polymer modulate the signal, and wherein a non-canonical polymer unit modulates the signal differently from a corresponding canonical polymer unit. The series of measurements are analysed using a machine learning technique that attributes a measurement of a non-canonical polymer unit to being a measurement of a respective corresponding canonical polymer unit. The sequence of the target polymer, or part thereof, is determined from the analysed series of measurements. A non-canonical polymer unit identified from the analysis can be additionally or alternatively determined. Two or more types of non-canonical polymer units corresponding to the two or more types of canonical polymer unit can be used. The polynucleotide can be DNA.
1 . A method of determining a sequence of a target polymer, or part thereof, comprising polymer units comprising canonical and non-canonical polymer units, the method comprising:
taking a series of measurements of a signal relating to the target polymer, wherein a measurement of the signal is dependent upon a plurality of polymer units, and wherein the polymer units of the target polymer modulate the signal, and wherein a non-canonical polymer unit modulates the signal differently from a corresponding canonical polymer unit;
analysing the series of measurements using a machine learning technique that attributes a measurement of a non-canonical polymer unit to being a measurement of a respective corresponding canonical polymer unit; and
determining the sequence of the target polymer, or part thereof, from the analysed series of measurements.
2 . A method according to claim 1 , wherein a non-canonical polymer unit identified from the analysis is additionally or alternatively determined.
3 . A method of claim 1 , wherein the target polymer comprises two or more types of non-canonical polymer units corresponding to the two or more types of canonical polymer unit.
4 . A method according to claim 1 , wherein the identity and sequence position of a non-canonical polymer unit is determined.
5 . A method according claim 1 , wherein the target polymer comprises non-canonical polymer units corresponding to each type of canonical polymer unit.
6 . A method according to claim 1 , wherein the machine learning technique does not determine between whether a polymer unit is non-canonical or a corresponding canonical polymer unit
7 . The method according to claim 1 wherein, the target polymer comprises plural non-canonical polymer units for each of the one or more types of non-canonical polymer unit present.
8 . A method according to claim 1 , wherein a non-canonical polymer unit may correspond to more than one canonical polymer unit.
9 . A method according to claim 1 , wherein the target polymer comprises approximately 50% of non-canonical polymer units.
10 . A method according to claim 1 , wherein a non-canonical polymer unit is a modified canonical polymer unit.
11 . A method according to claim 1 , wherein the non-canonical polymer unit is naturally modified.
12 . A method according to claim 1 , wherein the series of measurements are taken during movement of the target polymer with respect to a nanopore.
13 . A method according to claim 1 , wherein the measurements are measurements indicative of ion current flow through the nanopore or measurements of a voltage across the nanopore during translocation of the target polymer.
14 . A method according to claim 1 wherein the machine learning technique is trainable by a method comprising the steps of:
providing a plurality of target polymers comprising non-canonical units that have been substituted for equivalent canonical units at varying sequence positions in the target polymer;
taking series of measurements of signals relating to the target polymers;
analysing the series of measurements using the machine learning technique; and
estimating the corresponding canonical polymer units of the polymer training strands.
15 . (canceled)
16 . A method according to claim 1 wherein the polymer is a polynucleotide and the polymer units are nucleotide bases.
17 . A method according to claim 1 , wherein the one or more non-canonical bases has been modified by means of an enzyme.
18 . A method according to claim 1 , further comprising the step of modifying a canonical polymer to provide the target polymer comprising one or more one or more non-canonical bases of one or more different types.
19 . A method according to claim 1 , wherein the polynucleotide comprising one or more non-canonical bases of one or more different types is generated from its complement by use of a polymerase and a proportion of non-canonical bases.
20 . A method according to claim 1 wherein the polynucleotide is DNA.
21 - 22 . (canceled)
23 . A method according to claim 14 , wherein a polynucleotide training strand comprises more than one type of non-canonical polymer unit.
24 - 42 . (canceled)