Methods and apparatus for genetic evaluation

Abstract

Rapid and definitive bioagent detection and identification can be carried out without nucleic acid sequencing. Analysis of a variety of bioagents and samples, such as air, fluid, and body samples, can be carried out to provide information useful for industrial, medical, and environmental purposes. Nucleic acid samples of unknown or suspected bioagents may be collected, optimal primer pairs may be selected, and the nucleic acid may be amplified. Expected mass spectra signal models may be generated and selected, the actual mass spectra of the amplicons may be obtained. The expected mass spectra most closely correlating with the actual mass spectra may be determined using a joint maximum likelihood analysis, and base counts for the actual mass spectra and the expected mass spectra may be obtained. The most likely candidate bioagents may then be determined.

1 . A method of automating the determination of a distinguishing genotypic sequence for a biological member comprising:
 (a) comparing in a computationally non-linear manner a plurality of genotypic sequence regions from a plurality of biological members; and   (b) determining a distinguishing genotypic sequence for said biological members, whereby said genotypic distinguishing sequence region differentiates said biological members.   
     
     
         2 . The method according to  claim 1  wherein the plurality of biological members are members selected from a family and said distinguishing genotypic sequence differentiates genus of said family. 
     
     
         3 . The method according to  claim 1  wherein the computationally non-linear manner is a gene-space search algorithm. 
     
     
         4 . The method according to  claim 3  wherein the gene-space search algorithm is a computer executable instruction set whereby said plurality of genotypic sequence regions are searched in a non-linear manner. 
     
     
         5 . The method according to  claim 1  further comprising:
 determining computationally at least one additional distinguishing genotypic sequence for said biological members, whereby said at least one additional genotypic distinguishing sequence region, is synergistically distinguishing with said distinguishing genotypic sequence to further differentiate said biological members. 
 
     
     
         6 . The method according to  claim 2  wherein the plurality of biological members are members selected from a genus and said distinguishing genotypic sequence differentiates species of said genus. 
     
     
         7 . The method according to  claim 2  wherein the plurality of biological members are members selected from a species and said distinguishing genotypic sequence differentiates sub-species of said species. 
     
     
         8 . The method according to  claim 1  wherein the computationally non-linear manner is a non-sequential gene-space search algorithm. 
     
     
         9 . The method according to clam  1  wherein the computationally nonlinear manner is a parallel gene-space search algorithm. 
     
     
         10 . A method of determining computationally in a non-linear manner a number of primer sets needed to provide a desired level of identification of a biological member of a biological sample comprising:
 (a) determining computationally in a non-linear manner a level of identification obtained from a first primer set as applied to said biological member of said biological sample, and;   (b) repeating step (a) with additional primer sets until said level of identification is at least equal to said desired level of identification and determining thereby said number of primer sets needed to provide said level of identification.   
     
     
         11 . The method according to  claim 10  wherein said non-linear manner is a gene-space search algorithm. 
     
     
         12 . The method according to  claim 11  wherein said gene-space search algorithm is a non-sequential search algorithm. 
     
     
         13 . The method according to  claim 10  wherein said level of identification is a likelihood of differentiation of said biological member. 
     
     
         14 . The method according to  claim 10  wherein said number of primer sets is a number of primer pair combinations wherein each primer pair combination synergistically augments said level of identification of said biological member greater than 1-fold. 
     
     
         15 . The method according to  claim 12  wherein the gene-space search algorithm is a computer executable instruction set whereby a plurality of primer sets are searched in parallel. 
     
     
         16 . The method according to  claim 12  wherein the gene-space search algorithm is a computer executable instruction set whereby a plurality of primer sets are searched simultaneously. 
     
     
         17 . The method according to  claim 13  wherein said likelihood of differentiation of said biological member is a statistical likelihood of differentiation. 
     
     
         18 . A method of determining in a non-linear computational manner a number of primer sets needed to provide a desired level of identification of a member of a biological sample comprising:
 (a) obtaining a virtual amplicon of a portion of said member of said biological sample;   (b) comparing said virtual amplicon with a database of virtual amplicons, wherein said database contains virtual amplicons of corresponding identified portions of known members of biological samples, thereby determining a level of identification of said member of said biological sample;   (c) repeating step (b) with additional virtual amplicons of additional portions of said member of said biological sample until said level of identification is at or above said desired level for said member of said biological sample.   
     
     
         19 . The method according to  claim 18  wherein said non-linear manner is a gene-space search algorithm. 
     
     
         20 . The method according to  claim 19  wherein said gene-space search algorithm is a non-sequential search algorithm.