scholarly journals Evaluation of computational genotyping of structural variation for clinical diagnoses

GigaScience ◽  
2019 ◽  
Vol 8 (9) ◽  
Author(s):  
Varuna Chander ◽  
Richard A Gibbs ◽  
Fritz J Sedlazeck
Keyword(s):  
Dna Sequence ◽  
Structural Variation ◽  
Sequence Data ◽  
False Negative ◽  
Real Data ◽  
Clinical Diagnostics ◽  
Superior Performance ◽  
Short Read ◽  
False Discovery ◽  
The Impact

Abstract Background Structural 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. Results We 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. Conclusion Our results indicate that, although SV genotyping software methods have superior performance to SV callers, there are limitations that suggest the need for further innovation.

scholarly journals Evaluation of computational genotyping of Structural Variations for clinical diagnoses

2019 ◽  
Author(s):  
Varuna Chander ◽  
Richard A. Gibbs ◽  
Fritz J. Sedlazeck
Keyword(s):  
Dna Sequence ◽  
Structural Variation ◽  
Sequence Data ◽  
Real Data ◽  
Cost Effective ◽  
Clinical Diagnostics ◽  
Superior Performance ◽  
Structural Variations ◽  
False Discovery ◽  
Low Sensitivity

AbstractBackgroundIn recent years, Structural Variation (SV) has been identified as having a pivotal role in causing genetic disease. Nevertheless, the discovery of SVs based on short DNA sequence reads from next-generation DNA sequence methods is still error-prone, suffering from low sensitivity and high false discovery. These shortcomings can be partially overcome with the use of long reads, but the current expense precludes their application for routine clinical diagnostics. Structural Variation genotyping, on the other hand, offers cost-effective application as diagnostic tool in the clinic, with potentially no false positives and low occurrence of false negatives.ResultsWe assess five state- of-the- art SV genotyping software methods that test short read sequence data. These methods are characterized based on their ability to genotype certain SV types and size ranges. Furthermore, we analyze their applicability to parse different VCF file sub-formats, or to rely on specific meta information that is not always at hand. 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 their sensitivity and ability to filter out initial false discovery calls to assess their reliability.ConclusionOur results indicate that, although SV genotypers have superior performance to SV callers, there are performance limitations that suggest the need for further innovation.

scholarly journals REscan: inferring repeat expansions and structural variation in paired-end short read sequencing data

2020 ◽  
Author(s):  
Russell Lewis McLaughlin
Keyword(s):  
Structural Variation ◽  
Sequence Data ◽  
Neurological Diseases ◽  
Repeat Expansion ◽  
Sequencing Data ◽  
Short Read ◽  
Short Read Sequencing ◽  
Repeat Expansions ◽  
Paired End Sequencing

Abstract Motivation Repeat expansions are an important class of genetic variation in neurological diseases. However, the identification of novel repeat expansions using conventional sequencing methods is a challenge due to their typical lengths relative to short sequence reads and difficulty in producing accurate and unique alignments for repetitive sequence. However, this latter property can be harnessed in paired-end sequencing data to infer the possible locations of repeat expansions and other structural variation. Results This article presents REscan, a command-line utility that infers repeat expansion loci from paired-end short read sequencing data by reporting the proportion of reads orientated towards a locus that do not have an adequately mapped mate. A high REscan statistic relative to a population of data suggests a repeat expansion locus for experimental follow-up. This approach is validated using genome sequence data for 259 cases of amyotrophic lateral sclerosis, of which 24 are positive for a large repeat expansion in C9orf72, showing that REscan statistics readily discriminate repeat expansion carriers from non-carriers. Availabilityand implementation C source code at https://github.com/rlmcl/rescan (GNU General Public Licence v3).

scholarly journals Haplotype-aware variant calling enables high accuracy in nanopore long-reads using deep neural networks

2021 ◽  
Author(s):  
Kishwar Shafin ◽  
Trevor Pesout ◽  
Pi-Chuan Chang ◽  
Maria Nattestad ◽  
Alexey Kolesnikov ◽  
...  
Keyword(s):  
De Novo ◽  
Sequence Data ◽  
Variant Calling ◽  
High Accuracy ◽  
Superior Performance ◽  
Read Length ◽  
Single Nucleotide Variants ◽  
Single Nucleotide ◽  
Short Read ◽  
Long Read

