Analysis of polymers

Abstract

A nanopore device and a method of controlling a nanopore device for analyzing a polymer, the nanopore device comprising at least one sensor element, the sensor element comprising a nanopore and a sensor, the method comprising: displacing a polymer through the nanopore; generating a series of measurements using the sensor as the polymer is displaced through the nanopore; comparing the series of measurements with reference data to determine a similar measurement; if it is determined that the measure of similarity is below a threshold, operating the nanopore device to eject the polymer from the nanopores; and determining whether the polymer has been successfully ejected from the nanopore by analyzing another set of measurements taken by the sensor.

Description

[0001] The present invention relates to analysis of polymer analytes via control of a nanopore device. More specifically, the present invention relates to control of a nanopore device comprising a sensor element. The polymer may be, for example but not limited to, a polynucleotide in which the polymer units are nucleotides.

[0002] The use of nanopores to sense interactions with molecular entities such as polynucleotides is a powerful technology that has recently undergone significant development. Nanopore devices including arrays of nanopore sensing elements have been developed to enhance data collection by allowing multiple nanopores to sense interactions in parallel, typically from the same sample.

[0003] Nanopore devices can generally employ electrical signals across nanopore channels to generate measurement signals that are interpreted to sense and / or characterize molecular entities as they interact with the nanopore. Typically, the electrical signal is applied as a potential difference or current across an array of sensor elements (also referred to as nanopore channels), which will provide a meaningful measurement signal to be interpreted. The measurement can include, for example, one of ionic current, resistance, or voltage.

[0004] Such nanopore devices can provide long-term continuous readouts of polymers, for example, in the case of polynucleotides ranging from hundreds to tens of thousands (and potentially more) of nucleotides. Data collected in this manner include measurements such as those of ionic currents, where each translocation of a sequence through a sensitive portion of the nanopore results in a slight change in the property being measured.

[0005] While such nanopore devices may offer significant advantages, there remains a desire to increase analytical speed and efficiency with respect to the use of available sensor elements in nanopore devices.

[0006] According to a first aspect of the present invention, there is provided a method for controlling a nanopore device for analyzing a polymer, the nanopore device comprising at least one sensor element comprising a nanopore and a sensor electrode, the method comprising: causing a polymer to shift through the nanopore; determining a series of measurements from the sensor electrode as the polymer shifts through the nanopore; analyzing the series of measurements against at least one reference sequence to determine measurements of similarity; in response to a measure of similarity, operating the nanopore device to eject the polymer from the sensor element; and determining whether the sensor element is free of polymer by analyzing the measurements taken by the sensor electrode.

[0007] The polymer may comprise a series of polymer units to be identified by the nanopore device. It has been found that the desire to increase the speed of analysis, particularly in polymers having a greater number of polymer units (i.e., polymers of longer lengths), may result in unforeseen inefficiencies in the nanopore device. One particular method that may be used to increase the speed of analysis, particularly during the analysis of polymers of longer lengths, would be to compare the polymer being read with reference data or consensus data and determine whether (a) the analysis should continue; or (b) the polymer should be removed from the sensor element because the polymer is considered to be of no interest. In scenario (b), a new polymer should be introduced into the sensor element for analysis. Such methods involve analyzing measurements taken from the polymer when it has been partially displaced through the nanopore (i.e., during the period when the polymer is displaced through the nanopore). In particular, a series of measurements taken from the polymer during the partial displacement are analyzed using reference data derived from at least one reference sequence of polymer units. The analysis provides a measure of similarity between the sequence of polymer units of the partially displaced polymer and at least one reference sequence. Responsive to the measure of similarity, if the similarity to the reference sequence indicates that further analysis of the polymer is not required, for example because the polymer being measured is not of interest, action can be taken to eliminate the polymer from taking measurements from another polymer.

[0008] The removal of polymers allows the measurement of additional polymers to be performed without completing the measurement of the polymer initially measured. This provides a time saving in obtaining the measurement results because the action is taken "on the fly", i.e., during the period of obtaining the measurement results from the polymer. In typical applications, this time saving may be significant because a biochemical analysis system using a nanopore can provide a long continuous reading of a polymer, and the analysis can identify at an early stage in such a reading that no further measurement is required for the polymer currently being measured. However, if, for example, the polymer is not successfully removed from the sensor element, the use of this method may result in device inefficiencies.

[0009] Device inefficiency due to failure to remove undesirable polymers may manifest itself in a variety of ways. In one instance, the polymer is only partially removed, and the device continues to analyze the undesirable polymer, thereby generating inaccurate data and using resources on the device (i.e., limited sensor elements, power, computing resources) to analyze unnecessary data. In an alternative example, the undesirable polymer may be blocked in the nanopore. In this scenario, when multiple unsuccessful attempts are used to unclog the nanopore, device resources may be used inefficiently, especially when the device is trapped in a rejection / unblocking feedback loop. Any continuous attempts to unclog the nanopore may use relatively high voltages, which is an unnecessary and inefficient use of power and may disturb or interfere with adjacent sensor elements in the sensor element array.

[0010] In a first aspect, the present invention provides an improved method for controlling a nanopore device for analyzing a polymer, the nanopore device comprising at least one sensor element comprising a nanopore and a sensor, the method comprising: causing a polymer to shift through the nanopore; generating a series of measurements using the sensor as the polymer shifts through the nanopore; comparing the series of measurements with reference data to determine a measure of similarity; if the measure of similarity is determined to be below a threshold, operating the nanopore device to eject the polymer from the nanopore; and determining whether the polymer has been successfully ejected from the nanopore by analyzing another set of measurements obtained by the sensor; wherein if it has been determined that the polymer has not been successfully ejected from the nanopore, the method further comprises operating the nanopore device to perform any of the following: (i) at least one additional step of ejecting the polymer from the nanopore; or (ii) ceasing to obtain measurements from the nanopore.

[0011] In an example, the predetermined value can be a measurement result obtained by the sensor when the nanopore does not contain a polymer. For example, the predetermined value is a configuration or measurement result based on a known characteristic of the nanopore device. In other words, when it is known that the nanopore does not contain a polymer, a baseline measurement result can be obtained by the sensor element. This can be obtained, for example, before the analyte or sample polymer is introduced into the nanopore device or before the analyte or sample polymer has been introduced into the sensor element. This is generally referred to as an open-pore measurement, i.e., there is an increase in the ion flux through the hole due to the absence of a polymer. This will, for example, cause an increase in the observed current signal. Alternatively, the predetermined value can be a range of values ​​or a value indicating a specific molecule or chemical part or a recognizable noise or pulse. In one such example, the signal may be a customized and recognizable leader sequence of a polymer unit at the end of the polymer to be analyzed. The nanopore device will be configured to identify the signal measured from the leader sequence of the shift through the nanopore.

[0012] Additionally or alternatively, once it is known that the polymer has completely displaced through the sensor, a predetermined value may be taken. The predetermined value may be attributed to a baseline measurement for each sensor element, or taken from one sensor element but attributed to all sensor elements in the array device. Essentially, a measurement from the system is expected to be a specific value or between a range of values ​​to identify whether injection has been successful.

[0013] For example, at least one sensor element may be operable to eject a polymer that is being displaced through a nanopore. More specifically, the sensor may include an electrode, and at least one sensor element may be operable to eject the polymer that is being displaced through a nanopore by applying an ejection bias voltage for ejecting the polymer, such that the step of operating the sensor element to eject the polymer from the nanopore is performed by applying an ejection bias voltage. The ejection bias voltage is provided to eject the polymer from the nanopore of the sensor element, which may be a reverse displacement of the polymer that is fully displaced through the nanopore or partially displaced, depending on the length of the polymer and the amount that has been displaced through the nanopore.

[0014] In an example, if the polymer has been successfully ejected, the method may further include the additional step of operating the sensor element to accept additional polymer translocation through the nanopore, wherein operating the sensor element to accept additional polymer is performed by applying a translocation bias voltage sufficient to enable the additional polymer to be translocated through the sensor element. The improved method of the present invention has enabled the nanopore device to correctly determine that the sensor element is free of the rejected polymer and is ready to accept additional polymer for analysis, thereby ensuring that the sensor element of the device is used as efficiently as possible given the volume / amount of polymer to be analyzed (i.e., the reading of the analyte), the length of the polymer to be analyzed, and the rate / speed at which the polymer can be analyzed by the nanopore device. Prior art methods and devices suffer from the inherent problem that the ejection step is not always successful when ejecting or translocating particularly long polynucleotide chains (such as ≥ 5kb). After the ejection fails, measurements of the existing polymer continue. It is unclear to the user whether these measurements are from new chains or existing chains because it is assumed that all ejections are successful. If a failed ejection occurs, the device is less efficient because the measurements produced are further unwanted measurements from the undesired chain for analysis.

[0015] Conversely, if the polymer has not been successfully ejected, the method includes the additional step of applying a second ejection bias voltage to eject the polymer, the second ejection bias voltage being higher than the first ejection bias voltage. In this scenario, the improved method of the present invention has enabled the nanopore device to correctly determine that the sensor element is not free of the rejected polymer and is not ready to accept additional polymer for analysis. The nanopore of the affected sensor element is determined to be blocked or an ejection bias voltage has been applied that is insufficient to completely eject the polymer (due to length, resistance to movement through the hole, etc.), and an increased ejection bias voltage can be applied to attempt to unclog the hole. In this scenario, the sensor elements of the device are used as efficiently as possible because they are not used to analyze the polymer that has been determined to be rejected.

[0016] Similarly, in an example, if the polymer has not been successfully ejected after applying the second ejection bias voltage, the method may include the additional step of applying a third ejection bias voltage to eject the polymer, wherein the third ejection bias voltage is higher than the second ejection bias voltage. In this scenario, the improved method of the present invention has enabled the nanopore device to correctly determine that the sensor element is still affected by the rejected polymer and is not yet ready to accept additional polymer for analysis (and that measurements from the currently affected sensor element should not be recorded or should be ignored). The nanopore of the affected sensor element is still determined to be blocked or an ejection bias voltage has been applied that is insufficient to completely eject the polymer (due to length, resistance to movement through the hole, etc.), and an increased ejection bias voltage can be applied to attempt to unclog the hole. In this scenario, the sensor elements of the device are used as efficiently as possible because they are not used to analyze the polymer that has been determined to be rejected.

[0017] In an example, a nanopore device may include: a detection circuit including a plurality of detection channels, each of which is capable of taking electrical measurements from a sensor element, the number of sensor elements in the array being greater than the number of detection channels; and a switching arrangement capable of selectively connecting the detection channels to respective sensor elements in a multiplexed manner. In this regard, if desired, the nanopore device can easily switch from receiving a signal from an affected or blocked nanopore of a sensor element to receiving a signal from another sensor element.

[0018] If it is determined that successful polymer ejection has not been completed at this stage, the nanopore device may determine to turn off or ignore the signal generated by the sensor element. In an example, if the polymer has not been successfully ejected, the method may further include operating the nanopore device to stop taking measurements from the currently selected sensor element.

[0019] In an example, reference data derived from at least one reference sequence of polymer units may represent actual or simulated measurements taken by the nanopore device, and the step of analyzing a series of measurements taken from the polymer during the partial shift includes comparing the series of measurements to the reference data. The reference data may relate to a portion of the sequence of the polymer of interest. Alternatively or additionally, the reference data may relate to a synthetic or custom portion or tag of the polymer of interest. The reference data is used to ensure that the polymer being analyzed is the polymer of interest so that device resources are not exhausted to analyze signals generated by polymers that are not of interest to the end user. The reference data may be a relatively short sequence so that when longer polymers are to be analyzed, the device can easily determine and eliminate polymers that are not of interest.

[0020] The rejection of polymers allows the measurement of additional polymers to be made without completing the measurement of the polymer initially measured. This provides a time saving in obtaining the measurement results, because the action is taken "on the fly", i.e., during the period in which the measurement results are obtained from the polymer. In typical applications, this time saving can be significant, because biochemical analysis systems using nanopores can provide long-term continuous readings of polymers, and the analysis can identify at an early stage in such a reading that no further measurement is needed for the polymer currently being measured.

[0021] For example, in a typical application where the polymer is a polynucleotide, sequencing at 100% accuracy will allow a preliminary determination to be made after measuring approximately 30 nucleotides. Thus, given the practically achievable accuracy, a determination may be made after measuring a few hundred nucleotides, typically 250 nucleotides. In contrast, nanopore devices are capable of measuring sequences ranging from hundreds to tens of thousands (and potentially more) nucleotides in length.

[0022] Reference data derived from at least one reference sequence of polymer units can represent a feature vector representing a time-ordered characteristic of a characteristic of a measurement result obtained by a biochemical analysis system, and the step of analyzing a series of measurement results obtained from the polymer during a partial shift includes: deriving a feature vector representing a time-ordered characteristic of a characteristic of the measurement results from the series of measurement results; and comparing the derived feature vector with the reference data.

[0023] Reference data derived from at least one reference sequence of polymer units may represent the identity of the polymer units of the at least one reference sequence, and the step of analyzing a series of measurements taken from the polymer during the partial shift comprises: analyzing the series of measurements to provide an estimate of the identity of the polymer units in the sequence of polymer units of the partially shifted polymer, and comparing the estimate to the reference data to provide a measure of similarity.

[0024] When estimating and determining the identity of polymer units, there are many techniques that can be used to interpret and resolve the measured signals from the nanopore device. Two well-known data processing techniques commonly used in this field are machine learning (such as improved neural networks) and probabilistic methods (such as HMMs involving k-mer analysis). Exemplary methods are disclosed in WO2013121224A1 and WO2018203084A1, which are incorporated herein by reference in their entirety.

