.png){kind=icon}
Return
FusorSV: an algorithm for optimally combining data from multiple structural variation detection methods
.png){kind=icon}
OA
AI
DOI：10.1186/s13059-018-1404-6.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Comprehensive and accurate identification of structural variations (SVs) from next generation sequencing data remains a major challenge. We develop FusorSV, which uses a data mining approach to assess performance and merge callsets from an ensemble of SV-calling algorithms. It includes a fusion model built using analysis of 27 deep-coverage human genomes from the 1000 Genomes Project. We identify 843 novel SV calls that were not reported by the 1000 Genomes Project for these 27 samples. Experimental validation of a subset of these calls yields a validation rate of 86.7%. FusorSV is available at https://github.com/TheJacksonLaboratory/SVE.
Keywords:
Structural variation
Copy number variation
Next generation sequencing
Genome rearrangements
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
G
IF:
9.4
Papers:
6.4K
Citations:
7.3W
Organization
View more organizations
Cited Papers
ShareSaveDiscovery and genotyping of genome structural polymorphism by sequencing on a population scale.png)
NATURE GENETICSOAAI
NATURE GENETICSIF31.8
OAAIShareSaveCNVnator: An approach to discover, genotype, and characterize typical and atypical CNVs from family and population genome sequencingGENOME RESEARCHOAAI
GENOME RESEARCHIF5.5
OAAIShareSaveShareSaveObservation of a many-body dynamical phase transition with a 53-qubit quantum simulatorNatureOAAI
NatureIF0
OAAIShareSaveVIEWPOINT Missing heritability and strategies for finding the underlying causes of complex diseaseOAAI
OAAIShareSaveNucleotide-resolution analysis of structural variants using BreakSeq and a breakpoint libraryNATURE BIOTECHNOLOGYOAAI
NATURE BIOTECHNOLOGYIF41.7
OAAIShareSave View more