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Annotation-informed integration of '...

Baumann, Douglas D.

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Annotation-informed integration of 'omic data in next-generation sequencing.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Annotation-informed integration of 'omic data in next-generation sequencing./
Author:	Baumann, Douglas D.
Description:	176 p.
Notes:	Source: Dissertation Abstracts International, Volume: 74-07(E), Section: B.
Contained By:	Dissertation Abstracts International74-07B(E).
Subject:	Statistics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3555222
ISBN:	9781267967022

Annotation-informed integration of 'omic data in next-generation sequencing.
Baumann, Douglas D.

Annotation-informed integration of 'omic data in next-generation sequencing. - 176 p.

Source: Dissertation Abstracts International, Volume: 74-07(E), Section: B.

Thesis (Ph.D.)--Purdue University, 2012.

Next-generation sequencing (NGS) technologies have opened the door to a wealth of knowledge and information about biological systems, particularly in genomics and epigenomics. These tools, although useful, carry with them additional technological and statistical challenges that need to be understood and addressed. Four separate, but related, NGS issues are addressed in this work. Specifically, we address amplification bias, an improvement in statistical power for differential methylation tests, boundary definitions and analyses for histone modifications, and the integration of genomic and epigenomic data in joint analyses.

ISBN: 9781267967022Subjects--Topical Terms:

517247
Statistics.

Annotation-informed integration of 'omic data in next-generation sequencing.
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100 1 $a Baumann, Douglas D. $3 2092579
245 1 0 $a Annotation-informed integration of 'omic data in next-generation sequencing.
300 $a 176 p.
500 $a Source: Dissertation Abstracts International, Volume: 74-07(E), Section: B.
500 $a Adviser: Rebecca W. Doerge.
502 $a Thesis (Ph.D.)--Purdue University, 2012.
520 $a Next-generation sequencing (NGS) technologies have opened the door to a wealth of knowledge and information about biological systems, particularly in genomics and epigenomics. These tools, although useful, carry with them additional technological and statistical challenges that need to be understood and addressed. Four separate, but related, NGS issues are addressed in this work. Specifically, we address amplification bias, an improvement in statistical power for differential methylation tests, boundary definitions and analyses for histone modifications, and the integration of genomic and epigenomic data in joint analyses.
520 $a The majority of NGS technologies effectively sample small amounts of DNA or RNA that are amplified (i.e., copied) prior to sequencing. The amplification process is not perfect, and thus sequenced read counts can be extremely biased. Unfortunately, current amplification bias controlling procedures introduce a dependence of gene expression on gene length. This issue is addressed via a novel procedure that accounts for amplification bias, and is effective in estimating true gene expression independent of gene length.
520 $a NGS technologies generate whole-genome epigenomic profiles at single base-pair resolution. Several novel statistical approaches are presented that incorporate genomic annotation information into the analysis of differential methylation and histone modification profiles for the purpose of increasing the statistical power. A novel application of hierarchical Bayesian modeling, that incorporates DNA methylation information into a differential gene expression testing framework, is also presented. The advantages of these approaches are demonstrated through extensive simulations and real data applications.
590 $a School code: 0183.
650 4 $a Statistics. $3 517247
650 4 $a Biology, Bioinformatics. $3 1018415
690 $a 0463
690 $a 0715
710 2 $a Purdue University. $b Statistics. $3 1279962
773 0 $t Dissertation Abstracts International $g 74-07B(E).
790 1 0 $a Doerge, Rebecca W., $e advisor
790 1 0 $a Craig, Bruce A. $e committee member
790 1 0 $a Chun, Hyonho $e committee member
790 1 0 $a Zhu, Michael $e committee member
790 $a 0183
791 $a Ph.D.
792 $a 2012
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3555222