Methods and apparatus for genetic evaluation

• US 20040209260A1
• Filed: 04/18/2003
• Published: 10/21/2004
• Est. Priority Date: 04/18/2003
• Status: Active Grant

• Current Assignee: Ibis Biosciences Incorporated (Abbott Laboratories), Science Applications International Corporation
• Original Assignee: Ibis Biosciences Incorporated (Abbott Laboratories)
• Inventors: Hofstadler, Steven A., Sampath, Rangarajan, Griffey, Richard H., McNeil, John, Ecker, David J., Sofianos, Dino J., Crooke, Stanley T., Stoughton, Roland B., Knize, Duane J., Murray, Dennis P., Lewis, Clifford T., Rudnick, Karl H., Robbins, David W., Larson, Brons M., Penhune, John P., Bar-on, Jonathan R., Basham, Cecil L. Jr.
• Granted Patent: US 8,046,171 B2
• Time in Patent Office: Days
• US Class Current: 435/6
• CPC Class Codes: G16B 30/00 ICT specially adapted for sequence analysis involving nucleotides or amino acids
• CPC Class Codes: G16B 40/10 Signal processing, e.g. from mass spectrometry [MS] or from PCR
• CPC Class Codes: Y10T 436/24 Nuclear magnetic resonance, electron spin resonance or other spin effects or mass spectrometry

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.

Claims

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.

21. The method according to claim 18 wherein said first level of identification is a likelihood of differentiation of said biological member.

22. The method according to claim 18 wherein said number of primer sets is a number of primer pair combinations wherein each primer pair synergistically augments said level of differentiation of said biological member provided from any other primer pair combination.

23. The method according to claim 21 wherein the gene-space search algorithm is a computer executable instruction set whereby a plurality of primer sets are searched in parallel.

24. The method according to claim 21 wherein the gene-space search algorithm is a computer executable instruction set whereby a plurality of primer sets are searched simultaneously.

25. The method according to claim 22 wherein said likelihood of differentiation of said biological member is a statistical likelihood of differentiation.

26. The method according to claim 18 further comprising:

estimating the quantity of said member in said biological sample.

27. A method of obtaining a real and a virtual mass spectrum comprising:

(a) providing a virtual sample to be analyzed according to a virtual mass spectrometer having a virtual background noise; and

(b) obtaining said real and virtual mass spectrum.

28. The method according to claim 27 wherein said virtual background noise of said virtual mass spectrometer corresponds computationally with a real background noise of a corresponding real spectrometer.

29. The method according to claim 28 wherein said real background noise includes ambient noise, spectrometer variables or sample variables corresponding with real spectra from said real mass spectrometer.

30. The method according to claim 27 further comprising:

(a) providing a second virtual sample to be analyzed according to said virtual mass spectrometer having said virtual background noise; and

(b) obtaining a second real and virtual mass spectrum of said second virtual sample.

31. The method according to claim 29 wherein said second real and virtual mass spectrum is computationally subtracted from said first real and virtual mass spectrum providing thereby a subtractive virtual mass spectrum.

32. A method of determining a similarity criteria of a first virtual mass spectrum of an unknown bioagent as compared to a second virtual mass spectrum of a known bioagent comprising:

(a) obtaining said mass spectrum of said first unknown bioagent corresponding to an amplicon secondary to amplification of a known segment delineated by a primer set;

(b) comparing the mass spectrum of step (a) with at least said second virtual mass spectrum of said known bioagent, where said second virtual mass spectrum is of a virtual amplicon that corresponds to an identified portion of said known bioagent whereby said virtual mass spectrum is assigned a rank according to said similarity criteria with said mass spectrum of said first unknown bioagent.

33. A method for the generation of a synthetic mass spectrum template comprising:

obtaining a mass spectrum of a primer pair amplicon from a sample;

transforming said mass spectra into a mass spectrum model; and

storing said template on computer readable medium in computer readable format.

34. A method of grouping a plurality of biological members according to a grouping criteria comprising:

obtaining at least one grouping criteria by which each biological member is grouped;

comparing the grouping criteria of at least one biological member with the grouping criteria of at least one other biological member, thereby determining an interrelatedness between said at least one biological member and said at least one other biological member; and

grouping said plurality of biological members according to said interrelatedness.

35. The method according to claim 34 wherein said grouping criteria is a biological constraint.

36. The method according to claim 35 wherein said biological members have phylum interrelatedness.

37. The method according to claim 36 wherein said biological constraint is an evolutionary constraint.

38. The method according to claim 36 wherein said biological members have class interrelatedness.

39. The method according to claim 36 wherein said biological members have family interrelatedness.

40. The method according to claim 36 wherein said biological members have genus interrelatedness.

41. The method according to claim 36 wherein said biological members have species interrelatedness.