VALOR2: characterization of large-scale structural variants using linked-reads

Karaoğlanoğlu, Fatih; Ricketts, Camir; Ebren, Ezgi; Rasekh, Marzieh Eslami; Hajirasouliha, Iman; Alkan, Can

doi:10.1186/s13059-020-01975-8

Cited by 18 publications

(14 citation statements)

References 68 publications

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“…Because short reads (<<1,000 bp) are often smaller than or similar to the SV size, bioinformaticians have developed a variety of methods to infer SVs, including using split reads, discordant read pairs, depth of coverage and local de novo assembly. Linked reads add long-range (100+ kb) information to short reads, enabling phasing of reads for haplotype-specific deletion detection, large SV detection [11][12][13] and diploid de novo assembly 14 . Long reads (>>1,000 bp), which can fully traverse many more SVs, further enable SV detection, often sequence resolved, using mapped reads 15,16 , local assembly after phasing long reads 6,17 and global de novo assembly 18,19 .…”

Section: Nature Biotechnologymentioning

confidence: 99%

A robust benchmark for detection of germline large deletions and insertions

et al. 2020

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any diseases have been linked to SVs, most often defined as genomic changes at least 50 bp in size, but SVs are challenging to detect accurately. Conditions linked to SVs include autism 1 , schizophrenia, cardiovascular disease 2 , Huntington's disease and several other disorders 3. Far fewer SVs exist in germline genomes relative to small variants, but SVs affect more base pairs, and each SV might be more likely to affect phenotype 4-6. Although next-generation sequencing technologies can detect many SVs, each technology and analysis method has different strengths and weaknesses. To enable the community to

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Section: Nature Biotechnologymentioning

confidence: 99%

A robust benchmark for detection of germline large deletions and insertions

et al. 2020

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“…In our study, linked-read information was only used to improve the mapping against the reference genome (Marks et al, 2019). More recently, SV callers have been described that exploit linked information of linked-read data as VALOR2 (Karaoglanoglu et al, 2020) or LEVIATHAN (Morisse et al, 2021). However, the SV callers that were available at the time the simulations were performed had a very limited spectrum of SV types and SV length categories they could detect e.g.…”

Section: Discussionmentioning

confidence: 99%

Structural variants in the barley gene pool: precision and sensitivity to detect them using short-read sequencing and their association with gene expression and phenotypic variation

Weisweiler¹,

Arlt²,

Wu³

et al. 2022

Preprint

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In human genetics, several studies have shown that phenotypic variation is more likely to be caused by structural variants (SV) than by single nucleotide variants (SNV). However, accurate while cost-efficient discovery of SV in complex genomes remains challenging. The objectives of our study were to (i) facilitate SV discovery studies by benchmarking SV callers and their combinations with respect to their sensitivity and precision to detect SV in the barley genome, (ii) characterize the occurrence and distribution of SV clusters in the genomes of 23 barley inbreds that are the parents of a unique resource for mapping quantitative traits, the double round robin population, (iii) quantify the association of SV clusters with transcript abundance, and (iv) evaluate the use of SV clusters for the prediction of phenotypic traits. In our computer simulations based on a sequencing coverage of 25x, a sensitivity >70% and precision >95% was observed for all combinations of SV types and SV length categories if the best combination of SV callers was used. We observed a significant (P < 0.05) association of gene-associated SV clusters with global gene-specific gene expression. Furthermore, about 9% of all SV clusters that were within 5kb of a gene were significantly (P < 0.05) associated with the gene expression of the corresponding gene. The prediction ability of SV clusters was higher compared to that of single nucleotide polymorphisms from an array across the seven studied phenotypic traits. These findings suggest the usefulness of exploiting SV information when fine mapping and cloning the causal genes underlying quantitative traits as well as the high potential of using SV clusters for the prediction of phenotypes in diverse germplasm sets.

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“…In our study, linked-read information was only used to improve the mapping against the reference genome (Marks et al 2019). More recently, SV callers have been described that exploit linked information of linked-read data as VALOR2 (Karaoǧlanoǧlu et al 2020) or LEVIATHAN (Morisse et al 2021). However, the SV callers that were available at the time the simulations were performed had a very limited spectrum of SV types and SV length categories they could detect, e.g., LongRanger wgs (Zheng et al 2016) and NAIBR (Elyanow et al 2018).…”

Section: Precision and Sensitivity To Detect Sv In Complex Cereal Gen...mentioning

confidence: 99%

Structural variants in the barley gene pool: precision and sensitivity to detect them using short-read sequencing and their association with gene expression and phenotypic variation

Weisweiler

Arlt

et al. 2022

Theor Appl Genet

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Key message Structural variants (SV) of 23 barley inbreds, detected by the best combination of SV callers based on short-read sequencing, were associated with genome-wide and gene-specific gene expression and, thus, were evaluated to predict agronomic traits. Abstract In human genetics, several studies have shown that phenotypic variation is more likely to be caused by structural variants (SV) than by single nucleotide variants. However, accurate while cost-efficient discovery of SV in complex genomes remains challenging. The objectives of our study were to (i) facilitate SV discovery studies by benchmarking SV callers and their combinations with respect to their sensitivity and precision to detect SV in the barley genome, (ii) characterize the occurrence and distribution of SV clusters in the genomes of 23 barley inbreds that are the parents of a unique resource for mapping quantitative traits, the double round robin population, (iii) quantify the association of SV clusters with transcript abundance, and (iv) evaluate the use of SV clusters for the prediction of phenotypic traits. In our computer simulations based on a sequencing coverage of 25x, a sensitivity > 70% and precision > 95% was observed for all combinations of SV types and SV length categories if the best combination of SV callers was used. We observed a significant (P < 0.05) association of gene-associated SV clusters with global gene-specific gene expression. Furthermore, about 9% of all SV clusters that were within 5 kb of a gene were significantly (P < 0.05) associated with the gene expression of the corresponding gene. The prediction ability of SV clusters was higher compared to that of single-nucleotide polymorphisms from an array across the seven studied phenotypic traits. These findings suggest the usefulness of exploiting SV information when fine mapping and cloning the causal genes underlying quantitative traits as well as the high potential of using SV clusters for the prediction of phenotypes in diverse germplasm sets.

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VALOR2: characterization of large-scale structural variants using linked-reads

Cited by 18 publications

References 68 publications

A robust benchmark for detection of germline large deletions and insertions

A robust benchmark for detection of germline large deletions and insertions

Structural variants in the barley gene pool: precision and sensitivity to detect them using short-read sequencing and their association with gene expression and phenotypic variation

Structural variants in the barley gene pool: precision and sensitivity to detect them using short-read sequencing and their association with gene expression and phenotypic variation

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