Analysis of Measurements of a Polymer
The present invention relates generally to the field of analysing measurements of a
polymer comprising polymer units, for example but without limitation a polynucleotide,
made during translocation of the polymer through a nanopore.
A nanopore measurement is typically made by restricting the flow of material
between two pools of solution using a membrane. An aperture is provided within that
membrane to allow the transfer of material from one pool of solution to another. The aperture
has at least one dimension on the nanometre scale. As the material is translocated through the
pore, measurements are made of that material. The most commonly used setup relies on the
application of an applied potential to drive molecular species through the nanopore. An
electrode is placed in each solution volume and the solution contains an electrolyte, typically
a salt, such as 1M NaCl. The applied potential across the electrodes also drives the
electrolyte through the pore and generates a current. When material passes through the pore it
modifies the flow of ions which is directly observed in the current measurement. The degree
of current block and the duration the material spends in the nanopore are indicative of its
identity.
The original concept of analysing a polymer by passing it through a nanopore was
proposed by Branton et al. (US-5,795,782) in 1996. In this case, a DNA molecule is passed
through a nanopore embedded in a lipid membrane. An electrode is placed on each side of
the membrane and an applied potential is used to drive the DNA molecule from one side of
the membrane to the other. During the translocation of the DNA molecule, the trans
membrane current through the pore is measured. It was shown that different sequences of
DNA would give rise to different observed currents as the DNA passes through the nanopore.
These early experiments were performed using homopolymers of nucleotides where the
polymer freely translocates the nanopore. In these experiments, the rate of polymer
translocation is very fast (~ 5 causing the characteristics of individual nucleotides
within the polymer to be difficult to determine.
To overcome the limitations of rapid DNA translocation, Branton et al. disclose the
use of a polymerase to control the speed of DNA translocation through the nanopore. This
elegant solution has been adopted and adapted by many researchers in the field which has led
to a number of publications. The basic concept is to provide a ratchet to the motion of the
polymer, which could encompass a molecular motor or a molecular brake.
Early work concentrated on the use of polymerases to control the motion of DNA. A
number of studies were performed using Klenow fragment, but these experiments were
limited by the short duration of the DNA-enzyme complex on top of the nanopore. A number
of schemes were developed to compensate for this weak binding (e.g. see Olasagasti et al,
Nat Nanotechnol. 2010 Nov; 5(1 1):798-806, Ashkenasy et al., Angew Chem Int Ed Engl.
2005 Feb 18;44(9): 1401-4).
In 2010, it was disclosed by Akeson et al. that Phi29 DNA polymerase (DNAP) could
function on top of a nanopore (e.g. see Lieberman et al., J Am Chem Soc. 2010 Dec
22; 132(50): 17961-72, 61/402,903). The strength of the Phi29 DNAP binding to the template
DNA was sufficient to allow multiple enzyme cycles to be performed on top of the nanopore,
thus allowing the DNA to be pulled through the nanopore in a ratcheted fashion. The paper
also revealed that Phi29 DNAP could be used to control DNA motion through the nanopore
under conditions where the enzyme motion was inhibited. In these conditions, the Mg2+,
which is essential to enzymatic action, is effectively removed through the addition of the
metal chelator ethylenediaminetetraacetic acid (EDTA). The applied potential provides the
force on the DNA strand and the Phi29 DNAP limited the "unzipping" of the strand through
the pore. This work showed that enzymes in nanopore systems could either function as
molecular motors or as molecular breaks.
In addition to using polymerases as molecular ratchets, it has been demonstrated that
some helicase families can be used to provide controlled movement of polynucleotides
through a nanopore (e.g. see US 61/549,998 (Nl 15020), US 61/581,332 (Nl 15505), US
61/581,340 (Nl 15506)). Helicases have a number of properties that make them suitable for a
nanopore system.
An alternative method of slowing down translocation of a target single stranded DNA
is to hybridise additional sections of ssDNA (hyb-DNA) along the length of the target strand.
The target strand of DNA is rapidly fed through the pore under an applied potential. Once a
double stranded section of the strand reaches the constriction of the nanopore, the
translocation of the strand is halted, allowing the current to be read with the polymer at a
fixed position. The hyb-DNA section is un-hybridised by the force of the applied field, and
the target DNA strand continues to translocate the nanopore until another hyb-DNA is
encountered. In this way, the current signatures for the DNA strand at a number of fixed
positions are obtained. By employing complex sample preparation techniques, Derrington et
al. propose a method of sequencing a strand of DNA using this approach.
The data generated from these approaches shares key features; the translocation of DNA
occurs in discreet stages where each stage represents a position of the polymer in the
nanopore and each polymer position has a characteristic current level. The current levels can
sometimes exhibit fluctuations, termed variance. These features result in signals that take the
form of "noisy step waves".
More generally some property of the system depends on the polymer units in the
nanopore, and measurements of that property are taken. For example, a measurement system
may be created by placing a nanopore in an insulating membrane and measuring voltagedriven
ionic transport through the nanopore in the presence of analyte molecules. The
controlled movement of polymer through a nanopore results in a number of distinct levels of
measurement that are indicative of the polymer sequence.
In previous developments, the focus has been on determining the underlying sequence
of the polymer. Generally in these approaches, each of the states within the signal have been
analysed independently by comparing the current levels of these states to known current
levels from reference data. This process converts the current signal into an estimate of
polymer sequence. An alternative way of saying this is that the process converts the
information from signal space to sequence space. However, there are practical difficulties in
developing a measurement system that can reliably determine the sequence.
It is typical of many types of measurement system, including the majority of currently
known nanopores, for the value of each measurement to be dependent on a group of k
polymer units, where k is a plural integer, hereinafter referred to as a 'k-mer'. This is because
more than one polymer unit contributes to the observed ion current and might be thought of
conceptually as the measurement system having a "blunt reader head" that is bigger than the
polymer unit being measured. In such a situation, the number of different k-mers to be
resolved increases to the power of k . For example, if there are n possible polymer units, the
number of different k-mers to be resolved is nk. While it is desirable to have clear separation
between measurements for different k-mers, it is common for some of these measurements to
overlap. Especially with high numbers of k-mers, it can become difficult to resolve the
measurements produced by different k-mers, to the detriment of deriving information about
the polymer, for example an estimate of the underlying sequence of polymer units.
Much research has aimed at design of a measurement system that provides resolvable
measurements that are dependent on a single polymer unit. However, this has proved difficult
in practice, for example due to variation in measurements that can arise to varying extents
from inherent variation in the underlying physical or biological system and/or measurement
noise that is inevitable due the small magnitude of the properties being measured. Other work
has accepted measurements that are dependent on k-mers, but has aimed at design of a
measurement system in which the measurements from different k-mers are resolvable from
each other. However practical limitations mean again that this is very difficult. Distributions
of signals produced by some different k-mers can often overlap.
According to the present invention, there is provided a method of analyzing a timeordered
series of measurements of a polymer made during translocation of the polymer
through a nanopore, wherein the measurements are dependent on the identity of k-mers in the
nanopore, a k-mer being k polymer units of the polymer, where k is a positive integer, the
method comprising:
deriving, from the series of measurements, a feature vector of time-ordered features
representing characteristics of the measurements; and
determining similarity between the derived feature vector and at least one other
feature vector.
Although previous research has tried to derive the exact sequence from the
measurements, the present invention makes use of an appreciation that many applications do
not require the exact polymer sequence to be assigned. These include a significant number of
diagnostic, clinical, scientific, genetic applications where the desired result can be obtained
cheaply, quickly, and to a higher degree of accuracy without resorting to sequence
information. In particular the present invention involves derivation of a feature vector of
time-ordered features representing characteristics of the measurements. Similarity between
the derived feature vector and at least one other feature vector is then determined which
provides information that is useful in many applications.
Consequently, the present invention does not require the assignment of polymer
sequence, i.e. there is not necessarily a conversion of the measurement signal into sequence
space. This provides useful analysis of the polymer in many applications, but reduces the
burden on operation of the measurement system, because it is not necessary to resolve every
single polymer unit in the sequence. This reduction on the constraints of the measurement
system also increases the range of measurements systems. This may allow the use of a
measurement system that is easier to design or operate, or may allow the use of a
measurement system that is specifically adapted to analyse a particular characteristic of the
polymer, even without being able to provide complete sequence information.
An underlying feature of the invention is the conversion of the raw signal, that is the
time-ordered series of measurements into a feature vector of time-ordered features. The series
of measurements are derived as the polymer translocates through the nanopore and so
provide information on the overall sequence, even if this is not complete. The derivation of
the feature vector provides a representation which is also time-ordered but with a reduced
data set. This feature vector may be thought of as a "signature" of the polymer. The feature
vector is then compared to at least one other feature vector to determine the similarity. The at
least one other feature vector may be, for example, a feature vector stored in a memory or
another feature vector derived in the same manner. Based on the similarity, characteristics of
the polymer may be derived.
With some signals, there is sufficient resolution of each k-mer that groups of
consecutive measurements are dependent on a respective k-mer that is different for each
group. In this case, the step of deriving a feature vector may comprise identifying groups of
consecutive measurements, and, in respect of each group, deriving values of one or more
features that represent characteristics of the measurements of the group. For example, the
features may comprise: an average of the group of measurements; the period of the group of
measurements; a variance of the group of measurements; the distribution of the group of
measurements; or any combination thereof.
The present invention is also applicable to signals with a lesser resolution, such that
some k-mers may provide only a single measurement or no measurement at all.
As mentioned above, in some cases the derived feature vector may be compared with
at least one other feature vector stored in a memory in respect of at least one class. In this
case the similarity may be determined between between the entirety or part of the derived
feature vector and the entirety of the at least one other feature vector stored in the memory, or
alternatively between the entirety or part of the derived feature vector and a part of the at
least one other feature vector stored in the memory.
The method may further comprise classifying the polymer from which the derived
feature vector is derived as belonging to a said class on the basis of the determined similarity.
This provides for identification of the polymer under investigation.
The at least one other feature vector stored in the memory may be selected depending
upon the polymer to be measured, or alternatively a library of plural other feature vectors
stored in the memory may be used.
In some applications, a combined feature vector may be obtained from two or more
feature vectors having overlapping regions wherein the similarity of the derived feature
vector is determined between the combined feature vector. A non-overlapping region of the
combined feature vector may be used to determine similarity between the derived feature
vector, for example to identify a particular localised region of the derived feature vector.
Thus the method may be used to determine similarity between continuous or noncontinuous
regions of a derived feature vector and one or more feature vectors.
In some applications, plural parts of the derived feature vector may be compared all,
parts or plural parts of stored feature vectors.
As mentioned above, in other cases the derived feature vector may be compared with
at least one other feature vector that is a feature vector derived using the same method. This
provides for identification of characteristics of plural polymers that are under investigation,
relative to each other. In this case, the method may further comprise identifying clusters of
similar feature vectors as a class and classifying the polymers from which the feature vectors
are derived as belonging to an identified class.
In one example, where there are plural other feature vectors derived using the same
method, the method may further comprise identifying feature vectors that are derived from
polymers that are fragments of a common polymer on the basis of similarity in overlapping
parts of the feature vectors.
Where polymers are classified, the method may further comprise counting the
numbers of feature vectors belonging to different classes. This provides for analysis of a
population of polymers under investigation.
Where polymers are classified, the method may further comprise identifying localized
regions where the derived feature vector is dissimilar to a feature vector in respect of the
class in which the polymer is classified as belonging.
In a similar technique where the polymer has an expected identity, the derived feature
vector may be compared to a feature vector stored in a memory and the determination of
similarity comprises determining localized regions where the derived feature vector is
dissimilar to the at least one other feature vector stored in the memory.
Such identification of localized regions where the derived feature vector is dissimilar
to what is expected provides an analysis technique that is very powerful in many applications
where change in relatively small regions of long sequences of polymers is significant. One
example of such a technique is to identify mutations in a polymer that is a polynucleotide.
The method may be performed on a series of measurements that has been previously
made. Alternatively, the method may further comprise: translocating the polymer through a
nanopore; and making the continuous series of measurements of the polymer.
The method of analysing the series of measurements may be used in a method of
estimating the presence, absence or amount of a target polymer based on the analysis.
In that case, the polymer may comprise a mixture of two or more polymers and the
relative amounts of one or more polymers may be determined.
The method of estimating the presence, absence or amount of a target polymer may
be applied to a polymer analyte in a method comprising: fragmenting the polymer analyte
into polymers; and performing the method of estimating on the fragmented polymers. Where
the polymer is a polynucleotide, and the polymer units are nucleotides, the polymer analyte
may be fragmented by a restriction enzyme.
The method of analysing the series of measurements may be applied in a method of
determining an alteration in a polymer, comprising: translocating a polymer through a
nanopore repeatedly over a period of time; during each translocation, making a continuous
series of measurements of the polymer; analysing each series of measurements. In this case,
the step of determining similarity between the derived feature vector and at least one other
feature vector may comprise either (a) determining similarity between the derived feature
vector derived from each series of measurements and the same at least one other feature
vector or (b) determining similarity between all the derived feature vectors derived from the
series of measurements.
Where the polymer is a polynucleotide, and the polymer units are nucleotides, the
method may be used to determine the presence of a modified base or a point mutation.
Generally, the methods may be used to guide a therapy or diagnosis or to identify an
individual.
The present invention has numerous applications. Some non-limitative examples or
applications are as follows.
This invention can be applied to single molecule label free detection systems for
analysis of polymers, for example a nanopore system. It is common for such systems to
comprise a recognition element that is influenced by more than one monomer units at a given
polymer position. In these systems, extracting the relationship between measurement and
polymer sequence may be challenging or resource demanding.
This invention can be applied to any polymer analysis system where a polymer
signature is indicative of a characteristic of that polymer and where the exact polymer
sequence does not have to be known to determine said characteristic. Examples include but
are not limited to: detection of single nucleotide polymorphisms (S Ps), presence or absence
of specific sequences, grouping and counting of polymer sequences, design of labels and
biomarkers, and identification of modified or damaged DNA.
The method may be used for example to determine the presence, absence or amount
of a target polymer analyte in a sample. The method may be used to measure an amount with
respect to a threshold. The method may be used to determine the relative amounts of one or
more target polymers in a mixture of polymers.
The method may be used to guide a therapy or diagnosis based upon analysis of a
single sample. Alternatively the method may be carried out plural times over a period for
example to monitor progression of a disease or improvement of an individual. The method
may be used to monitor an efficacy of treatment, for example where used as a theranostic.
The method may be used in forensic applications for example to detect SNPs in
mitochondrial DNA for DNA profiling of individuals, for genetic fingerprinting of
individuals, for example by determining the presence of short tandem repeats, variable
tandem repeats and the like.
All the methods may be performed without estimating the sequence of polymer units
of the polymer.
To allow better understanding, embodiments of the present invention will now be
described by way of non-limitative example with reference to the accompanying drawings, in
which:
Fig. 1 is a schematic diagram of a measurement system comprising a nanopore;
Fig. 2 is a plot of a signal of an event measured over time by a measurement system;
Fig. 3 is a graph of the frequency distributions of measurements of two different
polynucleotides in a measurement system comprising a nanopore;
Figs. 4 and 5 are plots of 64 3-mer coefficients and 1024 5-mer coefficients,
respectively, against predicted values from a first order linear model applied to sets of
experimentally derived current measurements;
Fig. 6 is a flowchart of a method of analyzing an input signal comprising
measurements of a polymer;
Fig. 7 is a flowchart of a state detection step of Fig. 6;
Figs. 8 and 9 are plots, respectively, of an input signal subject to the state detection
step and of the resultant series of measurements;
Figs. 10 an 11 are flowcharts of examples of the similarity determination step of Fig.
