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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.
Single Nucleotide Polymorphisms-SNPs01:05

Single Nucleotide Polymorphisms-SNPs

A single nucleotide polymorphism or SNP is a single nucleotide variation at a specific genomic position in a large population. It is the most prevalent type of sequence variation found in the human genome. Point mutations that occur in more than 1% of the population qualify as SNPs. These are present once every 1000 nucleotides on an average in the human genome. Replacement of a purine with another purine (A/G) or a pyrimidine with another pyrimidine (C/T) is known as a transition. In contrast,...

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Related Experiment Video

Updated: May 28, 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

Fast MCMC sampling for hidden Markov Models to determine copy number variations.

Md Pavel Mahmud1, Alexander Schliep

  • 1Department of Computer Science, Rutgers University, 110 Frelinghuysen Road, Piscataway, NJ 08854, USA. pavelm@cs.rutgers.edu

BMC Bioinformatics
|November 4, 2011
PubMed
Summary

We developed a faster Bayesian method for analyzing genomic data using Hidden Markov Models (HMMs). This approach improves copy number variation detection by speeding up Markov Chain Monte Carlo (MCMC) sampling.

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Area of Science:

  • Genomics
  • Computational Biology
  • Statistical Modeling

Background:

  • Hidden Markov Models (HMM) are standard for analyzing Comparative Genomic Hybridization (CGH) data to detect chromosomal aberrations.
  • Maximum likelihood and Viterbi algorithms are efficient but introduce segmentation uncertainty.
  • Bayesian methods with Markov Chain Monte Carlo (MCMC) sampling offer improved accuracy but are computationally intensive, especially for high-density data.

Purpose of the Study:

  • To accelerate Bayesian inference for HMMs in genomic data analysis.
  • To reduce the computational cost of MCMC sampling without sacrificing accuracy.

Main Methods:

  • Developed an approximate sampling technique inspired by discrete sequence compression and kd-trees.
  • Leveraged spatial relationships in data for efficient sampling.

Main Results:

  • Achieved a significant speed-up in MCMC sampling for HMMs.
  • Demonstrated speed-ups of 10-60x on ArrayCGH and 90x on SNP array data.
  • Obtained results competitive with existing state-of-the-art Bayesian methods.

Conclusions:

  • The proposed approximate sampling method substantially accelerates Bayesian HMM analysis for genomic data.
  • This technique maintains accuracy comparable to traditional Bayesian approaches.
  • The method is applicable to various high-density genomic datasets, including ArrayCGH and SNP arrays.