Methods and systems for processing time-resolved signal intensity data

Abstract

Provided herein is technology relating to detecting and identifying nucleic acids and particularly, but not exclusively, to compositions, methods, kits, and systems for detecting, identifying, and quantifying target nucleic acids with high confidence at single-molecule resolution.

We claim: 
     
         1 . A method comprising:
 a) providing a dataset describing a time-resolved signal comprising a series of signal intensity transition events; and   b) classifying the series of signal intensity transition events according to at least one of the following criteria:
 i) a number of signal intensity transition events within an observation time is greater than a threshold number of signal intensity transition events; 
 ii) a median dwell time or a mean dwell time calculated for a number of signal intensity transition events is greater than a threshold dwell time; and/or 
 iii) a median dwell time or a mean dwell time calculated for a number of signal intensity transition events is less than a maximum dwell time. 
   
     
     
         2 . The method of  claim 1 , wherein the number of signal intensity transition events comprises a number of consecutive signal intensity transition events. 
     
     
         3 . The method of  claim 1 , wherein the time-resolved signal comprising a series of signal intensity transition events is produced by recording a signal produced by a process of continuous-time stochastic transitions between two or more states each having a different signal intensity. 
     
     
         4 . The method of  claim 1 , wherein the time-resolved signal comprising a series of signal intensity transition events is produced by recording a signal produced by a Poisson process. 
     
     
         5 . The method of  claim 1 , wherein the dataset describes a time-resolved signal comprising a series of signal intensity transition events that occur during a defined time interval. 
     
     
         6 . The method of  claim 5 , wherein the defined time interval is 10 to 100 minutes. 
     
     
         7 . The method of  claim 1 , wherein the time-resolved signal comprising a series of signal intensity transition events is produced by a repeated transient association of a detectably labeled query probe to a single analyte. 
     
     
         8 . The method of  claim 7 , wherein a dissociation rate of said transient association is at least 0.001 s −1 . 
     
     
         9 . The method of  claim 1 , further comprising analyzing the dataset to identify signal intensity transition events in the time-resolved signal. 
     
     
         10 . The method of  claim 9 , wherein analyzing the dataset to identify signal intensity transition events in the time-resolved signal comprises using a hidden Markov model and/or edge detection to identify signal intensity transition events in the time-resolved signal. 
     
     
         11 . The method of  claim 9 , wherein analyzing the dataset to identify signal intensity transition events comprises identifying portions of the signal that have an intensity value greater than a threshold intensity value. 
     
     
         12 . The method of  claim 11 , wherein the threshold intensity value is an intensity value greater than a background intensity value. 
     
     
         13 . The method of  claim 11 , wherein the threshold intensity value is an intensity value that is at least 1 or 2 standard deviations greater than a background intensity value. 
     
     
         14 . The method of  claim 1 , wherein the threshold number of signal intensity transition events is 10. 
     
     
         15 . The method of  claim 1 , wherein the threshold number of signal intensity transition events is a number of standard deviations above a mean number of transitions determined using a negative control. 
     
     
         16 . The method of  claim 1 , further comprising determining a dwell time for a signal intensity transition event by calculating an amount of time that a signal intensity is above a threshold value. 
     
     
         17 . The method of  claim 1 , further comprising identifying acceptable signals according to the results of the classifying. 
     
     
         18 . The method of  claim 1 , wherein providing the dataset describing a time-resolved signal comprising a series of signal intensity transition events comprises producing said dataset describing a time-resolved signal comprising a series of signal intensity transition events. 
     
     
         19 . The method of  claim 1 , wherein providing the dataset describing a time-resolved signal comprising a series of signal intensity transition events comprises receiving said dataset describing a time-resolved signal comprising a series of signal intensity transition events. 
     
     
         20 . A method comprising:
 a) receiving a dataset describing a plurality of time-resolved signals, wherein each time-resolved signal comprises a series of signal intensity transition events; and   b) classifying each time-resolved signal of the plurality of time-resolved signals according to at least one of the following criteria:
 i) a number of signal intensity transition events within an observation time is greater than a threshold number of signal intensity transition events; 
 ii) a median dwell time or a mean dwell time calculated for a number of signal intensity transition events is greater than a threshold dwell time; and/or 
 iii) a median dwell time or a mean dwell time calculated for a number of signal intensity transition events is less than a maximum dwell time. 
   
     
     
         21 . The method of  claim 18 , further comprising identifying acceptable time-resolved signals using the results of the classifying. 
     
     
         22 . The method of  claim 19 , further comprising counting the acceptable time-resolved signals to produce a count of acceptable time-resolved signals.