Structural variant calling from PromethION WGS of NA19240
Ontology highlight
ABSTRACT:
INSTRUMENT(S): Oxford Nanopore PromethION
ORGANISM(S): Homo Sapiens
SUBMITTER: VIB-CMN
PROVIDER: PRJEB29523 | EVA | 2018-11-06
REPOSITORIES: EVA
Similar Datasets
Project description:Background: Lung cancer is a heterogeneous disease and the primary cause of cancer-related mortality worldwide. Somatic mutations, including large structural variants, are important biomarkers in lung cancer for selecting targeted therapy. Genomic studies in lung cancer have been conducted using short-read sequencing. Emerging long-read sequencing technologies are a promising alternative to study somatic structural variants, however there is no current consensus on how to process data and call somatic events. In this study, we preformed whole genome sequencing of lung cancer and matched non-tumour samples using long and short read sequencing to comprehensively benchmark three sequence aligners and six structural variant callers comprised of four generic callers (Sniffles2, cuteSV, SVIM and DELLY in generic mode) and two somatic callers (nanomonsv and DELLY in somatic modes).
Results: Different combinations of aligners and variant callers influenced somatic structural variant detection. The choice of caller had a significant influence on somatic structural variant detection in terms of variant type, size, sensitivity, and accuracy. The performance of each variant caller was assessed by comparing to somatic structural variants identified by short read sequencing. When compared to somatic structural variants detected with short read sequencing, more events were detected with long read sequencing. The mean recall of somatic variant events identified by short read sequencing was higher for the somatic callers (64%) than generic callers (52%), with nanomonsv (69%) showing the best performance.
Conclusion: Long read sequencing can identify somatic structural variants. The longer reads have the potential to improve our understanding of cancer development and inform personalized cancer treatment.
| EGAS00001007819 | EGA
Project description: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.
| S-EPMC7751401 | biostudies-literature
Project description:<p>Recently developed methods that utilize partitioning of long genomic DNA fragments, and barcoding of shorter fragments derived from them, have succeeded in retaining long-range information in short sequencing reads. These so-called read cloud approaches represent a powerful, accurate, and cost-effective alternative to single-molecule long-read sequencing. We developed software, GROC-SVs, that takes advantage of read clouds for structural variant detection and assembly. We apply the method to two 10x Genomics data sets, one chromothriptic sarcoma with several spatially separated samples, and one breast cancer cell line, all Illumina-sequenced to high coverage. Comparison to short-fragment data from the same samples, and validation by mate-pair data from a subset of the sarcoma samples, demonstrate substantial improvement in specificity of breakpoint detection compared to short-fragment sequencing, at comparable sensitivity, and vice versa. The embedded long-range information also facilitates sequence assembly of a large fraction of the breakpoints; importantly, consecutive breakpoints that are closer than the average length of the input DNA molecules can be assembled together and their order and arrangement reconstructed, with some events exhibiting remarkable complexity. These features facilitated an analysis of the structural evolution of the sarcoma. In the chromothripsis, rearrangements occurred before copy number amplifications, and using the phylogenetic tree built from point mutation data, we show that single nucleotide variants and structural variants are not correlated. We predict significant future advances in structural variant science using 10x data analyzed with GROC-SVs and other read cloud-specific methods.</p>
| phs001255 | dbGaP
Project description:Whole-genome bisulfite sequencing (WGBS) is currently the gold standard for DNA methylation (5-methylcytosine, 5mC) profiling, however the destructive nature of sodium bisulfite results in DNA fragmentation and subsequent biases in sequencing data. Such issues have led to the development of bisulfite-free methods for 5mC detection. Nanopore sequencing is a long read non-destructive approach that directly analyzes DNA and RNA fragments in real time. Recently, computational tools have been developed that enable base-resolution detection of 5mC from Oxford Nanopore sequencing data. In this chapter we provide a detailed protocol for preparation, sequencing, read assembly and analysis of genome-wide 5mC using Nanopore sequencing technologies.
2021-12-03 | GSE179673 | GEO
Project description:New tools for improved long-read transcript assembly and coalescence with its short-read counterpart are required. Using our short- and long-read measurements from different cell lines with spiked-in standards, we systematically compared key parameters and biases in the read alignment and assembly of transcripts. We report a cDNA synthesis artifact in long-read datasets that impacts the identity and quantitation of assembled transcripts. We developed a computational pipeline to strand long-read cDNA libraries that markedly improves assembly of transcripts from long-reads. Incorporating stranded long-reads in a new hybrid assembly approach, we demonstrate its efficacy for improved characterization of challenging lncRNA transcripts. Our workflow can be applied to a wide range of transcriptomics datasets for superior demarcation of transcript ends and refined isoform structure, which can enable better differential gene expression analyses and molecular manipulations of transcripts.
