About this Research Topic
In recent years, growth of several computational resources and high throughput sequencing technologies has had a significant impact on the development of several cancer public resources/databases in an exponential way. This helps researchers to access several public resources to validate the research findings through large-scale biological data. Accessing these resources, through several potential computational tools, algorithms and packages help to achieve this in an efficient manner. As such, novel and promising ways of handling tumor high throughput sequencing techniques helps to explore more information on cancer models.
A number of multi-level biomedical problems are being reported through various sequencing techniques, including whole genome sequencing (WGS), whole-exome sequencing (WES), transcriptome sequencing, single-cell RNAseq, ATACseq, CITEseq and ChipSeq. Along with existing sequencing techniques, expression associated with spatial context play an important role in tumor immune microenvironment and their association in disease etiology, tumor killing, treatment response and overall survival benefits.
Tumor microenvironment and disease response analysis explore the spatial information vital to understanding tumor-immune crosstalk. Using spatial transcriptomics techniques, imaging mass cytometry analysis and their associated tools and techniques helps to achieve this goal. Various computational tools and techniques including artificial intelligence and machine learning algorithms help to understand and approach these questions in a robust way.
This research topic calls for original research articles on computational high throughput sequencing and analysis of big biological data including genomics, transcriptomics, proteomics, image and spatial data which fall under the scope of this special issue. Topics of interest include but are not limited to:
• Multi-omics high throughput sequencing methods for biomarker discovery.
• Image and spatial data segmentation, classification and drug response association.
• Imaging mass cytometry associated analysis, tools development and biomarker discovery through protein markers expression.
• Tools and algorithms for the high throughput sequencing data analysis.
• Single-cell omics methods to study the tumor microenvironment and drug-response association.
• Analysis of public multi-omics data analysis for the tumor microenvironment and biomarkers discovery and validation.
A number of multi-level biomedical problems are being reported through various sequencing techniques, including whole genome sequencing (WGS), whole-exome sequencing (WES), transcriptome sequencing, single-cell RNAseq, ATACseq, CITEseq and ChipSeq. Along with existing sequencing techniques, expression associated with spatial context play an important role in tumor immune microenvironment and their association in disease etiology, tumor killing, treatment response and overall survival benefits.
Tumor microenvironment and disease response analysis explore the spatial information vital to understanding tumor-immune crosstalk. Using spatial transcriptomics techniques, imaging mass cytometry analysis and their associated tools and techniques helps to achieve this goal. Various computational tools and techniques including artificial intelligence and machine learning algorithms help to understand and approach these questions in a robust way.
This research topic calls for original research articles on computational high throughput sequencing and analysis of big biological data including genomics, transcriptomics, proteomics, image and spatial data which fall under the scope of this special issue. Topics of interest include but are not limited to:
• Multi-omics high throughput sequencing methods for biomarker discovery.
• Image and spatial data segmentation, classification and drug response association.
• Imaging mass cytometry associated analysis, tools development and biomarker discovery through protein markers expression.
• Tools and algorithms for the high throughput sequencing data analysis.
• Single-cell omics methods to study the tumor microenvironment and drug-response association.
• Analysis of public multi-omics data analysis for the tumor microenvironment and biomarkers discovery and validation.
Keywords: Cancer, tumor microenvironment, sequencing analysis, image and spatial analysis, multi-omics
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