6;
Fig. 12 is plot of feature vectors for three fragments of a sequence identified by their
overlap, for Example 2 of the method;
Fig. 13 is a plot of similarity scores for candidate molecule as compared to all library
sequences in Example 2;
Fig. 14 is a plot of a candidate molecule aligned with best match library molecule in
Example 2;
Fig. 15 is a histogram of classification for 176 candidate molecules in Example 2;
Fig. 16 is a graph of the feature vector in Example 3 of the method, illustrating the
effect of S Ps on molecule 13;
Fig. 17 is a histogram of classification for 176 candidate molecules with three SNPs
in molecule 13 in Example 3;
Fig. 18 is a graph of the alignment of a measured molecule with the library feature
vector in Example 3;
Fig. 19 is a plot of position-resolved differences between measurements and library
feature vectors, illustrating position of SNPs, in Example 3;
Fig. 20 is a plot of position-resolved differences between measurements and library
feature vector without SNPs in Example 3;
Fig. 1 is a plot of the final alignment of data with consensus landmarks in Example 4
of the method;
Fig. 22 is a plot of position-resolved differences in candidate molecules 51-60 at
approximately position 337 in Example 4;
Figs. 23 and 24 are diagrams of trees formed by neighbour joining on alignment
similarity scores for a two cluster and a three cluster dataset respectively, in Example 5 of the
method;
Figs. 25 to 27 are graphs of landmark consensus with final alignment of data for each
identified cluster in Example 5;
Figs. 28 and 29 are histograms of classifications for the two cluster and three cluster
experiment respectively in Example 5;
Fig. 30 is a diagram of a tree formed by neighbour joining on alignment similarity
scores in Example 6 of the method; and
Fig. 3 1 is a graph of landmark consensus with final alignment of data for each of
three fragments in Example 6 .
Polymers that may be applied are as follows.
The polymer may be a biological polymer. The polymer may be natural or synthetic.
The polymer may be a polynucleotide (or nucleic acid), a polypeptide such as a protein, a
polysaccharide, or any other polymer. In the case of a polypeptide, the polymer units may be
amino acids that are naturally occurring or synthetic. In the case of a polysaccharide, the
polymer units may be monosaccharides.
Polynucleotides that may be applied are as follows.
A polynucleotide, such as a nucleic acid, is a macromolecule comprising two or more
nucleotides. The polynucleotide or nucleic acid may comprise any combination of any
nucleotides. The nucleotides can be naturally occurring or artificial. One or more
nucleotides in the target polynucleotide can be oxidized or methylated. One or more
nucleotides in the target polynucleotide may be damaged. One or more nucleotides in the
target polynucleotide may be modified, for instance with a label or a tag. The target
polynucleotide may comprise one or more spacers.
A nucleotide typically contains a nucleobase, a sugar and at least one phosphate
group. The nucleobase is typically heterocyclic. Nucleobases include, but are not limited to,
purines and pyrimidines and more specifically adenine, guanine, thymine, uracil and
cytosine. The sugar is typically a pentose sugar. Nucleotide sugars include, but are not
limited to, ribose and deoxyribose. The nucleotide is typically a ribonucleotide or
deoxyribonucleotide. The nucleotide typically contains a monophosphate, diphosphate or
triphosphate. Phosphates may be attached on the 5' or 3' side of a nucleotide.
Nucleotides include, but are not limited to, adenosine monophosphate (AMP),
adenosine diphosphate (ADP), adenosine triphosphate (ATP), guanosine monophosphate
(GMP), guanosine diphosphate (GDP), guanosine triphosphate (GTP), thymidine
monophosphate (TMP), thymidine diphosphate (TDP), thymidine triphosphate (TTP),
uridine monophosphate (UMP), uridine diphosphate (HDP), uridine triphosphate (UTP),
cytidine monophosphate (CMP), cytidine diphosphate (CDP), cytidine triphosphate (CTP), 5-
methylcytidine monophosphate, 5-methylcytidine diphosphate, 5-methylcytidine
triphosphate, 5-hydroxymethylcytidine monophosphate, 5-hydroxymethylcytidine
diphosphate, 5-hydroxymethylcytidine triphosphate, cyclic adenosine monophosphate
(cAMP), cyclic guanosine monophosphate (cGMP), deoxyadenosine monophosphate
(dAMP), deoxyadenosine diphosphate (dADP), deoxyadenosine triphosphate (dATP),
deoxyguanosine monophosphate (dGMP), deoxyguanosine diphosphate (dGDP),
deoxyguanosine triphosphate (dGTP), deoxythymidine monophosphate (dTMP),
deoxythymidine diphosphate (dTDP), deoxythymidine triphosphate (dTTP), deoxyuridine
monophosphate (dUMP), deoxyuridine diphosphate (dUDP), deoxyuridine triphosphate
(dUTP), deoxycytidine monophosphate (dCMP), deoxycytidine diphosphate (dCDP) and
deoxycytidine triphosphate (dCTP), 5-methyl-2' -deoxycytidine monophosphate, 5-methyl-
'-deoxycytidine diphosphate, 5-methyl-2'-deoxycytidine triphosphate, 5-hydroxymethyl-2'-
deoxycytidine monophosphate, 5-hydroxymethyl-2' -deoxycytidine diphosphate and 5-
hydroxymethyl-2' -deoxycytidine triphosphate. The nucleotides are preferably selected from
AMP, TMP, GMP, UMP, dAMP, dTMP, dGMP or dCMP. The nucleotides may be abasic
(i.e. lack a nucleobase). The nucleotides may contain additional modifications. In particular,
suitable modified nucleotides include, but are not limited to, 2'amino pyrimidines (such as
2'-amino cytidine and 2'-amino uridine), 2'-hyrdroxyl purines (such as , 2'-fluoro
pyrimidines (such as 2'-fluorocytidine and 2'fluoro uridine), hydroxyl pyrimidines (such as
5'-a-P-borano uridine), 2'-0-methyl nucleotides (such as 2'-0-methyl adenosine, 2'-0-
methyl guanosine, 2'-0-methyl cytidine and 2'-0-methyl uridine), 4'-thio pyrimidines (such
as 4'-thio uridine and 4'-thio cytidine) and nucleotides have modifications of the nucleobase
(such as 5-pentynyl-2'-deoxy uridine, 5-(3-aminopropyl)-uridine and l,6-diaminohexyl-N-5-
carbamoylmethyl uridine).
A nucleotide may be abasic (i.e. lack a nucleobase).
The polynucleotide may be single stranded or double stranded. The polynucleotide
may comprise one or more double stranded regions and one or more single regions. The
polynucleotide can be a nucleic acid, such as deoxyribonucleic acid (DNA) or ribonucleic
acid (RNA). The target polynucleotide can comprise one strand of RNA hybridized to one
strand of DNA. The polynucleotide may be any synthetic nucleic acid known in the art, such
as peptide nucleic acid (PNA), glycerol nucleic acid (GNA), threose nucleic acid (TNA),
locked nucleic acid (LNA) or other synthetic polymers with nucleotide side chains.
The whole or only part of the target polynucleotide may be characterised using this
method. The target polynucleotide can be any length. For example, the polynucleotide can
be at least 10, at least 50, at least 100, at least 150, at least 200, at least 250, at least 300, at
least 400 or at least 500 nucleotide pairs in length. The polynucleotide can be 1000 or more
nucleotide pairs, 5000 or more nucleotide pairs in length or 100000 or more nucleotide pairs
in length.
The target polynucleotide is present in any suitable sample. The invention is typically
carried out on a sample that is known to contain or suspected to contain the target
polynucleotide. Alternatively, the invention may be carried out on a sample to confirm the
identity of one or more target polynucleotides whose presence in the sample is known or
expected.
Samples that may be studied are as follows.
The sample may be a biological sample. The invention may be carried out in vitro on
a sample obtained from or extracted from any organism or microorganism. The organism or
microorganism is typically archaean, prokaryotic or eukaryotic and typically belongs to one
the five kingdoms: plantae, animalia, fungi, monera and protista. The invention may be
carried out in vitro on a sample obtained from or extracted from any virus. The sample is
preferably a fluid sample. The sample may be solid or semi-solid in origin which is
subsequently treated to provide a fluid sample. Examples of such are faecal, skin, tissue, hair,
bone and muscle. The sample typically comprises a body fluid of the patient. The sample
may be chosen for example from urine, blood, plasma, serum, lymph, saliva, interstitial
fluid, tears, mucus or amniotic fluid. Typically, the sample is human in origin, but
alternatively it may be from another mammal animal such as from commercially farmed
animals such as horses, cattle, sheep or pigs or may alternatively be pets such as cats or dogs.
Alternatively a sample of plant origin is typically obtained from a commercial crop, such as a
cereal, legume, fruit or vegetable, for example wheat, barley, oats, canola, maize, soya, rice,
bananas, apples, tomatoes, potatoes, grapes, tobacco, beans, lentils, sugar cane, cocoa, cotton.
The sample may be a non-biological sample. The non-biological sample is preferably
a fluid sample. Examples of a non-biological sample include surgical fluids, water such as
drinking water, sea water or river water, and industrial samples such as reagents for
laboratory tests, samples obtained from the synthesis of a polymer reagent.
The sample is typically processed prior to being assayed, for example by
centrifugation or by passage through a membrane that filters out unwanted molecules or
cells, such as red blood cells. The sample may be measured immediately upon being taken.
The sample may also be typically stored prior to assay, preferably below -70°C.
The sample may also be subject to any of the processes, designs, or modifications
presented in US 61/490860.
Membranes that may be used in a measurement system are as follows.
Any membrane may be used in accordance with the invention. Suitable membranes
are well-known in the art. The membrane is preferably an amphiphilic layer. An
amphiphilic layer is a layer formed from amphiphilic molecules, such as phospholipids,
which have both hydrophilic and lipophilic properties. The amphiphilic layer may be a
monolayer or a bilayer. The membrane may be a co-block polymer such as disclosed by
(Gonzalez-Perez et al, Langmuir, 2009, 25, 10447-10450).
The membrane may be a lipid bilayer. Lipid bilayers are models of cell membranes
and serve as excellent platforms for a range of experimental studies. For example, lipid
bilayers can be used for in vitro investigation of membrane proteins by single-channel
recording. Alternatively, lipid bilayers can be used as biosensors to detect the presence of a
range of substances. Suitable amphiphilic layers include, but are not limited to, a planar
lipid bilayer, a supported bilayer or a liposome. The lipid bilayer is preferably a planar lipid
bilayer. Suitable lipid bilayers are disclosed in International Application No.
PCT/GB08/000563 (published asWO 2008/102121), International Application No.
PCT/GB08/004127 (published asWO 2009/077734) and International Application No.
PCT/GB2006/001057 (published asWO 2006/100484).
Methods for forming lipid bilayers are known in the art. Suitable methods are
disclosed in the Example. Lipid bilayers are commonly formed by the method of Montal and
Mueller (Proc. Natl. Acad. Sci. USA., 1972; 69: 3561-3566), in which a lipid monolayer is
carried on aqueous solution/air interface past either side of an aperture which is
perpendicular to that interface.
The method of Montal & Mueller is popular because it is a cost-effective and
relatively straightforward method of forming good quality lipid bilayers that are suitable for
protein pore insertion. Other common methods of bilayer formation include tip-dipping,
painting bilayers and patch-clamping of liposome bilayers.
In a preferred embodiment, the amphiphilic layer is formed as described in
International Application No. PCT/GB08/004127 (published asWO 2009/077734).
In another preferred embodiment, the membrane is a solid state layer. A solid-state
layer is not of biological origin. In other words, a solid state layer is not derived from or
isolated from a biological environment such as an organism or cell, or a synthetically
manufactured version of a biologically available structure. Solid state layers can be formed
from both organic and inorganic materials including, but not limited to, microelectronic
materials, insulating materials such as Si3N4, A 120 3, and SiO, organic and inorganic polymers
such as polyamide, plastics such as Teflon® or elastomers such as two-component additioncure
silicone rubber, and glasses. The solid state layer may be formed from monatomic
layers, such as graphene, or layers that are only a few atoms thick. Suitable graphene layers
are disclosed in International Application No. PCT/US2008/010637 (published asWO
2009/035647). The solid state membrane can also support a nanopore derived from
biological material, non-limiting examples have been disclosed by Hall et al. (Nat
Nanotechnol. 2010 Dec;5(12):874-7) and Bell et al. (Nano Lett. 2012 Jan l l;12(l):512-7),
and International Application No. PCT/US201 1/039621 (published as WO/2012/005857).
The method is typically carried out using (i) an artificial amphiphilic layer comprising
a pore, (ii) an isolated, naturally-occurring amphiphilic layer comprising a pore, or (iii) a cell
having a pore inserted therein. The method is preferably carried out using an artificial
amphiphilic layer. The bilayer may comprise other transmembrane and/or intramembrane
proteins as well as other molecules in addition to the pore. Suitable apparatus and conditions
are discussed below. The method of the invention is typically carried out in vitro.
Nanopores that may be applied are as follows.
The measurement system comprises a nanopore. The measurements are taken during
translocation of the polymer through the nanopore. The translocation of the polymer through
the nanopore generates a characteristic signal in the measured property that may be observed,
and may be referred to overall as an "event".
The nanopore is a pore, typically having a size broadly speaking of the order of
nanometres, that allows the passage of polymers therethrough. Herein, references to a "pore"
mean a nanopore in this sense.
The nanopore may be a biological pore or a solid state pore.
A solid state pore, is typically an aperture in a solid state layer. A solid state pore may
be used in combination with additional components which provide an alternative or
additional measurement of the polymer such as tunnelling electrodes (Ivanov AP et al., Nano
Lett. 201 1 Jan 12; 1l(l):279-85), or a field effect transistor (FET) device (International
Application WO 2005/124888). Solid state pores may be formed by known processes
including for example those described in WO 00/79257.
The nanopore is preferably a transmembrane protein pore. A transmembrane protein
pore is a polypeptide or a collection of polypeptides that permits hydrated ions to flow from
one side of a membrane to the other side of the membrane. In the present invention, the
transmembrane protein pore is capable of forming a pore that permits hydrated ions driven by
an applied potential to flow from one side of the membrane to the other. The transmembrane
protein pore allows a polymer, such as DNA or RNA, to be moved through the pore.
The transmembrane protein pore may be a monomer or an oligomer. The pore is
preferably made up of several repeating subunits, such as 6, 7 or 8 subunits. The pore is
more preferably a heptameric or octameric pore.
The transmembrane protein pore typically comprises a barrel or channel through
which the ions may flow. The subunits of the pore typically surround a central axis and
contribute strands to a transmembrane b-barrel or channel or a transmembrane a-helix bundle
or channel.
The barrel or channel of the transmembrane protein pore typically comprises amino
acids that facilitate interaction with analyte, such as polymers, nucleotides, polynucleotides
or nucleic acids. These amino acids are preferably located near a constriction of the barrel
or channel. The transmembrane protein pore typically comprises one or more positively
charged amino acids, such as arginine, lysine or histidine, or aromatic amino acids, such as
tyrosine or tryptophan. These amino acids typically facilitate the interaction between the
pore and polymers, nucleotides, polynucleotides or nucleic acids.
