Single-Cell Omics Biology
A special issue of Biology (ISSN 2079-7737). This special issue belongs to the section "Genetics and Genomics".
Deadline for manuscript submissions: 1 July 2024 | Viewed by 226
Special Issue Editor
Special Issue Information
Dear Colleagues,
Single-cell omics is an emerging research area, offering a means to individually study attributes of cells, including their gene expression, epigenetics, transcriptomics, proteomics, and metabolomics. This progress is fueled by the continual refinement of advanced techniques such as DNA sequencing, mass spectroscopy, imaging, and microfluidics.
Bioinformatics plays an indispensable role in the growth of single-cell multi omics research. It deploys various computational approaches to increase the speed of data preprocessing, enhance data quality, integrate data at multiple biological levels, create data analysis and statistical models, develop user-friendly visualization tools, and utilize machine learning algorithms to predict outcomes or allow predictive models.
Single-cell omics allows for multidisciplinary research by integrating various readouts in biological systems, thus accelerating our understanding of biological processes. This method provides a unique approach to investigating cellular heterogeneity, as it allows for high levels of replication within a cell population, enabling advanced statistical modeling.
By focusing on individual cells, researchers can identify rare cell populations, discover new cell types, and explore cellular diversity within tissues and organs. However, single-cell multiomics research faces significant challenges, including improving the sensitivity and precision of the methods, developing standardized protocols, managing large and complex datasets, and increasing the statistical complexity of data analysis.
Dr. Nicolas Casadei
Guest Editor
Manuscript Submission Information
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Keywords
- epigenetics
- cellular heterogeneity
- developmental biology
- disease mechanisms
- therapeutic strategies
- multi-omics integration
- rare cell populations
- computational biology
- data visualization and interpretation