[0025] It has been found that probabilistic methods for measuring signal interpretation, such as HMM-based k-mer modeling, while resource intensive and computationally complex, provide a robust and easily trainable system for comparing signals to reference data. A review of probabilistic analysis tools used in the field of nanopore devices can be found in the paper Martin et al., GenomeBiology (2022) 23:11, which is incorporated herein by reference in its entirety.

[0026] In an instance, the measurement results depend on k-mers, i.e., k polymer units of a polymer, where k is an integer; the reference data represents a reference model that treats the measurement results as observations of a reference series of k-mer states corresponding to a reference sequence of polymer units, wherein the reference model includes: transition weights, which are used for transitions between the k-mer states in the reference series of k-mer states; and emission weights, which are used for each k-mer state for different measurements being observed when the k-mer state is observed, and the step of analyzing the series of measurements taken from the polymer during the partial shift includes fitting the model to the series of measurements to provide a measure of similarity as the fit of the model to the series of measurements.

[0027] In other examples, the measurement may be based on a k-mer, ie, k polymer units of a polymer, where k is an integer.

[0028] Such methods involve analyzing measurements taken from a polymer when the polymer has been partially translocated through a nanopore (i.e., during the period in which the polymer is translocated through the nanopore). In particular, a series of measurements taken from the polymer during the partial translocation period are analyzed using reference data derived from at least one reference sequence of polymer units. The analysis provides a measure of fit to the model. In response to the measure of fit, if the measure of fit indicates that the measurement quality is poor, action can be taken to remove the polymer and take measurements from another polymer.

[0029] The rejection of polymers allows the measurement of additional polymers to be performed without completing the measurement of the polymer initially measured. This provides a time saving in obtaining the measurement results, because the action is taken "on the fly", i.e., during the period in which the measurement results are obtained from the polymers. In typical applications, this time saving can be significant, because biochemical analysis systems using nanopores can provide long-term continuous readings of polymers, while the analysis may identify poor quality measurements at an early stage.

[0030] The nanopore can be a solid-state pore or a biological pore. In a specific example, the nanopore can be a biological pore.

[0031] In an example, the polymer is a polynucleotide and the polymer units are nucleotides. The polymer translocation through the nanopore is performed in a ratchet manner using, for example, a molecular motor or enzyme to control the translocation rate. This at least ensures that there is an accurate and precise comparison between the polymer being analyzed and the reference data.

[0032] In an example, a nanopore device may include a sensor electrode and the measurement includes an electrical measurement. In a specific example, a nanopore device may include a sensor electrode and the measurement taken by the sensor indicates ion flow through the nanopore.

[0033] In a second aspect, the present invention provides a nanopore device for analyzing a polymer comprising a sequence of polymer units, wherein the nanopore device comprises at least one sensor element, the at least one sensor element comprising a nanopore, a sensor and a data processor, and the nanopore device is operable to obtain continuous measurements of the polymer from the sensor element during the displacement of the polymer through the nanopore of the sensor element, wherein the data processor of the nanopore device is arranged to analyze a series of measurements obtained from the polymer during its partial displacement when the polymer has partially displaced through the nanopore, compare the series of measurements with reference data to determine measurements of similarity; wherein the data processor of the nanopore device is further arranged to eject the polymer in response to a measure of similarity and determine whether the polymer has been successfully ejected from the nanopore by analyzing a further set of measurements obtained by the sensor.

[0034] In a third aspect, the invention provides a method of controlling a nanopore device for analyzing a polymer comprising a sequence of polymer units, wherein the nanopore device comprises at least one sensor element, the at least one sensor element comprising a nanopore and a sensor, and the nanopore device is operable to take continuous measurements of the polymer from the sensor element during translocation of the polymer through the nanopore of the sensor element, and the nanopore device is operable to generate continuous measurements of the polymer from the sensor element during translocation of the polymer through the nanopore of the sensor element, wherein the method comprises: analyzing the polymer during its partial translocation by deriving a measure of fit to a model when the polymer has partially translocated through the nanopore. The method comprises the steps of: providing a biochemical analysis system to eject the polymer and determining whether the polymer has been successfully ejected from the nanopore by analyzing another set of measurements taken by the sensor. The method comprises: a biochemical analysis system to eject the polymer and ...

[0035] In a fourth aspect, the invention provides a nanopore device for analyzing a polymer comprising a sequence of polymer units, wherein the nanopore device comprises at least one sensor element, the at least one sensor element comprising a nanopore and a sensor, and the nanopore device is operable to obtain continuous measurements of the polymer from the sensor element during translocation of the polymer through the nanopore of the sensor element, wherein a biochemical analysis system is arranged to analyze a series of measurements taken from the polymer during its partial translocation when the polymer has partially translocated through the nanopore by deriving a measure of fit to a model, the model treating the measurements as observations of a series of k-mer states of different possible types and comprising: a transition weight, for each transition between consecutive k-mer states in the series of k-mer states, for possible transitions between the possible types of k-mer states; and an emission weight, for each type of k-mer state, representing the chance of observing a given value of a measurement of the k-mer, and the biochemical analysis system is arranged to eject the polymer in response to the measure of fit and determine whether the polymer has been successfully ejected from the nanopore by analyzing a further set of measurements taken by the sensor.

[0036] In order to achieve a better understanding, embodiments of the invention will now be described by way of non-limiting examples with reference to the accompanying drawings, in which:

[0037] Figure 1 is a schematic diagram of the nanopore device;

[0038] Figure 2 is a cross-sectional view of a nanopore sensor device;

[0039] Figure 3 is a schematic diagram of the sensor element of the nanopore device;

[0040] Figure 4 is a graph of a typical signal trace of events measured by a sensor element over time;

[0041] Figure 5 is a diagram of the electronic circuit of the sensor element;

[0042] Figure 6 is a diagram of an electronic circuit for an array of sensor elements;

[0043] Figure 7 is a flow chart of a prior art method for controlling a nanopore device for analyzing polymers;

[0044] Figure 8 is a flow chart of the state detection steps;

[0045] Fig. 9 is a detailed flow chart of an instance of the state detection step;

[0046] Fig.10a is a graph of a series of original measurements and the resulting series of measurements that undergo a state detection step;

[0047] Fig.10b is a graph of an atypical signal trace of events measured over time by a sensor element, where successful ejection of a polymer from a nanopore is followed by capture of a new chain;

[0048] Fig.10c is a graph of an atypical signal trace of events measured over time by a sensor element, wherein multiple unsuccessful attempts to eject a polymer from a nanopore are followed by a natural end to the polymer displacement;

[0049] Fig.10d is a graph of an atypical signal trace of events measured over time by a sensor element where there is a single unsuccessful attempt to eject a polymer from a nanopore and the polymer is left to translocate through the nanopore;

[0050] Fig.11 and 12 is a flow chart of a method of controlling a biochemical analysis system;

[0051] Figures 13 to 16 is a flow chart of different methods used to analyze different forms of reference data;

[0052] Fig.17 is a state diagram of an instance of the reference series of k-mer states;

[0053] Fig.18 is a state diagram of a reference series of k-mer states, showing possible types of transitions between k-mer states;

[0054] Fig.19 is a flow chart of a first process for generating a reference model;

[0055] Fig. 20 is a flow chart of a first process for generating a reference model;

[0056] Fig.21 is a flow chart of a method for estimating an alignment map; and

[0057] Fig. 22 It is a graph of the comparison map.

[0058] The various features described below are examples rather than limitations. In addition, the features described do not necessarily apply together and can be applied in any combination.

[0059] Figure 1 A nanopore device 1 for analyzing a polymer is shown. First, the properties of the polymer analyzed will be described.

[0060] A polymer comprises a sequence of polymer units. Each given polymer unit can be of a different type (or identity), depending on the properties of the polymer.

[0061] The polymer can be a polynucleotide (or nucleic acid), a polypeptide (such as a protein), a polysaccharide, an oligosaccharide, or any other polymer. The polymer can be natural or synthetic. The polymer unit can be a nucleotide. Nucleotides can be of different types, including different nucleobases.

[0062] The polynucleotide may be a deoxyribonucleic acid (DNA), a ribonucleic acid (RNA), a cDNA or a synthetic nucleic acid known in the art, such as a peptide nucleic acid (PNA), a glycerol nucleic acid (GNA), a threose nucleic acid (TNA), a locked nucleic acid (LNA) or other synthetic polymers having nucleotide side chains. The polynucleotide may be single-stranded, double-stranded or contain single-stranded and double-stranded regions. Typically, cDNA, RNA, GNA, TNA or LNA is single-stranded.

[0063] The methods described herein can be used to identify any nucleotide. Nucleotides can be naturally occurring or artificial. Nucleotides typically contain a nucleobase (which may be referred to herein as a "base"), a sugar, and at least one phosphate group. Nucleobases are typically heterocyclic. Suitable nucleobases include purines and pyrimidines, and more specifically, adenine, guanine, thymine, uracil, and cytosine. Sugars are typically pentoses. Suitable nucleobases include, but are not limited to, ribose and deoxyribose. Nucleotides are typically ribonucleotides or deoxyribonucleotides. Nucleotides typically contain monophosphates, diphosphates, or triphosphates.

[0064] Nucleotides can include damaged or epigenetic bases. Nucleotides can be labeled or modified to be used as markers with unique signals. This technology can be used to identify the absence of bases, for example, abasic units or spacers in polynucleotides.

[0065] When considering measurements of modified or damaged DNA (or similar systems), methods that take into account complementary data are particularly useful. The additional information provided allows for discrimination between a large number of underlying states.

[0066] The polymer may also be a type of polymer other than a polynucleotide, some non-limiting examples of which are listed below.

[0067] The polymer may be a polypeptide, in which case the polymer units may be naturally occurring or synthetic amino acids.

[0068] The polymer may be a polysaccharide, in which case the polymer units may be monosaccharides.

[0069] Polymer can comprise polymer unit of any length.When polynucleotide is displaced through nanopore, length range can be between 5kB and 4MB or larger.When polynucleotide is displaced to the anti-side from the cis side of nanopore, the inventor has observed that when quite long polynucleotide has left this nanopore at this anti-side and needs one or more other voltage biases that this polynucleotide is ejected from this nanopore, it may be difficult for this polymer to be ejected from this nanopore.Therefore, when the polymer (such as polynucleotide) that wherein at least 50kB, 100kB, 500kB or 1MB has been displaced through the polymer of nanopore carries out ejection step, method of the present invention is particularly beneficial.

[0070] As used herein, the term "k-mer" refers to a group of k-polymer units, where k is a positive integer, including the case where k is one, in which case the k-mer is a single polymer unit. In some contexts, reference to a k-mer, where k is a plural integer, is generally a subset of a k-mer, generally excluding the case where k is one.

[0071] Thus, each given k-mer may also be of different types, corresponding to different combinations of different types for each polymer unit of the k-mer.

[0072] Back to Figure 1 , the nanopore device 1 comprises a sensor device 2 connected to an electronic circuit 4 , which in turn is connected to a data processor 6 .

[0073] First some examples will be described, in which the sensor device 2 comprises an array of sensor elements, each sensor element comprising a biological nanopore.

[0074] In a first form, the sensor device 2 may have a Figure 2 The structure shown in cross section in Figure 1 comprises a body 20 in which an array of holes 21 is formed, each hole being a recess in which a sensor electrode 22 is arranged. A large number of holes 21 are provided to optimize the data collection rate of the device 1. In general, there may be any number of holes 21, typically 256 or 1024, although Figure 2 Only some of the holes 21 are shown. The body 20 is covered by a cover 23 which extends over the body 20 and is hollow to define a chamber 24 into which each hole 21 opens. A common electrode 25 is disposed within the chamber 23. In this first form, the sensor device 2 may be a device as described in further detail in WO 2009 / 077734, the teachings of which may be applied to the nanopore device 1 and which is incorporated herein by reference.

[0075] In a second form, the sensor device 2 may have a construction as described in detail in WO 2014 / 064443, the teachings of which may be applied to the nanopore device 1 and which is incorporated herein by reference. In this second form, the sensor device 2 has a substantially similar construction to the first form, including an array of compartments generally similar to the pores 21, although they have a more complex construction and each compartment contains a sensor electrode 22.

[0076] The sensor device 2 is prepared to form an array of sensor elements 30, one of which is located at Figure 3 Schematically shown in . Each sensor element 30 is made by forming a membrane 31 across the corresponding hole 21 in the first form of the sensor device 2 or across each compartment in the second form of the sensor device 2 and then by inserting a hole 32 into the membrane 31. The membrane 31 can be made of amphiphilic molecules such as lipids. The hole 32 is a biological nanopore. The preparation can be carried out using the techniques and materials described in detail in WO 2009 / 077734 for the first form of the sensor device 2 or using the techniques and materials described in detail in WO 2014 / 064443 for the second form of the sensor device 2.

[0077] Each sensor element 30 is operable to take electrical measurements from the polymer 33 during displacement of the polymer through the aperture 32 using the sensor electrodes 22 for each sensor element 30 and the common electrode 25. The displacement of the polymer 33 through the aperture 32 produces a characteristic signal in the measured property which can be observed and can be generally referred to as an "event."

