東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Computational Identification of B Ce...

Gupta, Namita.

FindBook

Google Book

Amazon

博客來

Computational Identification of B Cell Clones in High-Throughput Immunoglobulin Sequencing Data.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Computational Identification of B Cell Clones in High-Throughput Immunoglobulin Sequencing Data./
作者:	Gupta, Namita.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2017,
面頁冊數:	137 p.
附註:	Source: Dissertation Abstracts International, Volume: 78-11(E), Section: B.
Contained By:	Dissertation Abstracts International78-11B(E).
標題:	Bioinformatics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10633249
ISBN:	9780355105414

Computational Identification of B Cell Clones in High-Throughput Immunoglobulin Sequencing Data.
Gupta, Namita.

Computational Identification of B Cell Clones in High-Throughput Immunoglobulin Sequencing Data. - Ann Arbor : ProQuest Dissertations & Theses, 2017 - 137 p.

Source: Dissertation Abstracts International, Volume: 78-11(E), Section: B.

Thesis (Ph.D.)--Yale University, 2017.

Humoral immunity is driven by the expansion, somatic hypermutation, and selection of B cell clones. Each clone is the progeny of a single B cell responding to antigen. with diversified Ig receptors. The advent of next-generation sequencing technologies enables deep profiling of the Ig repertoire. This large-scale characterization provides a window into the micro-evolutionary dynamics of the adaptive immune response and has a variety of applications in basic science and clinical studies. Clonal relationships are not directly measured, but must be computationally inferred from these sequencing data. In this dissertation, we use a combination of human experimental and simulated data to characterize the performance of hierarchical clustering-based methods for partitioning sequences into clones. Our results suggest that hierarchical clustering using single linkage with nucleotide Hamming distance identifies clones with high confidence and provides a fully automated method for clonal grouping. The performance estimates we develop provide important context to interpret clonal analysis of repertoire sequencing data and allow for rigorous testing of other clonal grouping algorithms. We present the clonal grouping tool as well as other tools for advanced analyses of large-scale Ig repertoire sequencing data through a suite of utilities, Change-O. All Change-O tools utilize a common data format, which enables the seamless integration of multiple analyses into a single workflow. We then apply the Change-O suite in concert with the nucleotide coding se- quences for WNV-specific antibodies derived from single cells to identify expanded WNV-specific clones in the repertoires of recently infected subjects through quantitative Ig repertoire sequencing analysis. The method proposed in this dissertation to computationally identify B cell clones in Ig repertoire sequencing data with high confidence is made available through the Change-O suite and can be applied to provide insight into the dynamics of the adaptive immune response.

ISBN: 9780355105414Subjects--Topical Terms:

553671
Bioinformatics.

Computational Identification of B Cell Clones in High-Throughput Immunoglobulin Sequencing Data.
LDR:02951nmm a2200277 4500 001 2165964
005 20181203094030.5
008 190424s2017 ||||||||||||||||| ||eng d
020 $a 9780355105414
035 $a (MiAaPQ)AAI10633249
035 $a AAI10633249
040 $a MiAaPQ $c MiAaPQ
100 1 $a Gupta, Namita. $3 1279614
245 1 0 $a Computational Identification of B Cell Clones in High-Throughput Immunoglobulin Sequencing Data.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2017
300 $a 137 p.
500 $a Source: Dissertation Abstracts International, Volume: 78-11(E), Section: B.
500 $a Adviser: Steven H Kleinstein.
502 $a Thesis (Ph.D.)--Yale University, 2017.
520 $a Humoral immunity is driven by the expansion, somatic hypermutation, and selection of B cell clones. Each clone is the progeny of a single B cell responding to antigen. with diversified Ig receptors. The advent of next-generation sequencing technologies enables deep profiling of the Ig repertoire. This large-scale characterization provides a window into the micro-evolutionary dynamics of the adaptive immune response and has a variety of applications in basic science and clinical studies. Clonal relationships are not directly measured, but must be computationally inferred from these sequencing data. In this dissertation, we use a combination of human experimental and simulated data to characterize the performance of hierarchical clustering-based methods for partitioning sequences into clones. Our results suggest that hierarchical clustering using single linkage with nucleotide Hamming distance identifies clones with high confidence and provides a fully automated method for clonal grouping. The performance estimates we develop provide important context to interpret clonal analysis of repertoire sequencing data and allow for rigorous testing of other clonal grouping algorithms. We present the clonal grouping tool as well as other tools for advanced analyses of large-scale Ig repertoire sequencing data through a suite of utilities, Change-O. All Change-O tools utilize a common data format, which enables the seamless integration of multiple analyses into a single workflow. We then apply the Change-O suite in concert with the nucleotide coding se- quences for WNV-specific antibodies derived from single cells to identify expanded WNV-specific clones in the repertoires of recently infected subjects through quantitative Ig repertoire sequencing analysis. The method proposed in this dissertation to computationally identify B cell clones in Ig repertoire sequencing data with high confidence is made available through the Change-O suite and can be applied to provide insight into the dynamics of the adaptive immune response.
590 $a School code: 0265.
650 4 $a Bioinformatics. $3 553671
690 $a 0715
710 2 $a Yale University. $3 515640
773 0 $t Dissertation Abstracts International $g 78-11B(E).
790 $a 0265
791 $a Ph.D.
792 $a 2017
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10633249