Transmembrane protein pores for use in accordance with the invention can be derived
from b-barrel pores or a-helix bundle pores b-barrel pores comprise a barrel or channel that
is formed from b-strands. Suitable b-barrel pores include, but are not limited to, a-toxins,
such as a-hemolysin, anthrax toxin and leukocidins, and outer membrane proteins/porins of
bacteria, such as Mycobacterium smegmatis porin (Msp), for example MspA, outer
membrane porin F (OmpF), outer membrane porin G (OmpG), outer membrane
phospholipase A and Neisseria autotransporter lipoprotein (NalP). a-helix bundle pores
comprise a barrel or channel that is formed from a-helices. Suitable a-helix bundle pores
include, but are not limited to, inner membrane proteins and outer membrane proteins, such
as WZA and ClyA toxin. The transmembrane pore may be derived from Msp or from ahemolysin
(a-HL).
The transmembrane protein pore is preferably derived from Msp, preferably from
MspA. Such a pore will be oligomeric and typically comprises 7, 8, 9 or 10 monomers
derived from Msp. The pore may be a homo-oligomeric pore derived from Msp comprising
identical monomers. Alternatively, the pore may be a hetero-oligomeric pore derived from
Msp comprising at least one monomer that differs from the others. Preferably the pore is
derived from MspA or a homolog or paralog thereof.
A monomer derived from Msp comprises the sequence shown in SEQ ID NO: 2 or a
variant thereof. SEQ ID NO: 2 is the MS-(B1)8 mutant of the MspA monomer. It includes
the following mutations: D90N, D91N, D93N, Dl 18R, D134R and E139K. A variant of
SEQ ID NO: 2 is a polypeptide that has an amino acid sequence which varies from that of
SEQ ID NO: 2 and which retains its ability to form a pore. The ability of a variant to form a
pore can be assayed using any method known in the art. For instance, the variant may be
inserted into a lipid bilayer along with other appropriate subunits and its ability to
oligomerise to form a pore may be determined. Methods are known in the art for inserting
subunits into membranes, such as lipid bilayers. For example, subunits may be suspended in
a purified form in a solution containing a lipid bilayer such that it diffuses to the lipid bilayer
and is inserted by binding to the lipid bilayer and assembling into a functional state.
Alternatively, subunits may be directly inserted into the membrane using the "pick and
place" method described in M.A. Holden, H. Bayley. J . Am. Chem. Soc. 2005, 127, 6502-
6503 and International Application No. PCT/GB2006/001057 (published asWO
2006/100484).
Over the entire length of the amino acid sequence of SEQ ID NO: 2, a variant will
preferably be at least 50% homologous to that sequence based on amino acid identity. More
preferably, the variant may be at least 55%, at least 60%, at least 65%, at least 70%, at least
75%, at least 80%, at least 85%, at least 90% and more preferably at least 95%, 97% or 99%
homologous based on amino acid identity to the amino acid sequence of SEQ ID NO: 2 over
the entire sequence. There may be at least 80%, for example at least 85%, 90% or 95%,
amino acid identity over a stretch of 100 or more, for example 125, 150, 175 or 200 or more,
contiguous amino acids ("hard homology").
Standard methods in the art may be used to determine homology. For example the
UWGCG Package provides the BESTFIT program which can be used to calculate homology,
for example used on its default settings (Devereux et al (1984) Nucleic Acids Research 12,
p387-395). The PILEUP and BLAST algorithms can be used to calculate homology or line
up sequences (such as identifying equivalent residues or corresponding sequences (typically
on their default settings)), for example as described in Altschul S. F. (1993) J Mol Evol
36:290-300; Altschul, S.F et al (1990) J Mol Biol 215:403-10. Software for performing
BLAST analyses is publicly available through the National Center for Biotechnology
Information (http://www.ncbi.nlm.nih.gov/).
SEQ ID NO: 2 is the MS-(B1)8 mutant of the MspA monomer. The variant may
comprise any of the mutations in the MspB, C or D monomers compared with MspA. The
mature forms of MspB, C and D are shown in SEQ ID NOs: 15 to 17. In particular, the
variant may comprise the following substitution present in MspB: A138P. The variant may
comprise one or more of the following substitutions present in MspC: A96G, N102E and
A138P. The variant may comprise one or more of the following mutations present in MspD:
Deletion of Gl, L2V, E5Q, L8V, D13G, W21A, D22E, K47T, I49H, I68V, D91G, A96Q,
N102D, S103T, VI 041, S136K and G141A. The variant may comprise combinations of one
or more of the mutations and substitutions from Msp B, C and D. The variant may comprise
the mutation L88N. The variant of SEQ ID NO: 2 has the mutation L88N in addition to all
the mutations of MS-B1 and is called MS-B2. The pore used in the invention may be MS-
(B2)8 or MS-(B2C)8.
Amino acid substitutions may be made to the amino acid sequence of SEQ ID NO: 2
in addition to those discussed above, for example up to 1, 2, 3, 4, 5, 10, 20 or 30
substitutions. Conservative substitutions replace amino acids with other amino acids of
similar chemical structure, similar chemical properties or similar side-chain volume. The
amino acids introduced may have similar polarity, hydrophilicity, hydrophobicity, basicity,
acidity, neutrality or charge to the amino acids they replace. Alternatively, the conservative
substitution may introduce another amino acid that is aromatic or aliphatic in the place of a
pre-existing aromatic or aliphatic amino acid. Conservative amino acid changes are wellknown
in the art and may be selected in accordance with the properties of the 20 main amino
acids as defined in Table 2 below. Where amino acids have similar polarity, this can also be
determined by reference to the hydropathy scale for amino acid side chains in Table 3 .
Table 2 - Chemical properties of amino acids:
Side Chain Hydropathy
e 4.5
Val 4.2
Leu 3.8
Phe 2.8
Cys 2.5
Met 1.9
Ala 1.8
Gly -0.4
Thr -0. 7
Ser -0.8
Trp -0.9
Tyr -1.3
Pro -1.6
His -3.2
Glu -3.5
Gin -3.5
Asp -3.5
Asn -3.5
Lys -3.9
Arg -4.5
One or more amino acid residues of the amino acid sequence of SEQ ID NO: 2 may
additionally be deleted from the polypeptides described above. Up to 1, 2, 3, 4, 5, 10, 20 or
30 residues may be deleted, or more.
Variants may include fragments of SEQ ID NO: 2 . Such fragments retain pore
forming activity. Fragments may be at least 50, 100, 150 or 200 amino acids in length. Such
fragments may be used to produce the pores. A fragment preferably comprises the pore
forming domain of SEQ ID NO: 2 . Fragments must include one of residues 88, 90, 91, 105,
118 and 134 of SEQ ID NO: 2 . Typically, fragments include all of residues 88, 90, 91, 105,
118 and 134 of SEQ ID NO: 2 .
One or more amino acids may be alternatively or additionally added to the
polypeptides described above. An extension may be provided at the amino terminal or
carboxy terminal of the amino acid sequence of SEQ ID NO: 2 or polypeptide variant or
fragment thereof. The extension may be quite short, for example from 1 to 10 amino acids in
length. Alternatively, the extension may be longer, for example up to 50 or 100 amino acids.
A carrier protein may be fused to an amino acid sequence according to the invention. Other
fusion proteins are discussed in more detail below.
As discussed above, a variant is a polypeptide that has an amino acid sequence which
varies from that of SEQ ID NO: 2 and which retains its ability to form a pore. A variant
typically contains the regions of SEQ ID NO: 2 that are responsible for pore formation. The
pore forming ability of Msp, which contains a b-barrel, is provided by b-sheets in each
subunit. A variant of SEQ ID NO: 2 typically comprises the regions in SEQ ID NO: 2 that
form b-sheets. One or more modifications can be made to the regions of SEQ ID NO: 2 that
form b-sheets as long as the resulting variant retains its ability to form a pore. A variant of
SEQ ID NO: 2 preferably includes one or more modifications, such as substitutions,
additions or deletions, within its a-helices and/or loop regions.
The monomers derived from Msp may be modified to assist their identification or
purification, for example by the addition of histidine residues (a hist tag), aspartic acid
residues (an asp tag), a streptavidin tag or a flag tag, or by the addition of a signal sequence
to promote their secretion from a cell where the polypeptide does not naturally contain such a
sequence. An alternative to introducing a genetic tag is to chemically react a tag onto a
native or engineered position on the pore. An example of this would be to react a gel-shift
reagent to a cysteine engineered on the outside of the pore. This has been demonstrated as a
method for separating hemolysin hetero-oligomers (Chem Biol. 1997 Jul; 4(7):497-505).
The monomer derived from Msp may be labelled with a revealing label. The
revealing label may be any suitable label which allows the pore to be detected. Suitable
labels include, but are not limited to, fluorescent molecules, radioisotopes, e.g. 1251, 35S,
enzymes, antibodies, antigens, polynucleotides and ligands such as biotin.
The monomer derived from Msp may also be produced using D-amino acids. For
instance, the monomer derived from Msp may comprise a mixture of L-amino acids and Damino
acids. This is conventional in the art for producing such proteins or peptides.
The monomer derived from Msp contains one or more specific modifications to
facilitate nucleotide discrimination. The monomer derived from Msp may also contain other
non-specific modifications as long as they do not interfere with pore formation. A number of
non-specific side chain modifications are known in the art and may be made to the side
chains of the monomer derived from Msp. Such modifications include, for example,
reductive alkylation of amino acids by reaction with an aldehyde followed by reduction with
NaBH 4, amidination with methylacetimidate or acylation with acetic anhydride.
The monomer derived from Msp can be produced using standard methods known in
the art. The monomer derived from Msp may be made synthetically or by recombinant
means. For example, the pore may be synthesized by in vitro translation and transcription
(IVTT). Suitable methods for producing pores are discussed in International Application
Nos. PCT/GB09/001690 (published asWO 2010/004273), PCT/GB09/001679 (published as
WO 2010/004265) or PCT/GB 10/000 133 (published asWO 2010/086603). Methods for
inserting pores into membranes are discussed.
The transmembrane protein pore is also preferably derived from a-hemolysin (a-HL).
The wild type a-HL pore is formed of seven identical monomers or subunits (i.e. it is
heptameric). The sequence of one monomer or subunit of a-hemolysin-NN is shown in SEQ
ID NO: 4 . The transmembrane protein pore preferably comprises seven monomers each
comprising the sequence shown in SEQ ID NO: 4 or a variant thereof. Amino acids 1, 7 to
21, 3 1 to 34, 45 to 51, 63 to 66, 72, 92 to 97, 104 to 111, 124 to 136, 149 to 153, 160 to 164,
173 to 206, 210 to 213, 217, 218, 223 to 228, 236 to 242, 262 to 265, 272 to 274, 287 to 290
and 294 of SEQ ID NO: 4 form loop regions. Residues 113 and 147 of SEQ ID NO: 4 form
part of a constriction of the barrel or channel of a-HL.
In such embodiments, a pore comprising seven proteins or monomers each
comprising the sequence shown in SEQ ID NO: 4 or a variant thereof are preferably used in
the method of the invention. The seven proteins may be the same (homoheptamer) or
different (heteroheptamer).
A variant of SEQ ID NO: 4 is a protein that has an amino acid sequence which varies
from that of SEQ ID NO: 4 and which retains its pore forming ability. The ability of a
variant to form a pore can be assayed using any method known in the art. For instance, the
variant may be inserted into a lipid bilayer along with other appropriate subunits and its
ability to oligomerise to form a pore may be determined. Methods are known in the art for
inserting subunits into membranes, such as lipid bilayers. Suitable methods are discussed
above.
The variant may include modifications that facilitate covalent attachment to or
interaction with the helicase. The variant preferably comprises one or more reactive cysteine
residues that facilitate attachment to the helicase. For instance, the variant may include a
cysteine at one or more of positions 8, 9, 17, 18, 19, 44, 45, 50, 51, 237, 239 and 287 and/or
on the amino or carboxy terminus of SEQ ID NO: 4 . Preferred variants comprise a
substitution of the residue at position 8, 9, 17, 237, 239 and 287 of SEQ ID NO: 4 with
cysteine (A8C, T9C, N17C, K237C, S239C or E287C). The variant is preferably any one of
the variants described in International Application No. PCT/GB09/001690 (published asWO
2010/004273), PCT/GB09/001679 (published asWO 2010/004265) or PCT/GB 10/000 133
(published asWO 2010/086603).
The variant may also include modifications that facilitate any interaction with
nucleotides.
The variant may be a naturally occurring variant which is expressed naturally by an
organism, for instance by a Staphylococcus bacterium. Alternatively, the variant may be
expressed in vitro or recombinantly by a bacterium such as Escherichia coli. Variants also
include non-naturally occurring variants produced by recombinant technology. Over the
entire length of the amino acid sequence of SEQ ID NO: 4, a variant will preferably be at
least 50% homologous to that sequence based on amino acid identity. More preferably, the
variant polypeptide may be at least 55%, at least 60%, at least 65%, at least 70%, at least
75%, at least 80%, at least 85%, at least 90% and more preferably at least 95%, 97% or 99%
homologous based on amino acid identity to the amino acid sequence of SEQ ID NO: 4 over
the entire sequence. There may be at least 80%, for example at least 85%, 90% or 95%,
amino acid identity over a stretch of 200 or more, for example 230, 250, 270 or 280 or more,
contiguous amino acids ("hard homology"). Homology can be determined as discussed
above.
Amino acid substitutions may be made to the amino acid sequence of SEQ ID NO: 4
in addition to those discussed above, for example up to 1, 2, 3, 4, 5, 10, 20 or 30
substitutions. Conservative substitutions may be made as discussed above.
One or more amino acid residues of the amino acid sequence of SEQ ID NO: 4 may
additionally be deleted from the polypeptides described above. Up to 1, 2, 3, 4, 5, 10, 20 or
30 residues may be deleted, or more.
Variants may be fragments of SEQ ID NO: 4 . Such fragments retain pore-forming
activity. Fragments may be at least 50, 100, 200 or 250 amino acids in length. A fragment
preferably comprises the pore-forming domain of SEQ ID NO: 4 . Fragments typically
include residues 119, 121, 135. 113 and 139 of SEQ ID NO: 4 .
One or more amino acids may be alternatively or additionally added to the
polypeptides described above. An extension may be provided at the amino terminus or
carboxy terminus of the amino acid sequence of SEQ ID NO: 4 or a variant or fragment
thereof. The extension may be quite short, for example from 1 to 10 amino acids in length.
Alternatively, the extension may be longer, for example up to 50 or 100 amino acids. A
carrier protein may be fused to a pore or variant.
As discussed above, a variant of SEQ ID NO: 4 is a subunit that has an amino acid
sequence which varies from that of SEQ ID NO: 4 and which retains its ability to form a
pore. A variant typically contains the regions of SEQ ID NO: 4 that are responsible for pore
formation. The pore forming ability of a-HL, which contains a b-barrel, is provided by b-
strands in each subunit. A variant of SEQ ID NO: 4 typically comprises the regions in SEQ
ID NO: 4 that form b-strands. The amino acids of SEQ ID NO: 4 that form b-strands are
discussed above. One or more modifications can be made to the regions of SEQ ID NO: 4
that form b-strands as long as the resulting variant retains its ability to form a pore. Specific
modifications that can be made to the b-strand regions of SEQ ID NO: 4 are discussed above.
A variant of SEQ ID NO: 4 preferably includes one or more modifications, such as
substitutions, additions or deletions, within its a-helices and/or loop regions. Amino acids
that form a-helices and loops are discussed above.
