Genome Wide Mapping
Genome Wide Mapping - Related Videos
10:43
Genome-wide Mapping of Protein-DNA Interactions with ChEC-seq in Saccharomyces cerevisiae
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Cited by 11 •
2017
We describe chromatin endogenous cleavage coupled with high-throughput sequencing (ChEC-seq), a chromatin immunoprecipitation (ChIP)-orthogonal method for mapping protein binding sites genome-wide with micrococcal nuclease (MNase) fusion proteins.
10:05
Genome-wide Mapping of Drug-DNA Interactions in Cells with COSMIC (Crosslinking of Small Molecules to Isolate Chromatin)
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Cited by 5 •
2016
Identifying the direct targets of genome-targeting molecules remains a major challenge. To understand how DNA-binding molecules engage the genome, we developed a method that relies on crosslinking of small molecules to isolate chromatin (COSMIC).
14:26
Genome-wide Purification of Extrachromosomal Circular DNA from Eukaryotic Cells
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Cited by 59 •
2016
This paper presents a sensitive method called Circle-Seq for purifying extrachromosomal circular DNA (eccDNA). The method encompasses column purification, removal of remaining linear chromosomal DNA, rolling-circle amplification and high-throughput sequencing. Circle-Seq is applicable to genome-scale screening of eukaryotic eccDNA and studying genome instability and copy-number variation.
10:41
An Integrated Platform for Genome-wide Mapping of Chromatin States Using High-throughput ChIP-sequencing in Tumor Tissues
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Cited by 34 •
2018
Here, we describe an optimized high-throughput ChIP-sequencing protocol and computational analyses pipeline for the determination of genome-wide chromatin state patterns from frozen tumor tissues and cell lines.
11:50
Genome-Wide Mapping of Histone Modifications and Transcription Factor Binding Sites in Neuroendocrine Small Cell Lung Cancer Cell Lines Using CUT&RUN
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2026
An optimized Cleavage Under Targets and Release Using Nuclease followed by next generation sequencing (CUT&RUN-seq) protocol is described for neuroendocrine small cell lung cancer cell lines. It enables genome-wide mapping of various histone modifications and transcription factor (e.g. E2F7) binding sites to investigate epigenetic and transcriptional deregulation in SCLC pathobiology.
FAQs
Recombination separates DNA segments as they are inherited, while linkage identifies markers that tend to be transmitted with a phenotype or genomic feature. Statistical analysis of these patterns narrows the search toward regions likely to contain relevant genes or variants. The resulting locations are associations, so additional experimental work is needed to determine the biological cause.
High-density markers provide broad coverage across the genome, increasing the opportunity to detect relationships between DNA variation and inherited traits. Genotyping or sequencing can generate these markers, which are then analyzed across many genomic positions. Greater marker coverage helps describe genome organization and inheritance more fully than an analysis based on only a small number of locations.
Allele-frequency patterns show how genetic variants are distributed within the data being studied. Their analysis can support population studies and help identify variation associated with genomic features or disease risk. Because frequency patterns describe variation across groups or samples rather than inheritance alone, they provide a complementary perspective to recombination and linkage analyses.
A typical analysis begins by generating high-density markers through genotyping or sequencing. Researchers then examine recombination, linkage, or allele-frequency patterns and apply statistical analysis to identify regions associated with genes or phenotypes. The resulting candidate regions can be interpreted in relation to inheritance, population variation, or disease risk and prioritized for further experimental validation.
The approach is useful when researchers need to connect DNA variation with an inherited trait across the genome rather than focus on a preselected gene. By locating associated genomic regions, it can support gene discovery and the study of heritable traits. These results guide follow-up investigations that test whether candidate regions have a functional relationship to the phenotype.
Mapping results can identify genomic regions whose variation is associated with disease risk, while allele-frequency analysis can characterize how variants are distributed in populations. Together, these perspectives help researchers relate genetic variation to biological function and population patterns. Findings remain a basis for interpretation and follow-up validation rather than a complete explanation of disease or inheritance.