Skip to main content
Download PDF
- Main
Discovery of large genomic inversions using long range information
Published Web Location
https://doi.org/10.1186/s12864-016-3444-1Abstract
Background
Although many algorithms are now available that aim to characterize different classes of structural variation, discovery of balanced rearrangements such as inversions remains an open problem. This is mainly due to the fact that breakpoints of such events typically lie within segmental duplications or common repeats, which reduces the mappability of short reads. The algorithms developed within the 1000 Genomes Project to identify inversions are limited to relatively short inversions, and there are currently no available algorithms to discover large inversions using high throughput sequencing technologies.Results
Here we propose a novel algorithm, VALOR, to discover large inversions using new sequencing methods that provide long range information such as 10X Genomics linked-read sequencing, pooled clone sequencing, or other similar technologies that we commonly refer to as long range sequencing. We demonstrate the utility of VALOR using both pooled clone sequencing and 10X Genomics linked-read sequencing generated from the genome of an individual from the HapMap project (NA12878). We also provide a comprehensive comparison of VALOR against several state-of-the-art structural variation discovery algorithms that use whole genome shotgun sequencing data.Conclusions
In this paper, we show that VALOR is able to accurately discover all previously identified and experimentally validated large inversions in the same genome with a low false discovery rate. Using VALOR, we also predicted a novel inversion, which we validated using fluorescent in situ hybridization. VALOR is available at https://github.com/BilkentCompGen/VALOR.Many UC-authored scholarly publications are freely available on this site because of the UC's open access policies. Let us know how this access is important for you.
Main Content
For improved accessibility of PDF content, download the file to your device.
Enter the password to open this PDF file:
File name:
-
File size:
-
Title:
-
Author:
-
Subject:
-
Keywords:
-
Creation Date:
-
Modification Date:
-
Creator:
-
PDF Producer:
-
PDF Version:
-
Page Count:
-
Page Size:
-
Fast Web View:
-
Preparing document for printing…
0%