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Probabilistic Graphical Models for Genetics, Genomics, and Postgenomics$
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Christine Sinoquet and Raphaël Mourad

Print publication date: 2014

Print ISBN-13: 9780198709022

Published to Oxford Scholarship Online: December 2014

DOI: 10.1093/acprof:oso/9780198709022.001.0001

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Detection of Copy Number Variations from Array Comparative Genomic Hybridization Data Using Linear-chain Conditional Random Field Models

Detection of Copy Number Variations from Array Comparative Genomic Hybridization Data Using Linear-chain Conditional Random Field Models

Chapter:
(p.409) Chapter 16 Detection of Copy Number Variations from Array Comparative Genomic Hybridization Data Using Linear-chain Conditional Random Field Models
Source:
Probabilistic Graphical Models for Genetics, Genomics, and Postgenomics
Author(s):

Xiaolin Yin

Jing Li

Publisher:
Oxford University Press
DOI:10.1093/acprof:oso/9780198709022.003.0016

Copy number variation (CNV) accounts for roughly 12% of the human genome. Beside their inherent role in cancer development, CNVs have been reported to underlie susceptibility to complex diseases. Each variation may range from around 1000 nucleotides to less than 5 megabases. Array comparative genomic hybridization (aCGH) allows the identification of copy number alterations across genomes. The key computational challenge in analyzing CNVs using aCGH data is the detection of segment boundaries of copy number changes and inference of the copy number state for each segment. Markov random fields and, more specifically, conditional random fields provide a unified framework for data preprocessing, segmentation and copy number state decoding.

Keywords:   copy number variation, aCGH, conditional random fields

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