Structural Variant Calling Research Articles

Whole Genome Sequencing in Combination with Transcriptome Sequencing Unravels the Pathogenetic Impact of Rare Balanced Rearrangements in Myeloid Malignancies and May Lead to Novel Personalized Therapeutic Strategies

Background: Gene fusions function as therapeutic targets, impressively shown for e.g. BCR - ABL1 and PML - RARA . Hence, the need for a comprehensive genetic analysis as a basis for personalized medicine is increasing allowing to select treatment based on genotype and to provide markers for disease monitoring. At ASH 2016 we presented targeted RNA sequencing data from 38 patients with hematological malignancies harboring rearrangements one partner gene of which had been identified by standard diagnostic work up. Using targeted RNA sequencing the second partner gene could be identified in 22/38 cases. All rearrangements involving KMT2A (n=8), JAK2 (n=1), and RARA (n=1) were resolved. However, fusion transcripts were identified in only 4/9 ETV6 rearrangements and 8/19 RUNX1 rearrangements. In the present study a subset of these cases was further evaluated.Aim: To evaluate the potential of whole genome sequencing (WGS) in combination with transcriptome (TS) sequencing to unravel the significance of rare translocations.Patient cohort and methods: Five cases with unresolved ETV6 rearrangement and 4 cases with unresolved RUNX1 rearrangement were further analyzed by WGS (~90x coverage) and TS (~61m reads). WGS was performed using the Truseq DNA PCR-Free HT sample preparation kit and RNA was prepared using TruSeq Stranded Total RNA HT (with Ribo-Zero HMR) kit for TS. Library was prepared according to manufacture's protocol using fresh/frozen samples and sequencing performed on NovaSeq instruments in multiplexed runs (Illumina, San Diego, CA). Tumor-only sample analysis was conducted with BaseSpace (BS) WGS App with default parameters for alignment and subsequent (structural) variant calling. For further downstream analysis we loaded the data into BS Variant Interpreter software to filter down and prioritize variants of interest. TS analysis was performed using BS RNAseq App with default parameters to assess gene fusions and expression (Illumina, San Diego, CA).Results: In all 9 cases WGS detected the translocation predicted by chromosome banding analysis and the genomic breakpoints (Bp) were determined. Details are depicted in the table 1. In cases 1 to 4 the genomic breakpoints were located in introns 1 or 2 of ETV6 or upstream of ETV6 . The corresponding breakpoints were upstream of CCND1, CCND2, SOX9 and PDGFRA . By TS no fusion transcripts involving ETV6 were identified. However, in line with the genomic data an overexpression of CCND1, CCND2, SOX9 and PDGFRA was observed in the respective cases by TS (figure 1). Data suggest that an enhancer in the region of ETV6 is driving the overexpression of the respective genes. In case 5 the genomic breakpoint was located in intron 2 and no ETV6 fusion transcript identified by TS. Most likely the translocation leads to a stop codon and thus to haploinsufficiency of ETV6 .In cases 6 and 7 a RUNX1 - GRXCR1 (RUNX1 exon 6 fused to GRXCR1 exon 2) and a RUNX1 - JARID2 (RUNX1 exon 2 fused to JARID2 exon 7) were detected by TS, respectively. The respective genomic breakpoints are depicted in the table 1. In cases 8 and 9 the breakpoints in RUNX1 were both located in intron 2. No RUNX1 fusion transcripts were detected by TS. Based on WGS and TS data the results of the RUNX1 translocations are most likely out of frame fusions and thus lead to loss of function of RUNX1 with an effect comparable to RUNX1 frameshift mutations.In summary the combination of TS and WGS revealed 3 different mechanisms of ETV6 - and RUNX1 -rearrangements: 1. Overexpression of the partner gene, 2. Novel fusion transcripts, and 3. Loss of function of ETV6 or RUNX1 .From the clinical point of view in case of PDGFRA overexpression therapy with a tyrosine kinase can be considered. The overexpression of CCND1 and CCND2 suggests that the mTOR inhibitor rapamycin might be a therapeutic option in these cases.Conclusions and Perspectives: Whole genome sequencing in combination with transcriptome sequencing is a promising approach to unravel the pathogenetic role of rare rearrangements. This provides new options for therapeutic interventions and monitoring of minimal residual disease on a personalized level. Studies to evaluate the potential role of WGS and TS in a future diagnostic setting have been started to improve patients outcome based on a comprehensive genetic work-up allowing optimal treatment choice and monitoring of minimal residual disease. [Display omitted] DisclosuresHaferlach:MLL Munich Leukemia Laboratory: Employment, Equity Ownership. Nadarajah:MLL Munich Leukemia Laboratory: Employment. Walter:MLL Munich Leukemia Laboratory: Employment. Meggendorfer:MLL Munich Leukemia Laboratory: Employment. Kern:MLL Munich Leukemia Laboratory: Employment, Equity Ownership. Haferlach:MLL Munich Leukemia Laboratory: Employment, Equity Ownership.

