Methods and computer software for detecting splice variants

Patent metadata

Field	Value
Publication number	US-10275568-B2
Application number	US-201213431021-A
Country	US
Kind code	B2
Filing date	Mar 27, 2012
Priority date	Sep 30, 2005
Publication date	Apr 30, 2019
Grant date	Apr 30, 2019

Abstract

Methods and software products for analysis of alternative splicing are disclosed. In general the methods involve normalizing probe set or exon intensity to an expression level measurement of the gene. The methods may be used to identify tissue-specific alternative splicing events.

First claim

The invention claimed is:

1. A method of identifying one or more alternative splicing events for one or more genes of a target genome using a plurality of exon array, each array comprising a plurality of nucleic acid probe sets having one or more nucleic acid probes that are complementary to at least a portion of an exon and do not comprise junction probes, and a computer system comprising at least one processor, a memory, and an interface, the method comprising:
providing the plurality of exon arrays, wherein a nucleic acid probe set in each exon array is designed to probe for at least a portion of a first probe selection region (PSR) corresponding to an exon derived from one or more input annotations for the target genome, wherein a nucleic acid probe set associated with the first PSR is included in a first gene expression level grouping of a first gene on the target genome and the exon corresponding to the PSR maps to the first gene;
hybridizing the plurality of nucleic acid probes of a first exon array to nucleic acids derived from one or more nucleic acid samples from a first tissue sample to generate first probe selection region (PSR) intensity data corresponding to the nucleic acid probe set associated with the first;
calculating a first normalized exon intensity comprising a ratio of first exon intensity data to a first gene expression level, wherein the first exon intensity data comprises data from the first PSR intensity data corresponding to the exon, and the first gene expression level comprises a first gene intensity value obtained from PSR intensity data assembled from nucleic acid probes associated with the first gene expression level grouping;
hybridizing the plurality of nucleic acid probes of a second exon array to nucleic acids derived from one or more nucleic acid samples from a second tissue sample to generate second probe selection region (PSR) intensity data corresponding to the nucleic acid probe set associated with the first PSR;
calculating a second normalized exon intensity comprising a ratio of second exon intensity data to a second gene expression level, wherein the second exon intensity data comprises data from the second PSR intensity data, and the second gene expression level comprises a second gene intensity value obtained from PSR intensity data assembled from nucleic acid probes associated with the first gene expression level grouping; and
detecting differential exon expression of the gene using the first and second normalized exon intensities.

2. The method of claim 1 , wherein the one or more nucleic acid samples comprise at least three nucleic acid samples, and wherein the one or more alternative splicing events are identified based upon detection of differential exon expression of one or more exons of the gene within the at least three nucleic acid samples.

3. The method of claim 2 , wherein detection of differential exon expression of the one or more exons is based upon a proportional correlation of one or more signals for an exon within the at least three nucleic acid samples.

4. The method of claim 3 , wherein a low proportional correlation of the one or more signals for the exon within the at least three nucleic acid samples is indicative of differential exon expression and occurrence of the one or more alternative splicing events for the gene.

5. The method of claim 3 , wherein the proportional correlation of the one or more signals for the exon is based upon, at least in part, a formula of:
e i,j,k =(α i,k )( g j,k ),

wherein e i,j,k is an exon signal estimate of the i exon, j experiment, and k gene,
wherein g j,k is a gene level signal estimate of the j experiment and k gene, and
wherein α i,k is a ratio of the exon signal estimate of the i exon to the gene level signal estimate of the k gene in the experiment in which the k gene is maximally expressed.

6. The method of claim 1 , wherein signals from one or more hybridization events on the exon array are normalized within identification of the one or more alternative splicing events.

7. The method of claim 6 , wherein normalization is performed for one or more signals associated with an exon of the gene with respect to one or more signals associated with the gene.

8. The method of claim 7 , wherein the normalization comprises calculating a normalized exon signal estimate based upon, at least in part, a formula of:

n

i
,
j
,
k

=

e

i
,
j
,
k

g

j
,
k

,

wherein e i,j,k is an exon signal estimate of the i exon, j experiment, and k gene,
wherein g j,k is a gene level signal estimate of the j experiment and k gene, and
wherein n i,j,k is the normalized exon signal estimate of the i exon, j experiment, and k gene.

9. The method of claim 8 , wherein each j experiment corresponds to a different nucleic acid sample of the at least three nucleic acid samples.

10. The method of claim 1 , wherein the one or more nucleic acid samples comprise at least two nucleic acid samples, wherein the one or more alternative splicing events for the gene are identified using a detection metric, and wherein the detection metric is based upon measuring differences between one or more exon signals and an aggregate signal of the gene.

11. The method of claim 10 , wherein the detection metric is based upon an assumption that alternative splicing does not occur within the gene and that the differences between the one or more exon signals and the aggregate signal of the gene remain constant across the at least two nucleic acid samples.

12. The method of claim 10 , wherein the detection metric is based upon a log ratio of the one or more exon signals to the aggregate signal of the gene.

13. The method of claim 12 , wherein a constant value is utilized within the detection metric.

14. The method of claim 13 , wherein the constant value is based upon a percentile of a background signal, and wherein the constant value is based upon a value approximately equal to the twentieth percentile of the background signal.

15. The method of claim 13 , wherein the constant value stabilizes the variance within the detection metric.

16. The method of claim 10 , wherein the detection metric is based upon a calculation of exon signal that is based upon, at least in part, a formula of:
e i,j,k =(α i,k )( P i,j,k )( g j,k ),

wherein e i,j,k is an exon signal estimate of the i exon, j experiment, and k gene,
wherein α i,k is a ratio of the exon signal estimate of the i exon to the gene level signal

estimate of the k gene in the experiment in which the k gene is maximally expressed,
wherein P i,j,k is a splicing index that provides an estimate of proportionate expression of the i exon, j experiment, and k gene, and
wherein g j,k is a gene level signal estimate of the j experiment and k gene.

Assignees

• Williams Alan
• Cawley Simon
• Blume John E
• Wang Hui
• Clark Tyson
• Affymetrix Inc

Inventors

• Williams Alan
• Cawley Simon
• Blume John E
• Wang Hui
• Clark Tyson

Classifications

• G16B25/00 Primary

  ICT specially adapted for hybridisation; ICT specially adapted for gene or protein expression · CPC title

• G06F19/20 Primary

  Physics · mapped topic

• G16B20/20 Primary

  Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection · CPC title