[0078] The nanopore channel is a pore 32, typically having a size on the order of nanometers. In embodiments where the molecular entity is a polymer that interacts with the nanopore channel 32 while translocating through the nanopore channel 32, in which case the nanopore channel 32 is of a suitable size to allow the polymer to pass therethrough.

[0079] The nanopore can be a protein pore or a solid-state pore. The size of the pore can be such that only one polymer can displace the pore at a time.

[0080] When the nanopore is a protein pore, it may have the following properties.

[0081] The nanopore may be a transmembrane protein pore. The transmembrane protein pore used according to the present invention includes, but is not limited to, beta-toxins such as α-hemolysin, anthrax toxin, and leukocidin; and outer membrane proteins / porins of bacteria such as Mycobacterium smegmatis porins (Msp) (e.g., MspA), lysin, outer membrane porin F (OmpF), outer membrane porin G (OmpG), outer membrane phospholipase A, and Neisseria autotransporter lipoprotein (NalP). α-helical bundle pores include barrels or channels formed by α-helices. Suitable α-helical bundle pores include, but are not limited to, inner membrane proteins and α outer membrane proteins such as WZA and ClyA toxins. The transmembrane pore may be derived from lysine. The pore may be derived from CsgG, such as disclosed in WO-2016 / 034591, WO-2017 / 149316, WO-2017 / 149317, WO-2017 / 149318 or WO-2019 / 002893, all of which are incorporated herein by reference in their entirety. The pore may be a DNA origami pore.

[0082] The protein pore may be a naturally occurring pore or may be a mutant pore. The pore may be entirely synthetic.

[0083] When the nanopore is a protein pore, it can be inserted into a membrane supported in the sensor element 30. Such a membrane can be an amphiphilic layer, such as a lipid bilayer. An amphiphilic layer is a layer formed by amphiphilic molecules such as phospholipids having hydrophilic and lipophilic properties. The amphiphilic layer can be a monolayer or a bilayer. The amphiphilic layer can be a coblock polymer, such as disclosed in WO 2014 / 064444. Alternatively, the protein pore can be inserted into an orifice provided in a solid layer, for example as disclosed in WO2012 / 005857.

[0084] The nanopore may include an orifice formed in the solid-state layer, which may be referred to as a solid-state pore. The orifice may be a well, gap, channel, groove or slit disposed in the solid-state layer, along which an analyte may pass or enter the orifice. The solid-state layer may be formed of both organic and inorganic materials, including but not limited to microelectronic materials, insulating materials (such as Si3N4, Al2O3 and SiO), organic and inorganic polymers (such as polyamides), plastics (such as Teflon®) or elastomers (such as two-component addition-cured silicone rubber) and glass. The solid-state layer may be formed of graphene.

[0085] The molecular entity interacts with the nanopore in the sensing element 30 , resulting in the output of an electrical signal at the electrode 31 that is dependent on the interaction.

[0086] In one type of sensor device 2, the electrical signal may be an ionic current flowing through the nanopore. Similarly, electrical properties other than ionic current may be measured. Some examples of alternative types of properties include, but are not limited to, ionic current, impedance, tunneling properties, such as tunneling current (e.g., as disclosed in Ivanov AP et al., Nano Lett. 2011 Jan 12; 11(1): 279-85, which is incorporated herein by reference in its entirety), and FET (field effect transistor) voltage (e.g., as disclosed in WO2005 / 124888, which is incorporated herein by reference in its entirety). One or more optical properties may be used, optionally in combination with electrical properties (Soni GV et al., Rev Sci Instrum. 2010 Jan; 81(1): 014301, which is incorporated herein by reference in its entirety). The property may be a transmembrane current, such as an ionic current flowing through a nanopore. The ionic current may typically be a DC ionic current, although in principle an alternative is to use an AC current (i.e., the amplitude of an AC current flowing under an applied AC voltage).

[0087] The interaction may occur during translocation of the molecular entity relative to, for example through, the nanopore.

[0088] The electrical signal provides a series of measurements of properties associated with the interaction between the molecular entity and the nanopore. This interaction may occur at a constriction region of the nanopore. For example, where the molecular entity is a polymer comprising a series of polymer units displaced relative to the nanopore, the measurements may be of properties that depend on the displacement of the successive polymer units relative to the pore.

[0089] The ionic solution can be provided on either side of the nanopore. The sample containing the molecular entity of interest as a polymer can be added to one side of the nanopore, e.g. Figure 2The nanopore is arranged in the membrane 31 and allows displacement relative to the nanopore 32, for example under a potential difference or a chemical gradient. An electrical signal can be obtained during the displacement of the polymer relative to the pore, for example during the displacement of the polymer 33 through the nanopore 32. The polymer 33 can be partially displaced relative to the nanopore 32.

[0090] In order to allow measurements to be taken when the polymer 33 is displaced through the nanopore, the displacement rate can be controlled by a binding moiety that is bound to the polymer 33. Typically, the binding moiety can move the polymer through the nanopore under the action of an applied field or against an applied field. The binding moiety can be a molecular motor, such as used when the binding moiety is an enzyme, an enzyme activity, or used as a molecular brake. When the polymer is a polynucleotide, a variety of methods for controlling the displacement rate are proposed, including the use of polynucleotide binding enzymes. Suitable enzymes for controlling the displacement rate of polynucleotides include, but are not limited to, polymerases, translocases, helicases, exonucleases, single-stranded and double-stranded binding proteins, and topoisomerases, such as gyrase. For other polymer types, a binding moiety that interacts with the polymer type can be used. The binding moiety may be any of the binding moieties disclosed in WO-2010 / 086603, WO-2012 / 107778 and Lieberman KR et al., J Am Chem Soc. 2010; 132(50): 17961-72), and the voltage gating scheme (Luan B et al., Phys Rev Lett. 2010; 104(23): 238103), all of which are incorporated herein by reference in their entirety.

[0091] Binding moieties can be used in a variety of ways to control polymer motion. Binding moieties can move polymers through nanopores under the action of an applied field or against an applied field. Binding moieties can be used as molecular motors, for example where the binding moiety is an enzyme, enzyme activity, or as molecular brakes. The displacement of polymers can be controlled by a molecular ratchet that controls the movement of the polymer through the pore. The molecular ratchet can be a polymer-bound protein.

[0092] The polynucleotide handling enzyme may be one of the types described in, for example, WO 2015 / 140535, WO 2015 / 055981 or WO-2010 / 086603.

[0093] Translocation of polymer 33 through nanopore 32 may occur, cis to trans or trans to cis, either under or against an applied potential. Translocation may occur under an applied potential that may control the translocation.

[0094] Nucleases that act gradually or continuously on double-stranded DNA can be used on the cis side under the applied potential to allow the remaining single strand to pass through the hole, or on the trans side at a reverse potential. Similarly, helicases that unwind double-stranded DNA can also be used in a similar manner. There is also the possibility of sequencing applications, which require chain translocation against the applied potential, but the DNA must first be "captured" by the enzyme under a reverse potential or no potential. In the case where the potential is switched back after binding, the chain will pass through the hole from cis to trans and be maintained in an extended conformation by the current. Single-stranded DNA exonucleases or single-stranded DNA-dependent polymerases can act as molecular motors, pulling the recently translocated single strand back through the hole in a controlled stepwise manner (trans to cis) against the applied potential. Alternatively, single-stranded DNA-dependent polymerases can act as molecular brakes that slow down the movement of polynucleotides through the hole. Any of the moieties, techniques or enzymes described in WO-2012 / 107778 or WO-2012 / 033524, both of which are incorporated herein by reference in their entirety, may be used to control polymer motion.

[0095] Control of polymer analyte translocation through the nanopore can be performed by other methods, such as using a clamp and applying a displacement force, as disclosed in WO 2019 / 006214. Other forces for controlling translocation, including hydrostatic pressure, voltage control, optical or magnetic field forces, and combinations of the above, can be used alternatively, such as disclosed in Lu et al., Nanoletters 13:3048-3052, 2013 and Keyser et al., Nature Physics, 2:473-477, 2008.

[0096] The sensing element 30 and / or the molecular entity may be adapted to capture the molecular entity near the corresponding nanopore. For example, the sensing element 30 may further include a capture portion arranged to capture the molecular entity near the corresponding nanopore. The capture portion may be any of the binding moieties or exonucleases described above, also having the purpose of controlling translocation, or may be provided separately.

[0097] The capture moiety may be attached to the nanopore of the sensing element. At least one capture moiety may be attached to the nanopore of each sensor element.

[0098] The capture moiety may be a tag or tether that binds to a molecular entity. In this case, the molecular entity may be tailored to achieve this binding.

[0099] Such tags or tethers can be attached to the nanopore, for example as disclosed in WO 2018 / 100370, which is incorporated herein by reference in its entirety, and as further described below.

[0100] Alternatively, where the nanopore is inserted into a membrane, such tags or tethers may be attached to the membrane, for example as disclosed in WO 2012 / 164270, which is incorporated herein by reference in its entirety.

[0101] The methods described herein may include using adapters attached to molecular entities to be determined (such as polynucleotides) to capture them in a nanopore. For example, polynucleotide adapters suitable for nanopore sequencing of polynucleotides are known in the art. Adapters for nanopore sequencing of polynucleotides may include at least one single-stranded polynucleotide or non-polynucleotide region. For example, Y-adapters for nanopore sequencing are known in the art. Y adapters typically include (a) a double-stranded region and (b) a single-stranded region or a non-complementary region at the other end. If the Y adapter includes a single-stranded region, it can be described as having an overhang. The presence of non-complementary regions in the Y adapter gives the adapter its Y shape because, unlike the double-stranded portion, the two chains do not usually hybridize to each other. The Y-shaped adapter may include one or more anchors.

[0102] The Y adapter preferably includes a leader sequence that preferentially penetrates the hole. The leader sequence typically includes a polymer. The polymer is preferably negatively charged. The polymer is preferably a polynucleotide, such as DNA or RNA, a modified polynucleotide (such as abasic DNA), PNA, LNA, polyethylene glycol (PEG) or a polypeptide. The leader preferably includes a polynucleotide, and more preferably includes a single-stranded polynucleotide. The adapter can be connected to the polymer analyte using any method known in the art. Compared with the signal generated by the polymer to be analyzed, the leader sequence can produce a significantly different signal pattern or measurement result on the signal trace, so that it can be used to determine whether a new polymer begins to shift through the hole.

[0103] The analyte can include a membrane anchor or a transmembrane pore anchor to attach the analyte to the membrane. For example, a membrane anchor or a transmembrane pore anchor can facilitate the positioning of the adaptor and the coupled polynucleotide near the nanopore. The anchor can be a polypeptide anchor and / or a hydrophobic anchor that can be inserted into the membrane. In one embodiment, the hydrophobic anchor is a lipid, a fatty acid, a sterol, a carbon nanotube, a polypeptide, a protein, or an amino acid, such as cholesterol, palmitate, or tocopherol.

[0104] The anchor may include a linker, or 2, 3, 4 or more linkers. Preferred linkers include, but are not limited to, polymers such as polynucleotides, polyethylene glycol (PEG), polysaccharides and polypeptides. These linkers may be linear, branched or cyclic. Suitable linkers are described in WO 2010 / 086602. Examples of suitable anchors and methods of attaching anchors to adapters are disclosed in WO 2012 / 164270 and WO 2015 / 150786, both of which are incorporated herein by reference in their entirety.

[0105] Examples of tags and tethers attached to a nanopore are as follows.

[0106] Nanopores used in the methods described herein can be modified to include one or more binding sites for binding to one or more analytes (e.g., molecular entities) and thereby act as a capture moiety. In some embodiments, the nanopore can be modified to include one or more binding sites for binding to an adaptor attached to the analyte. For example, in some embodiments, the nanopore can bind to a leader sequence of an adaptor attached to the analyte. In some embodiments, the nanopore can bind to a single-stranded sequence in an adaptor attached to the analyte.

[0107] In some embodiments, the nanopore is modified to include one or more tags or tethers, each tag or tether including a binding site for an analyte. In some embodiments, the nanopore is modified to include one tag or tether per nanopore, each tag or tether including a binding site for an analyte.

[0108] In some embodiments, the tag or tether may comprise or be an oligonucleotide.

[0109] Other examples of tags or tethers include, but are not limited to, a His tag, biotin or streptavidin, an antibody that binds to the analyte, an aptamer that binds to the analyte, an analyte binding domain such as a DNA binding domain (including, for example, a peptide zipper such as a leucine zipper, a single-stranded DNA binding protein (SSB)), and any combination thereof.

[0110] Any method known in the art can be used to attach a tag or tether to the outer surface of the nanopore, such as the cis side of the membrane. For example, one or more tags or tethers can be attached to the nanopore via one or more cysteines (cysteine ​​bonds), one or more primary amines (such as lysine), one or more non-natural amino acids, one or more histidines (His tags), one or more biotin or streptavidin, one or more antibody-based tags, one or more enzyme modifications of epitopes (including, for example, acetyltransferases) and any combination thereof. Suitable methods for performing such modifications are well known in the art. Suitable non-natural amino acids include, but are not limited to, 4-azido-L-phenylalanine (Faz) and Liu CC and Schultz PG, Annu. Rev. Biochem., 2010, 79, 413-444. Figure 1 Any of the amino acids numbered 1 to 71 in , which are incorporated herein by reference in their entirety.

[0111] In some embodiments where one or more tags or tethers are attached to the nanopore via cysteine ​​bonds, one or more cysteines may be introduced by substitution into one or more monomers forming the nanopore.