Whole Genome Sequencing in Hairy Cell Leukemia-Variant (HCL-v) and Splenic Marginal Zone Lymphoma (SMZL)

Background: The WHO 2016 classification defines a variety of lymphomas. Among the mature B-cell neoplasms different types of splenic lymphomas are separated from each other. In particular, hairy cell leukemia-variant (HCL-v) and splenic marginal zone lymphoma (SMZL) are defined as distinct entities, however, while they are readily separated from hairy cell leukemia based on cytomorphology, immunophenotype and lack of BRAF V600E mutation, HCL-v and SMZL feature partial overlaps of findings in cytomorphology and immunophenotype. Cytogenetic changes are rare and nonspecific. Thus, a comprehensive molecular work up may provide insight into shared and differentiating mutation.Aims: Molecular characterization of HCL-v and SMZL through whole genome sequencing (WGS) to provide a more distinct disease category.Patient cohort and methods: Diagnostic peripheral blood or bone marrow samples of 14 patients (7 HCL-v and 7 SMZL, median age 82, range 59-94; 8 female, 6 males) were selected for WGS with 90x coverage. Infiltration by mature B-lymphatic population ranged from 6%-72% (median 52%) by immunophenotyping. HCL-v cases were negative for BRAF . Library preparation was performed using Truseq DNA PCR-Free HT sample preparation kit (Illumina, San Diego, CA) according to manufacturer's protocol using fresh/frozen samples and sequenced on NovaSeq sequencing instruments. Tumor-only data was aligned by BaseSpace (BS) WGS app with default parameters and (structural) variant calling was performed with Tumor/Normal app using an unmatched reference DNA (Promega, Fitchburg, WI). Data was subsequently loaded into BS Variant Interpreter to filter and prioritize variants of interest. For the structural variants we looked at all high confidence variant calls passing filters with at least 5 paired reads and 3 split reads with a somatic QScore of >30. In addition, all calls were eliminated with a population frequency of >1% according to the DGV (Database of Genomic variants). For mutation analysis we filtered down the passed variant list to the coding regions of the CancerGeneCensus genes, with a variant read frequency of >0.2, ExAC population frequency of <1% and a damaging/deleterious mutation prediction score with PolyPhen-2 and SIFT. We performed mutation signature analysis by looking at the distribution of base substitutions across the sample with a 3mer context to decompose somatic mutation patterns and compared the results to the COSMIC signature database.Results: We observed a heterogeneous structural variation pattern with no obvious recurrent aberrations across both cohorts. The high diversity in molecular pattern was further confirmed on variant level. Mutation analysis yielded 164 mutations with a median of 12.5 (range 3-18) mutations per sample involving 122 of the 600 investigated genes. 19 genes were mutated in both entities, 44 were exclusive to HCL-v and 59 exclusive to SMZL. Recurrent mutations (n=3/14) were observed in the following genes: ASXL1, ATM, KMT2C, PCSK7, SMO, SPEN, TP53, TRIP11, WRN . PCSK7 is a transcriptional regulator and has been described to be associated with B-cell lymphoma. WRN is a helicase enzyme involved in DNA repair and associated with AML. Strikingly 3/7 (42%) of SMZL cases were mutated in these two genes. SPEN is known to block the differentiation of precursor B-cells into marginal zone B-cells. Additional exclusive recurrent mutations (n=2) were observed in ABL2, ARID1B, CREBBP for HCL-v, and BCL11A, NACA, NSD1, SF3B1 for SMZL. ABL2 is a tyrosine kinase and thus may provide a treatable target. Pathway analysis revealed that 30% of genes mutated (n=36) are involved in the MAPK/ERK pathway. We subsequently conducted a context-sensitive mutation signature analysis with the SNV data, using 96 substitution patterns rather than 6 to increase the complexity. In all instances we found a highly similar pattern across all 14 samples with a strong bias towards C>T substitutions.Conclusion: HCL-v and SMZL are highly diverse on the molecular level. Mutation signature analysis creates patterns associated with cancer and age. More data and further refinement of mutational entity specific signatures are needed to increase the granularity of these signatures. We identified several putative genes to be involved in HCL-v and SMZL, which were not associated with the two entities before. In two instances tyrosine kinase proteins were involved revealing a target for drugs. DisclosuresHaferlach:MLL Munich Leukemia Laboratory: Employment, Equity Ownership. Nadarajah:MLL Munich Leukemia Laboratory: Employment. Meggendorfer:MLL Munich Leukemia Laboratory: Employment. Haferlach:MLL Munich Leukemia Laboratory: Employment, Equity Ownership. Kern:MLL Munich Leukemia Laboratory: Employment, Equity Ownership.

