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Related Concept Videos

Sample Preparation for Analysis: Overview01:21

Sample Preparation for Analysis: Overview

Sample preparation is an essential step in the analytical process. It involves preparing a sample so that it can be analyzed accurately. The goal is to extract the analyte, the substance you want to measure, from the sample while removing any components that may interfere with the analysis. Sample preparation techniques vary depending on the physical state of the sample.
Bulk or large solid samples are typically reduced in size using grinding, crushing, or milling techniques to increase the...
Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...
Sample Preparation for Analysis: Advanced Techniques01:08

Sample Preparation for Analysis: Advanced Techniques

Accurate analysis of complex samples often requires advanced preparation techniques to achieve reliable and reproducible results. Samples containing inorganic or organic materials can be challenging to dissolve or decompose effectively. Standard sample preparation methods include acid digestion, fusion, dry ashing, and wet digestion.
Acid digestion with strong acids is commonly used to dissolve inorganic materials that are insoluble (do not dissolve) in water. This method can be useful for...

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Related Experiment Video

Updated: Jun 25, 2026

Detection of Copy Number Alterations Using Single Cell Sequencing
09:45

Detection of Copy Number Alterations Using Single Cell Sequencing

Published on: February 17, 2017

A single-sample method for normalizing and combining full-resolution copy numbers from multiple platforms, labs and

Henrik Bengtsson1, Amrita Ray, Paul Spellman

  • 1Department of Statistics, Life Sciences Division, University of California, Lawrence Berkeley National Laboratory, Berkeley, USA. hb@stat.berkeley.edu

Bioinformatics (Oxford, England)
|February 6, 2009
PubMed
Summary
This summary is machine-generated.

Combining copy number (CN) estimates from multiple sources, like different platforms or labs, is now possible. Our method normalizes these CN estimates, improving precision and effectively increasing genome-wide resolution for better analysis.

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Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization
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Measuring Single-Cell Mitochondrial DNA Copy Number and Heteroplasmy Using Digital Droplet Polymerase Chain Reaction
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Measuring Single-Cell Mitochondrial DNA Copy Number and Heteroplasmy Using Digital Droplet Polymerase Chain Reaction

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Related Experiment Videos

Last Updated: Jun 25, 2026

Detection of Copy Number Alterations Using Single Cell Sequencing
09:45

Detection of Copy Number Alterations Using Single Cell Sequencing

Published on: February 17, 2017

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization
08:27

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

Measuring Single-Cell Mitochondrial DNA Copy Number and Heteroplasmy Using Digital Droplet Polymerase Chain Reaction
09:15

Measuring Single-Cell Mitochondrial DNA Copy Number and Heteroplasmy Using Digital Droplet Polymerase Chain Reaction

Published on: July 12, 2022

Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Whole-genome copy number (CN) studies require high precision and resolution.
  • CN estimates vary across platforms, labs, and analytical methods, hindering direct combination.
  • Existing methods struggle to integrate CN data from diverse sources.

Purpose of the Study:

  • To develop a method for normalizing and combining CN estimates from multiple sources.
  • To improve the precision and resolution of CN estimates for genomic studies.
  • To enable more effective analysis of copy number variations across different datasets.

Main Methods:

  • A single-sample multi-source normalization technique was developed.
  • The method scales CN estimates to a common level across all sources.
  • Microarray-based CN estimates from The Cancer Genome Atlas (TCGA) were used for validation.

Main Results:

  • Normalized CN estimates show consistent mean levels across different sources.
  • Combined data demonstrate improved separation of copy number states at a given resolution.
  • The approach effectively increases the resolution and genome coverage by integrating multiple sources.

Conclusions:

  • It is feasible to combine CN estimates from multiple sources to achieve higher effective resolution.
  • This normalization method enhances the utility of multi-platform CN data.
  • The approach facilitates more robust identification of copy number alterations in genomic studies.