[0112] The transmembrane pore can be modified to enhance capture of polynucleotides. For example, the pore can be modified to increase the positive charge within the pore entrance and / or within the barrel of the pore. Such modifications are known in the art. For example, WO 2010 / 055307 discloses mutations in α-hemolysin that increase the positive charge within the barrel of the pore.

[0113] Modified MspA, lysine and CsgG pores comprising mutations that enhance polynucleotide capture are disclosed in WO 2012 / 107778, WO 2013 / 153359 and WO 2016 / 034591, respectively, all of which are incorporated herein by reference in their entirety. Any of the modified pores disclosed in these publications may be used herein.

[0114] Typically, when the measurement is a current measurement of ion current flow through the pore 32, the ion current may typically be a DC ion current, although in principle an alternative is to use an AC current (ie the amplitude of an AC current flowing under an applied AC voltage).

[0115] The nanopore device 1 may take electrical measurements of types other than current measurements of ionic current through the nanopore as described above.

[0116] Other possible electrical measurements include: current measurements, impedance measurements, tunneling measurements (eg, as described in Ivanov Ap et al., Nano Lett. 2011 Jan 12;11(1):279-85), and field effect transistor (FET) measurements (eg, as disclosed in WO2005 / 124888).

[0117] As an alternative to electrical measurements, the nanopore device 1 may take optical measurements. A suitable optical method involving fluorescence measurements is disclosed by J. Am. Chem. Soc. 2009, 131 1652-1653.

[0118] Optical measurements can be combined with electrical measurements (Soni GV et al., Rev Sci Instrum. 2010 Jan;81(1):014301).

[0119] The nanopore device 1 can take simultaneous measurements of different properties. The measurements can be of different properties because they are measurements of different physical properties, which can be any of those described above. Alternatively, the measurements can be of different properties because they are measurements of the same physical property but under different conditions, for example electrical measurements such as current measurements under different bias voltages.

[0120] Typically, each measurement taken by the nanopore device 1 depends on a k-mer, i.e., k polymer units of a corresponding sequence of polymer units, where k is a positive integer. Although ideally the measurement will depend on a single polymer unit (i.e., where k is one), for many typical types of nanopore devices 1, each measurement depends on a k-mer of multiple polymer units (i.e., where k is a plural integer). That is, each measurement depends on the sequence of each of the polymer units in the k-mer, where k is a plural integer.

[0121] In a series of measurements taken by the nanopore device 1, consecutive groups of multiple measurements depend on the same k-mer. The multiple measurements in each group have constant values, subject to some variance discussed below, and thus form a "level" in the series of raw measurements. Such a level can generally be formed by measurements that depend on the same k-mer (or consecutive k-mers of the same type), and thus correspond to a common state of the nanopore device 1.

[0122] The signal moves between a set of levels, which can be large. Given the sampling rate of the instrument and the noise on the signal, the transitions between levels can be considered instantaneous, so the signal can be approximated by an idealized step trace.

[0123] The measurement results corresponding to each state are constant on the time scale of events, but for most types of nanopore devices 1, will be affected by variance on short time scales. Variance may be caused by measurement noise (e.g., noise generated by circuits and signal processing, especially noise generated by amplifiers in the specific case of electrophysiology). Such measurement noise is unavoidable due to the small magnitude of the measured characteristics. Variance may also be caused by inherent variation or diffusion in the underlying physical or biological system of the nanopore device 1. Most types of nanopore devices 1 will experience such inherent variation to a greater or lesser extent. For any given type of nanopore device 1, both sources of variation may play a role, or one of these noise sources may dominate.

[0124] Furthermore, there is usually no a priori knowledge of the number of measurements in the group and its variation is unpredictable.

[0125] These two factors of variance and the lack of knowledge of the number of measurements may make it difficult to distinguish some of the groups, for example if the groups are short and / or the levels of the measurements of two consecutive groups are close to each other.

[0126] A series of raw measurements may take this form as a result of physical or biological processes occurring in the nanopore device 1. Thus, in some contexts, each set of measurements may be referred to as a "state".

[0127] For example, in certain types of nanopore devices 1, events consisting of polymer translocation through the pore 32 may occur in a ratcheting manner. During each step of the ratcheting motion, the ionic current flowing through the nanopore at a given voltage across the pore 32 is constant, but subject to the variance described above. Therefore, each set of measurements is associated with a step of the ratcheting motion. Each step corresponds to a state in which the polymer is in a corresponding position relative to the pore 32. Although there may be some variation in the exact position during a state, there is large-scale movement of the polymer between states. Depending on the nature of the nanopore device 1, the states may occur due to binding events in the nanopore.

[0128] The duration of each state may depend on a variety of factors, such as the potential applied to the pore, the type of enzyme used to spin the polymer, whether the polymer is pushed or pulled through the pore by the enzyme, pH, salt concentration, and the type of nucleoside triphosphates present. The duration of a state may typically vary between 0.5 ms and 3 s, depending on the nanopore device 1, and there will be some random variation between states for any given nanopore system. The expected distribution of durations can be determined experimentally for any given nanopore device 1.

[0129] The extent to which a given nanopore device 1 provides measurements that depend on the k-mer and the size of the k-mer can be examined experimentally. Methods by which this is possible are disclosed in WO-2013 / 041878.

[0130] Returning to the nanopore device 1 , electrical measurements of types other than current measurements of ionic current through the nanopore as described above may be taken.

[0131] Other possible electrical measurements include: current measurements, impedance measurements, tunneling measurements (e.g., as disclosed in Ivanov Ap et al., Nano Lett. 2011 Jan 12;11(1):279-85), and field effect transistor (FET) measurements (e.g., as disclosed in WO2005 / 124888, which is incorporated herein by reference in its entirety).

[0132] Back to Figure 1, the arrangement of the electronic circuit 4 will now be discussed. The electronic circuit 4 is connected to the sensor electrodes 22 for each sensor element 30 and the common electrode 25. The electronic circuit 4 may have a general arrangement as described in WO 2011 / 067559 (which is incorporated herein by reference in its entirety). The electronic circuit 4 is arranged as follows to control the application of a bias voltage on each sensor element 3 and to take measurements from each sensor element 3.

[0133] Figure 5 An arrangement for an electronic circuit 4 is shown showing components for a single sensor element 30, which are duplicated for each of the sensor elements 30. In this arrangement, the electronic circuit 4 comprises a detection channel 40 and a bias control circuit 41, each connected to a sensor electrode 22 of a sensor element 30.

[0134] The detection channel 40 takes measurements from the sensor electrodes 22. The detection channel 40 is arranged to amplify the electrical signals from the sensor electrodes 22. Therefore, the detection channel 40 is designed to amplify very small currents with sufficient resolution to detect characteristic changes caused by the interactions of interest. The detection channel 40 is also designed with a sufficiently high bandwidth to provide the time resolution required to detect each such interaction. These limitations require sensitive and therefore expensive components. Specifically, the detection channel 40 can be arranged as described in detail in WO 2010 / 122293 or WO 2011 / 067559, reference is made to these documents, and each of these documents is incorporated herein by reference.

[0135] The bias control circuit 41 provides a bias voltage to the sensor electrode 22 for biasing the sensor electrode 22 relative to the input of the detection channel 40 .

[0136] During normal operation, the bias voltage provided by the bias control circuit 41 is selected to enable polymer to be translocated through the pore 32. Such a bias voltage may typically have a level of up to -200 mV.

[0137] The bias voltage provided by the bias voltage control circuit 41 may also be selected so that it is sufficient to eject the displaced material from the hole 32. By having the bias control circuit 41 provide such a bias voltage, the sensor element 30 is operable to eject the polymer being displaced through the hole 32. To ensure reliable ejection, the bias voltage is typically reverse biased, although this is not always necessary. When this bias voltage is applied, the input of the detection circuit 40 is designed to remain at a constant bias potential even in the presence of a negative current (similar to the normal current amplitude, typically an amplitude of 50pA to 100pA).

[0138] Figure 4A typical signal trace of events measured by a sensor element over time is shown. Fluctuations in the signal level (current in this case) can be analyzed to determine the sequence of the polymer being analyzed. This will be discussed in more detail below.

[0139] Figure 5 The illustrated arrangement for the electronic circuit 4 requires a separate detection channel 40 for each sensor element 30 , which is complex to implement. Figure 6 Another arrangement for the electronic circuit 4 is shown, which reduces the number of detection channels 40 .

[0140] In this arrangement, the number of sensing elements 30 in the array is greater than the number of detection channels 40, and the biochemical sensing system is operable to obtain polymer measurements from selected sensor elements in a multiplexed manner, particularly an electrically multiplexed manner. This is achieved by providing a switching arrangement 42 between the sensor electrodes 23 of the sensor elements 30 and the detection channels 40. Figure 6 A simplified example with four sensor elements 30 and two detection channels 40 is shown, but the number of sensor elements 30 and detection channels 40 may be larger, typically much larger. For example, for some applications, the sensor device 2 may include a total of 4096 sensor elements 30 and 1024 detection channels 40.

[0141] The switching arrangement 42 may be arranged as described in detail in WO 2010 / 122293. For example, the switching arrangement 42 may include a plurality of 1 to N multiplexers, each multiplexer connected to a group of N sensor elements 30, and may include appropriate hardware (such as latches) to select the state of the switches.

[0142] Thus, by switching of the switching arrangement 42, the nanopore device 1 may be operated to take measurements of polymers from sensor elements 30 selected in an electrically multiplexed manner.

[0143] The switching arrangement 42 may be controlled in the manner described in WO 2010 / 122293 to selectively connect a detection channel 40 to a corresponding sensor element 30 of acceptable performance quality based on an amplified electrical signal output from the detection channel 40, but otherwise the switching arrangement is controlled as further described below.

[0144] The arrangement also comprises a bias control circuit 41 for each sensor element 30 .

[0145] Although in this example the sensor elements 30 are selected in an electrically multiplexed manner, other types of nanopore devices 1 may be configured to switch between sensor elements in a spatially multiplexed manner, for example by movement of a probe used to obtain electrical measurements, or by control of an optical system used to obtain optical measurements from different spatial locations of different sensor elements 30.

[0146] The data processor 5 connected to the electronic circuit 4 is arranged as follows. The data processor 5 can be a computer device running an appropriate program, can be implemented by a dedicated hardware device, or can be implemented by any combination thereof. The computer device can be any type of computer system in the case of use, but usually has a conventional structure. The computer program can be written in any suitable programming language. The computer program can be stored on a computer-readable storage medium, which can be of any type, such as: a recording medium that can be inserted into a drive of a computing system and can store information magnetically, optically or optically; a fixed recording medium of a computer system, such as a hard disk; or a computer memory. The data processor 5 may include a card for insertion into a computer (such as a desktop or laptop computer). The data used by the data processor 5 can be stored in its memory 10 in a conventional manner.

[0147] The data processor 5 controls the operation of the electronic circuit 3. In addition to controlling the operation of the detection channels 41, the data processor also controls the bias control circuit 41 and controls the switching of the switching arrangement 31. The data processor 5 also receives and processes a series of measurement results from each detection channel 40. The data processor 5 stores and analyzes the series of measurement results, as further described below.

[0148] The data processor 5 controls the bias control circuit 41 to apply a bias voltage sufficient to enable the polymer to be translocated through the pores 32 of the sensor element 30. This operation of the biochemical sensor element 41 allows a series of measurements from different sensor elements 30 to be collected, which can be analyzed by the data processor 5 or another data processing unit to estimate the sequence of polymer units in the polymer, for example, using the techniques described in WO 2013 / 041878. The data from different sensor elements 30 can be collected and combined.

[0149] The control will now be described Figure 7The method of the nanopore device 1 shown in Figure 5 increases the speed of analysis and ensures that the nanopore is free of ejected polymer by rejecting polymer when no further analysis is required. The method is implemented in a data processor 5. The method is performed in parallel for each sensor element 30 from which a series of measurements are taken (i.e. each sensor element 30 in the first arrangement for the electronic circuit 4 and each sensor element 30 connected to the detection channel 40 by the switching arrangement 42 in the second arrangement for the electronic circuit 4).

[0150] In step C1, the nanopore device 1 is operated by controlling the bias control circuit 30 to apply a bias voltage across the pore 32 of the sensor element 30 sufficient to enable displacement of the polymer. Based on the output signal from the detection channel 40, the displacement is detected and measurements are taken. A series of measurements are taken over time. In some cases, the following steps operate on a series of raw measurements 11 taken by the sensor device 2 (i.e., a series of measurements of the type described above, including a continuous group of multiple measurements dependent on the same k-mer without requiring a priori knowledge of the number of measurements in any group).

[0151] In other cases, such as Figure 8 As shown, the original measurement results 11 are pre-processed using a state detection step SD to derive a series of measurement results 12, which are used in place of the original measurement results in the following steps.

[0152] In such a state detection step SD, a series of raw measurements 11 is processed to identify consecutive groups of raw measurements and to derive a series of measurements 12 consisting of a predetermined number of measurements for each identified group. Thus, a series of measurements 12 is derived for each sequence of measured polymer units. The purpose of the state detection step SD is to reduce the series of raw measurements to a predetermined number of measurements associated with each k-mer in order to simplify subsequent analysis. For example, Figure 4 The noisy step wave signal shown can be reduced to states where the single measurement associated with each state can be the average current. The state can be called a level.

[0153] Fig. 9 An example of such a state detection step SD is shown which looks for a short-term increase in the derivative of a series of raw measurements 11 as follows.

[0154] In step SD-1, a series of raw measurement results 11 are differentiated to derive their derivatives.

