Skip Navigation



Bioinformatics Advance Access published online on October 29, 2008
Bioinformatics, doi:10.1093/bioinformatics/btn561
This Article
Right arrow Advance Access manuscript (PDF) Freely available
Right arrow Supplementary Data
Right arrow All Versions of this Article:
25/1/61    most recent
btn561v1
Right arrow Comments: Submit a response
Right arrow Alert me when this article is cited
Right arrow Alert me when Comments are posted
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrowRequest Permissions
Google Scholar
Right arrow Articles by Wu, L.-Y.
Right arrow Articles by Wong, S. T.C.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Wu, L.-Y.
Right arrow Articles by Wong, S. T.C.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

© The Author (2008). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Conditional Random Pattern Algorithm for LOH Inference and Segmentation

Ling-Yun Wu 1,2, Xiaobo Zhou 1,*, Fuhai Li 1, Xiaorong Yang 1, Chung-Che Chang 3 and Stephen T.C. Wong 1

1Center for Biotechnology & Informatics, Department of Radiology, The Methodist Hospital Research Institute, Weill Medical College, Cornell University, Houston, TX 77030, USA
2Institute of Applied Mathematics, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100080, China
3Department of Pathology, The Methodist Hospital, Weill Medical College, Cornell University, Houston, TX 77030, USA

*To whom correspondence should be addressed. Xiaobo Zhou, E-mail: xzhou{at}tmhs.org


  Abstract

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 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 paper 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.

Contact: xzhou{at}tmhs.org

Associate Editor: Dr. Alex Bateman

Received on July 18, 2007; revised on October 10, 2008; accepted on October 24, 2008

Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?




Disclaimer: Please note that abstracts for content published before 1996 were created through digital scanning and may therefore not exactly replicate the text of the original print issues. All efforts have been made to ensure accuracy, but the Publisher will not be held responsible for any remaining inaccuracies. If you require any further clarification, please contact our Customer Services Department.