System and method for determining matching patterns within gene expression data

• US 7,428,554 B1
• Filed: 05/20/2004
• Issued: 09/23/2008
• Est. Priority Date: 05/23/2000
• Status: Expired due to Fees

• Current Assignee: Ocimum Biosolutions Incorporated (Ocimum Bio Solutions (India) Ltd.)
• Original Assignee: Ocimum Biosolutions Incorporated (Ocimum Bio Solutions (India) Ltd.)
• Inventors: Elashoff, Michael, Dolginow, Doug, Mertz, Larry, Diggans, James C., Coberley, Carter
• Primary Examiner(s): Rones; Charles
• Assistant Examiner(s): Khoshnoodi; Fariborz
• Application Number:
  US10/850,232
• Time in Patent Office: 1,587 Days
• Field of Search: 707/104.1, 707/3, 707/35, 702/19
• US Class Current: 1/1
• CPC Class Codes: G16B 20/00 ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
• CPC Class Codes: G16B 20/20 Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
• CPC Class Codes: G16B 25/00 ICT specially adapted for hybridisation; ICT specially adapted for gene or protein expression
• CPC Class Codes: G16B 25/10 Gene or protein expression profiling; Expression-ratio estimation or normalisation
• CPC Class Codes: G16B 50/00 ICT programming tools or database systems specially adapted for bioinformatics
• CPC Class Codes: G16B 50/30 Data warehousing; Computing architectures
• CPC Class Codes: Y10S 707/99933 Query processing, i.e. searching
• CPC Class Codes: Y10S 707/99945 Object-oriented database structure processing
• CPC Class Codes: Y10S 707/99948 Application of database or data structure, e.g. distributed, multimedia, or image

Abstract

A computer-based system and method are provided for retrieving information from a number of data sources on a computer network containing biological data. The network database is organized in a b-tree configuration having a plurality of sample nodes. Each sample node includes a curated data set of pre-formatted and pre-computed summary biological data obtained from at least one biological sample. The plurality of sample nodes are organized in a hierarchical arrangement according to clinical relevance. A set of attributes is assigned to each sample node to facilitate navigation through the database using a browser accessible through a graphical user interface. The set of attributes including at least one taxonomy designation selected from the group including tissues, diseases, medications and sample parameters. Search results that are produced include automated reports of the summary biological data stored in the sample nodes and custom reports generated using the summary biological data.

Claims

1. In a computer network, a method for determining patterns within gene expression data stored in a database containing biological data, the method comprising:

(a) defining a plurality of sample nodes within the database, each sample node comprising a curated data set comprising a set of pre-formatted and pre-computed biological data obtained from at least one biological sample, wherein the plurality of sample nodes are organized in a hierarchical arrangement according to clinical relevance;

(b) assigning a set of clinical attributes to each sample node, the set of clinical attributes including at least one taxonomy designation selected from the group consisting of tissues, diseases, medications and sample parameters;

(c) providing a user interface for entry of a search query into the computer processor and displaying search results at a user interface;

(d) prompting entry of the search query by requesting user selection of a search category from the group consisting of biological materials, biological material family, biological pathways, and sample set taxonomy, and wherein each sample node of the plurality of sample nodes is associated with a plurality of search categories;

(e) searching the plurality of sample nodes for data responsive to the search query;

(f) selecting one or more sample nodes containing the data responsive to the search query;

(g) saving search results comprising the set of pre-formatted and pre-computed biological data responsive to the one or more selected sample nodes;

(h) receiving a user interface selection of an algorithm for performing gene expression pattern matching for identifying genes or gene fragments within the one or more selected sample nodes that have similar gene expression patterns to a gene of interest, the algorithm comprising;

(i) computing a plurality of pairwise comparisons between the gene of interest and the genes or gene fragments within the one or more sample nodes, wherein each comparison is encoded using a qualitative three-state encoding scheme, wherein up-regulation of gene expression in the gene of interest relative to the genes or gene fragments within the one or more sample nodes is assigned a first symbol, down-regulation of gene expression in the gene of interest relative to the genes or gene fragments within the one or more sample nodes is assigned a second symbol different from the first symbol and no change in gene expression in the gene of interest relative to the genes or gene fragments within the one or more sample nodes is assigned a third symbol different from the first and second symbols wherein the three-state encoding scheme comprises a non-quantitative indication of gene behavior;

(ii) generating a three-by-three contingency matrix for each pairwise comparison using the three-state encoding scheme;

(iii) determining a distance score for each pairwise comparison;

(iv) generating a listing of lowest distance scores, wherein the lowest distance scores correspond to genes or gene fragments having the highest similarity to the gene of interest; and

(i) generating an output display comprising the listing of genes or gene fragments having the lowest distance scores.

2. The method of claim 1, wherein the biological materials search category is selected from the group consisting of genes, proteins, metabolites and biomolecular assay.