[0155] In step SD-2, the derivative from step SD-1 is low-pass filtered to suppress high frequency noise, which the differentiation in step SD-1 tends to amplify.

[0156] In step SD-3, the filtered derivatives from step SD-2 are thresholded to detect transition points between measurement groups and thus identify the original measurement groups.

[0157] In step SD-4, a predetermined number of measurement results are derived from each set of raw measurement results identified in step SD-3. The measurement results output from step SD-4 form a series of measurement results 12.

[0158] The predetermined number of measurements may be one or more.

[0159] In the simplest approach, a single measurement is derived from each group of raw measurements, for example the mean, median, standard deviation or number of raw measurements in each identified group.

[0160] In other methods, predetermined multiple measurements of different properties are derived from each group, such as any two or more of the mean, median, standard deviation, or number of raw measurements in each identified group. In this case, the predetermined multiple measurements of different properties are considered to depend on the same k-mer because they are different measures of the same set of raw measurements.

[0161] The state detection step SD can be used with Fig. 9 The methods shown are different methods. For example, Fig. 9 A common simplification of the method shown is to use a sliding window analysis that compares the means of two adjacent windows of data. The threshold can then be set directly on the difference in means, or it can be set based on the variance of the data points in the two windows (e.g., by calculating Student's t-statistic). A particular advantage of these methods is that they can be applied without imposing many assumptions on the data.

[0162] Other information related to the measurement levels may be stored for later use in the analysis. Such information may include, but is not limited to: variance of the signal; asymmetry information; confidence of the observation; length of the group.

[0163] As an example, Fig.10a A series of raw measurements determined experimentally and reduced by a moving window are shown. In particular, Fig.10a A series of raw measurements are shown as light rays. The levels after state detection are shown as overlaid black lines.

[0164] When the polymer has been partially translocated through the nanopore, i.e. during translocation, step C2 is performed. At this point, a series of measurements taken from the polymer during the partial translocation are collected for analysis, the series of measurements being referred to herein as a "chunk" of measurements. Step C2 may be performed after a predetermined number of measurements have been taken, such that the measurement chunk has a predefined size, or may alternatively be after a predetermined amount of time. In the former case, the size of the measurement chunk may be defined by a parameter that is initialized at the start of the run, but is changed dynamically, such that the size of the measurement chunk changes.

[0165] In step C3, the blocks of measurements collected in step C2 are analyzed. The analysis uses reference data 50. As discussed in more detail below, the reference data 50 is derived from at least one reference sequence of polymer units. The analysis performed in step C3 provides a measure of similarity between (a) a sequence of polymer units of a partially displaced polymer from which measurements have been taken and (b) a reference sequence. Various techniques for performing the analysis are possible, some examples of which are described below.

[0166] The measure of similarity may indicate the similarity to the entire reference sequence or to a portion of the reference sequence, depending on the application. The technique applied in step C3 for deriving the measure of similarity may be selected accordingly, for example a global or local approach.

[0167] Moreover, measures of similarity can indicate similarity by a variety of different metrics, as long as it generally provides a measure of how similar the sequences are. Listed below are some examples of specific measures of similarity that can be determined from sequences in different ways.

[0168] In step C4, a decision is made in response to the measure of similarity determined in step C3: (a) reject the polymer being measured, (b) require further measurements to make a decision, or (c) continue taking measurements to the end of the polymer.

[0169] If the decision made in step C4 is to (a) reject the polymer being measured, the method proceeds to step C5 where the nanopore device 1 is controlled to reject the polymer so that measurements can be taken from another polymer.

[0170] Step C5 is performed in a different manner between the first arrangement and the second arrangement of the electronic circuit 4 , as follows.

[0171] In the electronic circuit 4, in step C5, the bias control circuit 30 is controlled to apply a bias voltage sufficient to eject the polymer currently being displaced across the aperture 32 of the sensor element 30. This assumes that the polymer is ejected and thereby the aperture 32 is made available to receive further polymer.

[0172] To determine whether the ejection was successful, a further step C7 is performed to check that the orifice 32 has no ejected polymer. The check in step C7 may include determining whether the orifice registers a measurement of the open orifice current after the bias voltage is applied. This will be considered by the control circuit 30 as a successful ejection of the polymer.

[0173] After successful ejection in step C5 , the method returns to step C1 and the bias control circuit 30 is thus controlled to apply a bias voltage across the aperture 32 of the sensor element 30 sufficient to enable further polymer to be captured and displaced through the aperture 32 .

[0174] Fig.10b An example of such a scenario is shown in the measured signal of . At about 2256.5 seconds, the polymer to be analyzed has been captured and has begun to shift through the nanopore, resulting in the signal seen after 2256.5 seconds. At step C4, a decision is made based on the measure of similarity determined in step C3 to eliminate the polymer measured at about 2257.75 seconds. When step C5 is implemented, it can be seen that the signal drops to a low current, after which the signal recovers to the open nanopore current (about 200 pA, i.e., the signal displayed before the polymer shifts through the nanopore). Therefore, the sensor element is ready to capture and analyze another polymer, which occurs around 2258 seconds. At step C4, a decision is made based on the measure of similarity determined in step C3 to eliminate the polymer measured at about 2259.25 seconds. When step C5 is implemented, it can be seen that the signal drops to a low current, after which the signal recovers to the open nanopore current and another polymer is captured by the sensor element.

[0175] Alternatively, check C7 may include a measurement to determine if a signal is returned to the chain after the bias voltage is applied. This would be considered a polymer ejection failure. Fig.10c An example of such a scenario is shown in the measurement signal of Fig.10c As shown, the polymer to be analyzed is captured by the sensor element immediately after 1616.5 seconds when the trace signal drops from the open pore reading (approximately 175 Pa) to the series of measurements of the polymer 63 (approximately 75 Pa). Step C4 of fitting the model to the series of measurements 63 to provide a measure of similarity 65 is performed as described above.

[0176] There may be alternative scenarios where checking C7 determines that there has been a polymer injection failure. This is now described, and these two examples are not exhaustive.

[0177] In another scenario, it may be determined that the nanopore 32 is unable to eject (ie, the polymer has become stuck during displacement through the pore). Fig.10d An example of a trace from this scene is shown in .

[0178] If polymer has not yet been ejected or the nanopore is determined to be blocked, then in step C5, after the arrangement of the electronic circuit 4, the nanopore device 1 can be stopped from taking measurements from the currently selected "discarded" or "blocked" sensor element 30 by controlling the switching arrangement 42 to disconnect the detection channel 40 currently connected to the sensor element 30 and selectively connect the detection channel 40 to a different sensor element 30. At the same time, in step C5, the bias control circuit 30 is controlled to apply a bias voltage across the pore 32 of the sensor element 30 sufficient to eject the polymer currently being displaced through the currently selected sensor element 30, so that the sensor element 30 is available to receive additional polymer in the future.

[0179] The method then returns to step C1 which is applied to the newly selected sensor element 30 so that the nanopore device 1 starts taking measurements therefrom.

[0180] If the decision made in step C4 is that (b) further measurements are required to make a decision, the method returns to step C2. Thus, measurements of the displaced polymer continue to be taken until a chunk of measurements is next collected in step C2 and analyzed in step C3. The chunk of measurements collected when step C2 is performed again may simply be new measurements to be analyzed separately, or may be new measurements combined with a previous chunk of measurements.

[0181] If the decision made in step C4 is (c) to continue taking measurements until the end of the polymer, the method proceeds to step C6 without repeating steps C2 and C3, so that no further data blocks are analyzed. The sensor element 1 continues to operate, so that measurements continue to be taken until the end of the polymer. Thereafter, the method returns to step C1 so that further polymers can be analyzed.

[0182] The degree of similarity indicated by the measure of similarity used as the basis for the decision in step C4 may vary depending on the application and nature of the reference sequence. Thus, assuming that the decision is responsive to the measure of similarity, there is generally no restriction on the degree of similarity used to make different decisions.

[0183] The following are some examples of how the dependence on the measure of similarity may vary.

[0184] In applications where the reference sequence of polymer units is an unwanted sequence, and a decision to exclude a polymer is made in step C4 in response to a measure of similarity indicating that the partially displaced polymer is an unwanted sequence, a relatively high degree of similarity can be used as a basis for excluding the polymer. Similarly, the similarity can vary depending on the nature of the reference sequence in the context of the application. When it is intended to distinguish between similar sequences, a higher degree of similarity may be required as a basis for exclusion.

[0185] Conversely, in applications where the reference sequence of polymer units from which reference data 50 is derived is the target, and a decision to exclude polymers is made in step C4 in response to a measure of similarity indicating that the partially shifted polymer is not the target, a relatively low degree of similarity can be used as a basis for excluding polymers.

[0186] As another example, if the application is to determine whether a known gene from a known bacterium is present in samples of various bacteria, then if the gene has a conserved sequence among different bacterial strains, the similarity required to determine whether a polynucleotide has the same sequence as the target will be higher than if the sequence is not conserved.

[0187] Similarly, in some of the embodiments of the invention, the measure of similarity will be equal to the degree of identity of the polymer to the target polymer, while in other embodiments the measure of similarity will be equal to the probability that the polymer is identical to the target polymer.

[0188] The similarity required as a basis for rejection may also vary depending on the potential time savings, which itself depends on the application as described below. The acceptable false positive rate may depend on the time savings. For example, when the potential time savings by rejecting unwanted polymers is relatively high, it is acceptable to reject an increased proportion of polymers that are targets, as long as there is an overall time savings from rejecting polymers that are actually unwanted.

[0189] Now return to Figure 7 In the method, if at any point during the taking of measurements of a polymer it is detected that no further measurements are being taken, indicating that the end of the polymer has been reached, the method immediately returns to step C1 so that further polymer can be analyzed.

[0190] Figure 7 The method shown may vary depending on the application. For example, in some variations, the decision in step C4 is never (c) continue taking measurements until the end of the polymer, such that the method repeats collecting and analyzing chunks of measurements until the end of the polymer.

[0191] In another variant, in step C3 , instead of using reference data 50 and determining a measure of similarity, the decision to reject a polymer in step C4 may be based on other analysis of a series of measurement results, in general on any analysis of a block of measurement results.

[0192] In one possibility, step C3 may analyze whether the block of measurements is of insufficient quality, for example has a noise level exceeding a threshold, has a wrong scaling, or is characteristic of a damaged polymer.

[0193] The decision in step C4 is made based on this analysis, rejecting the polymer based on internal quality control checks. This still involves making the decision to reject the polymer based on a chunk of measurements (i.e. a series of measurements taken from the polymer during a partial displacement), and is therefore in contrast to ejecting a polymer that results in a blockade, in which case the polymer is no longer displaced and therefore no k-mer-dependent measurements are taken.

[0194] In another possibility, the method is modified, such as Fig.11 This method is similar to Figure 7The method is the same as that of , except that step C3 is modified. In step C3, instead of using reference data 50 derived from at least one reference sequence of polymer units and determining a measure of similarity, a general model 60 is used, which regards the measurement results as observations of a series of k-mer states of different possible types and includes: transition weights 61, which are used for each transition between consecutive k-mer states in the series of k-mer states for possible transitions between possible types of k-mer states; and emission weights 62, which, for each type of k-mer state, represent the chance of observing a given value of the measurement result of the k-mer. Step C3 is modified to include deriving a measure of fit to the reference model 60. In this case, step C3 may include deriving a measure of fit to a model that regards the measurement results as observations of a series of k-mer states of different possible types and includes: transition weights, which are used for each transition between consecutive k-mer states in the series of k-mer states for possible transitions between possible types of k-mer states; and emission weights, which, for each type of k-mer state, represent the chance of observing a given value of the measurement result of the k-mer. In this case, the model may be of the type described in WO2013 / 041878 and WO2018203084. The details of the model are referenced to WO2013 / 041878, WO2018203084 and WO2020109773 (all of which are incorporated herein by reference in their entirety), but a summary is given below. A measure of fit is derived, for example, as the likelihood of the measurement result observed from the most likely sequence of k-mer states. Such a measure of fit indicates the quality of the measurement result.

[0195] The decision in step C4 is made based on this metric of fit, thereby rejecting polymers based on internal quality control checks.

[0196] Thus, if the similarity to the reference sequence of polymer units indicates that further analysis of the polymer is not required or if the measurements taken from the polymer are of poor quality, the method enables the polymer to be rejected. This provides a significant time saving, as the polymer can be rejected instantly while it is still displaced through the aperture 32. There are many applications where this is useful, some examples of which are described further below, as well as an indication of the extent of possible time saving.

[0197] Figure 7 and 11 The alternative methods may be applied independently or in combination, in which case they may be applied simultaneously (e.g., step C3 of both methods is performed in parallel, and the other steps are performed together) or sequentially (e.g., Figure 7 Before the method Fig.11method).

[0198] We will now describe Fig.12 The method of controlling the biochemical analysis system 1 to sort polymers is shown. The method is a method according to the third aspect of the present invention. In this case, the sample chamber 24 contains a sample containing polymers, which may be of different types, and the hole 21 acts as a collection chamber for collecting the sorted polymers.

[0199] The method is implemented in a data processor 5. The method is performed in parallel for a plurality of sensor elements 30, for example for each sensor element 30 in the first arrangement of the electronic circuit 4 and for each sensor element 30 connected to a detection channel 40 via a switching arrangement 42 in the second arrangement of the electronic circuit 4.

