NGS data provides several sources of information from which methods may detect structural variation, including read depth, paired-end orientation, distance between mapped ends, and pairs where one end is "split" mapped or "one-end anchored" (i.e., its mate is not mapped).
This method allowed us to validate the utility of the paired-end method to detect structural variants and analyze the amount of coverage that is required to accurately detect structural variants with paired-end data.
High-resolution microarray-based CNV analysis provides a method to detect structural genomic alterations.
An ultrasound scan in the second trimester (18 to 23 weeks) is an effective method to detect structural anomalies [ 29- 31].
Unfortunately, none of the available methods designed to detect structural variation events (e.g. Bashir et al., 2008; Chen et al., 2009; Hormozdiari et al., 2009; Kidd et al., 2008; Korbel et al., 2007; Lee et al., 2008, 2009; Sindi et al., 2009; Tuzun et al., 2005) considered these copy events, and their focus was mainly on the discovery of deletions, insertions and inversions.
The aim of the proposed method is to detect structural zones inducing such behavior.
We thoroughly benchmarked and validated our SRiC method against the best available methods for detecting structural variants at relevant resolutions by using several different approaches to extensively evaluate the performance of our method.
Alongside this work, several methods have been developed to detect structural damage by using location-dependent changes in the modal parameters.
The present paper proposes a novel cluster-based method, named as agglomerative concentric hypersphere (ACH), to detect structural damage in engineering structures.
Current methods for detecting structural variation from massively parallel data use either paired-end mapping or depth of coverage methods (see [ 4] for a recent review).
