Modeling and Predicting Differential Alternative Splicing Events and Applications Thereof

Patent metadata

Field	Value
Publication number	US-2016237487-A1
Application number	US-201615040514-A
Country	US
Kind code	A1
Filing date	Feb 10, 2016
Priority date	Feb 10, 2015
Publication date	Aug 18, 2016
Grant date	—

Abstract

The present disclosure includes a method for predicting differential alternative splicing events from ribonucleic acid (RNA) sequencing data that includes receiving RNA sequence reads for two or more samples; generating directed acyclic graphs from the RNA sequence reads, wherein each directed acyclic graph represents at least a portion of a gene model; extracting count data from the directed acyclic graphs; and generating differential alternative splicing event information from the count data using a Dirichlet multinomial (DMN) regression.

First claim

We claim:

1 . A method for predicting differential alternative splicing events from RNA comprising:
a) sequencing at least one said RNA sample to produce RNA sequence data reads per sample; b) generating one or more directed acyclic graphs from the RNA sequence reads, wherein each directed acyclic graph represents at least a portion of a gene model; c) extracting count data from the directed acyclic graphs; and d) generating differential alternative splicing information from the count data using a Dirichlet multinomial (DMN) regression.

2 . The method of claim 1 , wherein generating one or more directed acyclic graphs in step b) further comprises: Decomposing each directed acyclic graph into alternative splicing types;
and Summarizing the count data into a count table for each decomposed directed acyclic graph.

3 . The method of claim 1 , further comprising: Aligning the RNA sequence reads to a reference genome or known transcriptome; and Quantifying exon and exon-exon junction reads from the RNA sequence reads.

4 . The method of claim 1 , wherein the two or more samples are generated under two or more conditions in a multi-factorial experimental design.

5 . A method for predicting differential alternative splicing events from ribonucleic acid (RNA) sequence data, comprising:
a) sequencing at least one said RNA sample to produce RNA sequence data reads per sample; b) generating one or more directed acyclic graphs from the RNA sequence reads, wherein each directed acyclic graph represents at least a portion of a gene model; c) extracting count data from the directed acyclic graphs; d) generating differential alternative splicing information from the count data using a Dirichlet multinomial (DMN) regression; and e) using said alternative splicing information to create antisense RNA sequence which correspond to said alternative splicing information.

6 . A method for treating a subject with a disease comprising:
a) sequencing at least one said RNA sample from at least one diseased tissue and at least one control sample to produce RNA sequence data reads per sample; b) generating one or more directed acyclic graphs from the RNA sequence reads, wherein each directed acyclic graph represents at least a portion of a gene model; c) extracting count data from the directed acyclic graphs; d) generating differential alternative splicing information from the count data using a Dirichlet multinomial (DMN) regression; e) using said alternative splicing information to create antisense RNA sequences which correspond to said alterative splicing information relevant to said diseased tissue sample; and f) treating said subject with said antisense RNA sequence corresponding to said diseased tissue sample RNA sequence variants so as to alieviate at least one symptom of said disease.

7 . A method for predicting differential alternative splicing events from ribonucleic acid (RNA) sequence data, comprising: Receiving RNA sequence reads for two or more samples; Generating one or more directed acyclic graphs from the RNA sequence reads, wherein each directed acyclic graph represents at least a portion of a gene model; Extracting count data from the directed acyclic graphs; and Generating differential alternative splicing information from the count data using a Dirichlet multinomial (DMN) regression.

8 . The method of claim 7 , wherein generating one or more directed acyclic graphs further comprises: Decomposing each directed acyclic graph into alternative splicing types; and Summarizing the count data into a count table for each decomposed directed acyclic graph.

9 . The method of claim 7 , further comprising: Aligning the RNA sequence reads to a reference genome or known transcriptome; and Quantifying exon and exon-exon junction reads from the RNA sequence reads.

10 . The method of claim 7 , wherein the two or more samples are generated under two or more conditions in a multi-factorial experimental design.

Assignees

• Texas A & M Univ Sys

Inventors

• Yu Peng
• Shaw Chad A

Classifications

• C12N2320/30

  Special therapeutic applications · CPC title

• C12N2310/11

  Antisense · CPC title

• C12N15/113

  Non-coding nucleic acids modulating the expression of genes, e.g. antisense oligonucleotides; {Antisense DNA or RNA; Triplex- forming oligonucleotides; Catalytic nucleic acids, e.g. ribozymes; Nucleic acids used in co-suppression or gene silencing (when used in plants C12N15/8218)} · CPC title

• C12Q1/6874 Primary

  involving nucleic acid arrays, e.g. sequencing by hybridisation · CPC title

• G16B20/20

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