Method and systems for querying sequence-centric scientific information

• US 10,127,353 B2
• Filed: 03/06/2017
• Issued: 11/13/2018
• Est. Priority Date: 12/16/2005
• Status: Active Grant

• Current Assignee: Illumina Incorporated
• Original Assignee: NextBio
• Inventors: Kupershmidt, Ilya, Su, Qiaojuan Jane
• Primary Examiner(s): Syed, Farhan
• Application Number:
  US15/451,271
• Publication Number:
  US 20170177793A1
• Time in Patent Office: 617 Days
• CPC Class Codes: G06F 16/24578 using ranking
• CPC Class Codes: G06F 16/248 Presentation of query results
• CPC Class Codes: G16B 20/00 ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
• CPC Class Codes: G16B 20/10 Ploidy or copy number detection
• CPC Class Codes: G16B 20/20 Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
• CPC Class Codes: G16B 20/40 Population genetics; Linkage disequilibrium
• CPC Class Codes: G16B 30/00 ICT specially adapted for sequence analysis involving nucleotides or amino acids
• CPC Class Codes: G16B 30/10 Sequence alignment; Homology search
• 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 50/00 ICT programming tools or database systems specially adapted for bioinformatics
• CPC Class Codes: G16B 50/30 Data warehousing; Computing architectures

Abstract

According to various embodiments, aspects of the invention provide a highly efficient meta-analysis infrastructure for performing research queries across a large number of studies and experiments from diverse sequencing technologies as well as different biological and chemical assays, data types and organisms, as well as systems to build and add to such an infrastructure. The methods, systems and apparatuses described enable combining orthogonal types of data and available public knowledge to elucidate mechanisms governing normal development, disease progression, as well as susceptibility of individuals to disease or response to drug treatments.

Claims

1. A computer implemented method of conducting a query in a database comprising a plurality of sequence-centric feature sets and gene-centric feature sets, the sequence-centric feature sets comprising genomic sequence regions and associated statistics based on experiments on samples containing the genomic sequence regions and the gene-centric feature sets comprising genes and associated statistics based on experiments on samples containing the genes, the method comprising:

(a) receiving, at a server computer comprising a processor and system memory, a query identifying a sequence-centric feature set or a gene-centric feature set, wherein the query is received from a user input to a query computer;

(b) receiving, by the server computer, correlation scores indicating correlations between the queried sequence-centric feature set or the queried gene-centric feature set and two or more feature sets in the database, wherein each correlation score was calculated by an iterative rank-based process comprising;

obtaining a first feature set in the database corresponding to the queried sequence-centric feature set or the queried gene-centric feature set,

obtaining a second feature set in the database,

mapping one or more features in the first feature set to one or more features in the second feature set to provide mapping information between the first and second feature sets,

obtaining ranks of the one or more features in the first feature set and ranks of the one or more features in the second feature set, and

calculating the correlation score using (i) the mapping information between the first and second feature sets, and (ii) the ranks of the one or more features in the first feature set and the ranks of the one or more features in the second feature set, the correlation score indicating a correlation between the first feature set and the second feature set;

(c) calculating, by the server computer, feature set rankings for the two or more feature sets based on the correlation scores; and

(d) outputting, based on the feature set rankings and by the server computer, a ranked list of the two or more feature sets to be presented to the user.

2. The computer implemented method of claim 1, wherein the plurality of sequence-centric feature sets and gene-centric feature sets in the database were obtained from a plurality of experiments, a plurality of platforms, or a plurality of organisms, and wherein at least some features of the feature sets in the database have different names and are associated with each other.

3. The computer implemented method of claim 1, wherein the correlation score comprises or is based on a p value.

15. The computer implemented method of claim 3, wherein each p value is calculated by a Fisher'"'"'s Exact Test using the ranks of the one or more features in the first feature set and the ranks of the one or more features in the second feature set.

16. The computer implemented method of claim 1, wherein the mapping comprises mapping the one or more features in the first feature set to one or more globally unique mapping identifiers, and/or mapping the one or more features in the second feature set to said one or more globally unique mapping identifiers.

17. The computer implemented method of claim 1, wherein at least some features of the feature sets have different names and are associated with each other.

18. The computer implemented method of claim 1, wherein the statistics associated with features are selected from a group consisting of:

gene expression of said features, abundance of said features, responses of said features to a treatment or stimulus, and effects of said features on biological systems.

19. The method of claim 1, wherein the ranks of the one or more features in the first feature set and the ranks of the one or more features in the second feature set comprise normalized ranks.

