Universal gene chip for high throughput chemogenomic analysis

• US 20070021918A1
• Filed: 04/25/2005
• Published: 01/25/2007
• Est. Priority Date: 04/26/2004
• Status: Abandoned Application

• Current Assignee: US Department of Health and Human Services (Government of the United States of America)
• Original Assignee: US Department of Health and Human Services (Government of the United States of America)
• Inventors: Natsoulis, Georges, Browne, Leslie
• Application Number:
  US11/114,998
• Publication Number:
  US 20070021918A1
• Time in Patent Office: Days
• US Class Current: 702/19
• CPC Class Codes: B01J 2219/00693 Means for quality control
• CPC Class Codes: B01J 2219/00695 Synthesis control routines, e.g. using computer programs
• CPC Class Codes: B01J 2219/00722 Nucleotides
• CPC Class Codes: C12Q 1/6837 using probe arrays or probe chips C12Q1/6874 takes precedence
• CPC Class Codes: C12Q 1/6876 Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
• CPC Class Codes: C12Q 2600/136 Screening for pharmacological compounds
• CPC Class Codes: C12Q 2600/158 Expression markers
• CPC Class Codes: G16B 25/00 ICT specially adapted for hybridisation; ICT specially adapted for gene or protein expression
• CPC Class Codes: G16B 25/30 Microarray design
• CPC Class Codes: G16B 40/00 ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
• CPC Class Codes: G16B 40/20 Supervised data analysis

Abstract

The invention provides methods for preparing reagent sets based on small subsets of highly informative genes capable of carrying out a broad range of chemogenomic classification tasks. The invention also provides high-throughput diagnostic assays and devices based on these reduced subsets of information rich genes. In addition, the invention provides a general method for selecting a reduced subset of highly responsive variables from a much larger multivariate dataset, and thus, use of these variables to prepare diagnostic measurement devices, or other analytic tools, with little or no loss of performance relative to devices or tools incorporating the full set of variables.

Claims

1. A method for preparing a high-throughput chemogenomic assay reagent set comprising:

a. deriving a set of non-redundant classifiers, each comprising a plurality of genes, from a chemogenomic dataset, wherein the chemogenomic dataset comprises expression levels for a plurality of gene measured in response to a plurality of compound treatments;

b. ranking each gene in the set of non-redundant classifiers based on its contribution across all of the non-redundant classifiers;

c. selecting the subset of genes ranking in about the 50^th percentile or higher; and

d. preparing a plurality of isolated polynucleotides or polypeptides, wherein each polynucleotide or polypeptide is capable of detecting at least one gene of the selected subset.

2. The method of claim 1, wherein the chemogenomic dataset comprises expression levels for at least 5000 genes.

3. The method of claim 1, wherein the chemogenomic dataset comprises at least about 100 different compound treatments.

4. The method of claim 1, wherein the set of non-redundant classifiers comprises at least about 50 classifiers.

5. The method of claim 1, wherein the selected subset of genes ranks in about the 90^th percentile or higher.

6. The method of claim 1, wherein the selected subset of genes comprises about 800 or fewer genes.

7. The method of claim 1, wherein the selected subset of genes comprises about 100 or fewer genes.

8. The method of claim 1, wherein the method of ranking the genes across all classifiers is selected from the group consisting of:

determining the sum of weights;

determining the sum of absolute value of weights; and

determining the sum of impact factors.

9. The method of claim 1, wherein the redundancy of the classifiers is determined using a fingerprint of resulting classifiers against a set of reference treatments.

10. The method of claim 9, wherein the fingerprint is assessed using a hierarchical clustering method selected from the group consisting of:

UPGMA and WPGMA.

11. A reagent set made according to claim 1.

12. The reagent set of claim 11, wherein the number of reagents in the subset is less than about 10% of the number of genes in the full chemogenomic dataset.

13. The reagent set of claim 11, wherein the number of reagents in the subset is less than about 5% of the number of genes in the full chemogenomic dataset.

14. The subset of claim 11, wherein the number of genes is 800 or fewer.

15. The subset of claim 11, wherein the number of genes is 400 or fewer.

16. An array comprising a reagent set made according to claim 1.

17. The array of claim 16, wherein the reagent set consists of polynucleotides capable of detecting the genes listed in Table 4.

18. The array of claim 16, wherein the reagent set consists of polynucleotides capable of detecting the top ranking 800 genes listed in Table 4.

19. The array of claim 16, wherein the reagent set consists of polypeptides each capable of detecting a secreted protein encoded by the genes listed in Table 5.

20. A reagent set for chemogenomic analysis of a compound treated sample, wherein the set comprises a plurality of polynucleotides or polypeptides, wherein each polynucleotide or polypeptide is capable of detecting at least one member of a subset of less than about 10 percent of the genes in a full chemogenomic dataset, and wherein the subset of genes is capable of generating a set of signatures that exhibit at least about 85 percent of the average performance of the same set of signatures generated from the full chemogenomic dataset.

21. The reagent set of claim 20, wherein the reagent set comprises a plurality of polynucleotides.

22. The reagent set of claim 21, wherein the plurality of polynucleotides are immobilized on one or more substrates.

23. The reagent set of claim 20, wherein the full chemogenomic dataset comprises expression levels for at least about 5000 genes.

24. The reagent set of claim 20, wherein the full chemogenomic dataset comprises at least about 100 different compound treatments.

25. The reagent set of claim 20, wherein the subset comprises less than about 5% of the genes in the full chemogenomic dataset.

26. The reagent set of claim 20, wherein the set of signatures comprises at least about 50 signatures.

27. The reagent set of claim 20, wherein the signatures are linear classifiers generated using support vector machines.

28. The reagent set of claim 20, wherein the subset is capable of generating a set of signatures that exhibit at least about 95 percent of the average performance of the same set of signatures generated from the full chemogenomic dataset.