[0200] In step D1, the biochemical analysis system 1 is operated by controlling the bias control circuit 30 to apply a bias voltage sufficient to enable the polymer to be displaced across the pore 32 of the sensor element 30. This causes the polymer to begin to displace through the nanopore, and the following steps are performed during the displacement. Based on the output signal from the detection channel 40, the displacement is detected and measurements are started. A series of measurements of the polymer are taken from the sensor element 30 over time.

[0201] In some cases, the following steps operate on a series of raw measurements 11 taken by the sensor device 2 (i.e., a series of measurements of the type described above, comprising consecutive groups of multiple measurements depending on the same k-mer, without requiring a priori knowledge of the number of measurements in any group).

[0202] In other cases, the original measurement result 11 is preprocessed using a state detection step SD to derive a series of measurement results 12, which are used in place of the original measurement results in the following steps. The state detection SD can be used in conjunction with the above reference Figure 8 and 9 Proceed in the same way as described for step C1.

[0203] When the polymer has been partially translocated through the nanopore, i.e. during translocation, step D2 is performed. At this point, a series of measurements taken from the polymer during the partial translocation are collected for analysis, the series of measurements being referred to herein as a "chunk" of measurements. Step D2 may be performed after a predetermined number of measurements have been taken, such that the measurement chunk has a predefined size, or may alternatively be after a predetermined amount of time. In the former case, the size of the measurement chunk may be defined by a parameter that is initialized at the start of the run, but is changed dynamically, such that the size of the measurement chunk changes.

[0204] In step D3, the blocks of measurements collected in step D2 are analyzed. The analysis uses reference data 50. As discussed in more detail below, the reference data 50 is derived from at least one reference sequence of polymer units. The analysis performed in step D3 provides a measure of similarity between (a) a sequence of polymer units of a partially displaced polymer from which measurements have been taken and (b) a reference sequence. Various techniques for performing the analysis are possible, some examples of which are described below.

[0205] The measure of similarity may indicate similarity to the entire reference sequence or to a portion of the reference sequence, depending on the application. The technique applied in step D3 for deriving the measure of similarity may be selected accordingly, eg a global or local approach.

[0206] Moreover, measures of similarity can indicate similarity by a variety of different metrics, as long as it generally provides a measure of how similar the sequences are. Listed below are some examples of specific measures of similarity that can be determined from sequences in different ways.

[0207] In step D4, a decision is made based on the measure of similarity determined in step D3, either (a) that further measurements are needed to make a decision, (b) that polymer displacement into well 21 is complete, or (c) that the polymer being measured is ejected back into sample chamber 24. If the decision made in step D4 is that (a) further measurements are needed to make a decision, then the method returns to step D2. Thus, measurements of the displaced polymer continue to be taken until a chunk of measurements is next collected in step D2 and analyzed in step D3. The chunk of measurements collected when step D2 is performed again may simply be new measurements to be analyzed individually, or may be new measurements combined with a previous chunk of measurements.

[0208] If the decision made in step D4 is that (b) polymer translocation into pores 21 is complete, the method proceeds to step D6 without repeating steps D2 and D3, so that no further analysis of the measurement results is performed.

[0209] In step D6, the polymer is translocated into the pores 21. As a result, the polymer is collected in the pores 21.

[0210] Step D6 may be performed by continuing to apply the same bias voltage across the pores 32 of the sensor element 30 that enables the polymer to be displaced.

[0211] Alternatively, in step D6, the bias voltage can be changed to perform the remaining shift of the polymer at an increased rate, thereby reducing the time spent on the shift. This is advantageous because it increases the overall speed of the classification process. Increasing the shift speed is acceptable because it is no longer necessary to analyze the polymer. Typically, the change in bias voltage can be an increase. In a typical system, the increase may be significant. For example, in one embodiment, the shift speed can be increased from about 30 bases per second to about 10,000 bases per second. The possibility of changing the shift speed may depend on the configuration of the sensor element. For example, when a polymer binding portion such as an enzyme is used to control the shift, this may depend on the polymer binding portion used. Advantageously, a polymer binding portion that can control the rate can be selected.

[0212] During step D6 the sensor element 1 may continue to operate so that measurements continue to be taken until the end of the polymer, but this is optional as the remainder of the sequence does not need to be determined.

[0213] After step D6, the method returns to step D1 so that further polymer can be displaced.

[0214] If the decision made in step D4 is (c) to eject the polymer, the method proceeds to step D5 where the biochemical analysis system 1 is controlled to eject the polymer being measured back into the sample chamber 24 so that measurements can be taken from the additional polymer.

[0215] In step D5, the bias control circuit 30 is controlled to apply a bias voltage sufficient to eject the polymer currently being displaced across the aperture 32 of the sensor element 30. This ejects the polymer and thereby makes the aperture 32 available to receive further polymer. After such ejection in step D5, the method returns to step D1 and the bias control circuit 30 is thus controlled to apply a bias voltage sufficient to enable further polymer to be displaced through the aperture 32 across the aperture 32 of the sensor element 30.

[0216] The method repeats when returning to step D1. The repetition of the method causes a continuous polymer from the sample chamber 24 to be displaced and processed.

[0217] Thus, the method utilizes a measure of similarity provided by analysis of a series of measurements taken from the polymer during a partial shift as a basis for whether to collect a continuous polymer in the well 21. In this way, polymers from the sample in the sample chamber 24 are sorted and desired polymers are selectively collected in the well 21.

[0218] The collected polymer can be recovered. This can be done after the method has been run repeatedly by removing the sample from the sample chamber 24 and then recovering the polymer from the wells 21. Alternatively, this can be done during the displacement of the polymer from the sample, for example, by providing the biochemical analysis system 1 with a fluidic system that extracts the polymer from the wells 21.

[0219] The method can be applied to a wide range of applications. For example, the method can be applied to polymers that are polynucleotides, such as viral genomes or plasmids. Viral genomes typically have a length on the order of 10-15 kB (kilobases), and plasmids typically have a length on the order of 4 kB. In such instances, the polynucleotides do not have to be fragmented and can be collected intact. The collected viral genomes or plasmids can be used in any way, for example, for transfecting cells. Transfection is the process of introducing DNA into the nucleus of a cell, and is an important tool used in studies that study gene function and the regulation of gene expression, thus helping to facilitate basic cell research, drug discovery, and target validation. RNA and proteins can also be transfected.

[0220] The degree of similarity indicated by the measure of similarity used as the basis for the decision in step D4 may vary depending on the application and the nature of the reference sequence. Thus, provided that the decision depends on the measure of similarity, there is generally no restriction on the degree of similarity used to make different decisions.

[0221] The following are some examples of how the dependence on the measure of similarity may vary.

[0222] In many applications, the reference sequence of polymer units from which the reference data 50 is derived is the desired sequence. In this case, in step D4, a decision to complete the shift is made in response to a measure of similarity (indicating that the partially shifted polymer is the desired sequence), and a relatively high degree of similarity can be used as a basis for completing the shift.

[0223] However, this is not required. In some applications, the reference sequence of polymer units is an unwanted sequence. In this case, in step D4, a decision to complete the shift is made in response to a measure of similarity indicating that the partially shifted polymer is not an unwanted sequence.

[0224] Similarly, similarity may vary depending on the nature of the reference sequence in the context of the application. When the intention is to distinguish between similar sequences, a higher degree of similarity may be desirable as a basis for elimination.

[0225] The method can be carried out using the same reference data 50 and the same standards in step D4 for each sensor element 30. In this case, each well 21 collects the same polymer in parallel.

[0226] Alternatively, the method can be performed to collect different polymers in different wells 21. In this case, differential sorting is performed. In one example thereof, different reference data 50 are used for different sensor elements 30. In another example thereof, the same reference data 50 are used for different sensor elements 30, but step D4 is performed with different dependencies on the measure of similarity for different sensor elements.

[0227] Figure 7 , 11 The method shown in and 12 may be varied, depending on the application.

[0228] A variety of different types of reference sequences of polymer units may be used, depending on the application. Without limitation, when the polymer is a polynucleotide, the reference sequence of polymer units may comprise one or more reference genomes or regions of interest of the one or more genomes to which measurements are compared.

[0229] The source of the reference data 50 may vary, depending on the application.The reference data may be generated from a reference sequence of polymer units or from measurements taken from a reference sequence of polymer units.

[0230] In some applications, previously generated reference data 50 may be pre-stored. In other applications, the reference data 50 is generated when the method is performed.

[0231] The reference data 50 may be provided for a single reference sequence of polymer units or for a plurality of reference sequences of polymer units. In the latter case, either step C3 is performed for each sequence or one of the plurality of reference sequences is selected for use in step C3. In the latter case, the selection may be made based on various criteria, depending on the application. For example, the reference data 50 may be applicable to different types of nanopore devices 1 (e.g., different nanopores) and / or environmental conditions, in which case the selection of the reference model 8 is based on the type of nanopore device 1 actually used and / or the actual environmental conditions.

[0232] The nanopore device 1 described above is an example of a nanopore device comprising an array of sensor elements, each sensor element comprising a nanopore. However, the method can be extended to any nanopore device that is operable to obtain continuous measurements of polymers selected in a multiplexed manner without the use of nanopores. An example of such a nanopore device is a scanning probe microscope, which can be an atomic force microscope (AFM), a scanning tunneling microscope (STM), or another form of scanning microscope. In such cases, the nanopore device can be operated to obtain continuous measurements of polymers selected in a spatially multiplexed manner. For example, the polymers can be arranged on a substrate at different spatial locations, and spatial multiplexing can be provided by moving the probe of the scanning probe microscope.

[0233] In the case where the reader is an AFM, the resolution of the AFM tip may not be as fine as the size of a single polymer unit. Such measurements may therefore be a function of multiple polymer units. The AFM tip may be functionalized to interact with the polymer unit in an alternative manner, but not if not functionalized. The AFM may be operated in contact mode, non-contact mode, tapping mode, or any other mode.

[0234] Where the reader is an STM, the resolution of the measurement may not be as fine as the size of a single polymer unit and the measurement is therefore a function of a number of polymer units.The STM may be operated in a conventional manner or operated to perform spectroscopic measurements (STS) or in any other mode.

[0235] The reference data 50 may take various forms which are derived in different ways from the reference sequence of polymer units. The analysis performed in step C4 to provide a measure of similarity depends on the form of the reference data 50. Some non-limiting examples will now be described.

[0236] In a first example, the reference data 50 represent the identity of polymer units of at least one reference sequence. In this case, step C4 comprises Fig.13 The process shown in is as follows.

[0237] In step C4a-1, the measurements block 63 are analyzed to provide an estimate of the identity of the polymer units in the sequence of polymer units of the partially displaced polymer 64. Step C4a-1 may generally be performed using any method for analyzing measurements taken by a nanopore device.

[0238] Step C4a-1 can be specifically carried out using the method described in detail in WO-2013 / 041878, WO2018203084 and WO2020109773. The details of the method refer to WO 2013 / 041878, but are summarized as follows.

[0239] The method refers to a general model 60 that includes transition weights 61 and emission weights 62 for a series of k-mer states corresponding to a measurement result block 63.

[0240] Transition weights 61 are provided for each transition between consecutive k-mer states in a series of k-mer states. Each transition can be considered to be from a starting k-mer state to a destination k-mer state. Transition weights 61 represent the relative weights of possible transitions between possible types of k-mer states (which are from any type of starting k-mer state to any type of destination k-mer state). Typically, this includes weights for transitions between two k-mer states of the same type.

[0241] For each type of k-mer state, an emission weight 62 is provided. The emission weight 62 is the weight used for the different measurement outcomes being observed when the k-mer state is of that type. Conceptually, the emission weights 62 can be thought of as representing the chance of observing a given value of the measurement outcome for that k-mer state, although they need not be probabilities.

[0242] Conceptually, transition weights 61 can be thought of as representing the chances of possible transitions, although they need not be probabilities. Thus, transition weights 61 take into account the chances of the k-mer state on which the measurement depends transitioning between different k-mer states, which may be more or less likely depending on the types of the starting and destination k-mer states.

[0243] As an example and not limitation, the model may be a HMM where transition weights 61 and emission weights 62 are probabilities.

[0244] Step C4a-1 uses the reference model 60 to derive an estimate 64 of the identity of the polymer units in the sequence of polymer units of the partially displaced polymer. This can be done using known techniques appropriate to the properties of the reference model 60. Typically, such techniques derive the estimate 64 based on the likelihood of the measurement outcome predicted by the reference model 50 observed from the sequence of k-mer states.

[0245] Such methods can also provide a measure of the fit of the measurement to the model, for example, a quality score indicating the likelihood of the measurement predicted by the reference model 50 observed from the sequence of most likely k-mer states. Such measures are typically derived because they are used to derive estimates 64.

[0246] As an example of the case where the general model 60 is an HMM, the analysis technique can be a known algorithm for solving HMMs, such as the Viterbi algorithm well known in the art. In this case, the estimate 64 is derived based on the likelihood predicted by the general model 60 resulting from the entire sequence of k-mer states.

[0247] As another example where the general model 60 is an HMM, the analysis technique may be of the type disclosed in Fariselli et al., "The posterior-Viterbi: a new decoding algorithm for hidden Markov models", Department of Biology, University of Casadio, filed at Cornell University, submitted on January 4, 2005. In this approach, the posterior matrix (representing the probability of a measurement outcome observed from each k-mer state) is used and consistent paths, i.e., paths in which adjacent k-mer states are biased toward overlap, are obtained, rather than simply selecting the most likely k-mer state for each event. Essentially, this allows the recovery of the same information as would be obtained directly from the application of the Viterbi algorithm.