Comprehensive Analysis of Mixed Phenotype Acute Leukemia (MPAL) By Whole Genome Sequencing

Background: Mixed phenotype acute leukemias (MPAL) comprise a rare subgroup of acute leukemias with significant expression of lineage-specific antigens of more than one cell lineage not allowing assignment to a single cell lineage. While two MPAL subgroups are genetically defined according to the WHO 2016 classification, i.e. MPAL with BCR-ABL1 and MPAL with KMT2A rearrangement, the majority of cases are grouped as MPAL, B/myeloid, NOS or MPAL, T/myeloid, NOS and lack any specific or characteristic genetics so far.Aims: Molecular characterization of MPAL by whole genome sequencing (WGS).Patient cohort and methods: Diagnostic peripheral blood or bone marrow samples of 12 MPAL patients (median age 64 years, range 38-96; 5 male, 7 female) were selected for WGS with 90x coverage. One had MPAL with BCR-ABL1, 5 had MPAL, B/myeloid, NOS and 6 had MPAL, T/myeloid, NOS. Library preparation was performed using Truseq DNA PCR-Free HT sample preparation kit (Illumina, San Diego, CA). Sequencing was performed on NovaSeq instruments (Illumina). Tumor-only data was aligned by BaseSpace (BS) WGS app with default parameters and (structural) variant calling was performed with the Tumor/Normal app using an unmatched reference DNA (Promega, Fitchburg, WI). Data was subsequently loaded into BS Variant Interpreter to filter and prioritize variants of interest. For the structural variants we looked at all high confidence variant calls passing filters with at least 5 paired reads and 3 split reads with a somatic QScore of >30. In addition, all calls were eliminated with a population frequency of >1% according to the DGV (Database of Genomic variants). We performed mutation signature analysis by looking at the distribution of base substitutions across the sample with a 3mer context (including the base before and after the substitution) to decompose somatic mutation patterns and compared the results to the COSMIC signature database. For mutation analysis variants with a read frequency of >0.2% and ExAC population frequency of <1% were considered. For the first set of analysis we filtered down the variant list to the coding region of 65 genes described mutated in hematologic malignancies. For the second set of analysis we considered also genes beyond these 65 genes but required that variants had an entry in the COSMIC database and were classified with a “damaging” score by PolyPhen-2 and SIFT data bases.Results: Regarding the first set of analysis we found a median of 6 (range 2-7) mutations per patient, 63 overall, involving 35 of the 65 investigated genes. The majority were missense mutations (n=31), frameshift mutations (n=14) and splice-site mutations (n=13). The genes listed in table 1 were found most frequently mutated. Except for RUNX1 none of the frequently encountered mutations affected the case with MPAL with BCR-ABL1 while there was no restriction of mutations to either of the two other MPAL subgroups. 11 of these 63 mutations had been previously analyzed by routine diagnostics applying next generation sequencing (NGS) yielding identical results.Regarding the second set of analysis without limitation on the above mentioned 65 genes we found a median of 83 (range 63-129) mutations per patient, 1079 overall, involving 736 genes. Specifically, four candidate genes were found mutated in at least five patients (table 2). NBPF1 has been described as tumor suppressor, PRAMEF2 belongs to the PRAME family of genes associated with various malignancies and as a prognostic factor in B-ALL, MUC12 is a highly polymorphous gene involved in a variety of cancer types and described to be epigenetically regulated and thus a marker for early cancer detection, ARSD has been described overexpressed and prognostic in CLL. Again, mutations were not restricted to one MPAL subgroup.We subsequently conducted a context-sensitive mutation signature analysis with the SNV data, using 96 substitution patterns rather than only 6 to increase the complexity. In all instances we found a highly similar pattern across all 12 samples with a strong bias towards C>T substitutions (figure 1).Conclusion: WGS is capable of reproducing routine NGS diagnostics and of identifying mutations not yet described for MPAL which might be used to better understand and categorize this disease. [Display omitted] DisclosuresKern:MLL Munich Leukemia Laboratory: Employment, Equity Ownership. Nadarajah:MLL Munich Leukemia Laboratory: Employment. Meggendorfer:MLL Munich Leukemia Laboratory: Employment. Walter:MLL Munich Leukemia Laboratory: Employment. Haferlach:MLL Munich Leukemia Laboratory: Employment, Equity Ownership. Haferlach:MLL Munich Leukemia Laboratory: Employment, Equity Ownership.

LRez: a C++ API and toolkit for analyzing and managing Linked-Reads data.

Linked-Reads technologies combine both the high quality and low cost of short-reads sequencing and long-range information, through the use of barcodes tagging reads which originate from a common long DNA molecule. This technology has been employed in a broad range of applications including genome assembly, phasing and scaffolding, as well as structural variant calling. However, to date, no tool or API dedicated to the manipulation of Linked-Reads data exist. We introduce LRez, a C++ API and toolkit that allows easy management of Linked-Reads data. LRez includes various functionalities, for computing numbers of common barcodes between genomic regions, extracting barcodes from BAM files, as well as indexing and querying BAM, FASTQ and gzipped FASTQ files to quickly fetch all reads or alignments containing a given barcode. LRez is compatible with a wide range of Linked-Reads sequencing technologies, and can thus be used in any tool or pipeline requiring barcode processing or indexing, in order to improve their performances. LRez is implemented in C++, supported on Unix-based platforms and available under AGPL-3.0 License at https://github.com/morispi/LRez, and as a bioconda module. Supplementary data are available at Bioinformatics Advances online.

