Collection

Methods and applications of multi-omics data analysis in human genetic disease

Submission status
Closed

Multi-omics research is increasingly recognized as being important for understanding and prediction of complex diseases. Driven by technological advances, many cost-effective and high-throughput technologies have become available to generate omics data, from quantification of the expression level to comprehensive profiling of RNA, protein, lipids, microbiome, metabolites and the genome. The availability of multi-omics data stimulates the development of novel bioinformatics and statistical approaches. A number of integrative multi-omics analysis approaches have been developed for improving classification of disease into clinically relevant subgroups and identifying disease related biomarkers. By employing effective analytical approaches, omics data can also facilitate unravelling biological networks regulating transitions from health to disease and discovering novel mechanisms of human genetic diseases. With increasing quality, diversity, and quantity of the multi-omics data, the analysis and application of multi-omics data may further shift the practice of health monitoring, early screening, disease management, and therapeutic development.

Potential topics include, but are not limited to: 1) Methods of sequencing reads analysis, including long read sequencing, 2) Prioritizing pathogenic mutations through multi-omics data analysis, 3) Integrating omics data in drug discovery, and 4) Methods/applications of single-cell sequencing data analysis, including spatial transcriptomics analysis and single-cell multi-omics

NOTE: Manuscripts using existing bioinformatics tools on publically available data sets without experimental validation will not be considered.

Editors

  • Prof. Huiying Zhao Prof. Huiying Zhao  &

    Prof. Huiying Zhao

    Huiying Zhao is a Professor at the Sun Yat-sen Memorial Hospital of Sun Yat-sen University. Her research mainly focuses on developing computational approaches for integrating mult-omics data to reveal disease mechanisms and repurpose drugs.

  • Dr. Maggie Wang Dr. Maggie Wang

    Dr. Maggie Wang

    Maggie Wang is an Associate Professor at The Chinese University of Hong Kong. Her research develops statistical methods for human genome association testing and prediction for complex traits such as the Alzheimer’s disease. She also develops methods for modelling virus evolution and mutation patterns for improved vaccine design with applications on the influenza virus and SARS-CoV-2.

Articles (10 in this collection)