Name
Title | ||
|---|---|---|
Detection of Copy Number Variation Regions Using the DNA-Sequencing Data from Multiple Profiles with Correlated Structure. |
Abstract | ||
|---|---|---|
In this article, we investigate the problem of detecting boundaries of DNA copy number variation (CNV) regions using the DNA-sequencing data from multiple subject samples. Genomic features along the linear realization of the actual genome are correlated, especially within vicinity of a locus, so are the sequencing reads along the genome. It is then crucial to take the correlated structure of such high-throughput genomic data into consideration when modeling DNA-sequencing data for CNV detection from statistical and computational viewpoints. We use the framework of a fused Lasso latent feature model to solve the problem, and propose a modified information criterion for selecting the tuning parameter when search for common CNVs is shared by multiple subjects. Simulation studies and application on multiple subjects' next-generation sequencing data, downloaded from the 1000 Genome Project, showed that the proposed approach can effectively identify individual CNVs of a single subject profile and common CNVs shared by multiple subjects. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1089/cmb.2018.0053 | JOURNAL OF COMPUTATIONAL BIOLOGY |
Keywords | Field | DocType |
CNVs,DNA-sequencing,fused Lasso,Lasso latent feature model,modified Bayesian information criterion | DNA Copy Number Variation,Copy-number variation,Artificial intelligence,DNA sequencing,Computational biology,Machine learning,Mathematics | Journal |
Volume | Issue | ISSN |
25.0 | 10 | 1066-5277 |
Citations | PageRank | References |
0 | 0.34 | 1 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jie Chen | 1 | 91 | 38.15 |
| Shirong Deng | 2 | 0 | 0.68 |