About this Research Topic
The continuous development and innovation of sequencing technologies has provided transformative insights into biological research and greatly accelerated related clinical applications. Single-cell and bulk sequencing technologies have unique advantages and disadvantages. Generally, single-cell sequencing approaches allow the dissection of a multitude of single cells for a given sample with relatively high cost, while bulk sequencing protocols are more cost effective and enable researchers to profile a larger number of samples at a cell-population level. Specifically, each modality of genome, epigenome, transcriptome, and proteome mainly reflects one particular aspect of a cell or sample. Joint analysis of the data from different omics could facilitate better understanding of the molecular functions and cellular/individual phenotypes. On the other hand, as the scale and availability of sequencing datasets rapidly grow, reanalysis of publicly available single-cell and bulk sequencing data from distinct perspectives may further uncover novel and interesting findings. Additionally, integrative multi-modal analysis is still challenging; novel methods/tools are needed to efficiently and accurately integrate and process the multi-omics data.
In this Research Topic, we welcome papers related to analysis or novel computational methods of single-cell or bulk multi-omics data. The sequencing datasets can be generated or downloaded from public databases. This collection welcomes, but is not limited to, the following subtopics:
1. Joint analysis of at least two types of single-cell or bulk sequencing omics data;
2. Integrative profiling of genome, transcriptome, epigenome, and proteome with single-cell or bulk sequencing approaches;
3. Reanalysis of large-scale single-cell and/or bulk sequencing datasets from public databases;
4. Novel computational methods for multimodal and integrative single-cell or bulk sequencing data analysis.
Keywords: multi-omics, single-cell sequencing, next-generation sequencing, bioinformatics, integrative methods
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