[0248] The above description is given in terms of a general model 60, which is an HMM, in which the transition weights 61 and emission weights 62 are probabilities, and the method uses a probabilistic technique with reference to the general model 60. Alternatively, however, the general model 60 may use a framework in which the transition weights 61 and / or emission weights 62 are not probabilities but represent the chance of a transition or measurement outcome in some other way. In this case, the method may use an analysis technique other than a probabilistic technique that is based on the likelihood predicted by the general model 60 of a series of measurement outcomes produced by a sequence of polymer units. The analysis technique may explicitly use a likelihood function, but generally this is not necessary.

[0249] In step C4a-2, the estimate 64 is compared to the reference data 50 to provide a measure of similarity 65. This comparison may use any known technique for comparing two sequences of polymer units, typically an alignment algorithm that derives an alignment map between the sequences of polymer units, and a score for the accuracy of the alignment map, which score is therefore a measure of similarity 65. Any of a number of available fast alignment algorithms may be used, such as the Smith-Waterman alignment algorithm, BLAST or derivatives thereof, or k-mer counting techniques.

[0250] This example of a form of reference data 50 has the advantage that the process for deriving the measure of similarity 65 is fast, but other forms of reference data are possible.

[0251] In a second example, the reference data 50 represent actual or simulated measurements taken by the nanopore device 1. In this case, step C4 comprises Fig.14, which simply comprises a step C4b of comparing a block of measurement results 63 with the reference data 50 to derive a measure of similarity 65. Any suitable comparison may be performed, for example using a distance function to provide a measure of the distance between the two series of measurement results as the measure of similarity 65.

[0252] In a third example, the reference data 50 represent feature vectors of time-ordered characteristics of properties of measurements taken by the nanopore device 1. Such feature vectors may be derived as described in detail in WO-2013 / 121224, which is cited and incorporated herein by reference. In this case, step C4 comprises Fig.15 The process shown in , which is carried out as follows.

[0253] In step C4c-1, the measurement result block 63 is analyzed to derive a feature vector 66 representing a time-sequential characteristic of the characteristics of the measurement result.

[0254] In step C4c-2, the feature vector 66 is compared with the reference data 50 to derive a measure of similarity 65. The comparison may be performed using the method described in detail in WO-2013 / 121224.

[0255] In a fourth example, the reference data 50 represent a reference model 70. In this case, step C4 comprises Fig.16 , which comprises a step C4d of fitting a reference model 70 to a series of measurement result blocks 63 to provide a measure of similarity 65 as a fit of the reference model 70 to the measurement result blocks 63. This can be done as follows.

[0256] The reference model 70 is a model of a reference sequence of polymer units in the nanopore device 1. The reference model 70 treats the measurement results as observations of a reference series of k-mer states corresponding to the reference sequence of polymer units. The k-mer states of the reference model 70 may model the actual k-mers on which the measurement results depend, although this is not mathematically necessary and thus the k-mer states may be abstractions of the actual k-mers. Therefore, different types of k-mer states may correspond to different types of k-mers present in the reference sequence of polymer units.

[0257] The reference model 70 can be considered as an adaptation of the general model 60 to model the measurements specifically obtained when measuring a reference sequence. Thus, the reference model 70 treats the measurements as observations of a reference series of k-mer states 73 corresponding to the reference sequence of polymer units. Thus, the reference model 70 has the same form as the general model 60, including in particular transition weights 71 and emission weights 72, which will now be described.

[0258] The transition weights 71 represent transitions between the k-mer states 73 of the reference series. Those k-mer states 73 correspond to the reference sequence of polymer units. Thus, consecutive k-mer states 73 in the reference series correspond to consecutive overlapping groups of k polymer units. Thus, there is an intrinsic mapping between the k-mer states 73 of the reference sequence and the polymer units of the reference series. Similarly, each k-mer state 73 is of a type corresponding to a combination of different types of each polymer unit in the group of k polymer units.

[0259] This refers to Fig.17 The state diagram is as follows: Fig.17 An example of three consecutive k-mer states 73 in a reference series of estimated k-mer states 73 is shown. In this example, k is three and the reference sequence of polymer units includes consecutive polymer units labeled A, A, C, G, T (although of course those particular types of k-mer states 73 are not limiting). Thus, the reference series of consecutive k-mer states 73 corresponding to those polymer units are of type AAC, ACG, CGT, which corresponds to the measured sequence of polymer unit AACGT.

[0260] Fig.18 The state diagram of shows transitions between the reference series of k-mer states 73, as represented by transition weights 71. In this example, the state may only allow forward advancement through the reference series of k-mer states 73 (although backward advancement may also be allowed in general). Three different types of transitions 74, 75, and 76 are shown below.

[0261] A transition 74 is allowed from each given k-mer state 73 in the reference series to the next k-mer state 73. This models the likelihood of consecutive measurements in a series of measurements 12 taken from consecutive k-mers of the reference sequence of polymer units. Where the measurement group block 63 is pre-processed to identify groups of consecutive measurements and derive a series of processed measurements (consisting of a predetermined number of measurements for each identified group) for further analysis, the transition weight 71 represents the transition 74 as having a relatively high likelihood.

[0262] A transition 75 is allowed from each given k-mer state 73 in the reference series to the same k-mer state. This models the likelihood of consecutive measurements in a series of measurements 12 taken from the same k-mer of the reference sequence of polymer units. This may be referred to as a "dwell". In the case where the measurement group block 63 is pre-processed to identify groups of consecutive measurements and derive a series of processed measurements consisting of a predetermined number of measurements for each identified group, the transition weight 71 represents this transition 75 as having a relatively low likelihood compared to the transition 74.

[0263] A transition 76 is allowed from each given k-mer state 73 in the reference series to a subsequent k-mer state 73 other than the next k-mer state 73. This models the likelihood of not taking a measurement from the next k-mer state, such that consecutive measurements in a series of measurements 12 are taken from k-mers of the reference sequence of separated polymer units. This may be referred to as a "skip". Where the measurement group block 63 is pre-processed to identify groups of consecutive measurements and derive a series of processed measurements consisting of a predetermined number of measurements for each identified group, the transition weight 71 represents the transition 76 as having a relatively low likelihood compared to the transition 74.

[0264] The levels of transition weights 71 for transitions 75 and 76 representing skip and stay relative to the level of transition weight 71 representing transition 74 may be derived in the same manner as transition weights 61 for skip and stay in the general model 31 , as described above.

[0265] In the alternative, where the measurement chunk 63 is not pre-processed to identify consecutive groups of measurements and derive a series of processed measurements, such that further analysis is performed on the measurement chunk 63 itself, then the transition weights 71 are similar but adapted to increase the likelihood of transitions 75 representing skipped transitions to represent consecutive measurements taken from the same k-mer. The level of transition weight 71 for a transition 75 depends on the number of measurements expected to be taken from any given k-mer, and can be determined by experimentation for the particular nanopore device 1 being used.

[0266] An emission weight 72 is provided for each k-mer state. The emission weight 72 is the weight for the different measurements being observed when the k-mer state is observed. The emission weight 72 therefore depends on the type of k-mer state in question. In particular, the emission weight 72 for any given type of k-mer state is the same as the emission weight 62 for that type of k-mer state in the general model 60 as described above.

[0267] Use the same as above reference Figure 7 The same technique described (except that the reference model 70 replaces the generic model 60 ) is used to carry out step C4d of fitting the model to a series of measurement blocks 63 to provide a measure of similarity 65 as the fit of the reference model 70 to the measurement blocks 63 .

[0268] Due to the form of the reference model 70, and in particular the representation of transitions between reference series 73 of k-mer states, application of the model essentially derives an estimate of the alignment mapping between the measurement result chunk 63 and the reference series of k-mer states 73. This can be understood as follows. Since the general model 60 represents transitions between possible types of k-mer states, application of the model provides an estimate of the type of k-mer state from which each measurement result is observed. Since the reference model 70 represents transitions between reference series of k-mer states 73, application of the reference model 70 instead estimates the k-mer state 73 of the reference sequence from which each measurement result is observed, which k-mer state is the alignment mapping between a series of measurements and the reference series of k-mer states 73.

[0269] Additionally, the algorithm derives a score for the accuracy of the alignment mapping, e.g., representing the likelihood that the estimate of the alignment mapping is correct, e.g., because the algorithm derived the alignment mapping based on such scores for different paths through the model. Thus, the score for the accuracy of the alignment mapping is a measure of similarity65

[0270] As an example where the reference model 70 is a HMM and the analysis technique applied is the Viterbi algorithm as described above, then the score is simply the likelihood associated with the derived estimate of the alignment map predicted by the reference model 70.

[0271] As another example where the general model 60 is an HMM, the analysis technique may be of the type disclosed in Fariselli et al., "The posterior-Viterbi: a new decoding algorithm for hidden Markov models", Department of Biology, University of Casadio, filed at Cornell University, submitted on January 4, 2005, as described above. This again derives a score 65 of a measure of similarity.

[0272] The reference model 70 may be generated from a reference sequence of polymer units or from measurements (taken from a reference sequence of polymer units), as follows.

[0273] The reference model 70 can be obtained by Fig.19The process shown is generated from a reference sequence of polymer units 80, as follows. This is very useful in applications where the reference sequence is known (e.g., from a library or from an earlier experiment). The input data representing the reference sequence of the polymer unit 80 may already be stored in the data processor 5 or may be input therein.

[0274] The process uses stored emission weights 81, which include emission weights e1 to en for a set of possible types of k-mer states type 1 to type n. Advantageously, this allows a reference model for any reference sequence of polymer units 80 to be generated based solely on the stored emission weights 81 for the possible types of k-mer states.

[0275] The process proceeds as follows.

[0276] In step P1, a reference sequence of polymer units 80 is received and therefrom a reference sequence of k-mer states 73 is generated. For each k-mer state 73 in the reference sequence, this is a straightforward process of establishing the type of the k-mer state 73 based on the combination of types of polymer units 80 to which the k-mer state 73 corresponds.

[0277] In step P2, a reference model is generated as follows.

[0278] Transition weights 71 are derived for transitions between the reference series of k-mer states 73 derived in step P1. Transition weights 71 are defined relative to the reference series of k-mer states 73 in the form described above.

[0279] Emission weight 72 is derived for each k-mer state 73 in the series of k-mer states 73 derived in step P1 by selecting a stored emission weight 81 according to the type of the k-mer state 73. For example, if a given k-mer state 73 is of type 4, then emission weight e4 is selected.

[0280] The reference model 70 can be obtained by Fig. 20 The process shown is generated from a series of reference measurements 93 taken from a reference sequence of polymer units. This is useful, for example, in applications where a reference sequence of polymer units is measured simultaneously with a target polymer. In particular, in this example, it is not required that the identities of the polymer units in the reference sequence are known per se. A series of reference measurements 93 may be taken by the nanopore device 1 from a polymer comprising the reference sequence of polymer units.

[0281] The process uses an additional model 90 that treats a series of reference measurements as observations of an additional series of k-mer states of different possible types. The additional model 90 is a model of the nanopore device 1 for taking a series of reference measurements 93, and may be the same as the general model 60 described above, for example, of the type disclosed in WO-2013 / 041878. Thus, the additional model comprises: a transition weight 91 for each transition between consecutive k-mer states in the additional series of k-mer states, i.e., a transition weight 91 for a possible transition between possible types of k-mer states; and an emission weight 92 for each type of k-mer state, i.e., an emission weight 92 for a different measurement being observed when the k-mer state is of that type.

[0282] The process proceeds as follows.

[0283] In step Q1, a further model 90 is applied to a series of reference measurements 93 to estimate the reference series of k-mer states 73 as a series of discrete estimated k-mer states. This can be done using the techniques described above.

[0284] In step Q2, a reference model 70 is generated as follows.

[0285] Transition weights 71 are derived for transitions between the reference series of k-mer states 73 derived in step Q1. Transition weights 71 are defined relative to the reference series of k-mer states 73 in the form described above.

[0286] Emission weights 72 are derived for each k-mer state 73 in the series of k-mer states 73 derived in step Q1 by selecting emission weights from the weights of the further model 50 according to the type of the k-mer state 73. Thus, the emission weight for each type of k-mer state 73 in the reference model is the same as the emission weight for the k-mer state 73 of that type in the further model 50.

[0287] Examples of various applications have been explored from the disclosure of WO2016059427, which is incorporated herein by reference in its entirety, for the basis of the decision in step C4 and an indication of possible time savings. The polymer is a polynucleotide, and the usual assumption has been made that measuring the first 250 nucleotides, then comparing with a reference sequence, will be sufficient to determine a) whether it is related to the reference sequence, and b) its position relative to the entire sequence. However, it may be more or less than this number. The number of polymer units required to make a determination is not necessarily fixed. Typically, the measurement will be performed continuously until such a determination can be made.

[0288] For each type of application, Figure 7 The use of the method shown in may vary slightly. A mixture of multiple types of applications may also be used. The analysis performed in step C3 and / or the basis for the decision in step C4 may also be dynamically adjusted as the run progresses. For example, the decision logic may not be applied initially, and then the logic may be used later in the run when enough data has been accumulated to make a decision. Alternatively, the decision logic may change during the run.

[0289] Fig.16 The method shown in results in the generation of an alignment map. The method can be applied more generally as follows.

[0290] Fig.21 A method for estimating an alignment mapping between (a) a series of measurements of a polymer comprising polymer units and (b) a reference sequence of polymer units is shown. The method is performed as follows.

