Genomic Region Enrichment
Genomic Region Enrichment - Related Videos
09:26
Isolation of Specific Genomic Regions and Identification of Associated Molecules by enChIP
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Cited by 4 •
2016
The identification of molecules associated with specific genomic regions of interest is required to understand the mechanisms of regulation of the functions of these regions. This protocol describes procedures to perform engineered DNA-binding molecule-mediated chromatin imunoprecipitation (enChIP) for identification of proteins and RNAs associated with a specific genomic region.
10:44
Generating Whole Bacterial Genomes from Clinical Samples using a Target Enrichment Workflow
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Cited by 1 •
2025
Here, we present a protocol to enable whole-genome sequencing of bacterial sexually transmitted infections from clinical samples using target enrichment. This novel, syndromic panel-based method overcomes challenges of abundant human DNA and low bacterial loads, facilitating genomic surveillance for Chlamydia trachomatis, Neisseria gonorrhoeae, Treponema pallidum, and Mycoplasma genitalium.
06:40
G2-seq: A High Throughput Sequencing-based Technique for Identifying Late Replicating Regions of the Genome
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2018
We describe a technique for combining flow cytometry and high throughput sequencing to identify late replicating regions of the genome.
08:31
Isolation and Genome Analysis of Single Virions using 'Single Virus Genomics'
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Cited by 2 •
2013
Single Virus Genomics (SVG) is a method to isolate and amplify the genomes of single virons. Viral suspensions of a mixed assemblage are sorted using flow cytometry onto a microscope slide with discrete wells containing agarose, thereby capturing the virion and reducing genome shearing during downstream processing. Whole genome amplification is achieved using multiple displacement amplification (MDA) resulting in genomic material that is suitable for sequencing.
12:36
Genomic MRI - a Public Resource for Studying Sequence Patterns within Genomic DNA
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Cited by 2 •
2011
We present a public computational web site for the analysis of genomic sequences. It detects DNA sequence patterns with various non-random nucleotide compositions. This resource also generates randomized sequences with diverse levels of complexity.
FAQs
The genomic background establishes what regions could reasonably have appeared in the dataset by chance. Comparing observed overlaps with this reference helps distinguish a genuine concentration from one caused by uneven feature distribution or the way the dataset was assembled. An appropriate background therefore directly affects statistical interpretation and the biological conclusions drawn from enrichment results.
Statistical analysis evaluates whether the observed overlap between genomic features and annotated regions is unlikely under the selected background expectation. A stronger enrichment signal reflects a larger difference between observed and expected representation, but its interpretation still depends on the relevance of the comparison background. The resulting assessment supports judgments about whether a pattern may reflect biological organization rather than chance.
Genome annotations divide or label locations according to functional or biological categories, including promoters, enhancers, regulatory elements, disease-linked loci, and chromatin states. Enrichment analysis uses these labels to connect positional patterns with possible genomic roles. The choice of annotation determines which types of biological interpretation can be tested and which functional regions emerge as concentrated in the dataset.
The approach can be applied to sets of genes, variants, or other genomic features, allowing the same analytical logic to address different experimental outputs. For example, a variant dataset can be assessed for concentration in regulatory elements, while a gene set can be examined in relation to genomic locations. This flexibility makes the analysis useful across diverse genetics studies.
A typical workflow starts with a defined set of genes, variants, or other genomic features, then identifies their overlaps with selected annotated regions. The observed overlaps are compared with an appropriate genomic background, and statistical methods evaluate whether the concentration is unlikely to be random. Researchers then interpret the enriched annotations in relation to function, disease, regulation, or phenotype.
Genomic Region Enrichment is particularly useful when high-throughput sequencing produces many genomic features whose individual locations are difficult to interpret. Grouping those features by annotated regions can reveal broader patterns, such as concentration near regulatory elements or disease-linked loci. These patterns help researchers move from large lists of genomic locations toward prioritized regions and biologically relevant hypotheses.
Enrichment findings provide contextual evidence about where genomic features accumulate and which functional categories may be involved. In genetics, associations with promoters, enhancers, regulatory elements, disease-linked loci, or chromatin states can help prioritize candidate regions for further study. They can also connect genomic patterns with biological pathways and phenotypes, while remaining dependent on the chosen background and annotations.