Aquila_stLFR: diploid genome assembly based structural variant calling package for stLFR linked-reads.

Identifying structural variants (SVs) is critical in health and disease, however, detecting them remains a challenge. Several linked-read sequencing technologies, including 10X Genomics, TELL-Seq and single tube long fragment read (stLFR), have been recently developed as cost-effective approaches to reconstruct multi-megabase haplotypes (phase blocks) from sequence data of a single sample. These technologies provide an optimal sequencing platform to characterize SVs, though few computational algorithms can utilize them. Thus, we developed Aquila_stLFR, an approach that resolves SVs through haplotype-based assembly of stLFR linked-reads. Aquila_stLFR first partitions long fragment reads into two haplotype-specific blocks with the assistance of the high-quality reference genome, by taking advantage of the potential phasing ability of the linked-read itself. Each haplotype is then assembled independently, to achieve a complete diploid assembly to finally reconstruct the genome-wide SVs. We benchmarked Aquila_stLFR on a well-studied sample, NA24385, and showed Aquila_stLFR can detect medium to large size deletions (50 bp-10 kb) with high sensitivity and medium-size insertions (50 bp-1 kb) with high specificity. Source code and documentation are available on https://github.com/maiziex/Aquila_stLFR. Supplementary data are available at Bioinformatics Advances online.

Discovery of a potential predictive marker for eribulin treatment and novel target genes in BRAF V600E mutant metastatic colorectal cancer using an AI-driven RNA-seq analysis platform: Translational research of the BRAVERY study (EPOC1701).

e15532 Background: Patients (pts) with BRAF V600E mutant (mt) metastatic colorectal cancer (mCRC) still have a poor prognosis even when treated with encorafenib plus cetuximab. We reported that eribulin that inhibits G2-M cell cycle had limited activity for these pts in the phase II BRAVERY study (Masuishi T, et al. WCGC 2020). Barras D, et al. (2017) reported that BRAF V600E mt mCRCs were classified into BM1 and BM2 characterized by KRAS/AKT pathway activation and deregulation of the cell cycle with G2-M phase activation, respectively, based on gene expression. Methods: Whole transcriptome RNA-seq of FFPE pre-treatment tumor samples was performed using NovaSeq 6000 in the BRAVERY study. Molecular features were extracted using the Txome.ai platform (Ocean Genomics Inc., PA, USA) which included transcript- and gene-level expression quantification, expression-based clustering, and structural variant calling. Efficacy of eribulin was classified as “good” if pts had a tumor reduction and/or progression-free survival (PFS) of more than 6 months, and “poor” otherwise. The differential gene expression analysis was performed between pts with “good” and “poor” using Txome.ai. In addition, BM and consensus molecular subtype (CMS) classification were performed using the model developed by Barras D, et al. (2017) and Guinney J, et al. (2015), respectively. Results: Among 27 pts, 26 tumor samples were available to perform RNA-seq and analyze gene expression despite low mapping rates. Patient characteristics were as follows: median age of 58.5 (range, 33–71) years; ECOG PS of 0/1 (16/10); primary tumor location of right/left (11/15); and all 26 pts had MSS/pMMR. Four and 22 pts were classified into “good” and “poor” groups, respectively. Among 52 differentially expressed genes (GENCODE v31) with false discovery rate-adjusted P- value &lt; 0.05, the top 5 genes with the lowest P-values are provided in the table. All 4 pts in the “good” group were classified into BM2 and pts in the “poor” group were classified into BM1 (8/22) and BM2 (14/22) (p = 0.07). In addition, all but 2 pts were classified into CMS4. These two pts belong to the “poor” group with one of them classified into CMS1 and the other into CMS2. Conclusions: These gene expression analyses suggest that BM2 subtype could be a predictive marker for the efficacy of eribulin and some genes could be novel targets with the goal to improve prognosis of pts with BRAF V600E mt mCRC. This is the first finding for a potential biomarker in this subgroup using RNA-seq analysis tools. These findings will require additional validation. Clinical trial information: UMIN000031552. [Table: see text]

Optimizing Nanopore sequencing-based detection of structural variants enables individualized circulating tumor DNA-based disease monitoring in cancer patients

Here, we describe a novel approach for rapid discovery of a set of tumor-specific genomic structural variants (SVs), based on a combination of low coverage cancer genome sequencing using Oxford Nanopore with an SV calling and filtering pipeline. We applied the method to tumor samples of high-grade ovarian and prostate cancer patients and validated on average ten somatic SVs per patient with breakpoint-spanning PCR mini-amplicons. These SVs could be quantified in ctDNA samples of patients with metastatic prostate cancer using a digital PCR assay. The results suggest that SV dynamics correlate with and may improve existing treatment-response biomarkers such as PSA.https://github.com/UMCUGenetics/SHARC.

