東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Statistical Methods for Genomic and ...

Jiang, Yuchao.

Linked to FindBook

Google Book

Amazon

博客來

Statistical Methods for Genomic and Transcriptomic Sequencing.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Statistical Methods for Genomic and Transcriptomic Sequencing./
Author:	Jiang, Yuchao.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2017,
Description:	157 p.
Notes:	Source: Dissertation Abstracts International, Volume: 78-12(E), Section: B.
Contained By:	Dissertation Abstracts International78-12B(E).
Subject:	Bioinformatics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10266281
ISBN:	9780355094930

Statistical Methods for Genomic and Transcriptomic Sequencing.
Jiang, Yuchao.

Statistical Methods for Genomic and Transcriptomic Sequencing. - Ann Arbor : ProQuest Dissertations & Theses, 2017 - 157 p.

Source: Dissertation Abstracts International, Volume: 78-12(E), Section: B.

Thesis (Ph.D.)--University of Pennsylvania, 2017.

Part 1: High-throughput sequencing of DNA coding regions has become a common way of assaying genomic variation in the study of human diseases. Copy number variation (CNV) is an important type of genomic variation, but CNV profiling from whole-exome sequencing (WES) is challenging due to the high level of biases and artifacts. We propose CODEX, a normalization and CNV calling procedure for WES data. CODEX includes a Poisson latent factor model, which includes terms that specifically remove biases due to GC content, exon capture and amplification efficiency, and latent systemic artifacts. CODEX also includes a Poisson likelihood-based segmentation procedure that explicitly models the count-based WES data. CODEX is compared to existing methods on germline CNV detection in HapMap samples using microarray-based gold standard and is further evaluated on 222 neuroblastoma samples with matched normal, with focus on somatic CNVs within theATRX gene.

ISBN: 9780355094930Subjects--Topical Terms:

553671
Bioinformatics.

Statistical Methods for Genomic and Transcriptomic Sequencing.
LDR:03476nmm a2200349 4500 001 2164266
005 20181106104110.5
008 190424s2017 ||||||||||||||||| ||eng d
020 $a 9780355094930
035 $a (MiAaPQ)AAI10266281
035 $a (MiAaPQ)upenngdas:12666
035 $a AAI10266281
040 $a MiAaPQ $c MiAaPQ
100 1 $a Jiang, Yuchao. $3 3352311
245 1 0 $a Statistical Methods for Genomic and Transcriptomic Sequencing.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2017
300 $a 157 p.
500 $a Source: Dissertation Abstracts International, Volume: 78-12(E), Section: B.
500 $a Adviser: Nancy R. Zhang.
502 $a Thesis (Ph.D.)--University of Pennsylvania, 2017.
520 $a Part 1: High-throughput sequencing of DNA coding regions has become a common way of assaying genomic variation in the study of human diseases. Copy number variation (CNV) is an important type of genomic variation, but CNV profiling from whole-exome sequencing (WES) is challenging due to the high level of biases and artifacts. We propose CODEX, a normalization and CNV calling procedure for WES data. CODEX includes a Poisson latent factor model, which includes terms that specifically remove biases due to GC content, exon capture and amplification efficiency, and latent systemic artifacts. CODEX also includes a Poisson likelihood-based segmentation procedure that explicitly models the count-based WES data. CODEX is compared to existing methods on germline CNV detection in HapMap samples using microarray-based gold standard and is further evaluated on 222 neuroblastoma samples with matched normal, with focus on somatic CNVs within theATRX gene.
520 $a Part 2: Cancer is a disease driven by evolutionary selection on somatic genetic and epigenetic alterations. We propose Canopy, a method for inferring the evolutionary phylogeny of a tumor using both somatic copy number alterations and single nucleotide alterations from one or more samples derived from a single patient. Canopy is applied to bulk sequencing datasets of both longitudinal and spatial experimental designs and to a transplantable metastasis model derived from human cancer cell line MDA-MB-231. Canopy successfully identifies cell populations and infers phylogenies that are in concordance with existing knowledge and ground truth. Through simulations, we explore the effects of key parameters on deconvolution accuracy, and compare against existing methods.
520 $a Part 3: Allele-specific expression is traditionally studied by bulk RNA sequencing, which measures average expression across cells. Single-cell RNA sequencing (scRNA-seq) allows the comparison of expression distribution between the two alleles of a diploid organism and thus the characterization of allele-specific bursting. We propose SCALE to analyze genome-wide allele-specific bursting, with adjustment of technical variability. SCALE detects genes exhibiting allelic differences in bursting parameters, and genes whose alleles burst non-independently. We apply SCALE to mouse blastocyst and human fibroblast cells and find that, globally, cis control in gene expression overwhelmingly manifests as differences in burst frequency.
590 $a School code: 0175.
650 4 $a Bioinformatics. $3 553671
650 4 $a Statistics. $3 517247
650 4 $a Biostatistics. $3 1002712
650 4 $a Systematic biology. $3 3173492
690 $a 0715
690 $a 0463
690 $a 0308
690 $a 0423
710 2 $a University of Pennsylvania. $b Genomics and Computational Biology. $3 2101632
773 0 $t Dissertation Abstracts International $g 78-12B(E).
790 $a 0175
791 $a Ph.D.
792 $a 2017
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10266281