Conditional random pattern algorithm for LOH inference and segmentation

Bioinformatics. 2009 Jan 1;25(1):61-7. doi: 10.1093/bioinformatics/btn561. Epub 2008 Oct 29.


Motivation: Loss of heterozygosity (LOH) is one of the most important mechanisms in the tumor evolution. LOH can be detected from the genotypes of the tumor samples with or without paired normal samples. In paired sample cases, LOH detection for informative single nucleotide polymorphisms (SNPs) is straightforward if there is no genotyping error. But genotyping errors are always unavoidable, and there are about 70% non-informative SNPs whose LOH status can only be inferred from the neighboring informative SNPs.

Results: This article presents a novel LOH inference and segmentation algorithm based on the conditional random pattern (CRP) model. The new model explicitly considers the distance between two neighboring SNPs, as well as the genotyping error rate and the heterozygous rate. This new method is tested on the simulated and real data of the Affymetrix Human Mapping 500K SNP arrays. The experimental results show that the CRP method outperforms the conventional methods based on the hidden Markov model (HMM).

Availability: Software is available upon request.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms*
  • Cell Line, Tumor
  • Computational Biology
  • Computer Simulation
  • Databases, Genetic
  • Humans
  • Loss of Heterozygosity / genetics*
  • Markov Chains
  • Models, Genetic*
  • Myelodysplastic Syndromes / genetics
  • Oligonucleotide Array Sequence Analysis
  • Polymorphism, Single Nucleotide / genetics
  • ROC Curve