Cpg Loci Quantification
Cpg Loci Quantification - Related Videos
13:21
Comprehensive DNA Methylation Analysis Using a Methyl-CpG-binding Domain Capture-based Method in Chronic Lymphocytic Leukemia Patients
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Cited by 2 •
2017
This work describes an optimized methyl-CpG-binding domain (MBD) sequencing protocol and a computational pipeline to identify differentially methylated CpG-rich regions in chronic lymphocytic leukemia (CLL) patients.
13:19
Microarray-based Identification of Individual HERV Loci Expression: Application to Biomarker Discovery in Prostate Cancer
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Cited by 16 •
2013
Human endogenous retroviruses (HERV), which occupy 8% of the human genome, retain scarce coding capacities but a hundred thousand long terminal repeats (LTRs). A custom Affymetrix microarray was designed to identify individual HERV locus expression and was used on prostate cancer tissues as a proof of concept for future clinical studies.
01:23
Root Loci for Positive-Feedback Systems
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2024
The Hartley oscillator is a positive feedback system that sustains oscillations by feeding the output back to the input in phase, thereby reinforcing the signal. Positive feedback systems can be viewed as negative feedback systems with inverted feedback signals. In these systems, the root locus encompasses all points on the s-plane where the angle of the system transfer function equals 360 degrees. The construction rules for the root locus in positive feedback systems are similar to those in...
08:44
Invertebrate Lifespan Quantification
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2023
Many animals naturally stop growing upon reaching adulthood, after which they undergo aging or "senescence" until dying. The amount of time between an organism\'s birth and death is called its lifespan, which can be influenced by various biological and environmental factors. By exposing organisms to different growth conditions, scientists can better understand the factors affecting lifespan. Flies and worms are ideal organisms to perform such experiments, given their short generation time and...
08:03
Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
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Cited by 2 •
2021
This analytical computational platform provides practical guidance for microbiologists, ecologists, and epidemiologists interested in bacterial population genomics. Specifically, the work presented here demonstrated how to perform: i) phylogeny-guided mapping of hierarchical genotypes; ii) frequency-based analysis of genotypes; iii) kinship and clonality analyses; iv) identification of lineage differentiating accessory loci.
FAQs
Bisulfite conversion creates the chemical distinction used for methylation analysis: unmethylated cytosines are changed to uracil, whereas methylated cytosines remain protected. Subsequent sequencing, pyrosequencing, or methylation-specific PCR detects the resulting sequence or amplification differences. This conversion step therefore connects the original methylation state to a measurable signal at selected genomic loci.
The measured proportion indicates how much of the analyzed DNA population carries methylation at that locus. It provides a quantitative rather than purely categorical readout, allowing samples to be compared across engineered cell lines, differentiation states, or culture conditions. Interpreting these values helps identify changes in epigenetic regulation without treating every locus as simply methylated or unmethylated.
These methods serve as downstream readout options after bisulfite conversion. Each can be used to determine the methylation state or proportion of methylated molecules at targeted CpG regions. Their inclusion in the workflow makes locus-level analysis adaptable to different experimental designs while preserving the central measurement: a quantitative profile of methylation across selected genomic sites.
A typical workflow begins with DNA from the biological sample, followed by bisulfite conversion to distinguish methylated from unmethylated cytosines. The converted material is then analyzed by sequencing, pyrosequencing, or methylation-specific PCR. The resulting measurements are interpreted as methylation proportions at selected loci, producing a profile for comparison among samples or experimental conditions.
CpG profiles can reveal whether engineered cell lines maintain the intended epigenetic state and whether differentiation is accompanied by changes at relevant genomic regions. Comparing locus-level measurements between cell populations or developmental conditions provides evidence about cell identity and gene-regulatory behavior. In bioengineering, this supports characterization beyond examining the engineered design alone.
Quantitative profiles provide a reproducible way to monitor epigenetic changes during culture or therapeutic development. Consistent measurements can support quality control by identifying whether samples retain comparable regulatory states over an experimental process. The same data can also contribute to biomarker discovery, where locus-specific methylation patterns are evaluated as measurable features associated with biological or development-related states.