[0291] like Fig.21 As shown, the input to the method may be a series of measurements 12 derived by the biochemical analysis system 1 taking a series of raw measurements from the sequence of polymer units and preprocessing them as described above. Alternatively, the input to the method may be a series of raw measurements 11.

[0292] The method uses a reference model 70 of a reference sequence of polymer units, which is stored in the memory 10 of the data processor 5. The reference model 70 takes the same form as described above, treating the measurements as observations of a reference series of k-mer states corresponding to the reference sequence of polymer units.

[0293] The reference model 70 is used in the comparison step S1. In particular, in the comparison step S1, the reference model 70 is applied to a series of measurement results 12. The comparison step S1 is performed in the same manner as the above step C4d. In other words, the reference model 70 is used in the above reference step S1. Fig.13 The same technique described (except that a reference model 70 replaces the generic model 60 ) performs the comparison step S1 by fitting the model to a series of measurement chunks 63 to provide a measure of similarity 65 as the fit of the reference model 70 to the measurement chunks 63 .

[0294] Due to the form of the reference model 70, and in particular the representation of transitions between reference series 73 of k-mer states, application of the model essentially derives an estimate 13 of the alignment mapping between a series of measurement result chunks and a reference series of k-mer states 73. This can be understood as follows. Since the general model 60 represents transitions between possible types of k-mer states, application of the model provides an estimate of the type of k-mer state from which each measurement result is observed, i.e., an initial estimated series of k-mer states 34 and discrete estimated k-mer states 35, each of which estimates the type of k-mer state from which each measurement result is observed. Since the reference model 70 represents transitions between reference series of k-mer states 73, application of the reference model 70 instead estimates the k-mer state 73 of the reference sequence from which each measurement result is observed, which k-mer state is an alignment mapping between a series of measurement results and a reference series of k-mer states 73.

[0295] Since there is an inherent mapping between the reference series of k-mer states 73 and the polymer units of the reference sequence, the alignment mapping between a series of measurements and the reference series of k-mer states 73 also provides an alignment mapping between the series of measurements and the reference sequence of polymer units.

[0296] Fig. 22 An example of an alignment map is shown to illustrate its properties. In particular, Fig. 22 The comparison mapping between the polymer units p0 to p7 of the reference sequence, the k-mer states k1 to k6 of the reference series and the measurement results m1 to m7 is shown. As an illustration, k is three in this example. The horizontal line indicates the comparison between the k-mer state and the measurement result, or indicates the comparison with the gap in other series in the case of the dotted line. Therefore, the polymer units p0 to p7 of the reference sequence are essentially compared with the k-mer states k1 to k6 of the reference series, as shown in the figure. K-mer state k1 corresponds to polymer units p1 to p3 and is mapped to these polymer units. As for the mapping between the k-mer states k1 to k6 of the reference series and the measurement results m1 to m7: k-mer state k1 is mapped to the measurement result m1, k-mer state k2 is mapped to the measurement result m2, k-mer state k3 is mapped to the gap in a series of measurement results, k-mer state k4 is mapped to the measurement result m3, and the measurement results m4 and m5 are mapped to the gaps in the series of k-mer states.

[0297] Depending on the method applied, the form of the estimate 13 of the alignment mapping may vary, as has been explored from the disclosure of WO2016059427, which is incorporated herein by reference in its entirety.

[0298] In all applications, the improved methods of the present invention allow for more efficient and effective analysis of desired polymers. This is particularly relevant for the analysis of longer polymers to prevent nanopore device resources from being used to analyze polymers that are not of interest.

Claims

1. A method for controlling a nanopore device for analyzing a polymer, the nanopore device comprising at least one sensor element, the sensor element comprising a nanopore and a sensor, the method comprising: translocating a polymer through the nanopore; generating a series of measurements using the sensor as the polymer translocates through the nanopore; comparing the series of measurements to reference data to determine a measure of similarity; if the measure of similarity is determined to be below a threshold, operating the nanopore device to eject the polymer from the nanopore; as well as determining whether the polymer has been successfully ejected from the nanopore by analyzing an additional set of measurements taken by the sensor; Wherein if it has been determined that the polymer has not been successfully ejected from the nanopore, the method further comprises operating the nanopore device to perform any of the following: (i) at least one additional step of ejecting said polymer from said nanopore; or (ii) ceasing to take measurements from the nanopore.

2. A method according to claim 1, wherein a further series of measurements used to determine whether the polymer has been successfully ejected from the nanopore are compared to predetermined values.

3. The method of claim 2, wherein the predetermined value is a measurement taken by the sensor when the nanopore is free of polymer.

4. A method according to any one of the preceding claims, wherein the at least one sensor element is operable to eject a polymer being translocated through the nanopore.

5. A method according to claim 4, wherein the sensor comprises an electrode and the at least one sensor element is operable to eject the polymer being displaced through the nanopore by the at least one additional step, the at least one additional step comprising applying an ejection bias voltage to eject the polymer, such that the step of operating the sensor element to eject the polymer from the nanopore is performed by applying the ejection bias voltage.

6. A method according to claim 5, wherein if the polymer has been successfully ejected, the method comprises the additional step of operating the sensor element to accept additional polymer to be translocated through the nanopore, wherein operating the sensor element to accept additional polymer is performed by applying a translocation bias voltage sufficient to enable the additional polymer to be translocated through the sensor element.

7. The method of claim 5, if the polymer has not been successfully ejected, the method comprising the additional step of applying a second ejection bias voltage to eject the polymer, the second ejection bias voltage being higher than the first ejection bias voltage.

8. The method of claim 6, if the polymer has not been successfully ejected after applying the second ejection bias voltage, the method comprising an additional step of applying a third ejection bias voltage to eject the polymer, wherein the third ejection bias voltage is higher than the second ejection bias voltage.

9. A method according to any one of claims 5 to 8, wherein if the polymer has not been successfully sprayed, the method further comprises: The nanopore device is operated to stop taking measurements from the sensor element.

10. A method according to any preceding claim, wherein the nanopore device includes an alternative sensor element, and after operating the nanopore device to stop taking measurements from the sensor element, the method further comprises selecting the alternative sensor element and operating the device to begin taking measurements from the alternative sensor element.

11. A method according to any preceding claim, wherein the nanopore device comprises: A detection circuit comprising an array of detection channels, each of which is capable of obtaining electrical measurements from an array sensor element, the number of sensor elements in the array being greater than the number of detection channels; and a switching arrangement, the switching arrangement being capable of selectively connecting the detection channels to respective sensor elements in a multiplexed manner; wherein when the method further comprises operating the nanopore device to stop obtaining measurements from the nanopore, the method further comprises switching to an alternative detection channel in the array of detection channels via the switching arrangement.

12. A method according to any one of the preceding claims, wherein the reference data derived from at least one reference sequence of polymer units represents actual or simulated measurements taken by a nanopore device, and the step of analysing the series of measurements taken from the polymer during a partial translocation comprises: The series of measurements are compared to the reference data.

13. A method according to any one of claims 1 to 12, wherein said reference data derived from at least one reference sequence of polymer units represents a feature vector representing a time-ordered characteristic of a property of measurements taken by a biochemical analysis system, and said step of analyzing said series of measurements taken from said polymer during said partial shift comprises: deriving from the series of measurements a feature vector representing a time-ordered characteristic of a property of the measurements; and comparing the derived feature vector with the reference data.

14. A method according to any one of claims 1 to 12, wherein the reference data derived from at least one reference sequence of polymer units represents the identity of the polymer units of the at least one reference sequence, and the step of analysing the series of measurements taken from the polymer during the partial shift comprises: The series of measurements is analyzed to provide an estimate of the identity of the polymer units in the sequence of polymer units of the partially displaced polymer, and the estimate is compared to the reference data to provide the measure of similarity.

15. A method according to any one of claims 1 to 12, wherein the measurements depend on k-mers, i.e. k polymer units of a polymer, where k is an integer; the reference data represent a reference model that considers the measurements as observations of a reference series of k-mer states corresponding to the reference sequence of polymer units, wherein the reference model comprises: transition weights for transitions between the k-mer states in the reference series of k-mer states; and emission weights, for each k-mer state, for different measurements being observed when the k-mer state is observed, and the step of analyzing the series of measurements taken from the polymer during the partial shift includes: fitting the model to the series of measurements to provide the measure of similarity as the fit of the model to the series of measurements.

16. A method according to claim 15, wherein the measurement is based on k-mers, i.e. k polymer units of a polymer, where k is an integer.

17. A method according to any one of the preceding claims, wherein the nanopore is a biological pore.

18. A method according to any preceding claim, wherein the polymer comprises a series of polymer units to be recognised by the nanopore device.

19. A method according to claim 18, wherein the polymer is a polynucleotide and the polymer units are nucleotides.

20. A method according to any preceding claim, wherein the polymer translocation through the nanopore is performed in a ratcheting manner.

21. A method according to any preceding claim, wherein the nanopore device comprises a sensor electrode and the measurements comprise electrical measurements.

22. A method according to any preceding claim, wherein the nanopore device comprises a sensor electrode and the measurements taken by the sensor are indicative of ion flow through the nanopore.

23. A nanopore device for analyzing a polymer comprising a sequence of polymer units, wherein the nanopore device comprises at least one sensor element, the at least one sensor element comprising a nanopore, a sensor and a data processor, and the nanopore device is operable to obtain continuous measurements of the polymer from the sensor element during the polymer's translocation through the nanopore of the sensor element, wherein the data processor of the nanopore device is arranged to analyze a series of measurements taken from the polymer during the partial translocation of the polymer when the polymer has partially translocated through the nanopore, compare the series of measurements with reference data to determine measurements of similarity; and wherein the data processor of the nanopore device is further arranged to eject the polymer in response to a measure of similarity and determine whether the polymer has been successfully ejected from the nanopore by analyzing another set of measurements taken by the sensor.

24. A nanopore device according to claim 23, wherein the data processor is connected to the sensor element via a control circuit.

25. A nanopore device according to claim 23 or 24, wherein the nanopore is a biological pore.

26. A nanopore device according to any one of claims 23 to 25, wherein the polymer comprises a series of polymer units to be recognized by the nanopore device.

27. A nanopore device according to claim 26, wherein the polymer is a polynucleotide and the polymer units are nucleotides.

28. A nanopore device according to any one of claims 23 to 27, wherein translocation of the polymer through the nanopore is performed in a ratcheting manner.

29. A nanopore device according to any one of claims 23 to 28, wherein the nanopore device comprises a sensor electrode and the measurements comprise electrical measurements.

30. A nanopore device according to any one of claims 23 to 29, wherein the nanopore device comprises a sensor electrode and the measurements taken by the sensor are indicative of ion flow through the nanopore.

31. A nanopore device according to any one of claims 23 to 30, wherein the nanopore device comprises: a detection circuit comprising an array of detection channels, each of which is capable of taking an electrical measurement from an array of sensor elements, the number of sensor elements in the array being greater than the number of detection channels; and a switching arrangement capable of selectively connecting the detection channels to respective sensor elements in a multiplexed manner.

32. A nanopore device according to any one of claims 23 to 32, wherein if it has been determined that the polymer has not been successfully ejected from the nanopore, the data processor is further arranged to operate the nanopore device to do any of the following: (iii) at least one additional step of ejecting said polymer from said nanopore; or (iv) ceasing to take measurements from the nanopore.

33. A method of controlling a nanopore device for analyzing a polymer comprising a sequence of polymer units, wherein the nanopore device comprises at least one sensor element, the at least one sensor element comprising a nanopore and a sensor, and the nanopore device is operable to take continuous measurements of the polymer from the sensor element during translocation of the polymer through the nanopore of the sensor element, and the nanopore device is operable to generate continuous measurements of the polymer from the sensor element during translocation of the polymer through the nanopore of the sensor element, wherein the method comprises: When the polymer has partially translocated through the nanopore, a series of measurements taken from the polymer during the partial translocation of the polymer is analyzed by deriving a measure of fit to a model, the model treating the measurements as observations of a series of k-mer states of different possible types and comprising: transition weights, for each transition between consecutive k-mer states in the series of k-mer states, for possible transitions between the possible types of k-mer states; and emission weights, for each type of k-mer state, representing the chance of observing a given value of a measurement of that k-mer, and responsive to the measure of fit, operating a biochemical analysis system to eject the polymer and determining whether the polymer has been successfully ejected from the nanopore by analyzing a further set of measurements taken by the sensor.

34. A nanopore device for analysing a polymer comprising a sequence of polymer units, wherein the nanopore device comprises at least one sensor element, the at least one sensor element comprising a nanopore and a sensor, and the nanopore device is operable to take continuous measurements of the polymer from the sensor element during translocation of the polymer through the nanopore of the sensor element, wherein a biochemical analysis system is arranged to analyse a series of measurements taken from the polymer during the partial translocation of the polymer when the polymer has partially translocated through the nanopore by deriving a measure of fit to a model, the model treating the measurements as observations of a series of k-mer states of different possible types and comprising: a transition weight, the transition weight being for each transition between consecutive k-mer states in the series of k-mer states, for possible transitions between the possible types of k-mer states; and an emission weight, the emission weight being for each type of k-mer state, representing the chance of observing a given value of a measurement result for that k-mer, and the biochemical analysis system is arranged to eject the polymer in response to the fitted metric and to determine whether the polymer has been successfully ejected from the nanopore by analyzing a further set of measurements taken by the sensor.

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