The variant may be modified to assist its identification or purification as discussed
above.
Pores derived from a-HL can be made as discussed above with reference to pores
derived from Msp.
In some embodiments, the transmembrane protein pore is chemically modified. The
pore can be chemically modified in any way and at any site. The transmembrane protein
pore is preferably chemically modified by attachment of a molecule to one or more cysteines
(cysteine linkage), attachment of a molecule to one or more lysines, attachment of a molecule
to one or more non-natural amino acids, enzyme modification of an epitope or modification
of a terminus. Suitable methods for carrying out such modifications are well-known in the
art. The transmembrane protein pore may be chemically modified by the attachment of any
molecule. For instance, the pore may be chemically modified by attachment of a dye or a
fluorophore.
Any number of the monomers in the pore may be chemically modified. One or more,
such as 2, 3, 4, 5, 6, 7, 8, 9 or 10, of the monomers is preferably chemically modified as
discussed above.
The molecule (with which the pore is chemically modified) may be attached directly
to the pore or attached via a linker as disclosed in International Application Nos.
PCT/GB09/001690 (published asWO 2010/004273), PCT/GB09/001679 (published asWO
2010/004265) or PCT/GB 10/000 133 (published asWO 2010/086603).
Ratchets that may be used are as follows.
The translocation of the polymer through the nanopore may be performed in a
ratcheted manner. In this case successive k-mers of the polymer are registered with the
nanopore. In this manner each measurement is dependent on a particular k-mer. If the
registration is held for sufficient time, then a group of plural measurements will be dependent
on a particular k-mer. Depending on the nature of the translocation, the period of registration
can be unpredictable and may vary in length. Depending on the period of registration,
relative to the measurement sampling rate, it might be that there are not plural measurements,
or even a signal measurement, that are dependent on every k-mer in the sequence.
The translocation of the polymer may be controlled by a molecular ratchet that
controls the movement of the polymer through the pore. The molecular ratchet may be a
polymer binding protein. For polynucleotides, the polynucleotide binding protein is
preferably a polynucleotide handling enzyme. A polynucleotide handling enzyme is a
polypeptide that is capable of interacting with and modifying at least one property of a
polynucleotide. The enzyme may modify the polynucleotide by cleaving it to form
individual nucleotides or shorter chains of nucleotides, such as di- or trinucleotides. The
enzyme may modify the polynucleotide by orienting it or moving it to a specific position.
The polynucleotide handling enzyme does not need to display enzymatic activity as long as it
is capable of binding the target polynucleotide and controlling its movement through the
pore. For instance, the enzyme may be modified to remove its enzymatic activity or may be
used under conditions which prevent it from acting as an enzyme. Such conditions are
discussed in more detail below.
The polynucleotide handling enzyme may be derived from a nucleolytic enzyme.
The polynucleotide handling enzyme used in the construct of the enzyme is more preferably
derived from a member of any of the Enzyme Classification (EC) groups 3.1.1 1, 3.1.13,
3.1.14, 3.1.15, 3.1.16, 3.1.21, 3.1.22, 3.1.25, 3.1.26, 3.1.27, 3.1.30 and 3.1.31. The enzyme
may be any of those disclosed in International Application No. PCT/GB 10/000 133
(published asWO 2010/086603).
Preferred enzymes are polymerases, exonucleases, helicases and topoisomerases,
such as gyrases. Suitable enzymes include, but are not limited to, exonuclease I from E. coli
(SEQ ID NO: 8), exonuclease III enzyme from E. coli (SEQ ID NO: 10), RecJ from T.
thermophilus (SEQ ID NO: 12) and bacteriophage lambda exonuclease (SEQ ID NO: 14)
and variants thereof. Three subunits comprising the sequence shown in SEQ ID NO: 14 or a
variant thereof interact to form a trimer exonuclease. The enzyme is preferably derived from
a Phi29 DNA polymerase. An enzyme derived from Phi29 polymerase comprises the
sequence shown in SEQ ID NO: 6 or a variant thereof.
A variant of SEQ ID NOs: 6, 8, 10, 12 or 14 is an enzyme that has an amino acid
sequence which varies from that of SEQ ID NO: 6, 8, 10, 12 or 14 and which retains
polynucleotide binding ability. The variant may include modifications that facilitate binding
of the polynucleotide and/or facilitate its activity at high salt concentrations and/or room
temperature.
Over the entire length of the amino acid sequence of SEQ ID NO: 6, 8, 10, 12 or 14, a
variant will preferably be at least 50% homologous to that sequence based on amino acid
identity. More preferably, the variant polypeptide may be at least 55%, at least 60%>, at least
65%o, at least 70%, at least 75%, at least 80%, at least 85%, at least 90% and more preferably
at least 95%, 97% or 99% homologous based on amino acid identity to the amino acid
sequence of SEQ ID NO: 6, 8, 10, 12 or 14 over the entire sequence. There may be at least
80% , for example at least 85%, 90% or 95%, amino acid identity over a stretch of 200 or
more, for example 230, 250, 270 or 280 or more, contiguous amino acids ("hard homology").
Homology is determined as described above. The variant may differ from the wild-type
sequence in any of the ways discussed above with reference to SEQ ID NO: 2 . The enzyme
may be covalently attached to the pore as discussed above.
The two strategies for single strand DNA sequencing are the translocation of the
DNA through the nanopore, both cis to trans and trans to cis, either with or against an applied
potential. The most advantageous mechanism for strand sequencing is the controlled
translocation of single strand DNA through the nanopore under an applied potential.
Exonucleases that act progressively or processively on double stranded DNA can be used on
the cis side of the pore to feed the remaining single strand through under an applied potential
or the trans side under a reverse potential. Likewise, a helicase that unwinds the double
stranded DNA can also be used in a similar manner. There are also possibilities for
sequencing applications that require strand translocation against an applied potential, but the
DNA must be first "caught" by the enzyme under a reverse or no potential. With the
potential then switched back following binding the strand will pass cis to trans through the
pore and be held in an extended conformation by the current flow. The single strand DNA
exonucleases or single strand DNA dependent polymerases can act as molecular motors to
pull the recently translocated single strand back through the pore in a controlled stepwise
manner, trans to cis, against the applied potential. Alternatively, the single strand DNA
dependent polymerases can act as molecular brake slowing down the movement of a
polynucleotide through the pore.
In a preferred embodiment, strand sequencing is carried out using a pore derived from
Msp and a Phi29 DNA polymerase. The method comprises (a) adding the polynucleotide to
the solution; (b) allowing the target polynucleotide to interact with a detector in the
membrane, which detector comprises a pore derived from Msp and a Phi29 DNA
polymerase, such that the polymerase controls the movement of the target polynucleotide
through the pore and a proportion of the nucleotides in the target polynucleotide interacts
with the pore; and (c) measuring the current passing through the pore during each interaction
and thereby determining the sequence of the target polynucleotide, wherein steps (b) and (c)
are carried out with a voltage applied across the pore. When the target polynucleotide is
contacted with a Phi29 DNA polymerase and a pore derived from Msp, the target
polynucleotide firstly forms a complex with the Phi29 DNA polymerase. When the voltage is
applied across the pore, the target polynucleotide/Phi29 DNA polymerase complex forms a
complex with the pore and controls the movement of the target polynucleotide through the
pore.
Wild-type Phi29 DNA polymerase has polymerase and exonuclease activity. It may
also unzip double stranded polynucleotides under the correct conditions. Hence, the enzyme
may work in three modes. This is discussed in more detail below.
The Phi29 DNA polymerase may comprise the sequence shown in SEQ ID NO: 6 or
a variant thereof. A variant of SEQ ID NO: 6 is an enzyme that has an amino acid sequence
which varies from that of SEQ ID NO: 6 and which retains polynucleotide binding activity.
The variant must work in at least one of the three modes discussed below. Preferably, the
variant works in all three modes. The variant may include modifications that facilitate
handling of the polynucleotide and/or facilitate its activity at high salt concentrations and/or
room temperature.
Over the entire length of the amino acid sequence of SEQ ID NO: 6, a variant will
preferably be at least 40% homologous to that sequence based on amino acid identity. More
preferably, the variant polypeptide may be at least 50%, at least 55%, at least 60%>, at least
6 5%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90% and more preferably
at least 95%, 97% or 99% homologous based on amino acid identity to the amino acid
sequence of SEQ ID NO: 6 over the entire sequence. There may be at least 80%, for
example at least 85%, 90% or 95%, amino acid identity over a stretch of 200 or more, for
example 230, 250, 270 or 280 or more, contiguous amino acids ("hard homology").
Homology is determined as described above. The variant may differ from the wild-type
sequence in any of the ways discussed above with reference to SEQ ID NO: 2 .
Any of the systems, apparatus or conditions discussed above may be used in
accordance with this preferred embodiment. The salt concentration is typically from 0 .15M
to 0.6M. The salt is preferably KC1.
The method may be carried out in one of three preferred ways based on the three
modes of the Phi29 DNA polymerase. Each way includes a method of proof-reading the
sequence. First, the method is preferably carried out using the Phi29 DNA polymerase as a
polymerase. In this embodiment, steps (b) and (c) are carried out in the presence of free
nucleotides and an enzyme cofactor such that the polymerase moves the target
polynucleotide through the pore against the field resulting from the applied voltage. The
target polynucleotide moves in the 5' to 3' direction. The free nucleotides may be one or
more of any of the individual nucleotides discussed above. The enzyme cofactor is a factor
that allows the Phi29 DNA polymerase to function either as a polymerase or an exonuclease.
The enzyme cofactor is preferably a divalent metal cation. The divalent metal cation is
preferably Mg , Mn , Ca or Co . The enzyme cofactor is most preferably Mg . The
method preferably further comprises (d) removing the free nucleotides such that the
polymerase moves the target polynucleotide through the pore with the field resulting from
the applied voltage (i.e. in the 3' and 5' direction) and a proportion of the nucleotides in the
target polynucleotide interacts with the pore and (e) measuring the current passing through
the pore during each interaction and thereby proof reading the sequence of the target
polynucleotide obtained in step (c), wherein steps (d) and (e) are also carried out with a
voltage applied across the pore.
Second, the method is preferably carried out using the Phi29 DNA polymerase as an
exonuclease. In this embodiment, wherein steps (b) and (c) are carried out in the absence of
free nucleotides and the presence of an enzyme cofactor such that the polymerase moves the
target polynucleotide through the pore with the field resulting from the applied voltage. The
target polynucleotide moves in the 3' to 5' direction. The method preferably further
comprises (d) adding free nucleotides such that the polymerase moves the target
polynucleotide through the pore against the field resulting from the applied voltage (i.e. in
the 5' to 3' direction) and a proportion of the nucleotides in the target polynucleotide
interacts with the pore and (e) measuring the current passing through the pore during each
interaction and thereby proof reading the sequence of the target polynucleotide obtained in
step (c), wherein steps (d) and (e) are also carried out with a voltage applied across the pore.
Third, the method is preferably carried out using the Phi29 DNA polymerase in
unzipping mode. In this embodiment, steps (b) and (c) are carried out in the absence of free
nucleotides and the absence of an enzyme cofactor such that the polymerase controls the
movement of the target polynucleotide through the pore with the field resulting from the
applied voltage (as it is unzipped). In this embodiment, the polymerase acts like a brake
preventing the target polynucleotide from moving through the pore too quickly under the
influence of the applied voltage. The method preferably further comprises (d) lowering the
voltage applied across the pore such that the target polynucleotide moves through the pore in
the opposite direction to that in steps (b) and (c) (i.e. as it re-anneals) and a proportion of the
nucleotides in the target polynucleotide interacts with the pore and (e) measuring the current
passing through the pore during each interaction and thereby proof reading the sequence of
the target polynucleotide obtained in step (c), wherein steps (d) and (e) are also carried out
with a voltage applied across the pore.
In another preferred embodiment, a helicase is used as a ratchet for the polynucleotide
(for example as disclosed in US 61/549,998 (Nl 15020), US 61/581,332 (Nl 15505), US
61/581,340 that are incorporated herein by reference). It has been shown that helicases have a
surprisingly high salt tolerance. Helicases can move the target polynucleotide in two
directions, namely with or against the field resulting from the applied voltage. Hence, the
method may be carried out in one of two preferred modes. Different signals are obtained
depending on the direction the target polynucleotide moves through the pore, i.e. in the
direction of or against the field. Helicases typically move the target polynucleotide through
the pore one nucleotide at a time. Helicases can therefore function like a single-base ratchet.
This is of course advantageous when sequencing a target polynucleotide because
substantially all, if not all, of the nucleotides in the target polynucleotide may be identified
using the pore. Helicases are capable of controlling the movement of single stranded
polynucleotides and double stranded polynucleotides. Helicases appear very resistant to the
field resulting from applied voltages. Very little movement of the polynucleotide under an
"unzipping" condition was observed. This is important because it means that there are no
complications from unwanted "backwards" movements when moving polynucleotides
against the field resulting from an applied voltage.
The method comprises: (a) contacting the target polynucleotide with a transmembrane
pore and a helicase such that the helicase controls the movement of the target polynucleotide
through the pore and nucleotides in the target polynucleotide interact with the pore; and (b)
measuring the current passing through the pore during one or more interactions to measure
one or more characteristics of the target polynucleotide and thereby characterising the target
polynucleotide.
As discussed above, helicases may work in two modes with respect to the nanopore.
For a helicase that translocates in the 3' to 5' direction, the two modes are as follows. First,
the method is preferably carried out using the helicase such that it moves the target sequence
through the pore with the field resulting from the applied voltage. In this mode the 3' end of
the DNA is first captured in the nanopore, and the enzyme moves the DNA into the nanopore
such that the target sequence is passed through the nanopore with the field until it finally
translocates through to the trans side of the bilayer. Alternatively, the method is preferably
carried out such that the enzyme moves the target sequence through the pore against the field
resulting from the applied voltage. In this mode the 5' end of the DNA is first captured in the
nanopore, and the enzyme moves the DNA through the nanopore such that the target
sequence is pulled out of the nanopore against the applied field until finally ejected back to
the cis side of the bilayer.
For a helicase that translocates in the 5' to 3' direction, the two modes are as follows.
First, the method is preferably carried out using the helicase such that it moves the target
sequence through the pore with the field resulting from the applied voltage. For In this mode
the 5' end of the DNA is first captured in the nanopore, and the enzyme moves the DNA into
the nanopore such that the target sequence is passed through the nanopore with the field until
it finally translocates through to the trans side of the bilayer. Alternatively, the method is
preferably carried out such that the enzyme moves the target sequence through the pore
against the field resulting from the applied voltage. In this mode the 3' end of the DNA is
first captured in the nanopore, and the enzyme moves the DNA through the nanopore such
that the target sequence is pulled out of the nanopore against the applied field until finally
ejected back to the cis side of the bilayer.
Measurement systems that may be used are as follows.
The methods may be carried out using any apparatus that is suitable for investigating
a membrane/pore system in which a pore is inserted into a membrane. The method may be
carried out using any apparatus that is suitable for transmembrane pore sensing. For
example, the apparatus comprises a chamber comprising an aqueous solution and a barrier
that separates the chamber into two sections. The barrier has an aperture in which the
membrane containing the pore is formed.
The methods may be carried out using the apparatus described in International
Application No. PCT/GB08/000562 (WO 2008/102120).
