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Exploring Long Non-Coding RNAs in Congenital Heart Disease Etiology Through Multiomic Computational Techniques /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Exploring Long Non-Coding RNAs in Congenital Heart Disease Etiology Through Multiomic Computational Techniques /Jacqueline S Penaloza.
Author:	Penaloza, Jacqueline S.,
Description:	1 electronic resource (139 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 86-06, Section: B.
Notes:	Advisors: White, Peter; McBride, Kim Committee members: Wedemeyer, Michelle A.; Bundschuh, Ralf A.; Chandler, Dawn.
Contained By:	Dissertations Abstracts International86-06B.
Subject:	Bioinformatics.
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31836981
ISBN:	9798346853343

Exploring Long Non-Coding RNAs in Congenital Heart Disease Etiology Through Multiomic Computational Techniques /
Penaloza, Jacqueline S.,

Exploring Long Non-Coding RNAs in Congenital Heart Disease Etiology Through Multiomic Computational Techniques /Jacqueline S Penaloza. - 1 electronic resource (139 pages)

Source: Dissertations Abstracts International, Volume: 86-06, Section: B.

This dissertation explores the role of long non-coding RNAs in congenital heart disease (CHD), the most common birth defect worldwide, where many genetic contributors remain unknown. We investigate lncRNAs in two key areas: copy number variants (CNVs) and single nucleotide variants (SNVs), aiming to identify novel lncRNA candidates and develop new tools for predicting their pathogenicity. While CNVs are known to contribute to CHD, the involvement of lncRNAs within these regions has been largely unexplored. Additionally, no existing computational tools specifically assess the pathogenic potential of heart-specific lncRNA variants, presenting a critical gap in CHD research.The first study focuses on identifying lncRNAs within CNVs that are linked to CHD. By combining CNV data with transcriptomic information from human heart development, we discover lncRNA candidates that may play significant regulatory roles in heart formation. This study provides a reproducible platform for investigating lncRNAs in the context of CNVs, advancing our understanding of their contributions to CHD.The second study introduces HeartiLNC, a novel machine learning-based score designed to predict the pathogenicity of SNVs in lncRNAs. This tool integrates heart-specific lncRNA expression profiles, population frequency data, and RNA secondary structure analysis. HeartiLNC offers a unique approach to identifying potentially deleterious variants that may contribute to CHD. This method represents a significant advancement in computational genomics, offering new insights into the genetic regulation of heart development.Together, these studies contribute to the understanding of lncRNAs in CHD, providing new tools for genetic analysis. This work highlights the importance of investigating both CNVs and SNVs in the non-coding genome to uncover the complex genetic architecture underlying CHD. We hope that one day these findings will lead to improvement in CHD diagnosis and management of disease.

English

ISBN: 9798346853343Subjects--Topical Terms:

194415
Bioinformatics.
Subjects--Index Terms:

Congenital heart disease

Exploring Long Non-Coding RNAs in Congenital Heart Disease Etiology Through Multiomic Computational Techniques /
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520 $a This dissertation explores the role of long non-coding RNAs in congenital heart disease (CHD), the most common birth defect worldwide, where many genetic contributors remain unknown. We investigate lncRNAs in two key areas: copy number variants (CNVs) and single nucleotide variants (SNVs), aiming to identify novel lncRNA candidates and develop new tools for predicting their pathogenicity. While CNVs are known to contribute to CHD, the involvement of lncRNAs within these regions has been largely unexplored. Additionally, no existing computational tools specifically assess the pathogenic potential of heart-specific lncRNA variants, presenting a critical gap in CHD research.The first study focuses on identifying lncRNAs within CNVs that are linked to CHD. By combining CNV data with transcriptomic information from human heart development, we discover lncRNA candidates that may play significant regulatory roles in heart formation. This study provides a reproducible platform for investigating lncRNAs in the context of CNVs, advancing our understanding of their contributions to CHD.The second study introduces HeartiLNC, a novel machine learning-based score designed to predict the pathogenicity of SNVs in lncRNAs. This tool integrates heart-specific lncRNA expression profiles, population frequency data, and RNA secondary structure analysis. HeartiLNC offers a unique approach to identifying potentially deleterious variants that may contribute to CHD. This method represents a significant advancement in computational genomics, offering new insights into the genetic regulation of heart development.Together, these studies contribute to the understanding of lncRNAs in CHD, providing new tools for genetic analysis. This work highlights the importance of investigating both CNVs and SNVs in the non-coding genome to uncover the complex genetic architecture underlying CHD. We hope that one day these findings will lead to improvement in CHD diagnosis and management of disease.
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