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Related Concept Videos

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%...
Genome Copying Errors02:46

Genome Copying Errors

DNA replication is a well-evolved process that copies millions of base pairs with high fidelity during each cell division. Occasionally a wrong base or a long stretch of wrong bases may get added to the daughter strands. If the errors are left unchecked, cells might accumulate several mutations that might endanger their  survival. Therefore, the copying errors are checked and repaired at three levels.
Random and Systematic Errors01:20

Random and Systematic Errors

Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...
Random and Systematic Errors01:20

Random and Systematic Errors

Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...
Random Error01:04

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Random or indeterminate errors originate from various uncontrollable variables, such as variations in environmental conditions, instrument imperfections, or the inherent variability of the phenomena being measured. Usually, these errors cannot be predicted, estimated, or characterized because their direction and magnitude often vary in magnitude and direction even during consecutive measurements. As a result, they are difficult to eliminate. However, the aggregate effect of these errors can be...

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Related Experiment Video

Updated: Jun 13, 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

Conditional random pattern model for copy number aberration detection.

Fuhai Li1, Xiaobo Zhou, Wanting Huang

  • 1Center for Bioengineering and Informatics, Department of Radiology, The Methodist Hospital Research Institute, Weill Cornell Medical College, Houston, TX 77030, USA.

BMC Bioinformatics
|April 24, 2010
PubMed
Summary
This summary is machine-generated.

A new conditional random pattern (CRP) model improves DNA copy number aberration (CNA) detection from noisy SNP array data. This method enhances accuracy and reliability, outperforming existing software for cancer research.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Cancer Research

Background:

  • DNA copy number aberrations (CNAs) are crucial in disease pathogenesis, including cancer.
  • High-density single nucleotide polymorphism (SNP) array data is vital for CNA detection.
  • Low signal-to-noise ratio (SNR) in SNP array data complicates accurate CNA detection, leading to false positives and missed regions.

Purpose of the Study:

  • To develop a robust statistical model for accurate CNA detection using low SNR SNP array data.
  • To improve the reliability of CNA detection in the presence of noise.

Main Methods:

  • Introduction of a novel conditional random pattern (CRP) model.
  • Utilizing contextual cues within the data to suppress noise.
  • Evaluation using both simulated and real high-density SNP array data.

Main Results:

  • The proposed CRP model demonstrates enhanced robustness and reliability in CNA detection.
  • The CRP model effectively suppresses noise, improving detection accuracy.
  • Validation results show superior performance compared to widely used software packages.

Conclusions:

  • The conditional random pattern (CRP) model is effective for detecting CNA regions even with noisy data.
  • This model offers a more reliable approach to CNA detection from SNP array data.