The methods may involve measuring the current passing through the pore during one
or more interactions with the nucleotide(s). Therefore the apparatus may also comprise an
electrical circuit capable of applying a potential and measuring an electrical signal across the
membrane and pore. The methods may be carried out using a patch clamp or a voltage
clamp. The methods preferably involve the use of a voltage clamp.
The methods of the invention may involve the measuring of a current passing through
the pore during one or more interactions with the nucleotide. Suitable conditions for
measuring ionic currents through transmembrane protein pores are known in the art and
disclosed in the Example. The method is typically carried out with a voltage applied across
the membrane and pore. The voltage used is typically from +2 V to -2 V, typically -400 mV
to +400mV. The voltage used is preferably in a range having a lower limit selected from
-400 mV, -300 mV, -200 mV, -150 mV, -100 mV, -50 mV, -20mV and 0 mV and an upper
limit independently selected from +10 mV, + 20 mV, +50 mV, +100 mV, +150 mV, +200
mV, +300 mV and +400 mV. The voltage used is more preferably in the range 100 mV to
240mV and most preferably in the range of 120 mV to 220 mV. It is possible to increase
discrimination between different nucleotides by a pore by using an increased applied
potential.
The methods are typically carried out in the presence of any charge carriers, such as
metal salts, for example alkali metal salt, halide salts, for example chloride salts, such as
alkali metal chloride salt. Charge carriers may include ionic liquids or organic salts, for
example tetramethyl ammonium chloride, trimethylphenyl ammonium chloride,
phenyltrimethyl ammonium chloride, or l-ethyl-3 -methyl imidazolium chloride. In the
exemplary apparatus discussed above, the salt is present in the aqueous solution in the
chamber. Potassium chloride (KC1), sodium chloride (NaCl) or caesium chloride (CsCl) is
typically used. NaCl is preferred. The salt concentration may be at saturation. The salt
concentration may be 3M or lower and is typically from 0.1 to 2.5 M, from 0.3 to 1.9 M,
from 0.5 to 1.8 M, from 0.7 to 1.7 M, from 0.9 to 1.6 M or from 1M to 1.4 M. The salt
concentration is preferably from 150 mM to 1M. The method is preferably carried out using
a salt concentration of at least 0.3 M, such as at least 0.4 M, at least 0.5 M, at least 0.6 M, at
least 0.8 M, at least 1.0 M, at least 1.5 M, at least 2.0 M, at least 2.5 M or at least 3.0 M.
High salt concentrations provide a high signal to noise ratio and allow for currents indicative
of the presence of a polymer to be identified against the background of normal current
fluctuations.
The methods are typically carried out in the presence of a buffer. In the exemplary
apparatus discussed above, the buffer is present in the aqueous solution in the chamber. Any
buffer may be used in the method of the invention. Typically, the buffer is HEPES. Another
suitable buffer is Tris-HCl buffer. The methods are typically carried out at a pH of from 4.0
to 12.0, from 4.5 to 10.0, from 5.0 to 9.0, from 5.5 to 8.8, from 6.0 to 8.7 or from 7.0 to 8.8
or 7.5 to 8.5. The pH used is preferably about 7.5.
The methods may be carried out at from 0 °C to 100 °C, from 15 °C to 95 °C, from 16
°C to 90 °C, from 17 °C to 85 °C, from 18 °C to 80 °C, 19 °C to 70 °C, or from 20 °C to 60
°C. The methods are typically carried out at room temperature. The methods are optionally
carried out at a temperature that supports enzyme function, such as about 37 °C.
The method is typically carried out in the presence of free nucleotides or free
nucleotide analogues and an enzyme cofactor that facilitate the action of the molecular
ratchet or enzyme. The free nucleotides may be one or more of any of the individual
nucleotides discussed above. The free nucleotides include, but are not limited to, adenosine
monophosphate (AMP), adenosine diphosphate (ADP), adenosine triphosphate (ATP),
guanosine monophosphate (GMP), guanosine diphosphate (GDP), guanosine triphosphate
(GTP), thymidine monophosphate (TMP), thymidine diphosphate (TDP), thymidine
triphosphate (TTP), uridine monophosphate (UMP), uridine diphosphate (HDP), uridine
triphosphate (UTP), cytidine monophosphate (CMP), cytidine diphosphate (CDP), cytidine
triphosphate (CTP), cyclic adenosine monophosphate (cAMP), cyclic guanosine
monophosphate (cGMP), deoxyadenosine monophosphate (dAMP), deoxyadenosine
diphosphate (dADP), deoxyadenosine triphosphate (dATP), deoxyguanosine monophosphate
(dGMP), deoxyguanosine diphosphate (dGDP), deoxyguanosine triphosphate (dGTP),
deoxythymidine monophosphate (dTMP), deoxythymidine diphosphate (dTDP),
deoxythymidine triphosphate (dTTP), deoxyuridine monophosphate (dUMP), deoxyuridine
diphosphate (dUDP), deoxyuridine triphosphate (dUTP), deoxycytidine monophosphate
(dCMP), deoxycytidine diphosphate (dCDP) and deoxycytidine triphosphate (dCTP). The
free nucleotides are preferably selected from AMP, TMP, GMP, CMP, UMP, dAMP, dTMP,
dGMP or dCMP. The free nucleotides are preferably adenosine triphosphate (ATP). The
enzyme cofactor is a factor that allows the enzyme to function. The enzyme cofactor is
preferably a divalent metal cation. The divalent metal cation is preferably Mg2+,Mn2+, Ca2+
or Co2+. The enzyme cofactor is most preferably Mg .
The target polymer may be contacted with the molecular ratchet and the pore in any
order. In is preferred that, when the target polymer is contacted with the molecular ratchet
and the pore, the target polymer firstly forms a complex with the molecular ratchet. When
the voltage is applied across the pore, the target polymer/molecular ratchet complex then
forms a complex with the pore and controls the movement of the polymer through the pore.
The nature of the measurements may be as follows.
A property that depends on the polymer units translocating through the pore may be
measured. The property may be associated with an interaction between the polymer and the
pore. Interaction of the polymer may occur at a constricted region of the pore. The
measurement system measures the property, producing a measurement that is dependent on
the polymer units of the polymer.
A variety of different types of measurements may be made. This includes without
limitation: electrical measurements and optical measurements. Possible electrical
measurements include: ion current flow measurements, impedance measurements, tunnelling
measurements (Ivanov AP et al., Nano Lett. 201 1 Jan 12; 1l(l):279-85), and FET
measurements (International Application WO 2005/124888). Optical measurements may be
combined with electrical measurements (Soni GV et al., Rev Sci Instrum. 2010
Jan;81(l):014301). The measurement may be a transmembrane current measurement such as
measurement of ionic current flowing through the pore.
Electrical measurements may be made using standard single channel recording
equipment as describe in Stoddart D et al., Proc Natl Acad Sci, 12;106(19):7702-7,
Lieberman KR et al, J Am Chem Soc. 2010; 132(50): 17961-72, and International Application
WO-2000/28312. Alternatively, electrical measurements may be made using a multi-channel
system, for example as described in International Application WO-2009/077734 and
International Application WO-201 1/067559.
It is possible to use measurements of more than one property. For example, one
possibility is to use measurements of ion current flow together with measurements of at least
one additional property besides ion current flow, for example comprising FET
measurements, optical measurements, or both.
The measurement system may comprise a plurality of pores. The apparatus preferably
further comprise a plurality of a polymer ratchets. The apparatus preferably further
comprises instructions for carrying out the method of the invention. The apparatus may be
any conventional apparatus for polymer analysis, such as an array or a chip. Any of the
embodiments discussed above with reference to the methods of the invention are equally
applicable to the apparatus of the invention.
The apparatus is preferably set up to carry out the method of the invention.
The apparatus may comprises: a sensor device that is capable of supporting the
membrane and plurality of pores and being operable to perform polymer characterising using
the pores; at least one reservoir for holding material for performing the characterising;
a fluidics system configured to controllably supply material from the at least one reservoir to
the sensor device; and a plurality of containers for receiving respective samples, the fluidics
system being configured to supply the samples selectively from the containers to the sensor
device. The apparatus may be any of those described in International Application No. No.
PCT/GB08/004127 (published asWO 2009/077734), PCT/GB 10/000789 (published asWO
2010/122293), International Application No. PCT/GB 10/002206 (not yet published) or
International Application No. PCT/US99/25679 (published asWO 00/28312), all of which
are incorporated herein by reference.
The apparatus may be a diagnostic device. The diagnostic device may be a benchtop
or handheld device. The device may be operated in conjunction with a cartridge, the cartridge
comprising the nanopore assay components and for receiving the fluid sample. The cartridge
may be housed in the device or otherwise operably connectable with the device. The
cartridge may be subsequently removed or disconnected from the device in order to clean the
cartridge for re-use, or for disposal. Thereafter an unused or cleaned cartridge may be used
with the device. The cartridge may be an integral part of the device wherein the device is
disposable after use. The cartridge will typically have a sample application region for
receiving a fluid sample. The sample application region may be a microfluidic channel or a
porous sample pad for example to directly receive a urine sample. The size of sample would
typically range from 0.25uL to lOmL. The sample application region may serve to directly
receive a sample from a patient, for example a sample of blood obtained with a fingerstick.
The cartridge may comprise a red blood cell filter for filtering red blood cells. The cartridge
may comprise dried reagents such as a salt, an anticoagulant, or a buffer. The device will
typically comprise data input and output ports and a memory for sending or receiving and
storing data, such as information in relation to feature vectors, patient ID, and measurement
results. The device may have wireless connectivity for communicating with a remote server
or medical professional. Typically the device and cartridge are not restricted to measurement
of a particular analyte and may capable of measuring any particular analyte and feature
vectors relating to a particular analyte of interest may be uploaded and stored in the memory.
Although ideally the measurements would be dependent on a single polymer unit
(which may thought of as a k-mer comprising k polymer units where k=l), with many typical
measurement systems, the measurement is dependent on a k-mer comprising k polymer units
where k is a plural integer. That is, each measurement is dependent on the sequence of each
of the polymer units in a k-mer. Typically the measurements are of a property that is
associated with an interaction between the polymer and the measurement system.
In some embodiments of the present invention it is preferred to use measurements
that are dependent on small groups of polymer units, for example doublets or triplets of
polymer units (i.e. in which k=2 or k=3). In other embodiments, it is preferred to use
measurements that are dependent on larger groups of polymer units, i.e. with a "broad"
resolution. Such broad resolution may be particularly useful for examining homopolymer
regions.
Where measurements are dependent on a k-mer, it is desirable that the measurements
are resolvable (i.e. separated) for as many as possible of the possible k-mers. Typically this
can be achieved if the measurements produced by different k-mers are well spread over the
measurement range and/or have a narrow distribution. This may be achieved to varying
extents by different measurement systems. However, it is a particular advantage of the
present invention, that it is not essential for the measurements produced by different k-mers
to be resolvable.
Fig. 1 schematically illustrates an example of a measurement system 8 comprising a
nanopore that is a biological pore 1 inserted in a biological membrane 2 such as a lipid
bilayer. A polymer 3 comprising a series of polymer units 4 is translocated through the
biological pore 1 as shown by the arrows. The polymer 3 may be a polynucleotide in which
the polymer units 4 are nucleotides. The polymer 3 interacts with an active part 5 of the
biological pore 1 causing an electrical property such as the trans-membrane current to vary in
dependence on a k-mer inside the biological pore 1. In this example, the active part 5 is
illustrated as interacting with a k-mer of three polymer units 4, but this is not limitative.
Electrodes 6 arranged on each side of the biological membrane 2 are connected to a
measurement circuit 7 that measures the electrical property. Thus the measurements are
dependent on the k-mer inside the biological pore 1.
A typical type of signal output by a measurement system and which is an input signal
to be analysed in accordance with the present invention is a "noisy step wave", although
without limitation to this signal type. An example of an input signal having this form is
shown in Fig. 2 for the case of an ion current measurement obtained using a measurement
system comprising a nanopore.
This type of input signal comprises an input series of measurements in which
successive groups of plural measurements are dependent on the same k-mer. The plural
measurements in each group are constant, subject to some variance discussed below, and
therefore form a "level" in the signal, corresponding to a state of the measurement system.
The signal moves between a set of levels, which may be a large set. Given the sampling rate
of the instrumentation and the noise on the signal, the transitions between levels can be
considered instantaneous, thus the signal can be approximated by an idealised step trace.
The measurements corresponding to each state are constant over the time scale of the
event, but for most measurement systems will be subject to variance over a short time scale.
Variance can result from measurement noise, for example arising from the electrical circuits
and signal processing, notably from the amplifier in the particular case of electrophysiology.
Such measurement noise is inevitable due the small magnitude of the properties being
measured. Variance can also result from inherent variation or spread in the underlying
physical or biological system of the measurement system. Most measurement systems will
experience such inherent variation to greater or lesser extents, even in the idealised case that
measurement noise is avoided. For any given measurement system, both sources of variation
may contribute or one of these noise sources may be dominant.
In addition, typically there is no apriori knowledge of number of measurements in
the group, which varies unpredictably.
These two factors of variance and lack of knowledge of the number of measurements
can make it hard to distinguish some of the groups, for example where the group is short
and/or the levels of the measurements of two successive groups are close to one another.
The signal takes this form as a result of the physical or biological processes occurring
in the measurement system. Thus, each group of measurements may be referred to as a
"state".
For example, in some measurement systems comprising a nanopore, the event
consisting of translocation of the polymer through the nanopore may occur in a ratcheted
manner. During each step of the ratcheted movement, the ion current flowing through the
nanopore at a given voltage across the nanopore is constant, subject to the variance discussed
above. Thus, each group of measurements is associated with a step of the ratcheted
movement. Each step corresponds to a state in which the polymer is in a respective position
relative to the nanopore. Although there may be some variation in the precise position during
the period of a state, there are large scale movements of the polymer between states.
Depending on the nature of the measurement system, the states may occur as a result of a
binding event in the nanopore.
There may be other information available either as part of the measurement or from
additional sources that provides registration information. This other information may enable
states to be identified.
Alternatively, the signal may take an arbitrary form. In these cases, the measurements
corresponding to k-mers may also be described in terms of a set of emissions and transitions.
For example, a measurement that is dependent on a particular k-mer may comprise of a series
of measurements occurring in a fashion amenable to description by these methods.
The extent to which a given measurement system provides measurements that are
dependent on k-mers and the size of the k-mers may be examined experimentally. For
example, known polymers may be synthesized and held at predetermined locations relative to
the measurement system to investigate from the resultant measurements how the
measurements depend on the identity of k-mers that interact with the measurement system.
One possible approach is to use a set of polymers having identical sequences except
for a k-mer at a predetermined position that varies for each polymer of the set. The size and
identity of the k-mers can be varied to investigate the effect on the measurements.
Another possible approach is to use a set of polymers in which the polymer units
outside a k-mer under investigation at a predetermined position vary for each polymer of the
set. As an example of such an approach, Fig. 3 is a frequency distribution of current
measurements of two polynucleotides in a measurement system comprising a nanopore. In
one of the polynucleotides (labelled polyT), every base in the region of the nanopore is a T
(labelled polyT), and in the other of the polynucleotides (labelled Nl 1-TATGAT-N8), 11
bases to the left and 8 to the right of a specific fixed 6-mer (having the sequence TATGAT)
are allowed to vary. The example of Fig. 3 shows excellent separation of the two strands in
terms of the current measurement. The range of values seen by the Nl 1-TATGAT-N8 strand
is also only slightly broader than that seen by the polyT. In this way and measuring polymers
with other sequences also, it can be ascertained that, for the particular measurement system in
question, measurements are dependent on 6-mers to a good approximation.