4. A system comprising:

a memory for storing a database comprising a plurality of feature sets, wherein each feature set of the plurality of features sets is either a sequence-centric feature sets or a gene-centric feature set, wherein each sequence-centric feature set comprises a plurality of sequence regions and associated statistics, and each gene-centric feature set comprises a plurality of genes and associated statistics; and

one or more processors in communication with the memory, wherein the one or more processors are configured to;

(a) receive a query identifying a sequence-centric feature set or a gene-centric feature set, wherein the query is received from a user input to a query computer;

(b) receive, in response to said query, correlation scores indicating correlations between the queried sequence-centric feature set or the queried gene-centric feature set and two or more feature sets in the database, wherein each correlation score was calculated by an iterative rank-based process comprising;

obtaining a first feature set in the database corresponding to the queried sequence-centric feature set or the queried gene-centric feature set,

obtaining a second feature set in the database,

mapping one or more features in the first feature set to one or more features in the second feature set to provide mapping information between the first and second feature sets,

obtaining ranks of the one or more features in the first feature set and ranks of the one or more features in the second feature set, and

calculating the correlation score using (i) the mapping information between the first and second feature sets, and (ii) the ranks of the one or more features in the first feature set and the ranks of the one or more features in the second feature set, the correlation score indicating a correlation between the first feature set and the second feature set;

(c) calculate feature set rankings for the two or more feature sets based on the correlation scores; and

(d) output, based on the feature set rankings, a ranked list of the two or more feature sets to be presented to the user.

5. The system of claim 4, wherein the plurality of feature sets in the database were obtained from a plurality of experiments, a plurality of platforms, or a plurality of organisms, and wherein at least some features of the feature sets in the database have different names and are associated with each other.

6. The system of claim 4, wherein the correlation score comprises or is based on a p-value.

7. The system of claim 6, wherein the p-value is obtained by a Fisher'"'"'s Exact Test using the ranks of the one or more features in the first feature set and the ranks of the one or more features in the second feature set.

8. The system of claim 4, wherein the mapping is based on genomic coordinate, physical proximity, haplotype, function, or phenotype.

9. The system of claim 4, wherein the mapping comprises mapping the one or more features in the first feature set to one or more globally unique mapping identifiers, and/or mapping the one or more features in the second feature set to the one or more globally unique mapping identifiers.

10. The system of claim 4, wherein the ranks are obtained based on statistics associated with features of the first feature set and statistics associated with features of the second feature set.

11. The system of claim 4, the one or more processors are further configured to present the user with a graphical representation of associations between (i) features of a feature set in the ranked list of feature sets and (ii) other information in the database.

12. The system of claim 4, wherein the gene-centric feature sets in the database comprise microarray expression data.

13. The system of claim 4, wherein the sequence-centric feature sets in the database comprise one or more of methylated region data, genomic variation data, and epigenetic profiling data.

14. A computer program product comprising a non-transitory machine readable medium storing program code that, when executed by one or more processors of a computer system, causes the computer system to implement a method of querying a sequence-centric feature set or a gene-centric feature set against a database comprising a plurality of feature sets, wherein each feature set of the plurality of features sets is either a sequence-centric feature sets or a gene-centric feature set, each sequence-centric feature set comprises a plurality of sequence regions and associated statistics, and each gene-centric feature set comprises a plurality of genes and associated statistics, said program code comprising:

(a) code for receiving a query identifying a sequence-centric feature set or a gene-centric feature set, wherein the query is received from a user input to a query computer;

(b) code for correlating, in reply to said query, the queried sequence-centric feature set or the queried gene-centric feature set with two or more feature sets of the plurality of feature sets in the database, wherein the correlating comprises;

obtaining a first feature set in the database corresponding to the queried sequence-centric feature set or the queried gene-centric feature set,

obtaining a second feature set in the database,

mapping one or more features in the first feature set to one or more features in the second feature set to provide mapping information between the first and second feature sets,

obtaining ranks of the one or more features in the first feature set and ranks of the one or more features in the second feature set, and

calculate the correlation score using (i) the mapping information between the first and second feature sets, and (ii) the ranks of the one or more features in the first feature set and the ranks of the one or more features in the second feature set, the correlation score indicating a correlation between the first feature set and the second feature set;

(c) code for calculating feature set rankings for the two or more feature sets based on the correlation scores; and

(d) code for outputting, based on the feature set rankings, a ranked list of the two or more feature sets to be presented to the user.