Genomic Alteration Analysis
Genomic Alteration Analysis - Related Videos
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.
09:45
Detection of Copy Number Alterations Using Single Cell Sequencing
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Cited by 9 •
2017
Single cell sequencing is an increasingly popular and accessible tool for addressing genomic changes at high resolution. We provide a protocol that uses single cell sequencing to identify copy number alterations in single cells.
10:34
Ultra-long Read Sequencing for Whole Genomic DNA Analysis
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Cited by 28 •
2019
Long-read sequences greatly facilitate the assembly of complex genomes and characterization of structural variation. We describe a method to generate ultra-long sequences by nanopore-based sequencing platforms. The approach adopts an optimized DNA extraction followed by modified library preparations to generate hundreds of kilobase reads with moderate coverage from human cells.
11:52
Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations
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Cited by 15 •
2016
We describe a targeted RNA sequencing-based method that includes preparation of indexed cDNA libraries, hybridization and capture with custom probes and data analysis to interrogate selected transcripts for gene expression, mutations, and gene fusions. Targeted RNAseq permits cost-effective, rapid evaluation of selected transcripts on a desktop sequencer.
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.
FAQs
Statistical models provide a formal way to evaluate whether an observed genomic difference is more consistent with a genuine alteration or with technical noise in the measurements. By modeling the data rather than treating every deviation as meaningful, the analysis produces more defensible calls. This distinction is essential when downstream conclusions depend on which events are considered real.
A reference or control supplies the comparison point needed to interpret genomic measurements. The analysis asks how strongly a sample departs from that baseline, rather than evaluating the measurement in isolation. This comparison supports estimates of alteration frequency, effect size, and statistical significance, while making results more interpretable across samples or study groups.
Frequency describes how often an alteration appears, effect size describes the magnitude of its difference or association, and statistical significance summarizes the strength of evidence for a non-random pattern under the selected model. Considering all three prevents a common interpretive mistake: treating a frequent event, a large effect, or a significant result as interchangeable evidence.
Multiple-testing correction limits misleading significance claims when many genomic events are evaluated at once. Without accounting for the number of tests, some results may appear significant simply because numerous hypotheses were examined. Applying correction helps distinguish more credible signals from chance findings and strengthens comparisons of alteration patterns across a study.
An analysis generally begins with genomic measurements and a suitable reference or control. Investigators then apply statistical models to separate potential alterations from technical noise, quantify frequency and effect size, test significance, and correct for multiple comparisons. The final stage interprets the resulting pattern in relation to genetic variation, disease-related patterns, or other study outcomes.
In cancer research, the approach can characterize mutation profiles and identify recurrent genomic events, allowing investigators to summarize which alterations repeatedly appear across analyzed material. In genetic research, it supports broader assessment of variation and disease-related patterns. Its value lies in converting collections of genomic measurements into statistically evaluated patterns rather than isolated observations.
To examine biological or clinical relevance, researchers can assess relationships between genomic alterations and measured outcomes. The resulting analysis can indicate whether particular alteration patterns are associated with those outcomes, while frequency, effect size, and significance provide complementary evidence for interpretation. This statistical framing also supports more reproducible genomic studies by making evaluation criteria explicit.