This approach, or similar, can be generalised for any measurement system enabling
the location and a minimal k-mer description to be determined.
Similar methodology may be used to identify location and width of wellapproximating
k-mers in a general measurement system. In the example of Fig. 3, this is
achieved by changing the position of the 6-mer relative to the pore (e.g. by varying the
number of Ns before and after) to detect location of the best approximating k-mer and
increasing and decreasing the number of fixed bases from 6 . The value of k can be minimal
subject to the spread of values being sufficiently narrow. The location of the k-mer can be
chosen to minimise peak width.
For typical measurement systems, it is usually the case that measurements that are
dependent on different k-mers are not all uniquely resolvable. For example, in the
measurement system to which Fig. 3 relates, it is observed that the range of the
measurements produced by DNA strands with a fixed 6-mer is of the order of 2 pA and the
approximate measurement range of this system is between 30 pA and 70 pA. For a 6-mer,
there are 4096 possible k-mers. Given that each of these has a similar variation of 2 pA, it is
clear that in a 40 pA measurement range these signals will not be uniquely resolvable. Even
where measurements of some k-mers are resolvable, it is typically observed that
measurements of many other k-mers are not.
For many actual measurement systems, it is not possible to identify a function that
transforms k measurements, that each depend in part on the same polymer unit, to obtain a
single value that is resolved at the level of a polymer unit, or more generally the k-mer
measurement is not describable by a set of parameters smaller than the number of k-mers.
By way of example, it will now be demonstrated for a particular measurement system
comprising a nanopore experimentally derived ion current measurements of polynucleotides
are not accurately describable by a simple first order linear model. This is demonstrated for
the two training sets described in more detail below. The simple first order linear model used
for this demonstration is:
Current = Sum [ fn(Bn) ] + E
where fn are coefficients for each base Bn occurring at each position n in the measurement
system and E represents the random error due to experimental variability. The data are fit to
this model by a least squares method, although any one of many methods known in the art
could alternatively be used. Figs. 4 and 5 are plots of the best model fit against the current
measurements. If the data was well described by this model, then the points should closely
follow the diagonal line within a typical experimental error (for example 2 pA).This is not
the case showing that the data is not well described by this linear model for either set of
coefficients.
There will now be described a specific method of analysing a time-ordered sequence
of measurements.
The method is illustrated in Fig. 6 and may be computer-implemented in an analysis
device 10 illustrated schematically in Fig. 6 . The analysis device 10 may be implemented by
a computer program executed in a computer apparatus or may be implemented by a dedicated
hardware device, or any combination thereof. In either case, the data used by the method is
stored in a memory in the analysis device 10. The computer apparatus, where used, may be
any type of computer system but is typically of conventional construction. The computer
program may be written in any suitable programming language. The computer program may
be stored on a computer-readable storage medium (i.e. a non-transitory medium), which may
be of any type, for example: a recording medium which is insertable into a drive of the
computing system and which may store information magnetically, optically or optomagnetically;
a fixed recording medium of the computer system such as a hard drive; or a
computer memory.
There is first described the method that is performed on an input signal 11 that has
sufficient time resolution that it comprises a series of measurements (or more generally any
number of series, as described further below) of the type described above in which the
measurements are time-ordered and comprise successive groups of plural measurements that
are dependent on the same k-mer without apriori knowledge of number of measurements in
any group.
An example of such an input signal 11 is shown in Fig. 2 as previously described.
In a state detection step SI, the input signal 11 is processed to identify successive
groups of measurements.
The state detection step SI may be performed using the method shown in Fig. 7 that
looks for short-term increases in the derivative of the input signal 11 as follows.
In step S1-1, the input signal 1 1 is differentiated to derive its derivative.
In step SI -2, the derivative from step Sl-1 is subjected to low-pass filtering to
suppress high-frequency noise (which the differentiation tends to amplify).
In step Sl-3, the filtered derivative from step Sl-2 is thresholded to detect transition
points between the groups of measurements, and thereby identify the groups of data.
In step S2, the measurements in each identified group are to derive values of one or
more features that represent characteristics in respect of each group. In the simplest approach,
a single value is derived, for example the mean, but plural values of features that represent
the same or different characteristics may be used to increase the information content.
Examples of features that may be used include: an average (a mean or a median or other
average) of the group of measurements; the period of the group of measurements; a variance
of the group of measurements; the distribution of the group of measurements, asymmetry
information; the confidence of the measurements; or any combination thereof.
The values of the features output from step S2 form a feature vector 12 in which the
values are time-ordered in the same order as the groups from which they are derived.
Step S2 has the result of providing a representation of the input signal 11 in which the
amount of information is reduced, but in which the significant characteristics of the signal are
maintained.
In general, other methods may alternatively be used in place of steps SI an/or S2 to
derive the feature vector 12 of values of one or more features that represent characteristics of
the input signal 11, time-ordered in the same order as the input signal 11.
In particular, it is not necessary to specifically identify the groups, and as such the
methods may be applied to input signals where the time resolution is lower to the extent that
some k-mers may provide only a single measurement or no measurement at all.
A possible simplification of the state detection step is to use a sliding window
analysis whereby one compares the means of two adjacent windows of data. A threshold can
then be either put directly on the difference in mean, or can be set based on the variance of
the data points in the two windows (for example, 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.
By way of example, Fig. 8 illustrates an experimentally determined input signal 11
reduced by a moving window t-test. In particular, Fig. 8 shows the input signal 11 as the light
line. Levels following state detection are shown overlayed as the dark line. Fig. 9 shows the
values derived for the entire trace, calculating the level of each state from the mean value
between transitions.
In step S3, the feature vector 12 derived in step S2 is compared with at least one other
feature vector 13 to determine the similarity there between. As shown by the dotted lines,
that other feature vector 13 may be one or more feature vectors 14 stored in memory 15 of
the analysis device 10, or alternatively may be one or more feature vectors 12 derived using
steps SI and S2 from input signals 11 that are series of measurements of other polymers.
Step S3 may be implemented in a variety of manners to derive useful information
about the polymer under investigation. Some non-limitative examples of step S3 are as
follows.
In a first example of step S3 shown in Fig. 10, the feature vector 12 derived in step S2
is compared with the other feature vector that is one or more of plural feature vectors 14
stored in a memory 15 of the analysis device 10 in respect of at least one class, as a library.
In this case, in step S3 produces classification data 16 that classifies the polymer from which
the derived feature vector 12 is derived as belonging to one of the classes on the basis of the
determined similarity.
Depending on the nature of the polymers represented by the feature vectors 14 in the
memory 15, similarity may be determined between the entirety or part of the derived feature
vector 12 and the entirety of the feature vector 14 stored in the memory 15, or between the
entirety or part of the derived feature vector 12 and a part of the feature vector 14 stored in
the memory 15.
In this case, optionally the method may be repeated on input signals 11 that are series
of measurements of other polymers, for example from the same sample. In that case either or
both of the following steps S4 and S5 may be performed.
In step S4 the numbers of polymers in each class may be counted. That provides
information on the profile of the population of polymers under investigation.
In step S5, the derived feature vector 12 is compared again with the feature vector
feature vector 14 stored in a memory 15 of the class within which the polymer of the derived
feature vector 12 is classified as belonging. In this comparison, similarity is again
determined, but this time to identify localized regions where the derived feature vector 12 is
dissimilar to that feature vector 14 in respect of the class. Such identification of localized
regions where the derived feature vector is dissimilar to what is expected provides an
analysis technique that is very powerful in many applications where change in relatively
small regions of long sequences of polymers is significant. One example of such a technique
is to identify mutations in a polymer that is a polynucleotide.
In step S3, the feature vector 13 used for the comparison may be selected from the
feature vectors 14 stored in the memory depending upon the polymer to be measured.
The feature vectors 14 stored in the memory 15 may comprise two or more feature
vectors having overlapping regions. In that case, the similarity may be determined in step S3
with the non-overlapping regions of the feature vectors 14 are used in the determination of
similarity with the derived feature vector 12.
In a second example shown in Fig. 11, step S3 is performed in respect of plural
feature vectors 12 derived by performing steps SI and S2 on plural polymers, for example
polymers from the same sample or polymers that are fragments of a common polymer.
In this second example, step S3 comprises the following steps.
In step S3-1, the plural derived feature vectors 12 are compared with each other and
the similarity therebetween is determined.
In step S3-2, the plural derived feature vectors 12 are clustered on the basis of their
similarity. In particular, clusters of similar feature vectors 12 are identified as a class. Step
S3-2 produces classification data 16 that classifies the polymers from which each derived
feature vector 12 is derived as belonging to one of the classes.
The classification data 16 may be processed by steps S4 and/or S5 as described
above.
In a third example, step S3 is performed in respect of plural feature vectors 12 derived
by performing steps SI and S2 on plural polymers that are fragments of a common polymer.
In this case, in step S3, the plural derived feature vectors 12 are compared with each other
and the similarity therebetween is determined in overlapping parts of the feature vectors 12.
This allows information on the common polymer to be built up from the input signals of the
fragments.
A fourth example of step S3 is similar to step S5, but involves comparison of the
derived feature vector 12 a feature vector 14 stored in a memory 15. In this comparison,
similarity is determined to identify localized regions where the derived feature vector 12 is
dissimilar to that feature vector 14 in the memory. This fourth example has similar
advantages to step S5 above, but is applicable where the expected type of the polymer is
known in advance and so the comparison can be made with a feature vector 14 in respect of
that expected type, without needing to classify the derived feature vector 12 first.
There will now be discussed some of the mathematical techniques that may be
applied in steps S3 and S5 to determine similarity.
One approach is to modify existing pairwise dynamic programming sequence
alignment algorithms e.g. the Needleman-Wunsch algorithm for global alignment or the
Smith-Waterman algorithm for local alignment.
The modifications may include replacing the substitution matrix with a distance
measure operating on the feature vector. For example the distance measure may be a
measurement of the absolute difference in current between the data points. The distance
function could also consider multiple measurements at each position e.g. mean and variance
of a current measurement.
Modification may also be made to the to the gap scoring mechanism as are known in
the art, for example constant gap penalties, linear gap penalties or affine gap penalties.
These algorithms output an alignment score that is a function of the two feature
vectors, the distance function and the gap penalties. The alignment score can be used to
determine similarity.
These modified alignment algorithms can be used for clustering, consensus building,
and pattern matching although other methods can also be used to achieve these tasks.
Multiple alignment algorithms may also be modified in similar ways to those
described for pairwise alignments.
Rather than match feature vectors by using gapped alignment techniques as described
above, an alternative approach is to represent the feature vector in terms of shorter subvectors,
typically comprising consecutive entries in the feature vector. For example, if the
feature vector was (1,2,3,4,5) then we could represent it by length 3 sub-vectors to give the
new representation {(1,2,3),(2,3,4),(3,4,5)}. For our application the sub-vectors are
frequently considerably longer (>10) so maintaining much of the time-ordering information.
Similarity of feature vectors on the basis of sub-vectors is then defined on the basis of
how closely the set of sub-vectors match. This has the potential to be a more efficient means
of comparison than gapped alignment type algorithms, since we may compare sub-vectors
directly without allowing for gaps.
If the feature sub-vectors are suitably discretized (for example by rounding each
number to the nearest 0 .1) then exact or partial matches of sub-vectors may be used, and
similarity calculated in terms of what proportion of sub-vectors match or partially match.
Discretisation also enables integer arithmetic to be used for comparison. Alternatively hash
functions may be applied to sub-vectors to give fixed length "fingerprints" (see for instance
Karp, R., Rabin, M. (1987) "Efficient randomized pattern matching algorithms"/ IBM J . Res.
Development 31:249-260.) denoting presence or absence of sub-vectors which can be rapidly
compared.
Similar ideas in terms of matching sub-strings are used by algorithms like BLAST
(Altschul, S.F., Gish, W., Miller, W., Myers, E.W. & Lipman, D.J. (1990) "Basic local
alignment search tool." J . Mol. Biol. 215:403-410.) that split data into short fragments and
match these against a large library.
An alternative approach is to use an HMM (Hidden Markov Model) Viterbi path as
follows.
In general, alignment-based and sub-vector based measures of pairwise similarity
treat the pair of feature vectors that are being compared in the same way. The result is that
given a pair of feature vectors A and B, the similarity of A to B is equal to the similarity of B
to A.
However, where one of the feature vectors to be compared is a library feature vector,
it is natural to treat the problem as if that feature vector were the "model" or "training
sequence". In this case, an alignment can be performed using HMM methods with models
constructed in a similar manner to the "forced path" training models described previously
(US 61/538,721, GB 1117574.2). Algorithms other than Viterbi that are known in the art may
also be applied, for example the Forwards-Backwards algorithm. As in the case of alignment
algorithms, there is an output score that can be used as the measure of similarity. In the case
of Viterbi this is the total likelihood of the path. The total likelihood is not guaranteed to be
equal if we swapped the roles of the two feature vectors, however for classification problems
in particular, this is not generally an issue.
For clustering, the following approaches may be applied.
Clustering is performed on input signals 11 from a measured population of polymers,
and involves determining the number and/or types of polymer present according to some
similarity criteria.
Given a matrix of distances (or similarities/dissimilarities), methods for hierarchical
clustering are well known and covered in standard monographs (for example Gordon, A.D.
(1999) Classification, 2nd edition. Chapman and Hall/CRC). Hierarchical agglomerative
methods are also used for sequence alignment in packages such as CLUSTAL (Higgins,D.G.
and Sharp,P.M. (1988). CLUSTAL: a package for performing multiple sequence alignment
on a microcomputer. Gene, 73, 237-244.)
Using global or local alignment algorithms, all feature vectors are pairwise aligned
with each other such that we have a measure of similarity (or in some cases distance)
between each pair of feature vectors. These similarity values can be written down as a
similarity matrix with the (m,n)th entry containing the similarity of the m'th to the n'th
feature vector. A clustering technique is then used (typically hierarchical agglomerative
clustering) based on that similarity matrix.
Two extremes of agglomerative clustering are single-link (score a pair of clusters
during the agglomerative step on the basis of the most similar feature vector pair) and
complete-link (score a pair of clusters based on the most dissimilar feature vector pair)
clustering. The best combination of algorithm to determine similarity and clustering
technique is dependent on the nature of the clusters expected for a given application.
For example, if clusters are expected to be made up of feature vectors with
overlapping fragments of pairs of feature vectors showing high similarity, local alignment
scores and single-link agglomerative clustering would be one appropriate choice. An
example of this is shown in Worked Example 2, where sequences 1 and 2 overlap as do
sequences 2 and 3 . If in our clustering task we wished to identify these as a single cluster
amid some other feature vectors, we would be most likely to be successful using a local
alignment score to correctly identify the short overlapping regions. Single-link clustering
would join the sequences into the same cluster because 1 has an overlap with 2 and 2 has an
overlap with 3, however complete-link agglomerative clustering would be a poor choice
since sequences 1 and 3 have no actual overlap in sequence space and hence are likely to
have low similarity in terms of feature vectors.
Where clusters are expected to be near-identical across the entire feature vector (for
example, where feature vectors have already been identified to begin and end in
approximately the same place relative to a known reference, and we are looking to discover
classes that vary subtly from that reference) global alignment scores and complete-link
agglomerative clustering would be more appropriate.
In many contexts, it is useful to be able to generate a single reference feature vector to
represent a group/cluster/class of similar and overlapping feature vectors. The following is an
outline of an iterative algorithm that can be used to achieve this.
