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Variant Interpretation in Personal G...

Chen, Jieming.

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Variant Interpretation in Personal Genomes Using Repeat Protein Sequences, Networks and Allele-specific Analyses.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Variant Interpretation in Personal Genomes Using Repeat Protein Sequences, Networks and Allele-specific Analyses./
Author:	Chen, Jieming.
Description:	183 p.
Notes:	Source: Dissertation Abstracts International, Volume: 77-06(E), Section: B.
Contained By:	Dissertation Abstracts International77-06B(E).
Subject:	Genetics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10005370
ISBN:	9781339434575

Variant Interpretation in Personal Genomes Using Repeat Protein Sequences, Networks and Allele-specific Analyses.
Chen, Jieming.

Variant Interpretation in Personal Genomes Using Repeat Protein Sequences, Networks and Allele-specific Analyses. - 183 p.

Source: Dissertation Abstracts International, Volume: 77-06(E), Section: B.

Thesis (Ph.D.)--Yale University, 2015.

Variant annotation has always been an important challenge in genomics. With hundreds of thousands of personal genomes and a preponderance of variants being sequenced now, there is a greater urgency to address this challenge. A plausible way is to scan a genome in silico and very quickly prioritize variants according to some specific functional significance. By providing a ranked list of variants, such approaches can facilitate the design of expensive and difficult experiments. Here, the thesis focuses on developing interdisciplinary approaches to uncover variants that are involved in protein-protein interactions and allele-specific behavior. To identify variants involved in protein-protein interactions, the human reference genome is analyzed separately with concepts from network theory, protein-protein recognition and protein engineering. The network approach uses a combination of networks built from protein databases, and evolutionary features computed from variants to construct a systems-based machine-learning classifier to accurately estimate the impact of variants involved in diseases. The protein recognition approach analyzes the relationship between the strength of the interaction and the physicochemical aspects of protein interfaces and shows that only a small portion of the protein interface is responsible for most of the binding strength. This refines the broad annotation provided by the networks approach. The protein domain perspective offers an intermediate approach by using multiple sequence alignments of a class of protein-interacting domains known as repeat domains, in order to identify subsets of key residues that are important in protein interactions. Jointly, the three approaches demonstrate the ability to annotate protein interaction variants at different levels of granularity. To identify variants associated with allele-specific behavior, 382 personal genomes are constructed and used in place of the human reference genome to reduce reference bias. They are integrated with functional genomics assays and processed en masse in a uniform fashion to assess allelic imbalances in transcription factor binding and gene expression. Altogether, these studies present methods that can annotate variants based on a given functional significance and demonstrate the utility of cross-disciplinary research and personal genomes in meeting the challenge of genome annotation.

ISBN: 9781339434575Subjects--Topical Terms:

530508
Genetics.

Variant Interpretation in Personal Genomes Using Repeat Protein Sequences, Networks and Allele-specific Analyses.
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245 1 0 $a Variant Interpretation in Personal Genomes Using Repeat Protein Sequences, Networks and Allele-specific Analyses.
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500 $a Source: Dissertation Abstracts International, Volume: 77-06(E), Section: B.
500 $a Advisers: Mark Gerstein; Lynne Regan.
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520 $a Variant annotation has always been an important challenge in genomics. With hundreds of thousands of personal genomes and a preponderance of variants being sequenced now, there is a greater urgency to address this challenge. A plausible way is to scan a genome in silico and very quickly prioritize variants according to some specific functional significance. By providing a ranked list of variants, such approaches can facilitate the design of expensive and difficult experiments. Here, the thesis focuses on developing interdisciplinary approaches to uncover variants that are involved in protein-protein interactions and allele-specific behavior. To identify variants involved in protein-protein interactions, the human reference genome is analyzed separately with concepts from network theory, protein-protein recognition and protein engineering. The network approach uses a combination of networks built from protein databases, and evolutionary features computed from variants to construct a systems-based machine-learning classifier to accurately estimate the impact of variants involved in diseases. The protein recognition approach analyzes the relationship between the strength of the interaction and the physicochemical aspects of protein interfaces and shows that only a small portion of the protein interface is responsible for most of the binding strength. This refines the broad annotation provided by the networks approach. The protein domain perspective offers an intermediate approach by using multiple sequence alignments of a class of protein-interacting domains known as repeat domains, in order to identify subsets of key residues that are important in protein interactions. Jointly, the three approaches demonstrate the ability to annotate protein interaction variants at different levels of granularity. To identify variants associated with allele-specific behavior, 382 personal genomes are constructed and used in place of the human reference genome to reduce reference bias. They are integrated with functional genomics assays and processed en masse in a uniform fashion to assess allelic imbalances in transcription factor binding and gene expression. Altogether, these studies present methods that can annotate variants based on a given functional significance and demonstrate the utility of cross-disciplinary research and personal genomes in meeting the challenge of genome annotation.
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