SKSV: ultrafast structural variation detection from circular consensus sequencing reads.

Circular consensus sequencing reads are promising for the comprehensive detection of structural variants (SVs). However, alignment-based SV calling pipelines are computationally intensive due to the generation of complete read-alignments and its post-processing. Herein, we propose a SKeleton-based analysis toolkit for Structural Variation detection (SKSV). Benchmarks on real and simulated datasets demonstrate that SKSV has an order of magnitude of faster speed than state-of-the-art SV calling approaches; moreover, it achieves higher F1 scores for various types of SVs. SKSV is available from https://github.com/ydLiu-HIT/SKSV. Supplementary data are available at Bioinformatics online.

StLFRsv: A Germline Structural Variant Analysis Pipeline Using Co-barcoded Reads.

Co-barcoded reads originating from long DNA fragments (mean length >30 kbp) maintain both single base level accuracy and long-range genomic information. We propose a pipeline, stLFRsv, to detect structural variation using co-barcoded reads. stLFRsv identifies abnormal large gaps between co-barcoded reads to detect potential breakpoints and reconstruct complex structural variants (SVs). Haplotype phasing by co-barcoded reads increases the signal to noise ratio, and barcode sharing profiles are used to filter out false positives. We integrate the short read SV caller smoove for smaller variants with stLFRsv. The integrated pipeline was evaluated on the well-characterized genome HG002/NA24385, and 74.5% precision and a 22.4% recall rate were obtained for deletions. stLFRsv revealed some large variants not included in the benchmark set that were verified by long reads or assembly. For the HG001/NA12878 genome, stLFRsv also achieved the best performance for both resource usage and the detection of large variants. Our work indicates that co-barcoded read technology has the potential to improve genome completeness.

Robust Benchmark Structural Variant Calls of An Asian Using State-of-the-art Long-read Sequencing Technologies

The importance of structural variants (SVs) for human phenotypes and diseases is now recognized. Although a variety of SV detection platforms and strategies that vary in sensitivity and specificity have been developed, few benchmarking procedures are available to confidently assess their performances in biological and clinical research. To facilitate the validation and application of these SV detection approaches, we established an Asian reference material by characterizing the genome of an Epstein-Barr virus (EBV)-immortalized B lymphocyte line along with identified benchmark regions and high-confidence SV calls. We established a high-confidence SV callset with 8938 SVs by integrating four alignment-based SV callers, including 109× Pacific Biosciences (PacBio) continuous long reads (CLRs), 22× PacBio circular consensus sequencing (CCS) reads, 104× Oxford Nanopore Technologies (ONT) long reads, and 114× Bionano optical mapping platform, and one de novo assembly-based SV caller using CCS reads. A total of 544 randomly selected SVs were validated by PCR amplification and Sanger sequencing, demonstrating the robustness of our SV calls. Combining trio-binning-based haplotype assemblies, we established an SV benchmark for identifying false negatives and false positives by constructing the continuous high-confidence regions (CHCRs), which covered 1.46 gigabase pairs (Gb) and 6882 SVs supported by at least one diploid haplotype assembly. Establishing high-confidence SV calls for a benchmark sample that has been characterized by multiple technologies provides a valuable resource for investigating SVs in human biology, disease, and clinical research.

PopDel identifies medium-size deletions simultaneously in tens of thousands of genomes

Thousands of genomic structural variants (SVs) segregate in the human population and can impact phenotypic traits and diseases. Their identification in whole-genome sequence data of large cohorts is a major computational challenge. Most current approaches identify SVs in single genomes and afterwards merge the identified variants into a joint call set across many genomes. We describe the approach PopDel, which directly identifies deletions of about 500 to at least 10,000 bp in length in data of many genomes jointly, eliminating the need for subsequent variant merging. PopDel scales to tens of thousands of genomes as we demonstrate in evaluations on up to 49,962 genomes. We show that PopDel reliably reports common, rare and de novo deletions. On genomes with available high-confidence reference call sets PopDel shows excellent recall and precision. Genotype inheritance patterns in up to 6794 trios indicate that genotypes predicted by PopDel are more reliable than those of previous SV callers. Furthermore, PopDel’s running time is competitive with the fastest tested previous tools. The demonstrated scalability and accuracy of PopDel enables routine scans for deletions in large-scale sequencing studies.

