MetaSV: an accurate and integrative structural-variant caller for next generation sequencing
| Autor: | Marghoob Mohiyuddin, Wing Hung Wong, Jian Li, John C. Mu, Alexej Abyzov, Hugo Y. K. Lam, Narges Bani Asadi, Mark Gerstein |
|---|---|
| Rok vydání: | 2015 |
| Předmět: |
Statistics and Probability
Biology computer.software_genre Biochemistry DNA sequencing 03 medical and health sciences 0302 clinical medicine Software Code (cryptography) Molecular Biology Sequence Deletion 030304 developmental biology computer.programming_language Supplementary data Genetics 0303 health sciences Extramural business.industry Genetic Variation High-Throughput Nucleotide Sequencing Structural variant Python (programming language) Genome Analysis Applications Notes Computer Science Applications Dynamic programming Mutagenesis Insertional Computational Mathematics Computational Theory and Mathematics Data mining business computer 030217 neurology & neurosurgery |
| Zdroj: | Bioinformatics |
| ISSN: | 1367-4811 1367-4803 |
| Popis: | Summary: Structural variations (SVs) are large genomic rearrangements that vary significantly in size, making them challenging to detect with the relatively short reads from next-generation sequencing (NGS). Different SV detection methods have been developed; however, each is limited to specific kinds of SVs with varying accuracy and resolution. Previous works have attempted to combine different methods, but they still suffer from poor accuracy particularly for insertions. We propose MetaSV, an integrated SV caller which leverages multiple orthogonal SV signals for high accuracy and resolution. MetaSV proceeds by merging SVs from multiple tools for all types of SVs. It also analyzes soft-clipped reads from alignment to detect insertions accurately since existing tools underestimate insertion SVs. Local assembly in combination with dynamic programming is used to improve breakpoint resolution. Paired-end and coverage information is used to predict SV genotypes. Using simulation and experimental data, we demonstrate the effectiveness of MetaSV across various SV types and sizes. Availability and implementation: Code in Python is at http://bioinform.github.io/metasv/. Contact: rd@bina.com Supplementary information: Supplementary data are available at Bioinformatics online. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|