Genotyping Of Structural Variation Research Articles

MosaiCatcher v2: a single-cell structural variations detection and analysis reference framework based on Strand-seq.

Single-cell DNA template strand sequencing (Strand-seq) allows a range of various genomic analysis including chromosome length haplotype phasing and structural variation (SV) calling in individual cells. Here, we present MosaiCatcher v2, a standardized workflow and reference framework for single-cell SV detection using Strand-seq. This framework introduces a range of functionalities, including: an automated upstream Quality Control (QC) and assembly sub-workflow that relies on multiple genome assemblies and incorporates a multistep normalization module, integration of the single-cell nucleosome occupancy and genetic variation analysis SV functional characterization and of the ArbiGent SV genotyping modules, platform portability, as well as a user-friendly and shareable web report. These new features of MosaiCatcher v2 enable reproducible computational processing of Strand-seq data, which are increasingly used in human genetics and single-cell genomics, toward production environments. MosaiCatcher v2 is compatible with both container and conda environments, ensuring reproducibility and robustness and positioning the framework as a cornerstone in computational processing of Strand-seq data. MosaiCatcher v2 is a standardized workflow, implemented using the Snakemake workflow management system. The pipeline is available on GitHub: https://github.com/friendsofstrandseq/mosaicatcher-pipeline/ and on the snakemake-workflow-catalog: https://snakemake.github.io/snakemake-workflow-catalog/?usage=friendsofstrandseq/mosaicatcher-pipeline. Strand-seq example input data used in the publication can be found in the Data availability statement. Additionally, a lightweight dataset for test purposes can be found on the GitHub repository.

SVJedi-graph: improving the genotyping of close and overlapping structural variants with long reads using a variation graph.

Structural variation (SV) is a class of genetic diversity whose importance is increasingly revealed by genome resequencing, especially with long-read technologies. One crucial problem when analyzing and comparing SVs in several individuals is their accurate genotyping, that is determining whether a described SV is present or absent in one sequenced individual, and if present, in how many copies. There are only a few methods dedicated to SV genotyping with long-read data, and all either suffer of a bias toward the reference allele by not representing equally all alleles, or have difficulties genotyping close or overlapping SVs due to a linear representation of the alleles. We present SVJedi-graph, a novel method for SV genotyping that relies on a variation graph to represent in a single data structure all alleles of a set of SVs. The long reads are mapped on the variation graph and the resulting alignments that cover allele-specific edges in the graph are used to estimate the most likely genotype for each SV. Running SVJedi-graph on simulated sets of close and overlapping deletions showed that this graph model prevents the bias toward the reference alleles and allows maintaining high genotyping accuracy whatever the SV proximity, contrary to other state of the art genotypers. On the human gold standard HG002 dataset, SVJedi-graph obtained the best performances, genotyping 99.5% of the high confidence SV callset with an accuracy of 95% in less than 30 min. SVJedi-graph is distributed under an AGPL license and available on GitHub at https://github.com/SandraLouise/SVJedi-graph and as a BioConda package.

A ∼ 4.1 kb deletion in IRX1 gene upstream is completely associated with rumplessness in Piao chicken

Piao chicken, a Chinese indigenous rumpless chicken breed, lacks pygostyle, caudal vertebra, uropygial gland and tail feathers. The rumplessness in Piao chicken presents an autosomal dominant inheritance pattern. However, the molecular genetic mechanisms underlying the rumplessness in Piao chicken remains unclear. In this study, whole-genome resequencing was performed for 146 individuals from 10 chicken breeds, including 9 tailed chicken breeds and Piao rumpless breed. Tailbone CT scan for Piao chickens and WL chickens, revealed that some Piao chicken tails were normal in number, and for a few Piao chickens tail length and tail bone numbers were between the rumpless and the normal tailed chickens. The results showed that the rumpless phenotype has not been completely fixed in Piao chicken breed. Using selection signature analysis and structural variation detection, we found a 4174 bp deletion located in the upstream region of IRX1 gene on chromosome 2 related to rumpless phenotype. Structural variation genotyping showed that the deletion was present in all 32 rumpless Piao chickens (del/del, wild/del) and absent from all 112 tailed chickens included in the dataset for the other 9 breeds and 2 tailed Piao chickens (wild/wild). In summary, all rumpless Piao chickens tested here carry this deletion mutation, to show a complete linkage association with rumplessness trait. We suggested that the 4174 bp deletion could be causative for rumpless phenotype in Piao chicken since this is the only mutation to show the complete linkage disequilibrium with rumplessness on whole genome level across all of 146 chickens from the 10 breeds. This study could facilitate a better understanding of the genetic characteristics of Piao chicken.