NanotatoR: a tool for enhanced annotation of genomic structural variants

BackgroundWhole genome sequencing is effective at identification of small variants, but because it is based on short reads, assessment of structural variants (SVs) is limited. The advent of Optical Genome Mapping (OGM), which utilizes long fluorescently labeled DNA molecules for de novo genome assembly and SV calling, has allowed for increased sensitivity and specificity in SV detection. However, compared to small variant annotation tools, OGM-based SV annotation software has seen little development, and currently available SV annotation tools do not provide sufficient information for determination of variant pathogenicity.ResultsWe developed an R-based package, nanotatoR, which provides comprehensive annotation as a tool for SV classification. nanotatoR uses both external (DGV; DECIPHER; Bionano Genomics BNDB) and internal (user-defined) databases to estimate SV frequency. Human genome reference GRCh37/38-based BED files are used to annotate SVs with overlapping, upstream, and downstream genes. Overlap percentages and distances for nearest genes are calculated and can be used for filtration. A primary gene list is extracted from public databases based on the patient’s phenotype and used to filter genes overlapping SVs, providing the analyst with an easy way to prioritize variants. If available, expression of overlapping or nearby genes of interest is extracted (e.g. from an RNA-Seq dataset, allowing the user to assess the effects of SVs on the transcriptome). Most quality-control filtration parameters are customizable by the user. The output is given in an Excel file format, subdivided into multiple sheets based on SV type and inheritance pattern (INDELs, inversions, translocations, de novo, etc.).nanotatoR passed all quality and run time criteria of Bioconductor, where it was accepted in the April 2019 release. We evaluated nanotatoR’s annotation capabilities using publicly available reference datasets: the singleton sample NA12878, mapped with two types of enzyme labeling, and the NA24143 trio. nanotatoR was also able to accurately filter the known pathogenic variants in a cohort of patients with Duchenne Muscular Dystrophy for which we had previously demonstrated the diagnostic ability of OGM.ConclusionsThe extensive annotation enables users to rapidly identify potential pathogenic SVs, a critical step toward use of OGM in the clinical setting.

SVIM-asm: structural variant detection from haploid and diploid genome assemblies.

MotivationWith the availability of new sequencing technologies, the generation of haplotype-resolved genome assemblies up to chromosome scale has become feasible. These assemblies capture the complete genetic information of both parental haplotypes, increase structural variant (SV) calling sensitivity and enable direct genotyping and phasing of SVs. Yet, existing SV callers are designed for haploid genome assemblies only, do not support genotyping or detect only a limited set of SV classes.ResultsWe introduce our method SVIM-asm for the detection and genotyping of six common classes of SVs from haploid and diploid genome assemblies. Compared against the only other existing SV caller for diploid assemblies, DipCall, SVIM-asm detects more SV classes and reached higher F1 scores for the detection of insertions and deletions on two recently published assemblies of the HG002 individual.Availability and implementationSVIM-asm has been implemented in Python and can be easily installed via bioconda. Its source code is available at github.com/eldariont/svim-asm.Supplementary information Supplementary data are available at Bioinformatics online.

Parliament2: Accurate structural variant calling at scale.

BackgroundStructural variants (SVs) are critical contributors to genetic diversity and genomic disease. To predict the phenotypic impact of SVs, there is a need for better estimates of both the occurrence and frequency of SVs, preferably from large, ethnically diverse cohorts. Thus, the current standard approach requires the use of short paired-end reads, which remain challenging to detect, especially at the scale of hundreds to thousands of samples.FindingsWe present Parliament2, a consensus SV framework that leverages multiple best-in-class methods to identify high-quality SVs from short-read DNA sequence data at scale. Parliament2 incorporates pre-installed SV callers that are optimized for efficient execution in parallel to reduce the overall runtime and costs. We demonstrate the accuracy of Parliament2 when applied to data from NovaSeq and HiSeq X platforms with the Genome in a Bottle (GIAB) SV call set across all size classes. The reported quality score per SV is calibrated across different SV types and size classes. Parliament2 has the highest F1 score (74.27%) measured across the independent gold standard from GIAB. We illustrate the compute performance by processing all 1000 Genomes samples (2,691 samples) in <1 day on GRCH38. Parliament2 improves the runtime performance of individual methods and is open source (https://github.com/slzarate/parliament2), and a Docker image, as well as a WDL implementation, is available.ConclusionParliament2 provides both a highly accurate single-sample SV call set from short-read DNA sequence data and enables cost-efficient application over cloud or cluster environments, processing thousands of samples.

