Name
Abstract | ||
|---|---|---|
There are several approaches for copy number variation (CNV) detection using next-generation sequencing data. Among them, read-depth based methods have become widely used, especially for targeted and whole exome sequencing data. However, read-depth based approaches suffer from noise and biases and also poor breakpoint detection. In this work, we present a novel efficient segmentation algorithm that integrates information from partially mapped (soft-clipped and split) reads with read depth data for more precise CNV detection. The proposed method employs an efficient implementation of the solution to the change-point optimization problem, Taut String, to smooth the read depth data and to generate piecewise constant signals as CNV segments. Using simulated and real data, we show that our proposed method has a much faster runtime and can improve the sensitivity of CNV detection compared to the commonly used CBS method. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/BIBM.2018.8621529 | PROCEEDINGS 2018 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE (BIBM) |
Keywords | Field | DocType |
Copy number variation, soft-clipped read, split read, Breakpoints | Pattern recognition,Copy-number variation,Computer science,Segmentation,Artificial intelligence,Optimization problem,Piecewise,Machine learning,Exome sequencing | Conference |
ISSN | Citations | PageRank |
2156-1125 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fatima Zare | 1 | 0 | 2.37 |
| Sardar Ansari | 2 | 12 | 7.05 |
| Kayvan Najarian | 3 | 262 | 59.53 |
| Sheida Nabavi | 4 | 18 | 8.68 |