Sequence-centric scientific information management

• US 9,183,349 B2
• Filed: 06/08/2010
• Issued: 11/10/2015
• 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 M
• Application Number:
  US12/796,545
• Publication Number:
  US 20100318528A1
• Time in Patent Office: 1,981 Days
• US Class Current: 1/1
• 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 integrating a sequence-centric feature set into a knowledge base on a storage device comprising sequence-centric feature sets and gene-centric feature sets, the method comprising:

receiving by one or more processors of a computer system a sequence-centric feature set provided by a user, wherein the sequence-centric feature set comprises a plurality of sequence regions and associated statistics, wherein the plurality of sequence regions comprise one or more SNPs, methylated regions, or genomic variations;

mapping by the one or more processors of the computer system the plurality of sequence regions to genes within the knowledge base to provide a set of mapped genes for the received sequence-centric feature set, wherein the plurality of sequence regions and the genes within the knowledge base are related by genomic coordinate, physical proximity, haplotype, function, or phenotype;

mapping by the one or more processors of the computer system the plurality of sequence regions to other sequence regions within the knowledge base to provide a set of mapped sequence regions for the received sequence-centric feature set, wherein the plurality of sequence regions and the genes within the knowledge base are related by genomic coordinate, physical proximity, haplotype, function, or phenotype;

providing ranks of the set of mapped sequence regions in the received sequence-centric feature set and in other sequence-centric feature sets in the knowledge base, wherein the other sequence-centric feature sets comprise a plurality of sequence regions and associated statistics;

performing by the one or more processors of the computer system iterative rank based processes to calculate sequence-sequence scores indicating correlations between the received sequence-centric feature set and other sequence-centric feature sets in the knowledge base using the ranks of the set of mapped sequence regions;

providing ranks of the set of mapped genes in the received sequence-centric feature set and in the gene-centric feature sets in the knowledge base, wherein the gene-centric feature sets comprise one or more of genes ranked by activity and microarray-based gene expression data;

performing by one or more processors of the computer system iterative rank based processes to calculate sequence-gene scores indicating the correlations between the received sequence-centric feature set and the gene-centric feature sets using the ranks of the set of mapped genes;

storing the received sequence-centric feature set, the sequence-sequence scores, and the sequence-gene scores on the storage device;

receiving a query sequence region or a query gene as a query input; and

displaying information based on one or more sequence-sequence scores or one or more sequence-gene scores that correspond to the query sequence region or the query gene.

2. The computer-implemented method of claim 1, wherein the gene-centric feature sets comprise microarray expression data and the plurality of sequence regions comprises SNPs.

3. The computer-implemented method of claim 1, wherein the gene-centric feature sets comprise microarray expression data and the plurality of sequence regions comprises methylated regions.

4. The computer-implemented method of claim 1, wherein the gene-centric feature sets comprise microarray expression data and wherein the plurality of sequence regions comprises genomic variations.

5. The computer-implemented method of claim 1, wherein the gene-centric features sets comprise microarray expression data and wherein at last one sequence-centric feature set in the knowledge base comprises epigenetic profiling data.

6. The computer-implemented method of claim 1, wherein sequence-sequence scores indicating a correlation between a received sequence-centric features set to thousands of other sequence-centric feature sets in the knowledge base are calculated.

7. The computer-implemented method of claim 1, wherein sequence-gene scores indicating correlations between a received sequence-centric feature set to thousands ofgene-centric feature sets in the knowledge base are calculated.

8. The computer-implemented method of claim 1, wherein at least one of the plurality of sequence regions is mapped to a gene based on being within the gene, overlapping the gene, being within a certain proximity of the gene, or covering the gene.

9. The computer-implemented method of claim 1, wherein at least one of the plurality of sequence regions does not overlap with any gene in the knowledge base.

10. The computer-implemented method of claim 1, further comprising displaying on a screen a graphical representation of a sequence region mapping.

11. A computer program product comprising a machine readable non-transitory medium on which is provided program instructions for integrating a sequence-centric feature set into a knowledge based on a storage device comprising sequence-centric feature sets and gene-centric feature sets, the program instructions comprising:

code for receiving a sequence-centric feature set provided by a user, wherein the sequence-centric feature set comprises a plurality of sequence regions and associated statistics, wherein the plurality of sequence regions comprise one or more SNPs, methylated regions, or genomic variations;

code for mapping the plurality of sequence regions to genes within the knowledge base to provide a set of mapped genes for the received sequence-centric feature set, wherein the plurality of sequence regions and the genes within the knowledge base are related by genomic coordinate, physical proximity, haplotype, function, or phenotype;

