{'fraction_non_alphanumeric': 0.035993061578490894, 'fraction_numerical': 0.009106678230702514, 'mean_word_length': 6.055045871559633, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Cardiovascular Transcriptomics and Epigenomics Using Next-Generation Sequencing: Challenges, Progress, and Opportunities

Cardiovascular disease (CVD) is the leading cause of death worldwide. Prediction and prevention of CVD, such as coronary artery disease and atherosclerosis, traditionally depend on identification of risk factors.1,2 These factors are effective in the general assessment of CVD risk but are not consistent indicators for all individuals.3 Therefore, CVD research has been recently expanded to include the identification of omic biomarkers (eg, genomic, transcriptomic, and epigenomic) that may (1) improve our understanding of the molecular mechanisms of CVD, (2) facilitate the development of personalized CVD care, and (3) reduce CVD mortality rates by accurately identifying high-risk individuals.4 Next-generation sequencing (NGS) is a promising technology to identify omic biomarkers. Because of its high-throughput capability in discovering novel genomic features with base-pair resolution, NGS is projected to play an increasingly important role in clinical diagnostics and personalized medicine for CVD.5,6

NGS and associated bioinformatics methods have been applied to cardiovascular genomics, transcriptomics, and epigenomics. Figure 1 illustrates 4 NGS applications such as (1) identification of differentially expressed genes (DEGs) using RNA sequencing (RNA-seq), (2) identification of protein-binding regions in the genome using chromatin immunoprecipitation sequencing (ChIP-seq), (3) identification of genetic variants in exon regions using exome sequencing, and (4) identification of genomic methylation patterns using methyl-CpG-binding domain sequencing. These applications identify and quantify omic biomarkers that may be clinically viable for early disease diagnosis and effective disease treatment and management. In this article, we focus on 2 major applications of NGS technology: (1) RNA-seq, which has enabled researchers to characterize CVD by studying transcriptome-wide expression profiles,7 alternative splicing patterns,8 and miRNA regulatory networks9 and (2) ChIP-seq, which has enabled researchers to examine the epigenetic mechanisms of CVD by profiling the genome-wide pattern of protein-binding regions (eg, transcription factors and enhancers) …