About this Research Topic
One of the common outputs of omics studies is a list of genomic regions or genes. When the output is a list of genes, interpretation of the findings is typically carried out through a routine gene ontology or pathway analysis. When the output is a list of genomic regions, the interpretation is often carried out using some form of genomic colocalization and enrichment analyses. Unlike pathway analysis of a list of genes, genomic colocalization and enrichment analyses do not refer to a single approach. Instead, depending upon the research question, a wide variety of approaches are employed to interpret a list of genomic regions. For instance, the relationship between a set of genomic regions and any other genomic features (e.g., annotations in public databases) could be determined by searching for statistically significant overlap. In another example, windows of genomic sequence harboring genomic features of interest could be determined as significant/relevant by relating to some form of expected “null” or background. Further, cis biological functions of a set of genomic regions could be studied by assigning genomic regions to their nearest target genes based on certain definitions of proximity and subsequently performing a routine gene ontology/pathway analysis.
A first generation of tools/methodology to perform genomic colocalization and enrichment analyses have already been developed. Such tools have been used widely to generate novel hypotheses and to create new knowledge. With the availability of massive public datasets through resources such as ENCODE, Roadmap Epigenomics, GTEx and so on, the time is ripe to create a second generation of tools/methodology and to use both the existing and novel methodologies in innovative ways to unearth new knowledge about the genomes and their complexities.
Contributions are welcome in several areas:
• Novel methodologies for core colocalization analysis – improved statistical handling of complexities of core
aspects of genome structure and genomic features
• Novel methodologies for integrative colocalization analysis – incorporating multiple features, potentially across
multiple omics layers.
• New user-friendly tools for colocalization analysis, in the form of libraries/APIs for main programming
languages, as command line interface and/or as web interface
• Updates/improvements to existing tools are welcome given that the new features warrant communication.
• Applied work of remarkable importance using genomic colocalization and enrichment analyses.
Topic Editor Ryan Matthew Layer is a co-founder of Base2 Genomics. The rest of Topic Editors declare no competing interests with regards to the Research Topic.
This topic has been realized and is in collaboration with Dr. Kanduri, Post Doctoral Researcher at the University of Oslo, Norway.
Keywords: BED format, Colocalization analysis, Co-occurrence analysis, Region set enrichment analysis, Enrichment analysis, Genomic overlap analysis, Genome analysis, Genomic tracks, Statistical genomics
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