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Statistical methods for detecting al...

Jacobson, Sean Robert.

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Statistical methods for detecting allelic imbalance in RNA-Seq data.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Statistical methods for detecting allelic imbalance in RNA-Seq data./
Author:	Jacobson, Sean Robert.
Description:	57 p.
Notes:	Source: Masters Abstracts International, Volume: 52-04.
Contained By:	Masters Abstracts International52-04(E).
Subject:	Biology, Biostatistics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1550031
ISBN:	9781303638046

Statistical methods for detecting allelic imbalance in RNA-Seq data.
Jacobson, Sean Robert.

Statistical methods for detecting allelic imbalance in RNA-Seq data. - 57 p.

Source: Masters Abstracts International, Volume: 52-04.

Thesis (M.S.)--University of Colorado Denver, Anschutz Medical Campus, 2013.

Gene expression studies are a key component of investigations related to identification and characterization of genetic risk factors for disease. Gene expression refers to the abundance of messenger ribonucleic acid (mRNA) present in a sample, which is presumably related to the abundance of the protein for which the mRNA codes. Comparisons in gene expression between groups of individuals with informative dichotomous phenotypes can provide insight into disease etiology.

ISBN: 9781303638046Subjects--Topical Terms:

1018416
Biology, Biostatistics.

Statistical methods for detecting allelic imbalance in RNA-Seq data.
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020 $a 9781303638046
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100 1 $a Jacobson, Sean Robert. $3 2104924
245 1 0 $a Statistical methods for detecting allelic imbalance in RNA-Seq data.
300 $a 57 p.
500 $a Source: Masters Abstracts International, Volume: 52-04.
500 $a Adviser: Tasha Fingerlin.
502 $a Thesis (M.S.)--University of Colorado Denver, Anschutz Medical Campus, 2013.
520 $a Gene expression studies are a key component of investigations related to identification and characterization of genetic risk factors for disease. Gene expression refers to the abundance of messenger ribonucleic acid (mRNA) present in a sample, which is presumably related to the abundance of the protein for which the mRNA codes. Comparisons in gene expression between groups of individuals with informative dichotomous phenotypes can provide insight into disease etiology.
520 $a The majority of expression studies have relied on measures of total gene expression which ignore the potential for variability in expression between pairs of homologous chromosomes within an individual. Sequencing of mRNA molecules (RNA-Seq) is a technology which allows for quantification of variability in expression between pairs of chromosomes and is increasingly being used for that purpose. Measurement of allelic imbalance (AI) from RNA-Seq data relies on counting RNA molecules (sequencing reads) coming from a certain gene or transcript and determining what proportion came from one chromosome versus the other. Simply put, for a single individual, reads can be assigned to a specific chromosome for a given transcript/gene based on the difference in their sequence; the AI is determined by the proportion of reads that have one sequence vs. another sequence.
520 $a Challenges related to the analysis of RNA-Seq data for measuring AI include the ability to use multiple sequence variants in the RNA molecules to determine whether there is evidence for AI. The published methods to date use either an ad hoc approach or a complex statistical method called a hidden Markov model (HMM) to combine information from multiple variants across a transcript. The goal of this thesis was to compare the ad-hoc to a simple yet statistically well-justified meta-analysis approach for combining information across multiple variants in a transcript. I show that the ad hoc approach has an inflated type I error rate and that the meta-analysis approach maintains the appropriate type I error rate while achieving similar power. I also demonstrate that the HMM approach is not applicable for most transcripts/genes across the genome for RNA-seq based investigations of AI.
590 $a School code: 1639.
650 4 $a Biology, Biostatistics. $3 1018416
650 4 $a Biology, Bioinformatics. $3 1018415
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710 2 $a University of Colorado Denver, Anschutz Medical Campus. $b Biostatistics. $3 2104925
773 0 $t Masters Abstracts International $g 52-04(E).
790 $a 1639
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792 $a 2013
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1550031