3. The method of claim 1, wherein the gene expression pattern matching algorithm identifies co-regulated and inverse co-regulated gene expression patterns relative to the gene of interest.

4. The method of claim 3, wherein the algorithm is adapted to identify coordinate or inverse coordinate gene expression patterns within a specified therapeutic area.

5. The method of claim 3, wherein the algorithm is adapted to identify coordinate or inverse coordinate gene expression patterns across normal tissues.

6. The method of claim 2, wherein the selected search category is biological material family, wherein the biological material family comprises a gene family, and wherein the search report further comprises differential expression for gene family members and a listing of gene family members.

7. The method of claim 1, wherein the selected search category is biological pathway and the search report further comprises differential expression for pathway-linked expressed entities and a listing of member expressed entities of the biological pathway.

8. The method of claim 1, wherein the selected search category is sample set taxonomy, wherein the search query comprises:

selecting a taxonomy designation,

selecting a category from a plurality of categories within the selected taxonomy designation; and

selecting a sub-category from a plurality of sub-categories within the selected category, wherein each sub-category comprises a group of sample nodes having attributes corresponding to the selected taxonomy designation, selected category and the selected sub-category.

9. The method of claim 8, wherein the selected taxonomy comprises tissues and the plurality of categories comprises organ systems.

10. The method of claim 9, wherein the category is selected from the organ system group consisting of cardiovascular system, central nervous system, GI/digestive system, respiratory system, reproductive system, skeletal system, endocrine system, excretory system, skeletal system, and muscular system.

11. The method of claim 9, wherein the plurality of sub-categories are tissue types and the group of sample nodes comprises disease data.

12. The method of claim 9, wherein the search query comprises a tissue of interest.

13. The method of claim 8, wherein the selected taxonomy comprises diseases and the plurality of categories comprises therapeutic areas.

14. The method of claim 13, wherein the therapeutic areas comprise oncology/cancer, cardiovascular diseases, inflammation disorders, central nervous system disorders and metabolic diseases.

15. The method of claim 13, wherein the selected therapeutic area comprises oncology/cancer and the sub-categories comprise types of cancer.

16. The method of claim 13, wherein the plurality of sub-categories comprises disease types and the group of sample nodes comprises tissue data.

17. The method of claim 8, wherein the selected taxonomy comprises medications and the plurality of categories comprises medication.

18. The method of claim 17, wherein the plurality of sub-categories comprises tissue type and the group of sample nodes comprises tissue data.

19. The method of claim 1, further comprising:

storing a text description of each sample node in the database;

within the database, associating the text description with each sample node.

20. The method of claim 1, wherein the user interface comprises an Internet web site and each search category has a corresponding distinct web page.

21. The method of claim 1, further comprising:

pre-computing a plurality of pairwise node comparisons between two different sample nodes for a subset of the plurality of sample nodes, comprising;

selecting the subset from sample nodes having a pathological relationship with at least one other sample node in the subset;

comparing gene expression data within the two different sample nodes to determine a differential expression between the two different sample nodes;

storing the plurality of pairwise node comparisons in the database; and

associating each pairwise node comparison with each of its corresponding sample nodes.

22. The method claim 21, wherein the subset of sample nodes is user selected and the step of computing is performed on-the-fly.

23. The method of claim 21, wherein the pairwise node comparison comprises comparison of sample nodes selected from the group consisting of normal versus disease, morphologies of two different diseases, normal versus normal on different tissues, normal versus secondary malignancy, and primary versus secondary malignancy.

24. The method of claim 1, further comprising:

providing a tracking module for saving search results;

repeating steps (d) through (g) for a plurality of iterations;

prompting the user to save in the tracking module selected search results corresponding to at least a portion of the plurality of iterations;

prompting the user to select one or more search results saved in the tracking module.

25. The method of claim 1, further comprising associating at least one histopathology image with each sample node.

26. A network-based system for determining patterns within gene expression data stored in a database containing biological data, the system comprising:

a processor including a search engine;

a database comprising;

a plurality of sample nodes, each sample node comprising a curated data set comprising a set of pre-formatted and pre-computed biological data obtained from at least one biological sample, wherein the plurality of sample nodes are organized in a hierarchical arrangement according to clinical relevance;

a set of clinical attributes assigned to each sample node, the set of clinical attributes including at least one taxonomy designation selected from the group consisting of tissues, diseases, medications and sample parameters;

a user interface to enter a search query and display search results, wherein the search query comprises an instruction to the search engine to search a category from the group consisting of biological materials, biological material families, biological pathways, and sample set taxonomy, and wherein each sample node of the plurality of sample nodes is associated with a plurality of search categories; and

a tracking module for storing data corresponding to a plurality of user selected sample nodes and further comprising means for receiving a user interface selection of an algorithm to be executed by the processor for performing gene expression pattern matching for identifying genes or gene fragments within the user selected sample nodes that have similar gene expression patterns to a gene of interest, wherein the algorithm comprises the steps of;