2023-10-14 | GSE215357 | GEO
Project description:New tools for improved long-read transcript assembly and coalescence with its short-read counterpart are required. Using our short- and long-read measurements from different cell lines with spiked-in standards, we systematically compared key parameters and biases in the read alignment and assembly of transcripts. We report a cDNA synthesis artifact in long-read datasets that impacts the identity and quantitation of assembled transcripts. We developed a computational pipeline to strand long-read cDNA libraries that markedly improves assembly of transcripts from long-reads. Incorporating stranded long-reads in a new hybrid assembly approach, we demonstrate its efficacy for improved characterization of challenging lncRNA transcripts. Our workflow can be applied to a wide range of transcriptomics datasets for superior demarcation of transcript ends and refined isoform structure, which can enable better differential gene expression analyses and molecular manipulations of transcripts.
2023-10-14 | GSE215355 | GEO
Project description:Recent advances in throughput and accuracy mean that the Oxford Nanopore Technologies (ONT) PromethION platform is a now a viable solution for WGS. New bioinformatic methods have been developed to take advantage of this long read data, however much of the validation of these tools has focussed on calling germline variants (both SNVs and structural variants). Somatic variants are outnumbered many-fold by germline variants and their detection is further complicated due to their frequency varying depending on tumour purity/subclonality. Here, we evaluate the extent to which Nanopore WGS enables genome-wide detection and analysis of somatic variation. We do this through sequencing tumour and germline genomes for a patient with diffuse B-cell lymphoma. We examine the capability of currently available tools for calling somatic variants in ONT data by comparing the data with results from 150bp short-read sequencing of the same samples. We then conduct a detailed analysis of the performance of multiple long-read mappers and structural variant callers for calling large, somatic structural variants (SVs) in ONT data. Our protocol achieved yields of up to 96 mapped Gb per PromethION flow cell with average read lengths of ~5kb. Calling germline SNVs from these data achieved good specificity and sensitivity. However, results of somatic SNV calling highlight the need for the development of specialized joint calling algorithms. Our analysis of structural variants shows that the comparative performance of different tools varies significantly between SV types, and suggest long reads are especially advantageous for calling large somatic deletions and duplications. Finally, we highlight the utility of long reads for phasing clinically relevant variants by using the ONT data to confirm that a somatic 1.6Mb deletion and a p.(Arg249Met) mutation involving TP53 are oriented in trans.
| EGAS00001004266 | EGA
Project description:BackgroundArtifact chimeric reads are enriched in next-generation sequencing data generated from formalin-fixed paraffin-embedded (FFPE) samples. Previous work indicated that these reads are characterized by erroneous split-read support that is interpreted as evidence of structural variants. Thus, a large number of false-positive structural variants are detected. To our knowledge, no tool is currently available to specifically call or filter structural variants in FFPE samples. To overcome this gap, we developed 2 R packages: SimFFPE and FilterFFPE.ResultsSimFFPE is a read simulator, specifically designed for next-generation sequencing data from FFPE samples. A mixture of characteristic artifact chimeric reads, as well as normal reads, is generated. FilterFFPE is a filtration algorithm, removing artifact chimeric reads from sequencing data while keeping real chimeric reads. To evaluate the performance of FilterFFPE, we performed structural variant calling with 3 common tools (Delly, Lumpy, and Manta) with and without prior filtration with FilterFFPE. After applying FilterFFPE, the mean positive predictive value improved from 0.27 to 0.48 in simulated samples and from 0.11 to 0.27 in real samples, while sensitivity remained basically unchanged or even slightly increased.ConclusionsFilterFFPE improves the performance of SV calling in FFPE samples. It was validated by analysis of simulated and real data.
| S-EPMC8458033 | biostudies-literature
Project description:Structural variation (SV) plays a fundamental role in genome evolution and can underlie inherited or acquired diseases such as cancer. Long-read sequencing technologies have led to improvements in the characterization of structural variants (SVs), although paired-end sequencing offers better scalability. Here, we present dysgu, which calls SVs or indels using paired-end or long reads. Dysgu detects signals from alignment gaps, discordant and supplementary mappings, and generates consensus contigs, before classifying events using machine learning. Additional SVs are identified by remapping of anomalous sequences. Dysgu outperforms existing state-of-the-art tools using paired-end or long-reads, offering high sensitivity and precision whilst being among the fastest tools to run. We find that combining low coverage paired-end and long-reads is competitive in terms of performance with long-reads at higher coverage values.
| S-EPMC9122538 | biostudies-literature
Project description:Recent research into structural variants (SVs) has established their importance to medicine and molecular biology, elucidating their role in various diseases, regulation of gene expression, ethnic diversity, and large-scale chromosome evolution-giving rise to the differences within populations and among species. Nevertheless, characterizing SVs and determining the optimal approach for a given experimental design remains a computational and scientific challenge. Multiple approaches have emerged to target various SV classes, zygosities, and size ranges. Here, we review these approaches with respect to their ability to infer SVs across the full spectrum of large, complex variations and present computational methods for each approach.
| S-EPMC6868818 | biostudies-literature