Hybrid model for DNA probe design and validation using nonlinear and linear regression methods

Abstract

Methods and systems for selecting oligonucleotide probes for use in microarray applications are provided herein. The described methods use a combination of measured probe performance and predicted probe performance to select probes. Nucleic acid arrays containing probes selected by the described methods are described. Also included are algorithms for performing the subject methods recorded on computer-readable media and computational systems for analysis.

1 . A method for selecting an oligonucleotide probe for use on a microarray, comprising: 
 generating two or more candidate oligonucleotide probes;    analyzing the two or more candidate probes with one or more metrics that indicate probe performance to obtain an individual probe score for each metric;    combining the individual probe score for each metric into a single combined score for the probe; and    selecting the probe with a combined score closest to an optimal score value for use on a microarray, wherein the optimal score value is the score at, or nearest to, the highest end of a numerical scale of probe scores.    
     
     
         2 . The method of  claim 1 , wherein the optimal score value is about 1.0 on a scale of probe scores ranging from 0.0 to 1.0.  
     
     
         3 . The method of  claim 1 , wherein the optimal score value is about 100 on a scale of probe scores ranging from 50 to 100.  
     
     
         4 . The method of  claim 1 , wherein generating a candidate set of oligonucleotide probes comprises: 
 selecting one or more target sequences within a region of interest; and    tiling subsequences of each target sequence across each region of interest to generate the candidate set of potential probes.    
     
     
         5 . The method of  claim 4 , further comprising: 
 generating a large set of potential probes by tiling the target sequences in single base steps across the region of interest; and    applying pairwise reduction to reduce the number of probes by a factor of greater than about 2 and less than about 1000.    
     
     
         6 . The method of  claim 1 , wherein the metrics used to analyze the candidate probes comprise direct metrics, indirect metrics, in silico metrics, or combinations thereof.  
     
     
         7 . The method of  claim 6 , wherein direct metrics used to analyze the candidate probes comprise the changes in probe response based on experimentally measured quantities, further comprising known changes in copy number of a target molecule.  
     
     
         8 . The method of  claim 6 , wherein indirect metrics used to analyze the candidate probes comprise changes in predicted probe response resulting from experimentally measured quantities for a target molecule.  
     
     
         9 . The method of  claim 6 , wherein indirect metrics used to analyze the candidate probes comprise changes in predicted probe response measured using empirical relationships based on direct responses from other probe-target molecule duplexes.  
     
     
         10 . The method of  claim 6 , wherein in silico metrics used to analyze the candidate probes comprise changes in probe response based on calculated quantities for a target molecule.  
     
     
         11 . The method of  claim 6 , wherein in silico metrics used to analyze the candidate probes comprise changes in probe response measured using empirical relationships based on direct responses from other probe-target molecule duplexes.  
     
     
         12 . The method of  claim 1 , wherein analyzing the candidate probes with one or more metrics to obtain individual probe scores further comprises: 
 calculating the slope for each candidate probe;    plotting the slope against the corresponding value for each of the metrics to obtain a trend curve; and    fitting the trend curve with a polynomial function with order n to generate an individual probe score.    
     
     
         13 . The method of  claim 12 , wherein the order n of the polynomial function ranges from n=1 to n=20.  
     
     
         14 . The method of  claim 1 , wherein combining individual probe scores for each metric to obtain a combined score comprises adding or averaging the probe score for each metric.  
     
     
         15 . The method of  claim 1 , wherein combining the individual probe scores to obtain a combined score comprises fitting the scores with a linear additive multivariate fitting function.  
     
     
         16 . The method of  claim 15 , wherein combining the individual probe scores further comprises fitting measured slope responses for a well-characterized training data set to a change in copy number.  
     
     
         17 . The method of  claim 1 , wherein combining the individual probe scores to obtain a combined score comprises fitting the scores with a linear multiplicative curve-fitting function.  
     
     
         18 . The method of  claim 17 , wherein combining the individual probe scores further comprises: 
 combining metrics in each category using a linear model to obtain intermediate scores; and    multiplying together the intermediate scores to generate the combined score.    
     
     
         19 . The method of  claim 1 , wherein combining the individual probe scores further comprises synthetically modifying the combined score to obtain probes with more robust performance, the synthetic modification further comprising: 
 generating a candidate set of probes;    applying pairwise reduction to reduce the number of probes in the candidate set;    calculating the slope for each probe;    plotting the slope against the corresponding value for each of the metrics to obtain a trend curve;    fitting the trend curve to generate a measured probe score;    replacing the fitted trend curve with a synthetic curve; and    using the synthetic curve to generate a predicted score for each probe.    
     
     
         20 . The method of  claim 1 , wherein selecting the probe for use in a microarray application comprises: 
 combining experimentally measured scores with predicted scores to obtain a combined score value for the probe; and    selecting the probe with a combined score value closest to an optimal score value, wherein the optimal score value is the score at, or nearest to, the highest end of a numerical scale of probe scores.    
     
     
         21 . A computer-readable medium having recorded thereon a program that selects a probe for use in microarray applications according to the method of  claim 1 .  
     
     
         22 . A computational analysis system comprising the computer-readable medium according to  claim 21 .  
     
     
         23 . A method of fabricating a nucleic acid microarray, comprising producing at least two different oligonucleotide probes on a microarray substrate, wherein at least one of the two different oligonucleotide probes is a probe selected according to the method of  claim 1 .  
     
     
         24 . A nucleic acid microarray produced according to the method of  claim 23.