VanillaICE. A Hidden Markov Model for high throughput genotyping arrays. Bioconductor. Hidden Markov Models for characterizing chromosomal alterations in high throughput SNP arrays. Background (http://www.biostat.jhsph.edu/ iruczins/software/vanillaice.html): High density single nucleotide polymorphism microarrays (SNP chips) provide information on a subject’s genome, such as the chromosomal copy numbers and the genotype (heterozygosity/homozygosity). In contrast to fluorescence in situ hybridization and karyotyping, SNP chips provide a high resolution map of the human genome that can be used to detect, for example, microdeletions, microduplications, and loss of heterozygosity. As a variety of diseases are linked to such chromosomal alterations, SNP chips promise new insights for these diseases by aiding in the discovery of such regions, and may suggest targets for intervention. The R package VanillaICE contains the software for fitting hidden Markov models on genomic array data to infer chromosomal alterations, including deletions, amplifications, and regions with loss of heterozygosity. In addition, measures of uncertainty for the genotype and copy number estimates can be incorporated, which can be crucial for the detection of micro-deletions and micro-amplifications
Keywords for this software
References in zbMATH (referenced in 3 articles )
Showing results 1 to 3 of 3.
- Polat, Huseyin; Du, Wenliang; Renckes, Sahin; Oysal, Yusuf: Private predictions on hidden Markov models (2010)
- Zhang, Zhongyang; Lange, Kenneth; Ophoff, Roel; Sabatti, Chiara: Reconstructing DNA copy number by penalized estimation and imputation (2010)
- Scharpf, Robert B.; Parmigiani, Giovanni; Pevsner, Jonathan; Ruczinski, Ingo: Hidden Markov models for the assessment of chromosomal alterations using high-throughput SNP arrays (2008)