High-throughput technologies such as microarray and next-generation sequencing (NGS) allow for monitoring the molecular profile of thousands of genes in a single experiment. Yet, gaining insight into the underlying biology of phenotypes or conditions under study utilizing such data still remains a challenge.
Various phenotypes and conditions are often the results of the coordinated activity of a group of genes or biomolecules. Therefore, the study of changes in the expression pattern of groups of genes is essential for a better understanding of the biological mechanisms underlying variations of the phenotype under study. These groups of biologically related genes are available through gene set and pathway knowledge bases. Computational methodologies that utilize these knowledge bases alongside the data from high-throughput technologies are widely utilized to study various biological phenomena. Over-representation analysis, functional class scoring methods, and (topology-based) pathway analysis methods are the main categories of such methods. We use "gene set analysis" as an umbrella term to refer to such methods.
Despite being widely used, there is little to no consensus for the best practices in gene set analysis, including the choice of method, collection of gene sets or pathways, and sample size to be used, to name a few. In addition, utilizing other data sources, such as DNA copy number and epigenetic markers, may also help shed light on functional molecular mechanisms, on their own or in combination with other types of omics data.
This Research Topic is focussed on the methodologies that could further improve the utility of gene set analysis in gaining biological insight from high-throughput expression studies. Topics of interest include, but are not limited to:
? Novel methods for gene set analysis using single- or multi-omics data types
? Benchmarking and evaluation of gene set analysis methods
? Reproducibility and reliability of gene set analysis methods
? Shortcomings of commonly used gene set analysis methods and recipes for addressing them
? Criteria or guidelines for choosing a method most appropriate to a given experiment or circumstances.
High-throughput technologies such as microarray and next-generation sequencing (NGS) allow for monitoring the molecular profile of thousands of genes in a single experiment. Yet, gaining insight into the underlying biology of phenotypes or conditions under study utilizing such data still remains a challenge.
Various phenotypes and conditions are often the results of the coordinated activity of a group of genes or biomolecules. Therefore, the study of changes in the expression pattern of groups of genes is essential for a better understanding of the biological mechanisms underlying variations of the phenotype under study. These groups of biologically related genes are available through gene set and pathway knowledge bases. Computational methodologies that utilize these knowledge bases alongside the data from high-throughput technologies are widely utilized to study various biological phenomena. Over-representation analysis, functional class scoring methods, and (topology-based) pathway analysis methods are the main categories of such methods. We use "gene set analysis" as an umbrella term to refer to such methods.
Despite being widely used, there is little to no consensus for the best practices in gene set analysis, including the choice of method, collection of gene sets or pathways, and sample size to be used, to name a few. In addition, utilizing other data sources, such as DNA copy number and epigenetic markers, may also help shed light on functional molecular mechanisms, on their own or in combination with other types of omics data.
This Research Topic is focussed on the methodologies that could further improve the utility of gene set analysis in gaining biological insight from high-throughput expression studies. Topics of interest include, but are not limited to:
? Novel methods for gene set analysis using single- or multi-omics data types
? Benchmarking and evaluation of gene set analysis methods
? Reproducibility and reliability of gene set analysis methods
? Shortcomings of commonly used gene set analysis methods and recipes for addressing them
? Criteria or guidelines for choosing a method most appropriate to a given experiment or circumstances.