Genomic Target Identification
Genomic Target Identification - Related Videos
08:40
Genome-wide Screen for miRNA Targets Using the MISSION Target ID Library
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Cited by 1 •
2012
The Target ID Library is a plasmid-based, genome-wide collection of cloned cDNA used to identify miRNA targets. Here we demonstrate its use and application.
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.
11:19
Genome-wide Analysis using ChIP to Identify Isoform-specific Gene Targets
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Cited by 13 •
2010
Here we are presenting a chromatin immunoprecipitation (ChIP) procedure for genome-wide location analysis of protein isoforms that differ in a histone-binding domain. We are applying it to ChIP-Seq analysis to identify the targets of the KDM5A/JARID1A/RBP2 histone demethylase.
09:51
Establishment of Genome-edited Human Pluripotent Stem Cell Lines: From Targeting to Isolation
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Cited by 18 •
2016
Genome editing of human pluripotent stem cells (hPSCs) can be done quickly and efficiently. Presented here is a robust experimental procedure to genetically engineer hPSCs as exemplified by editing the AAVS1 safe harbor locus to express EGFP and introduce antibiotic resistance.
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.
FAQs
Prioritization combines several types of evidence rather than relying on association alone. Researchers compare genomic patterns, examine gene-expression profiles, and assess whether a variant, gene, or region has a plausible biological connection to the measured phenotype. Candidates supported by converging evidence become stronger targets for functional testing, while isolated statistical patterns require additional validation before interpretation.
Gene-expression profiling helps connect genomic differences with changes in biological activity. When a candidate region or gene is associated with a phenotype, its expression pattern can indicate whether that genomic signal corresponds to altered cellular function. This information strengthens or weakens the proposed mechanism and helps researchers select targets that can be evaluated in relation to disease or therapeutic response.
CRISPR-based perturbation changes or disrupts a selected genomic element so researchers can observe the resulting cellular or organismal outcome. A measurable change that follows the perturbation supports a functional relationship, whereas no relevant change may weaken the proposed target hypothesis. Researchers also evaluate specificity, because effects must be connected to the intended target rather than an unrelated genomic consequence.
A typical workflow begins by identifying candidate variants or regions through genome sequencing, comparative analysis, expression profiling, or association studies. Researchers then prioritize candidates using biological plausibility and available evidence, perturb selected targets when appropriate, and measure the resulting phenotype. Molecular validation provides an additional check that the observed outcome is linked to the proposed genomic target.
The approach can link genomic variation or regional patterns with measurable changes in cells or organisms. Depending on the study, evidence may include an association with a phenotype, altered gene expression, or an outcome following targeted perturbation. These layers help distinguish correlation from a more plausible functional relationship and clarify how genomic information may relate to disease or treatment response.
Genomic target identification supports several connected applications. In disease-mechanism research, it helps investigate genomic contributors to biological outcomes. In biomarker development, it can connect genomic patterns with measurable states, while drug discovery uses prioritized targets for therapeutic investigation. Precision medicine applies these links to understand differences in disease or therapeutic response among individuals.