1. Generate a long initial feature vector. We call this the landmark vector.
2 . Align each feature vector to the landmark vector.
3 . Generate a new, empty, landmark vector.
4 . Moving from start to finish along the aligned feature vectors from step 2, whenever a
proportion p of the aligned feature vectors lie within a range r, add the mean value at
that position to the landmark vector.
5 . Repeat 2-4 until the landmark vector produced at step 4 is identical for consecutive
iterations, or a maximum number of iterations is reached.
Alternatively, the landmark vector can be updated based on many or all possible
alignments.
The landmark vector produced as a result of this process with the feature vectors
aligned to it produces a "consensus" of the feature vectors.
In step 1, all pairs of feature vectors may be aligned and the aligned pair with the
most states picked, subject to some minimum level of similarity, taking the mean at each
position where the states align to generate the initial feature vector. Alternatives are possible,
for instance just picking the longest feature vector.
The pairwise alignment algorithms used in step 2 are described above.
In step 4, p and r can be varied according to the particular situation, mean may be
replaced by some other measure of location, and r may be replaced by some other measure of
spread.
This consensus building process provides a multiple alignment algorithm in terms of
feature vectors. The landmark-aligned states give a fixed length vector representing each
feature vector.
Some approaches to classification are as follows.
The task for classification is to assign a "query" feature vector to one of m classes for
integer m>l. There is a library of "target" feature vectors 14 in the memory 15 belonging to
these m classes.
Method of solution is dependent on whether the target feature vectors are
heterogeneous (mutually dissimilar at a global level) or are homogeneous (all globally
similar to each other, with some relatively subtle differences, typically localized,
differences), although clearly there are cases that lie between these extremes where mixtures
of methods are appropriate.
In the heterogeneous case, the simplest method for class determination is to calculate
similarity between the query feature vector and the target feature vectors by one of the
methods described above and to assign the query feature vector to the class with the target
feature vector of maximum similarity.
If there are multiple target feature vectors per class, then a summary target feature
vector may be derived for each class, containing for instance the mean value across target
feature vectors in that class, and proceed as before. For alignment-based similarity measures,
it is needed first to perform a multiple alignment of the feature vectors using, for example,
the "Consensus Building" process described above.
Alternatively each target feature vector may be treated independently. For instance, in
the simplest case, the query feature vector is assigned to the class of the closest target feature
vector. For this approach to be as successful as possible, a re-weighting of statistics to
account for the different number of target feature vectors per class is frequently desirable.
Although an alignment of all target feature vectors across all classes is not generally
possible in the heterogeneous case, we can nonetheless use learning algorithms to derive
classifiers. The vector of distances or dissimilarities to target feature vectors can be used as
the input to multivariate learning techniques such as multi-class linear discriminant analysis
to produce an improved classifier. Alternatively, a fixed length vector may be produced from
sub-vectors using standard hashing algorithms as described earlier and this used as the input
to learning algorithms. More about learning algorithms in the homogeneous case follows.
It is generally possible for many methods to output not just a most likely class, but a
probability of the classification being correct.
In the homogeneous case, the same or similar methods may be applied as in the
heterogeneous case, however random variation across the feature vector may well mask the
systematic local variations that are of primary interest and provide the key information to
correctly discriminate between classes.
Hence it is frequently more efficient to learn what the key differences between the
target feature vectors are; or more generally, given a training set of feature vectors with
known classes, to learn rules for correct classification that allow us to predict the class of
feature vectors.
Unlike the heterogeneous case, feature vectors may initially be aligned to a common
reference feature vector (for instance the landmarks from consensus alignment of the target
feature vectors), similarly to the "Consensus Building" case above, and the states aligning to
landmarks fed as a fixed length input vector to learning algorithms.
Given a training set of feature vectors of known class, standard statistical and
machine-learning classification techniques may be used to predict the class of a new feature
vector. For instance, a decision tree classifier (for example, but not limited, to C4.5. Quinlan,
J . R. (1993) C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers) can learn
that particular positions of the reference-aligned feature vector are above a particular value
for one class only. So-called black box methods such as neural networks, random forests and
support vector machines may be used to make predictions of class membership, while not
necessarily generating interpretable rules. In an alternative method, Bayesian networks may
be implemented, where expert knowledge may also be incorporated.
It may be of particular interest where classes vary around the same position after
alignment to the reference (for instance corresponding to less conserved regions of the
genome). In this case, given an alignment, one can look directly for one-or-more consecutive
positions with high between-class variation compared to within-class variation.
It is generally beneficial to use standard techniques such as cross-validation and hold
out sets with these methods to avoid over-fitting and gain an idea of generalizability.
Rather than begin with an alignment step, we also use sub-vectors as inputs to
learning algorithms. A fixed length vector may be produced from sub-vectors using standard
hashing algorithms as discussed earlier and this used as the input to learning algorithms.
Alternatively, the sub-vectors themselves may be used directly - for instance with an
algorithm searching for sub-vectors that only have near neighbours within-class.
Problems that do not obviously fall into the homogeneous or heterogeneous cases
may be treated using a mixture of methods from the two cases, in particular by first
subdividing the problem space into homogeneous groups of classes using clustering
(similarly to "Clustering" described above).
There will now be described approaches to determination of localized regions where
the derived feature vector 12 is dissimilar to another feature vector, e.g. in step S5 or the
fourth example of step S3.
Generally, an alignment to the target feature vector is performed and then positions
that vary between the query feature vector and the target feature vector are identified.
Where there is more than one target feature vector from a single class, a reference
feature vector is generated (for example the landmarks described in the "Consensus
Building" above) from the target feature vectors, and the target feature vectors are aligned to
the reference feature vector to gain an idea of location and variability at each position in the
reference (for example by calculating the mean and standard deviation of the aligned target
feature vectors at that position). Localised regions where the query feature vector shows a
pattern of values unlikely to be produced in the target class may then be identified, for
example by looking at the total likelihood across a number of consecutive reference-aligned
states if we assume each distribution to be Gaussian with mean and standard deviation
estimated from the target feature vectors.
The methods may be extended to look at differences between classes of feature
vectors as discussed in the homogenous case of classification above. These classes may be
pre-defined, for example they may be DNA samples from patients with and without a
particular disease. Alternatively, they may be derived by clustering in the first instance.
Equally, many of the statistical and machine learning techniques (such as decision
trees) discussed above in the context of homogeneous classification methods are also used to
discover localized regions that differ between pairs or classes of feature vector.
There will now be described approaches to assembly of large feature vectors from
fragments of feature vectors, e.g. in the third example of step S3 above.
The majority of existing assembly algorithms can be modified to use the feature
vectors of the present type. The consensus methods described above may be appropriate for
some assembly applications. Generally the following method may be used.
Feature vectors are first "discretised". A transformation is applied to each series of
measurments may include any one or combination of the following:
1. Representing the feature vector as a series of deltas.
2 . Representing the feature vector as a series of classes based on current level.
3 . Representing the feature vector as a series of milestone (well characterised) features.
Once the trace is discretised, standard assembly algorithms may be used. For example
seed sequences maybe extracted, and used in overlapping. The overlapper will then orientate
the reads using the feature vector space transformation.
Existing assembly algorithms that may be applied include Zerbino & Birney, "Velvet:
Algorithms for de novo short read assembly using de Bruijn graphs", Genome Res. 2008. 18:
821-829 and Batzoglou, S. "Algorithmic challenges in mammalian genome sequence
assembly", (2005) Encyclopaedia of genomics, proteomics and bioinformatics, ed Dunn, M.,
et al. (John Wiley and Sons, New York)
Some specific applications of the present invention are now set out by way of nonlimitative
example.
A first application is in counting molecules against a known library or panel of
molecules, which may use a method involving the first example of step S3.
The library comprises the feature vectors 14 stored in the memory 15. Such libraries
may be generated for later use, using either supervised or un-supervised learning, based on
individual experiments for each molecule or for sets of molecules to learn the feature vectors.
For example one may have a set of DNA/RNA sequences of known disease. The
fingerprints of these molecules may be known in advance, either from measurements or
generated from a model. Given a measurement of a molecule, this can be compared against
the known library and the similarity of the molecule to the library members measured. This
allows identification of each molecule measured (this identification could be "other") and
quantification of the relative numbers of each type of molecule measured.
Examples of things that can be counted with reference to a library or reference panel
are as follows:
Expression profiles: comparing abundance of mRNA transcripts by matching feature
vectors. This can be used to measure changes in expression levels. Such gene expression
might change during development, disease, treatment for the disease, between one organ and
another.
Abundance of biomarker miRNAs: these are, typically, 20-25-mer RNA
oligonucleotides that circulate in blood, and changes in the expression level of groups of
these is associated with certain diseases, particularly cancers. One could compare to a
defined panel, so there would be a relatively small search space for pattern matching.
Foetal copy number variation in circulating blood: fragmented foetal DNA circulates
in maternal blood. If the foetus has an aneuploidy, e.g. an additional copy of chromosome 21,
18, 11 (the main ones that are not immediately fatal) it would be possible to design capture
probes to, for example, exons of the chromosomes of interest, so as to enrich them for pore
analysis, and to then compare these to reference feature vectors and to count. The main
limitation of current methods for this is the inability to distinguish between maternal and
foetal chromosomes. There are differences in methylation status between foetal and maternal
DNA that are not visible to next gen sequencing that uses PCR, but which would be visible
as differences in feature vectors.
Comparative genomic hybridization (CGH): changes in copy number of various
genomic regions can be altered in tumour cells (and also in foetuses, as described above). For
a while, this was identified by comparative genome hybridisation, i.e. where patient / sample
is compared to reference by hybridising fragmented genomic DNA to a set of probes on an
array. As with foetal testing, feature vector space can be used to profile these copy number
changes.
Viral or bacterial load: a measure of the severity of infection. Possibly in conjunction
with some form of enrichment, the number of pathogen RNA or DNA copies per ml of blood
is measured. It would not have to be done on the whole pathogen genome. Early stage and
late stage measurements may be carried out to identify antigenic drift and/or antigenic
variation.
The method may be applications in epidemiology, for example in identification
(strain typing) and how a disease is spreading or evolving. The method may be used for
example to monitor the efficacy of a particular drug therapy or monitor the spread of disease
from one area of the body to another or spread of disease between patients.
Probes: There is provided a small population of probes (eg. aptamers to a biomarker
panel), some of which attach to a target molecule. Those that didn't bind are separated from
those that did, and molecules are counted in the unbound population or in the bound
population to quantify the target molecules.
The identity of organisms could be determined, e.g. in food or in cultures.
A second application is in quantification of major population(s) and measurement of
"other" present in a sample.
As an example, one can consider synthesis of a DNA oligomer. Current quality
control methods typically involve Polyacrylamide Gel Electrophoresis (PAGE), Highperformance
liquid chromatography (HPLC) and mass spectrometry. One could measure a
sample of the synthesised DNA and determine the signature of the major population present.
It is then possible to count the number of molecules in the sample that are different from the
major population, possibly indicating errors in synthesis. In particular if the differences occur
at particular positions in the feature vectors it may be due to a systematic error that can be
rectified by adjusting the synthesis conditions. Any improvements could then be verified by
repeating the nanopore measurement.
A third application is in measurement of modifications/differences at positions and
quantification of those modifications/differences within a population of molecules.
One example is calling of single-nucleotide polymorphisms (SNP). Known positions
compared to the four (or more) allowed nucleotides at that position. The presence and/or
absence of a SNP at a known location compared to the "wild-type". This may enable
identification of new loci. Similarly it may enable identification of paralog-specific variants
in non-allelic homologous recombination (NAHR) as discussed later.
Another example concerns methylation. Measurements can be made at known
methylation sites. The method allows identification of the presence, absence and/or
quantification of methylation at those sites. The method also allows identification of
unknown sites. The method allows estimation of "bulk" methylation state of individual
molecules, for example whether 100 % of the population is 50 % modified or 50 % of the
population is 100 % modified, e.g. for use in foetal screening as described above. The
methylation state of certain genes can be used as a biomarker for cancer
Another example is identification of splice variants and/or translocation breakpoints.
This is similar to the examples described earlier, but one identifies the position where feature
vectors stop matching, or where one half of a feature vector maps to one locus and the other
half maps elsewhere.
A fourth application is in identification of the presence and/or absence to a desired
confidence of a particular known molecule.
This is similar to comparison to the first application, but here there may be interest in
one particular molecule.
This method may be used to identify populations of molecules that are related, but not
identical to the known molecule to a particular degree of confidence (similar to
measurements of homology of DNA or protein sequences), for example in rapidly mutating
diseases.
Another example concerns fusion transcripts, as in splice variants. Detection of
specific fusion transcripts is used in cancer diagnosis, e.g. the presence of the Bcl-abl fusion
transcript indicates leukaemia.
Another example concerns diagnosis of NAHR. During meiosis, recombination
between similar but non-allelic loci results in deletion or duplication of fairly large chunks of
genome, with catastrophic consequences for the foetus arising from such a gamete. This will
cause a change in copy number of the affected loci (see CGH above), but also results in a
fusion of the non-allelic homologs, which would be detectable by looking at PSVs (like
S Ps but not the same).
Another example concerns the case where plural parts of the derived feature vector
are compared to plural stored feature vectors. For example the DNA sequence for known
protein domains may be used to generate the library feature vectors and the DNA that codes
for an unknown protein measured. Part of the derived feature vector may be identified with
for example a catalytic domain and another part with for example a DNA binding domain.
Thus the function of the protein may be deduced.
A fifth application concerns assembly.
From a collection of molecules reading smaller, partially overlapping feature vectors,
either randomly split, systematically split, or split by some other mechanism from a larger
molecule, one can assemble the complete larger feature vector. Similar algorithms (adapted)
to those used for sequence assembly may be used. Alternatively, one may generate a rough
template feature vector from known properties of the molecule (e.g. if the DNA sequence is
known) and the small fragments mapped to that template feature vector. In the case where the
template was approximate, the template can be refined throughout the process.
Libraries may be derived experimentally or be generated informatically.
Examples of the type of library used may include without limitation feature vectors
constructed from known DNA sequences using a model, from known protein sequences,
from known polymers, feature vectors derived experimentally, feature vectors assembled
from overlapping derived feature vectors, feature vectors derived from the consensus of
clustered measurements. Libraries may comprise plural related feature vectors, plural
unrelated feature vectors, heterogeneous or homogeneous sizes of feature vectors, similar
feature vectors with localised differences.
An example where libraries of feature vectors corresponding to DNA fragments are
derived experimentally may use fragments produced systematically, for example by
enzymatic fragmentation, or fragments produced randomly, for example by mechanical
shearing or through non-selective enzyme action. Randomly fragmented derived feature
vectors may preferentially be assembled into larger derived feature vectors for use in a
library. Systematically fragmented libraries may preferentially be used as library feature
vectors covering similar regions to the fragmentation pattern.
An example where a library of feature vectors is derived informatically may utilise
available databases, for example the NIH Genbank database (Nucleic Acids Research, 201 1
Jan;39(Database issue):D32-7) which contains publically available DNA sequences. To
derive, for example, the feature vectors for mean current corresponding to those sequence, a
model may be used derived from a training process such as that used previously (US
61/538,721, GB 1117574.2, Nl 14722). The library may be reduced to those sequences that
are of interest for the particular application, for example the library may be reduced to coding
regions of the human genome.
There will now be described some examples of use of the present invention.
