High-throughput sequencing-based investigation of chronic disease markers and mechanisms

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Editorial

21 June 2022

Editorial: High-Throughput Sequencing-Based Investigation of Chronic Disease Markers and Mechanisms

Yuriy L. Orlov

,

Wen-Lian Chen

,

Marina I. Sekacheva

,

Guoshuai Cai

and

Hua Li

1,692 views

4 citations

Editors

4

Hua Li

Shanghai Jiao Tong University

Wen-Lian Chen

Longhua Hospital, Shanghai University of Traditional Chinese Medicine

Yuriy L Orlov

The Digital Health Institute, I.M.Sechenov First Moscow State Medical University

Guoshuai Cai

University of Florida

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With the recent development of sequencing technology and the rapid reduction of sequencing costs, high-throughput sequencing (including second and third-generation sequencing) is revolutionizing basic life science research and clinical research from various aspects. High-throughput sequencing often produces millions of sequencing reads at a time, and the alignment or assembly of these reads allows determination of various mutations (e.g., SNV and Indels) at the genomic level, accurate gene expression quantification at the transcriptomic level, and identification of histone or DNA modification at the epigenomic level. The resulting accumulation of enormous multi-omics information has opened up a new era of finding effective disease markers and studying their roles in disease occurrence and development.

Using high-throughput sequencing, various markers of chronic diseases (such as cancer, heart disease, diabetes, and arthritis) have been developed at all omics levels, which have been used for diagnosis and classification of diseases, prediction of treatment effects, and prevention of diseases. The quickly and massively acquired multi-omics data, together with newly developed algorithms, provide an excellent chance for the identification of more reliable biomarkers. This research topic aims at (1) developing new chronic disease markers at four levels (i.e., genome, epigenome, transcriptome, and translatome) with the help of high-throughput sequencing, and (2) delineating potential marker-related mechanisms for chronic disease occurrence or development. This research topic covers a broad spectrum of interests, and studies including both wet lab and dry lab results are more welcomed. More specifically, this research topic welcomes contributions including but not limited to the following areas:

1. Identification of novel biomarkers for chronic disease detection (especially in early-stage) or prognosis prediction using high-throughput sequencing;

2. characterize the possible pathological causes of markers as well as the potential roles they play in disease initiation and development;

3. New high-throughput sequencing techniques that facilitate the development of more effective biomarkers of chronic disease;

4. New algorithms or tools for in silico identification of effective chronic disease markers based on high-throughput sequencing data.

Please note that: (1) the high-throughput sequencing for genome, epigenome, transcriptome, and translatome (i.e., ribosome-associated RNA) is preferred for this topic; (2) at least some dry lab results need to be validated with wet-lab experiments; (3) studies successfully uncovering biomarker-related disease mechanisms will be highly preferred.

With the recent development of sequencing technology and the rapid reduction of sequencing costs, high-throughput sequencing (including second and third-generation sequencing) is revolutionizing basic life science research and clinical research from various aspects. High-throughput sequencing often produces millions of sequencing reads at a time, and the alignment or assembly of these reads allows determination of various mutations (e.g., SNV and Indels) at the genomic level, accurate gene expression quantification at the transcriptomic level, and identification of histone or DNA modification at the epigenomic level. The resulting accumulation of enormous multi-omics information has opened up a new era of finding effective disease markers and studying their roles in disease occurrence and development.

Using high-throughput sequencing, various markers of chronic diseases (such as cancer, heart disease, diabetes, and arthritis) have been developed at all omics levels, which have been used for diagnosis and classification of diseases, prediction of treatment effects, and prevention of diseases. The quickly and massively acquired multi-omics data, together with newly developed algorithms, provide an excellent chance for the identification of more reliable biomarkers. This research topic aims at (1) developing new chronic disease markers at four levels (i.e., genome, epigenome, transcriptome, and translatome) with the help of high-throughput sequencing, and (2) delineating potential marker-related mechanisms for chronic disease occurrence or development. This research topic covers a broad spectrum of interests, and studies including both wet lab and dry lab results are more welcomed. More specifically, this research topic welcomes contributions including but not limited to the following areas:

1. Identification of novel biomarkers for chronic disease detection (especially in early-stage) or prognosis prediction using high-throughput sequencing;

2. characterize the possible pathological causes of markers as well as the potential roles they play in disease initiation and development;

3. New high-throughput sequencing techniques that facilitate the development of more effective biomarkers of chronic disease;

4. New algorithms or tools for in silico identification of effective chronic disease markers based on high-throughput sequencing data.

Please note that: (1) the high-throughput sequencing for genome, epigenome, transcriptome, and translatome (i.e., ribosome-associated RNA) is preferred for this topic; (2) at least some dry lab results need to be validated with wet-lab experiments; (3) studies successfully uncovering biomarker-related disease mechanisms will be highly preferred.

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Editors

Hua Li

Shanghai Jiao Tong University

Wen-Lian Chen

Longhua Hospital, Shanghai University of Traditional Chinese Medicine

Yuriy L Orlov

The Digital Health Institute, I.M.Sechenov First Moscow State Medical University

Guoshuai Cai

University of Florida

Impact

54,503 Total views

34,786 Article views

18,266 Article downloads

1,451 Topic views

Published In

Frontiers in Genetics

Human and Medical Genomics

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