code for mapping the plurality of sequence regions to other sequence regions within the knowledge base to provide a set of mapped sequence regions for the received sequence-centric feature set, wherein the plurality of sequence regions and the genes within the knowledge base are related by genomic coordinate, physical proximity, haplotype, function, or phenotype;

code for providing ranks of the set of mapped sequence regions in the received sequence-centric feature set and in other sequence-centric feature sets in the knowledge base, wherein the other sequence-centric feature sets comprise a plurality of sequence regions and associated statistics;

code for performing iterative rank based processes to calculate sequence-sequence scores indicating correlations between the received sequence-centric feature set and other sequence-centric feature sets in the knowledge base using the ranks of the set of mapped sequence regions;

code for providing ranks of the set of mapped genes in the received sequence-centric feature set and in the gene-centric feature sets in the knowledge base, wherein the gene-centric feature sets comprise one or more of genes ranked by activity and microarray-based gene expression data;

code for performing iterative rank based processes to calculate sequence-gene scores indicating the correlations between the received sequence-centric feature set and the gene-centric feature sets using the ranks of the set of mapped genes;

code for storing the received sequence-centric feature set, the sequence-sequence scores, and the sequence-gene scores on the storage device;

code for receiving a query sequence region or a query gene as a query input; and

code for displaying information based on one or more sequence-sequence scores or one or more sequence-gene scores that correspond to the query sequence region or the query gene.

12. The computer program product of claim 11, wherein the gene-centric feature sets comprise microarray expression data and the plurality of sequence regions comprises SNPs.

13. The computer program product of claim 11, wherein the gene-centric feature sets comprise microarray expression data and the plurality of sequence regions comprises methylated regions.

14. The computer program product of claim 11, wherein the gene-centric feature sets comprise microarray expression data and wherein the plurality of sequence regions comprises genomic variations.

15. The computer program product of claim 11, wherein the gene-centric feature sets comprise microarray expression data and wherein at least one sequence-centric feature set in the knowledge base comprises epigenetic profiling data.

16. The computer program product of claim 11, wherein the program instructions comprise code for calculating sequence-sequence scores indicating a correlation between a received sequence-centric feature set to thousands of other sequence-centric feature sets in the knowledge base.

17. The computer program product of claim 11, wherein the program instructions comprise code for calculating sequence-gene scores indicating correlations between a received sequence-centric feature set to thousands of gene-centric feature sets in the knowledge base.

18. The computer program product of claim 11, wherein the program instructions comprise code for mapping at least one of the plurality of sequence regions to a gene based on being within the gene, overlapping the gene, being within a certain proximity of the gene, or covering the gene.

19. The computer program product of claim 11, wherein the program instructions comprise code for displaying on a screen a graphical representation of a sequence region mapping.

20. An apparatus for integrating a sequence-centric feature set into a knowledge base comprising sequence-centric feature sets and gene-centric feature sets, comprising:

a memory for storing a knowledge base of scientific information; and

one or more processors in communication with the memory and configured to;

receive sequence-centric feature set provided by a user, wherein the sequence-centric feature set comprises a plurality of sequence regions and associated statistics, wherein the plurality of sequence regions comprise one or more SNPs, methylated regions, or genomic variations;

map the plurality of sequence regions to genes within the knowledge base to provide a set of mapped genes for the received sequence-centric feature set, wherein the plurality of sequence regions and the genes within the knowledge base are related by genomic coordinate, physical proximity, haplotype, function, or phenotype;

map the plurality of sequence regions to other sequence regions within the knowledge base to provide a set of mapped sequence regions for the received sequence-centric feature set, wherein the plurality of sequence regions and the other sequence regions within the knowledge base are related by genomic coordinate, physical proximity, haplotype, function, or phenotype;

provide ranks of the set of mapped sequence regions in the received sequence-centric feature set and in other sequence-centric feature sets in the knowledge base, wherein the other sequence-centric feature sets comprise a plurality of sequence regions and associated statistics;

perform iterative rank based processes to calculate sequence-sequence scores indicating correlations between the received sequence-centric feature set and other sequence-centric feature sets in the knowledge base using the ranks of the set of mapped sequence regions;

provide ranks of the set of mapped genes in the received sequence-centric feature set and in the gene-centric feature sets in the knowledge base, wherein the gene-centric feature sets comprise one or more of genes ranked by activity and microarray-based gene expression data;

perform iterative rank based processes to calculate sequence-gene scores indicating the correlations between the received sequence-centric feature set and the gene-centric feature sets using the ranks of the set of mapped genes;

store the received sequence-centric feature set, the sequence-sequence scores, and the sequence-gene scores on the memory;

receive a query sequence region or a query gene as a query input; and

display information based on one or more sequence-sequence scores or one or more sequence-gene scores that correspond to the query sequence region or the query gene.