Example 1 concerns data acquisition in a typical nanopore experiment with the
following experimental conditions:
Buffered solution: 1M NaCl, 100 mM Hepes pH 8.0, 1mM ATP, 1mM MgCl2, 1
mM DTT, lOmM Potassium Ferrocyanide (II), lOmM Potassium Ferricyanide (III), Pt
electrodes.
Nanopore: MS(B2C)8 MspA MS-
(G75 S/G77S/L88N/D90N/D9 1N/D93N/D 118R/Q 126R/D 134R/E1 39K)8
Enzyme: Helicase 100 nM
Electrical measurements were acquired from single MspA nanopores inserted in 1,2-
diphytanoyl-glycero-3-phosphocholine lipid (Avanti Polar Lipids) bilayers. Bilayers were
formed across -100 mih diameter apertures in 20 mih thick PTFE films (in custom Delrin
chambers) via the Montal-Mueller technique, separating two 1mL buffered solutions. All
experiments were carried out in the stated buffered solution. Single-channel currents were
measured on Axopatch 200B amplifiers (Molecular Devices) equipped with 1440A
digitizers. Pt electrodes were connected to the buffered solutions so that the cis compartment
(to which both nanopore and enzyme/DNA are added) is connected to the ground of the
Axopatch headstage, and the trans compartment is connected to the active electrode of the
headstage.
After achieving a single pore in the bilayer, DNA polynucleotide and helicase were
added to 100 mI_, of buffer and pre-incubated for 5mins (DNA = 1.5 nM, Enzyme = 1 mM) .
This pre-incubation mix was added to 900 mI_, of buffer in the cis compartment of the
electrophysiology chamber to initiate capture of the helicase-DNA complexes in the MspA
nanopore (to give final concentrations of DNA = 0.15 nM, Enzyme = 0.1 mM) . Helicase
ATPase activity was initiated as required by the addition of divalent metal ( 1 mM MgCl2)
and NTP ( 1 mM ATP) to the cis compartment. Experiments were carried out at a constant
potential of +120 mV.
The analyte DNA samples used in this study are shown as ANA ID NO. 1-19.
Example 2 concerns identification and quantification of particular DNA molecules
from a panel of DNA molecules. This example describes the process of identification of
DNA molecules in a solution from a pre-determined library of feature vectors.
Library Construction was performed as follows. The library was constructed by
taking 18 approximately 400 mer sequences (ANA ID NO 1 to 18), each overlapping the
previous sequence by approximately 100 bases from a 5 kilobase genome (PhiX174). For
example, ANA ID NO 2 will share 100 bases with ANA ID NO 1 and 100 bases with ANA
ID NO 3). These sequences contain a sequence at the beginning and a sequence at the end,
common to all strands and not part of the larger genome. The overlapping sequences allow a
demonstration of identification of different molecules, even in the presence of large similar
regions. The library feature vectors are constructed for the mean current by considering a
model of the current associated with each 5 mer position (1024 values). The determination of
this type of model has been disclosed previously (for example in US 61/538,721, GB
1117574.2, Nl 14722).
Feature Vectors for sequences 1, 2 and 3 are shown in Fig. 12, which illustrates
overlapping sections. Common ends (as described above) of each sequence have been
removed for this illustration.
Candidate molecule feature vectors were obtained as follows. Candidate molecules
were acquired using the experimental methods describe above and in Example 1. Candidates
are reduced to feature vectors consisting of the mean current between identified transitions as
described previously.
An example candidate belonging to one of the sequences (ANA ID NO 1 to 18) was
considered. This molecule was compared against the library (ANA ID NO 1 to 18) using an
alignment algorithm as described above. The output score from the alignment is used as a
measurement of similarity to each of the library members.
Comparison by alignment was performed. The output scores from the library
comparison are shown in Fig. 13. One can see that the score for one of the library members is
much higher than those for all the other library members. This is true across a range of
reasonable parameterisations of the alignment. Here a gap penalty of - 1 and a scoring
function of reciprocal absolute difference is shown (i.e. closer matches are higher scores).
Closer inspection of the alignment with library molecule 13 (ANA ID NO 13) shows
that a close match is indeed present, as shown in Fig. 14.
This was run for all the candidate molecules in this experiment (all molecule 13) and
one can see that in most cases the molecule was correctly identified as molecule 13. In cases
where the molecule was not correctly identified, these are mis-identified as molecule 12
(ANA ID NO 12). These are typically partial reads of the molecule, containing mostly the
shared overlapping sequence. A histogram of identifications is shown in Fig. 15. We count
168 instances of molecule 13, correctly identified in this experiment.
Example 3 concerns measurement of single-nucleotide polymorphisms (SNPs) in a
DNA fragment.
Library construction and feature vectors were generated using methods as presented
above, however in the library feature vector for molecule 13 (ANA ID NO 13), there were
made three changes to the sequence [old][position][new], T335A, G357T, C385A (ANA ID
NO 19). Any examples of molecule 13 will have changes at these positions relative to the
library molecule (i.e. 3 SNPs). The effect of these SNPs on the library feature vector is
shown in Fig. 16.
The alignment based identification method of the previous example was repeated,
demonstrating that these SNPs do not have a significant impact on the identification of the
molecules. The majority of molecules are still correctly identified with a slightly higher
tendency to mis-identify given the SNPs. The increased tendency is due to the sequences for
ANA ID NO 12 sharing the same sequence but without the SNPs. A histogram of
identifications is shown in Fig. 17.
For SNP calling, an HMM and a Viterbi path was used for alignment since this has a
better path constraint (i.e. will align better through the mismatches SNP regions) than e.g.
Needleman-Wunsch with parameters used previously. Alignments shown in Fig. 18 compare
well with the idealised library mutations shown earlier. The three SNPs are clearly
observable in Fig. 18.
Looking across a dataset of 176 molecules these SNP positions can be clearly
identified. Fig. 19 shows the difference in current between Viterbi aligned library and
candidate feature vectors. The three SNPs are visible, in the case of 335 and 357 at several
positions as several of the measured features are affected by each single change (i.e. a single
change to sequences affects several adjacent kmers).
The control version of this experiment was run, using the library feature vector for
ANA ID NO 13 without the SNPs. In this case no consistent difference is identified from the
library, as shown in Fig. 20 wherein no positions display a consistent deviation.
Example 4 concerns identification of a major population and measurement of a subpopulation
that is similar but different.
This example is worked through with simulated data. A set of 60 feature vectors (of
mean current) is simulated of ANA ID NO 13. Ten of the simulations also contain a SNP.
Gaussian noise with standard deviation of 1 pA is added to each value and 5 % of values
within each vector are deleted at random. Apart from simulating the data, no more
knowledge of the sequence is used.
Using this dataset (and no knowledge of the sequence) a consensus is constructed via
the landmark process described previously. Fig. 2 1 shows the final output of this process
with all the data aligned to the consensus. We clearly see the region where the SNP is
contained at approx. position 337.
Performing the same analysis as for Example 3 one can see the SNP usually identified
in molecules 51-60, as shown in Fig. 22.
Example 5 concerns identification of a number of populations, generation of a library
and a relative count.
Two cases are considered, firstly where there are two species present, and secondly
where there are three species. Data are simulated using the sequences from ANA ID NO 13,
9 and 5 from Example 2 . However for this example no sequence or model information is
utilised, other than for simulation of the initial dataset. Using the pairwise alignment scores
as measures of similarity a tree is constructed by neighbour joining as is known in the art. As
shown in Figs. 23 and 24, these datasets cluster well into two and three populations
respectively. It is also clear that a threshold could be defined (length of lines represents
similarity) to separate these clusters.
In the case of the three cluster experiment a landmark consensus for each cluster was
built. The results of this are shown in Figs. 25 to 27.
The identification as for Example 2 was run for both experiments. Figs. 28 and 29
show the counts against the three clusters for the two cluster and three cluster experiment.
We see we have correctly quantified the mixtures in each experiment.
Example 6 concerns assembly of a larger library feature vector from smaller feature
vectors.
This example uses simulated data from the overlapping sequences SI-SI 8 as
described above. However, to illustrate the assembly process we remove the sequences at the
start and end, common to all sequences (as described in Example 2) such that the sequences
overlap without any mis-matched regions (as was shown in Fig. 12). Since the sequences are
guaranteed to be overlapping a relatively simple method can be used. Were this not the case
one could use more complex assembly algorithms adapted from those known in the art as
described above.
A tree by neighbour joining on pairwise alignment scores was constructed, similar to
Example 5 . However since relatively large non-similar regions were expected, there was used
a scoring function that does not penalise gaps at the beginning or end of the alignment as
strongly as those within the alignment. The tree is shown in Fig. 30. Here it can be seen that
all the sequences have similar relation to two other sequences, representing the -100 base
overlap each sequence shares with the sequences either side.
Progressing through the tree in order of relatedness, consensus landmarks for the
aligned sequences are constructed with the output landmarks from a pair of sequences acting
as the feature vector where that pair is joined to another sequence. The output of the process
is a fully assembled feature vector. The original data was aligned to the assembled features
for illustration. Alignments for three fragments are shown in Fig. 31, wherein the overlaps
can be clearly seen.

Claims
1. A method of analyzing a time-ordered series of measurements of a polymer made
during translocation of the polymer through a nanopore, wherein the measurements are
dependent on the identity of k-mers in the nanopore, a k-mer being k polymer units of the
polymer, where k is a positive integer, the method comprising:
deriving, from the series of measurements, a feature vector of time-ordered features
representing characteristics of the measurements; and
determining similarity between the derived feature vector and at least one other
feature vector.
2 . A method according to claim 1, wherein the at least one other feature vector is at least
one other feature vector stored in a memory in respect of at least one class.
3. A method according to claim 2, wherein the at least one other feature vector stored in
the memory is selected depending upon the polymer to be measured.
4 . A method according to claim 2 or 3, wherein the at least one other feature vector
stored in the memory comprises an overall feature vector of a common polymer constructed
from the feature vectors of fragments.
5 . A method according to any one of claims 2 to 4, wherein said step of determining
similarity comprises determining similarity between the entirety or part of the derived feature
vector and the entirety of the at least one other feature vector stored in the memory.
6 . A method according to any one of claims 2 to 4, wherein said step of determining
similarity comprises determining similarity between the entirety or part of the derived feature
vector between the derived feature vector and a part of the at least one other feature vector
stored in the memory.
7 . A method according to any one of claims 2 to 6, further comprising classifying the
polymer from which the derived feature vector is derived as belonging to a said class on the
basis of the determined similarity.
8 . A method according to claim 1, wherein the at least one other feature vector is a
feature vector derived using the same method.
9 . A method according to claim 8, wherein the at least one other feature vector is plural
other feature vectors derived using the same method, and the method further comprises
identifying features vectors that are derived from polymers that are fragments of a common
polymer on the basis of similarity in overlapping parts of the feature vectors.
10. A method according to claim 8, further comprising constructing an overall feature
vector of the common polymer from the feature vectors of the identified fragments.
11. A method according to claim 8, wherein the at least one other feature vector is plural
other feature vectors derived using the same method, and the method further comprises
identifying clusters of similar feature vectors as a class and classifying the polymers from
which the feature vectors are derived as belonging to an identified class.
12. A method according to claim 7 or 11, further comprising counting the numbers of
feature vectors belonging to different classes.
13. A method according to claim 7, 11 or 12, further comprising identifying localized
regions where the derived feature vector is dissimilar to a feature vector in respect of the
class in which the polymer is classified as belonging.
14. A method according to claim 1, wherein the at least one other feature vector
comprises a feature vector stored in a memory and said step of determining similarity
comprises determining localized regions where the derived feature vector is dissimilar to the
at least one other feature vector stored in the memory.
15. A method according to any one of the preceding claims, wherein
groups of consecutive measurements are dependent on a respective k-mer that is
different for each group, and
the step of deriving a feature vector comprises identifying groups of consecutive
measurements, and, in respect of each group, deriving values of one or more features that
represent characteristics of the measurements of the group.
16. A method according to any one of the preceding claims, wherein the features
comprise:
an average of the group of measurements;
the period of the group of measurements;
a variance of the group of measurements;
asymmetry information;
confidence information of the measurements;
the distribution of the group of measurements; or
any combination thereof.
17. A method according to any one of the preceding claims, wherein said measurements
are electrical measurements.
18. A method according to any one of the preceding claims, wherein said measurements
comprise measurements of ion current flow through the nanopore.
19. A method according to claim 18, wherein the measurements further comprise
measurements of at least one additional property besides ion current flow.
20. A method according to claim 19, wherein the measurements of at least one additional
property comprise FET measurements, optical measurements, or both.
2 1. A method according to any one of the preceding claims, wherein the polymer is a
biological polymer.
22. A method according to any one of the preceding claims, wherein the polymer is a
polynucleotide, and the polymer units are nucleotides.
23. A method according to any one of the preceding claims, wherein the nanopore is a
biological pore.
24. A method according to any one of the preceding claims, wherein said translocation of
the polymer through the nanopore is performed in a ratcheted manner in which successive kmers
are registered with the nanopore.
25. A method according to any one of the preceding claims, wherein the translocation of
the polymer is controlled by a molecular ratchet.
26. A method according to claim 25, wherein the molecular ratchet is a polymer binding
protein.
27. A method according to any one of the preceding claims, further comprising:
translocating the polymer through a nanopore; and
making the continuous series of measurements of the polymer.
28. A method of estimating the presence, absence or amount of a target polymer, the
method comprising
translocating a polymer through a nanopore;
making the continuous series of measurements of the polymer;
analysing the series of measurements using a method according to any one of the
claims 1 to 26; and
estimating the presence, absence or amount of a target polymer based on the analysis.
29. A method according to claim 28, wherein the polymer comprises a mixture of two or
more polymers and the relative amounts of one or more polymers are determined.
30. A method of estimating the presence, absence or amount of a target polymer in a
polymer analyte, the method comprising:
fragmenting the polymer analyte into polymers; and
performing a method according to claim 28 or 29 on the fragmented polymers.
31. A method according to claim 30, wherein the polymer is a polynucleotide, and the
polymer units are nucleotides, and wherein the polymer analyte is fragmented by a restriction
enzyme.
32. A method according to any one of claims 28 to 31, wherein the presence, absence or
amount of a polymer is determined without estimating the entire sequence of polymer units
of the polymer.
33. A method of determining an alteration in a polymer, comprising
translocating a polymer through a nanopore repeatedly over a period of time;
during each translocation, making a continuous series of measurements of the
polymer; and
analysing each series of measurements using a method according to any one of the
claims 1 to 26, wherein the step of determining similarity between the derived feature vector
and at least one other feature vector comprises either (a) determining similarity between the
derived feature vector derived from each series of measurements and the same at least one
other feature vector or (b) determining similarity between all the derived feature vectors
derived from the series of measurements.
34. A method according to any one of claims 28 to 33, wherein the polymer is a
polynucleotide, and the polymer units are nucleotides, and the method is used to determine
the presence of a modified base or a point mutation.
35. A method according to any one of claims 28 to 34 used to guide a therapy or
diagnosis.
36. A computer program capable of execution by a computer apparatus and configured on
execution to perform a method according to any one of the preceding claims.
37. An analysis device configured to perform a method according to any one of claims 1
to 35.
38. A diagnostic device comprising:
an analysis device according to claim 37; and
a measurement system comprising a nanopore through which a polymer is capable of
being translocated, the measurement system being arranged to make a continuous series of
measurements of the polymer during translocation.