The development of single-cell and bulk sequencing technologies have provided transformative insights into biological and clinical researches, which greatly facilitated the translation of basic science to practical applications. Single-cell sequencing approaches enable the dissection of thousands of single cells for a given sample, while bulk sequencing protocols allow the investigation of a large number of samples at a cell-population level. Both single-cell and bulk sequencing can be applied to profile different omics layers or modalities of individual cells and samples, respectively. Each modality of genome, epigenome, transcriptome, proteome, metabolome and metagenome mainly reflects one particular aspect of a cell or sample. Joint analysis of multimodal or multi-omics data could largely facilitate a better understanding of the molecular functions and underlying mechanisms. On the other hand, as the scale and availability of sequencing datasets rapidly grow, novel methods/tools are also in urgent need to more efficiently and accurately integrate different omics data.
In this Research Topic, we welcome papers related to integrative analysis or novel computational methods of single-cell or bulk multi-omics data. Descriptive studies solely based on bioinformatics investigation of publicly available genomic/transcriptomic data do not fall within the scope unless significant biological or mechanistic insights are provided into the process being interrogated. This Research Topic welcomes, but is not limited to, the following subtopics:
1. Joint analysis of at least two types of omics data from single-cell and/or bulk sequencing to identify specific biomarkers ;
2. Molecular mechanism profiling with single-cell and/or bulk multimodal data;
3. Translational researches based on single-cell and/or bulk sequencing technologies;
4. Novel bioinformatics methods for integrative analysis of multi-omics data.
Topic editor Dr. Geng Chen is employed by GeneCast Biotechnology (and declares no competing interests with regards to the Research Topic Subject). All other Topic Editors declare no competing interests with regards to the Research Topic subject
The development of single-cell and bulk sequencing technologies have provided transformative insights into biological and clinical researches, which greatly facilitated the translation of basic science to practical applications. Single-cell sequencing approaches enable the dissection of thousands of single cells for a given sample, while bulk sequencing protocols allow the investigation of a large number of samples at a cell-population level. Both single-cell and bulk sequencing can be applied to profile different omics layers or modalities of individual cells and samples, respectively. Each modality of genome, epigenome, transcriptome, proteome, metabolome and metagenome mainly reflects one particular aspect of a cell or sample. Joint analysis of multimodal or multi-omics data could largely facilitate a better understanding of the molecular functions and underlying mechanisms. On the other hand, as the scale and availability of sequencing datasets rapidly grow, novel methods/tools are also in urgent need to more efficiently and accurately integrate different omics data.
In this Research Topic, we welcome papers related to integrative analysis or novel computational methods of single-cell or bulk multi-omics data. Descriptive studies solely based on bioinformatics investigation of publicly available genomic/transcriptomic data do not fall within the scope unless significant biological or mechanistic insights are provided into the process being interrogated. This Research Topic welcomes, but is not limited to, the following subtopics:
1. Joint analysis of at least two types of omics data from single-cell and/or bulk sequencing to identify specific biomarkers ;
2. Molecular mechanism profiling with single-cell and/or bulk multimodal data;
3. Translational researches based on single-cell and/or bulk sequencing technologies;
4. Novel bioinformatics methods for integrative analysis of multi-omics data.
Topic editor Dr. Geng Chen is employed by GeneCast Biotechnology (and declares no competing interests with regards to the Research Topic Subject). All other Topic Editors declare no competing interests with regards to the Research Topic subject
