VEGA: variational segmentation for copy number detection

VEGA: variational segmentation for copy number detection

MOTIVATION Genomic copy number (CN) information is useful to study genetic traits of many diseases. Using array comparative genomic hybridization (aCGH), researchers are able to measure the copy number of thousands of DNA loci at the same time. Therefore, a current challenge in bioinformatics is the development of efficient algorithms to detect the map of aberrant chromosomal regions. METHODS...

Multi-platform segmentation for joint detection of copy number variants

MOTIVATION With the expansion of whole-genome studies, there is rapid evolution of genotyping platforms. This leads to practical issues such as upgrading of genotyping equipment which often results in research groups having data from different platforms for the same samples. While having more data can potentially yield more accurate copy-number estimates, combining such data is not straightforw...

Statistical methods for DNA copy-number detection

A Stochastic Segmentation Model for Recurrent Copy Number Alteration Analysis

Recurrent DNA copy number alterations (CNAs) are key genetic events in the study of human genetics and disease. Analysis of recurrent DNA CNA data often involves the inference of individual samples’ true signal levels and the crosssample recurrent regions at each location. We propose for the analysis of multiple samples CNA data a new stochastic segmentation model and an associated inference pr...

biomvRCNS : Copy Number study and Segmentation for multivariate biological data

With high throughput experiments like tiling array and NGS, researchers are looking for continuous homogeneous segments or signal peaks, which would represent chromatin states, methylation ratio, transcripts or genome regions of deletion and amplification. While in a normal experimental set-up, these profiles would be generated for multiple samples or conditions with replicates. In the package ...