Patent Publication Number: US-2007112524-A1

Title: Correlation of DNA/amino acid sequences

Description:
The main idea is to use biochemical parameter values to quantify and to correlate DNA/amino acid sequences. The biochemical parameters eg, mutability, molecular weight, hydrophobicity, polarity, PkN, PkC, beta sheet probability, alpha helix probability, energy per residue, energy per atom, bulkiness, contribution of side chain to molecular weight, hydrophobicity, propensity for gaps, reading frame distance and other parameters to be added later, are quantified in order to correlate these DNA/amino acid values from species to species or from healthy cell to diseased cell (cancer patient to remission patient or from diabetes patient to obese non-diabetes patient etc.) to find significant, causal associations. The correlations can be run between two species to check for taxonomic or evolutionary association.  
      The correlations used would be PEARSONIAN as the data consisting of continuous, normally-distributed, biochemical parameter values would be suitable for non-discrete correlation model building. This idea has never been carried out and is statistically unique. 
    
    
      The flow chart begins with the introduction of the alphabetical letters for DNA/amino acids and ends with their quantification into biochemical parameters (hydrophobicity rating or polarity measurement etc.) and the application of the PEARSONIAN CORRELATION FORMULA for the calculation of the correlation coefficient: 
          1.) Use any language e.g. C++, VB.net etc.     2.) Introduce two files of sequence letters for a bivariate correlation.     3.) Do until END OF FILE.     4.) Assign the biochemical parameter value to each letter in file # 1 .     5.) Now assign a corresponding value to each letter in file # 2 .     6.) Do until END OF FILE.     7.) Use input command for entering data by hand.     8.) Input a value for each range desired from each of the two files e.g. 1,4 2,5 or whatever range one wants to correlate.     9.) Next, sum the parameter values in each of the two ranges.     10.) Divide the sum by the range value (example above would equal 4 in each file).     11.) Multiply each deviation from the mean value found in file  1  by each deviation found in file  2 .     12.) Sum this deviation value and divide by n−1. This is the covariance.     13.) Divide this deviation sum from above by standard deviation of file one times standard deviation of file two.     14.) PRINT this as the correlation coefficient.