A structural variation genotyping algorithm enhanced by CNV quantitative transfer

A structural variation genotyping algorithm enhanced by CNV quantitative transfer

Evaluation of computational genotyping of structural variation for clinical diagnoses.

BackgroundStructural variation (SV) plays a pivotal role in genetic disease. The discovery of SVs based on short DNA sequence reads from next-generation DNA sequence methods is error-prone, with low sensitivity and high false discovery rates. These shortcomings can be partially overcome with extensive orthogonal validation methods or use of long reads, but the current cost precludes their application for routine clinical diagnostics. In contrast, SV genotyping of known sites of SV occurrence is relatively robust and therefore offers a cost-effective clinical diagnostic tool with potentially few false-positive and false-negative results, even when applied to short-read DNA sequence data.ResultsWe assess 5 state-of-the-art SV genotyping software methods, applied to short-read sequence data. The methods are characterized on the basis of their ability to genotype different SV types, spanning different size ranges. Furthermore, we analyze their ability to parse different VCF file subformats and assess their reliance on specific metadata. We compare the SV genotyping methods across a range of simulated and real data including SVs that were not found with Illumina data alone. We assess sensitivity and the ability to filter initial false discovery calls. We determined the impact of SV type and size on the performance for each SV genotyper. Overall, STIX performed the best on both simulated and GiaB based SV calls, demonstrating a good balance between sensitivity and specificty.ConclusionOur results indicate that, although SV genotyping software methods have superior performance to SV callers, there are limitations that suggest the need for further innovation.

Corrigendum: Discovery and genotyping of structural variation from long-read haploid genome sequence data.

Corrigendum: Discovery and genotyping of structural variation from long-read haploid genome sequence data.

SV2: accurate structural variation genotyping and de novo mutation detection from whole genomes.

Structural variation (SV) detection from short-read whole genome sequencing is error prone, presenting significant challenges for population or family-based studies of disease. Here, we describe SV2, a machine-learning algorithm for genotyping deletions and duplications from paired-end sequencing data. SV2 can rapidly integrate variant calls from multiple structural variant discovery algorithms into a unified call set with high genotyping accuracy and capability to detect de novo mutations. SV2 is freely available on GitHub (https://github.com/dantaki/SV2). jsebat@ucsd.edu. Supplementary data are available at Bioinformatics online.

Discovery and genotyping of structural variation from long-read haploid genome sequence data.

In an effort to more fully understand the full spectrum of human genetic variation, we generated deep single-molecule, real-time (SMRT) sequencing data from two haploid human genomes. By using an assembly-based approach (SMRT-SV), we systematically assessed each genome independently for structural variants (SVs) and indels resolving the sequence structure of 461,553 genetic variants from 2 bp to 28 kbp in length. We find that >89% of these variants have been missed as part of analysis of the 1000 Genomes Project even after adjusting for more common variants (MAF > 1%). We estimate that this theoretical human diploid differs by as much as ∼16 Mbp with respect to the human reference, with long-read sequencing data providing a fivefold increase in sensitivity for genetic variants ranging in size from 7 bp to 1 kbp compared with short-read sequence data. Although a large fraction of genetic variants were not detected by short-read approaches, once the alternate allele is sequence-resolved, we show that 61% of SVs can be genotyped in short-read sequence data sets with high accuracy. Uncoupling discovery from genotyping thus allows for the majority of this missed common variation to be genotyped in the human population. Interestingly, when we repeat SV detection on a pseudodiploid genome constructed in silico by merging the two haploids, we find that ∼59% of the heterozygous SVs are no longer detected by SMRT-SV. These results indicate that haploid resolution of long-read sequencing data will significantly increase sensitivity of SV detection.

Genome structural variation discovery and genotyping

Comparisons of human genomes show that more base pairs are altered as a result of structural variation - including copy number variation - than as a result of point mutations. Here we review advances and challenges in the discovery and genotyping of structural variation. The recent application of massively parallel sequencing methods has complemented microarray-based methods and has led to an exponential increase in the discovery of smaller structural-variation events. Some global discovery biases remain, but the integration of experimental and computational approaches is proving fruitful for accurate characterization of the copy, content and structure of variable regions. We argue that the long-term goal should be routine, cost-effective and high quality de novo assembly of human genomes to comprehensively assess all classes of structural variation.