Long-read trio sequencing of individuals with unsolved intellectual disability

Long-read sequencing (LRS) has the potential to comprehensively identify all medically relevant genome variation, including variation commonly missed by short-read sequencing (SRS) approaches. To determine this potential, we performed LRS around 15×–40× genome coverage using the Pacific Biosciences Sequel I System for five trios. The respective probands were diagnosed with intellectual disability (ID) whose etiology remained unresolved after SRS exomes and genomes. Systematic assessment of LRS coverage showed that ~35 Mb of the human reference genome was only accessible by LRS and not SRS. Genome-wide structural variant (SV) calling yielded on average 28,292 SV calls per individual, totaling 12.9 Mb of sequence. Trio-based analyses which allowed to study segregation, showed concordance for up to 95% of these SV calls across the genome, and 80% of the LRS SV calls were not identified by SRS. De novo mutation analysis did not identify any de novo SVs, confirming that these are rare events. Because of high sequence coverage, we were also able to call single nucleotide substitutions. On average, we identified 3 million substitutions per genome, with a Mendelian inheritance concordance of up to 97%. Of these, ~100,000 were located in the ~35 Mb of the genome that was only captured by LRS. Moreover, these variants affected the coding sequence of 64 genes, including 32 known Mendelian disease genes. Our data show the potential added value of LRS compared to SRS for identifying medically relevant genome variation.

Towards a better understanding of the low recall of insertion variants with short-read based variant callers

BackgroundSince 2009, numerous tools have been developed to detect structural variants using short read technologies. Insertions >50 bp are one of the hardest type to discover and are drastically underrepresented in gold standard variant callsets. The advent of long read technologies has completely changed the situation. In 2019, two independent cross technologies studies have published the most complete variant callsets with sequence resolved insertions in human individuals. Among the reported insertions, only 17 to 28% could be discovered with short-read based tools.ResultsIn this work, we performed an in-depth analysis of these unprecedented insertion callsets in order to investigate the causes of such failures. We have first established a precise classification of insertion variants according to four layers of characterization: the nature and size of the inserted sequence, the genomic context of the insertion site and the breakpoint junction complexity. Because these levels are intertwined, we then used simulations to characterize the impact of each complexity factor on the recall of several structural variant callers. We showed that most reported insertions exhibited characteristics that may interfere with their discovery: 63% were tandem repeat expansions, 38% contained homology larger than 10 bp within their breakpoint junctions and 70% were located in simple repeats. Consequently, the recall of short-read based variant callers was significantly lower for such insertions (6% for tandem repeats vs 56% for mobile element insertions). Simulations showed that the most impacting factor was the insertion type rather than the genomic context, with various difficulties being handled differently among the tested structural variant callers, and they highlighted the lack of sequence resolution for most insertion calls.ConclusionsOur results explain the low recall by pointing out several difficulty factors among the observed insertion features and provide avenues for improving SV caller algorithms and their combinations.

Svpluscnv: analysis and visualization of complex structural variation data.

Despite widespread prevalence of somatic structural variations (SVs) across most tumor types, understanding of their molecular implications often remains poor. SVs are extremely heterogeneous in size and complexity, hindering the interpretation of their pathogenic role. Tools integrating large SV datasets across platforms are required to fully characterize the cancer's somatic landscape. svpluscnv R package is a swiss army knife for the integration and interpretation of orthogonal datasets including copy number variant segmentation profiles and sequencing-based structural variant calls. The package implements analysis and visualization tools to evaluate chromosomal instability and ploidy, identify genes harboring recurrent SVs and detects complex rearrangements such as chromothripsis and chromoplexia. Further, it allows systematic identification of hot-spot shattered genomic regions, showing reproducibility across alternative detection methods and datasets. https://github.com/ccbiolab/svpluscnv. Supplementary data are available at Bioinformatics online.

Using short read sequencing to characterise balanced reciprocal translocations in pigs

BackgroundA balanced constitutional reciprocal translocation (RT) is a mutual exchange of terminal segments of two non-homologous chromosomes without any loss or gain of DNA in germline cells. Carriers of balanced RTs are viable individuals with no apparent phenotypical consequences. These animals produce, however, unbalanced gametes and show therefore reduced fertility and offspring with congenital abnormalities. This cytogenetic abnormality is usually detected using chromosome staining techniques. The aim of this study was to test the possibilities of using paired end short read sequencing for detection of balanced RTs in boars and investigate their breakpoints and junctions.ResultsBalanced RTs were recovered in a blinded analysis, using structural variant calling software DELLY, in 6 of the 7 carriers with 30 fold short read paired end sequencing. In 15 non-carriers we did not detect any RTs. Reducing the coverage to 20 fold, 15 fold and 10 fold showed that at least 20 fold coverage is required to obtain good results. One RT was not detected using the blind screening, however, a highly likely RT was discovered after unblinding. This RT was located in a repetitive region, showing the limitations of short read sequence data. The detailed analysis of the breakpoints and junctions suggested three junctions showing microhomology, three junctions with blunt-end ligation, and three micro-insertions at the breakpoint junctions. The RTs detected also showed to disrupt genes.ConclusionsWe conclude that paired end short read sequence data can be used to detect and characterize balanced reciprocal translocations, if sequencing depth is at least 20 fold coverage. However, translocations in repetitive areas may require large fragments or even long read sequence data.