(i) computing a plurality of pairwise comparisons between the gene of interest and the genes or gene fragments within the user selected sample nodes, wherein each comparison is encoded using a qualitative three-state encoding scheme, wherein up-regulation of gene expression in the gene of interest relative to the genes or gene fragments within the one or more sample nodes is assigned a first symbol, down-regulation of gene expression in the gene of interest relative to the genes or gene fragments within the one or more sample nodes is assigned a second symbol different from the first symbol and no change in gene expression in the gene of interest relative to the genes or gene fragments within the one or more sample nodes is assigned a third symbol different from the first and second symbols wherein the three-state encoding scheme comprises a non-quantitative indication of gene behavior;

(ii) generating a three-by-three contingency matrix for each pairwise comparison using the three-state encoding scheme;

(iii) determining a distance score for each pairwise comparison;

(iv) generating a listing of lowest distance scores, wherein the lowest distance scores correspond to genes or gene fragments having the highest similarity to the gene of interest; and

(v) displaying the listing of the lowest distance scores and the corresponding genes or gene fragments.

27. The system of claim 26, wherein the user selected sample nodes comprises sample nodes having a pathological relationship with at least one other sample node in the subset.

28. The system of claim 27, wherein the user selected sample nodes are selected from the group consisting of normal versus disease, morphologies of two different diseases, normal versus normal on different tissues, normal versus secondary malignancy, and primary versus secondary malignancy.

29. The system of claim 26, wherein the tracking module further comprises means for requesting on-the-fly custom pairwise node comparison of two stored sample nodes.

30. The system of claim 26, wherein the tracking module further comprises means for generation of one or more additional reports selected from the group consisting of gene detail, eNorthern, differential expression, pathology report, and time study, using one or more user selected sample nodes.

31. The system of claim 26, wherein the user interface comprises an Internet web site and each search category has a corresponding web page.

32. The system of claim 26, wherein the biological material search category comprises the group consisting of genes, proteins, metabolites and biomolecular assay.

33. The system of claim 26, wherein the search category is sample set taxonomy and the search query further comprises:

a first user selection of taxonomy designation;

a second user selection of a category from a plurality of categories within the selected taxonomy designation;

a third user selection of a sub-category from a plurality of sub-categories within the selected category, wherein each sub-category comprises a group of sample nodes having attributes corresponding to the selected taxonomy designation, the selected category and the selected sub-category.

34. The system of claim 33, wherein the taxonomy designation is tissues and the plurality of categories comprises organ systems.

35. The system of claim 34, wherein the search query comprises a tissue of interest and further comprising executing a gene expression pattern matching algorithm for identifying marker genes expressed in the tissue of interest.

36. The system of claim 34, wherein the selected category is selected from the organ system group consisting of cardiovascular system, central nervous system, GI/digestive system, respiratory system, reproductive system, skeletal system, endocrine system, excretory system, skeletal system, and muscular system.

37. The system of claim 36, wherein the plurality of sub-categories are tissue types and the group of sample nodes comprises disease data.

38. The system of claim 33, wherein the taxonomy designation comprises diseases and the plurality of categories comprises therapeutic areas.

39. The system of claim 38, wherein the therapeutic areas comprise oncology/cancer, cardiovascular diseases, inflammation disorders, central nervous system disorders and metabolic diseases.

40. The system of claim 39, wherein the selected therapeutic area comprises oncology/cancer and the sub-categories comprise types of cancer.

41. The system of claim 39, wherein the plurality of sub-categories comprises disease types and the group of sample nodes comprises tissue data.

42. The system of claim 33, wherein the taxonomy designation comprises medications and the plurality of categories comprises medication.

43. The system of claim 42, wherein the plurality of sub-categories comprises tissue type and the group of sample nodes comprises tissue data.

44. The system of claim 26, wherein the database further comprises at least one histopathology image associated with each sample node.

45. The system of claim 26, wherein the gene expression pattern matching algorithm identifies co-regulated and inverse co-regulated gene expression patterns relative to the gene of interest.

46. The system of claim 26, wherein the algorithm is adapted to identify co-regulated and inverse co-regulated gene expression patterns within a specified therapeutic area.

47. The system of claim 26, wherein the algorithm is adapted to identify co-regulated and inverse co-regulated gene expression patterns across normal tissues.

48. The system of claim 26, wherein the selected search category is biological material family, the biological material family is gene family, and the search report further comprises differential expression for gene family members and a listing of gene family members.

49. The system of claim 26, wherein the selected search category is biological pathway and the search report further comprises differential expression for pathway-linked genes and a listing of member expressed entities of the biological pathway.