國立虎尾科技大學 |

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

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Computational Identification of B Cell Clones in High-Throughput Immunoglobulin Sequencing Data./
Author:	Gupta, Namita.
Description:	1 online resource (137 pages)
Notes:	Source: Dissertation Abstracts International, Volume: 78-11(E), Section: B.
Contained By:	Dissertation Abstracts International78-11B(E).
Subject:	Bioinformatics. -
Online resource:	click for full text (PQDT)
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. - 1 online resource (137 pages)

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

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

Includes bibliographical references

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.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355105414Subjects--Topical Terms:

583857
Bioinformatics.
Index Terms--Genre/Form:

554714
Electronic books.

Computational Identification of B Cell Clones in High-Throughput Immunoglobulin Sequencing Data.
LDR:03234ntm a2200313Ki 4500 001 920481
005 20181203094030.5
006 m o u
007 cr mn||||a|a||
008 190606s2017 xx obm 000 0 eng d
020 $a 9780355105414
035 $a (MiAaPQ)AAI10633249
035 $a AAI10633249
040 $a MiAaPQ $b eng $c MiAaPQ $d NTU
100 1 $a Gupta, Namita. $3 1195293
245 1 0 $a Computational Identification of B Cell Clones in High-Throughput Immunoglobulin Sequencing Data.
264 0 $c 2017
300 $a 1 online resource (137 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
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.
504 $a Includes bibliographical references
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.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Bioinformatics. $3 583857
655 7 $a Electronic books. $2 local $3 554714
690 $a 0715
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a Yale University. $3 1178968
773 0 $t Dissertation Abstracts International $g 78-11B(E).
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10633249 $z click for full text (PQDT)