SVJedi: Genotyping structural variations with long reads

Abstract

Motivation: Studies on structural variants (SV) are expanding rapidly. As a result, and thanks to third generation sequencing technologies, the number of discovered SVs is increasing, especially in the human genome. At the same time, for several applications such as clinical diagnoses, it is important to genotype newly sequenced individuals on well defined and characterized SVs. Whereas several SV genotypers have been developed for short read data, there is a lack of such dedicated tool to assess whether known SVs are present or not in a new long read sequenced sample, such as the one produced by Pacific Biosciences or Oxford Nanopore Technologies. Results: We present a novel method to genotype known SVs from long read sequencing data. The method is based on the generation of a set of reference sequences that represent the two alleles of each structural variant. Long reads are aligned to these reference sequences. Alignments are then analyzed and filtered out to keep only informative ones, to quantify and estimate the presence of each SV allele and the allele frequencies. We provide an implementation of the method, SVJedi, to genotype insertions and deletions with long reads. The tool has been applied to both simulated and real human datasets and achieves high genotyping accuracy. We also demonstrate that SV genotyping is considerably improved with SVJedi compared to other approaches, namely SV discovery and short read SV genotyping approaches.

Comprehensive Structural Variant Detection: From Mosaic to Population-Level

“…This is clearly inefficient as the bam/cram alignment files need to be assessed twice. Even so, this process can only be achieved by using a few of the existing methods (SVJedi 53 , Sniffles 27 , CuteSV 45 ). Sniffles2 strategy only requires an initial calling and merging to obtain a fully genotyped population-level VCF.…”

Quartet DNA reference materials and datasets for comprehensively evaluating germline variants calling performance

“…Large SVs over 10 Mb and the ones located in centromeres, peri-centromere, and gaps regions of the reference genome were excluded. The remaining 31,659 SVs were then re-genotyped in a pedigree using three genotypers (Sniffles 27 , SVjedi 35 , and LRcaller 36 ) with the reads of PacBio Sequel and ONT. Consensus genotypes (23,891) from at least six of the ten genotype call sets were then determined as the consensus genotype calls for each of the Quartet samples.…”

Regenotyping structural variants through an accurate force-calling method

“…We first evaluated the regenotyping performance of cuteSV2 and other state-of-the-art methods (i.e., Sniffles1 [18] , Sniffles2 [19] , and SVJedi [17] ) on a well-known human sample HG002 from the Ashkenazim trio-family group based on the Genome in a Bottle (GiaB) ground truth set (SV v0.6) from the National Institute of Standards and Technology (NIST). The target SV candidates were integrated from the Ashkenazim trio-family group (i.e., HG002, HG003, and HG004), as detected by PBSV (https://github.com/PacificBiosciences/pbsv).…”

“…Many force calling methods have been developed to achieve accurate SV regenotyping with long-read sequencing technologies. SVJedi [17] is used to filter out the informative alignments to quantify the presence of SV alleles. However, it still lacks high accuracy and only accepts the original reads as input, which requires a large amount of time to accomplish read alignment.…”