Abstract 1329: Identifying low allele frequency somatic variants using the Saphyr System

Abstract Cancer is known to be a dynamic disease with subclones evolving throughout the stages. These progresses create a sample of high heterogeneity, in which high throughput and high sensitivity structural variant calling sensitivity are essential to detect the low frequency events in order to have a thorough understanding of the cancer. Using the Bionano Saphyr® System, a genome imaging platform with a throughput of up to 1.3 Tbp per flowcell, sensitive detection of these low allele frequency structural rearrangements is facilitated by the Bionano data analysis tools, the Rare Variant Pipeline and Variant Annotation Pipeline. Here we compared three tumor-normal pairs of cell lines: the HCC2218, HCC1395 and HCC1954. We first collected about 1.3 Tbp for each of the six tumor and normal cell lines. Each dataset has molecule N50s above 270 kbp. These datasets were input into the Rare Variant Pipeline, where molecules deviating from the reference were locally assembled and structural variants were detected with the local assemblies. Running the Variant Annotation Pipeline then compared the structural variants between the tumor and the control samples and identified variants that are somatic in each pair. With the somatic calls found, we identified various numbers of low allele frequency events in each pair. For example, we observed a low frequency translocation t(2,12) that is unique to the HCC1954 tumor sample. This translocation involves the protein kinase ZAK, a gene known to take part in cell cycle checkpoint regulation (Tosti et al., 2004). In the HCC2218 tumor sample, we found a 381 kbp deletion at AUTS2 (autism susceptibility candidate 2), a gene which has also been associated with acute lymphoblastic leukemia (Denk et al., 2012) and other cancers (Stadler et al., 2012). These events are of low allele frequency and are found to be somatic. In conclusion, cancer samples are heterogeneous and thus, high throughput and sensitive structural variant calling methods are required for a complete examination of the structural mutations that may be present in tumor samples. Bionano has provided an efficient and streamlined data collection platform as well as a smooth and intuitive bioinformatics workflow for expeditious detection of low allele frequency events such as those herein illustrated. References Denk, Dagmar, et al. "PAX5-AUTS2: a recurrent fusion gene in childhood B-cell precursor acute lymphoblastic leukemia." Leukemia research 36.8 (2012): e178-e181. Stadler, Zsofia K., et al. "Rare de novo germline copy-number variation in testicular cancer." The American Journal of Human Genetics 91.2 (2012): 379-383. Tosti, Elena, et al. "The stress kinase MRK contributes to regulation of DNA damage checkpoints through a p38γ-independent pathway." Journal of Biological Chemistry 279.46 (2004): 47652-47660. Citation Format: Joyce Lee, Andy Wing Chun PANG, Caspar Groß, Jakob Admard, Elena Buena-Atienza, Stephan Ossowski, Thomas Anantharaman, Mark Oldakowski, Sven Bocklandt, Alex Hastie. Identifying low allele frequency somatic variants using the Saphyr System [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 1329.

Abstract 4897: Genome-wide structural variation analysis and characterization of Hepatitis B Virus integration sites in hepatocellular carcinomas using Bionano Genomics whole genome imaging

Abstract Large genomic rearrangements are known contributors to cancer formation. Hepatitis B virus (HBV) integration is a frequent driver of hepatocellular carcinoma, frequently associated with genomic rearrangements. Next generation sequencing (NGS) is commonly used to study these integration sites and rearrangements; however, due to short read lengths, NGS is often unable to characterize large and complicated genomic rearrangements. To solve this problem, whole genome imaging was used to perform genome-wide detection of large structural variants (SVs) in DNA extracted from liver tumors, including characterization of HBV viral integration sites and nearby rearrangements. Ultra-high molecular weight DNA was extracted from liver tissue of four patients diagnosed with hepatocellular carcinoma. The DNA was fluorescently labeled at the motif CTTAAG, linearized, and imaged in nanochannel arrays using a Bionano Genomics Saphyr platform. Images of molecules were converted into digital single-molecule reads. 400X coverage of DNA molecules at least 150 kbp in length was collected for each sample. For three of the patients, somatic structural variants (SV) were identified by comparing SV calls and single molecule DNA reads of liver tumor resections against those of matching physiologically normal liver tissue. For the fourth patient, putative somatic SVs were identified by comparing SV calls against a database of 59,490 known variants from 57 physiologically normal individuals. Among the matched-pair samples, 511 large tumor-unique structural variants were identified. For the fourth sample, 619 putative somatic events were identified. HBV viral integration sites, which had previously been identified via NGS, and nearby SVs were characterized. Complex SVs were resolved, including translocation events with several loci. These findings demonstrate the utility of whole genome imaging for studying the relationship between viral integration and cancer. Citation Format: Karl Hong, Camille Péneau, Quentin Bayard, Sandrine Imbeaud, Andy Pang, Alex Hastie, Eric Letouzé, Jessica Zucman-Rossi. Genome-wide structural variation analysis and characterization of Hepatitis B Virus integration sites in hepatocellular carcinomas using Bionano Genomics whole genome imaging [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 4897.