Long-read sequencing has the potential to transform variant detection by reaching currently difficult-to-map regions and routinely linking together adjacent variations to enable read based phasing. Third-generation nanopore sequence data has demonstrated a long read length, but current interpretation methods for its novel pore-based signal have unique error profiles, making accurate analysis challenging. Here, we introduce a haplotype-aware variant calling pipeline PEPPER-Margin-DeepVariant that produces state-of-the-art variant calling results with nanopore data. We show that our nanopore-based method outperforms the short-read-based single nucleotide variant identification method at the whole genome-scale and produces high-quality single nucleotide variants in segmental duplications and low-mappability regions where short-read based genotyping fails. We show that our pipeline can provide highly-contiguous phase blocks across the genome with nanopore reads, contiguously spanning between 85% to 92% of annotated genes across six samples. We also extend PEPPER-Margin-DeepVariant to PacBio HiFi data, providing an efficient solution with superior performance than the current WhatsHap-DeepVariant standard. Finally, we demonstrate de novo assembly polishing methods that use nanopore and PacBio HiFi reads to produce diploid assemblies with high accuracy (Q35+ nanopore-polished and Q40+ PacBio-HiFi-polished).

scholarly journals Predicting RNA splicing from DNA sequence using Pangolin

2021 ◽  
Author(s):  
Tony Zeng ◽  
Yang I Li
Keyword(s):  
Deep Learning ◽  
Dna Sequence ◽  
Rna Splicing ◽  
Rare Variants ◽  
Sequence Data ◽  
Learning Approaches ◽  
Loss Of Function ◽  
Pathogenic Variants ◽  
Uncertain Significance ◽  
The Impact

Recent progress in deep learning approaches have greatly improved the prediction of RNA splicing from DNA sequence. Here, we present Pangolin, a deep learning model to predict splice site strength in multiple tissues that has been trained on RNA splicing and sequence data from four species. Pangolin outperforms state of the art methods for predicting RNA splicing on a variety of prediction tasks. We use Pangolin to study the impact of genetic variants on RNA splicing, including lineage-specific variants and rare variants of uncertain significance. Pangolin predicts loss-of-function mutations with high accuracy and recall, particularly for mutations that are not missense or nonsense (AUPRC = 0.93), demonstrating remarkable potential for identifying pathogenic variants.

scholarly journals GRIDSS2: comprehensive characterisation of somatic structural variation using single breakend variants and structural variant phasing

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Daniel L. Cameron ◽  
Jonathan Baber ◽  
Charles Shale ◽  
Jose Espejo Valle-Inclan ◽  
Nicolle Besselink ◽  
...  
Keyword(s):  
Copy Number ◽  
Structural Variation ◽  
False Negative ◽  
False Negative Rate ◽  
Structural Variants ◽  
Structural Variant ◽  
Complex Rearrangement ◽  
False Discovery ◽  
Copy Number Changes ◽  
Paired End Sequencing

AbstractGRIDSS2 is the first structural variant caller to explicitly report single breakends—breakpoints in which only one side can be unambiguously determined. By treating single breakends as a fundamental genomic rearrangement signal on par with breakpoints, GRIDSS2 can explain 47% of somatic centromere copy number changes using single breakends to non-centromere sequence. On a cohort of 3782 deeply sequenced metastatic cancers, GRIDSS2 achieves an unprecedented 3.1% false negative rate and 3.3% false discovery rate and identifies a novel 32–100 bp duplication signature. GRIDSS2 simplifies complex rearrangement interpretation through phasing of structural variants with 16% of somatic calls phasable using paired-end sequencing.

scholarly journals Prioritisation of Structural Variant Calls in Cancer Genomes

2016 ◽  
Author(s):  
Miika J Ahdesmäki ◽  
Brad Chapman ◽  
Pablo E Cingolani ◽  
Oliver Hofmann ◽  
Aleksandr Sidoruk ◽  
...  
Keyword(s):  
Dna Sequence ◽  
Gene Fusion ◽  
Sequence Data ◽  
High Impact ◽  
Structural Variants ◽  
Short Read ◽  
Structural Variant ◽  
Dna Sequence Data ◽  
Cancer Genomes ◽  
Fusion Detection

AbstractSensitivity of short read DNA-sequencing for gene fusion detection is improving, but is hampered by the significant amount of noise composed of uninteresting or false positive hits in the data. In this paper we describe a tiered prioritisation approach to extract high impact gene fusion events. Using cell line and patient DNA sequence data we improve the annotation and interpretation of structural variant calls to best highlight likely cancer driving fusions. We also considerably improve on the automated visualisation of the high impact structural variants to highlight the effects of the variants on the resulting transcripts. The resulting framework greatly improves on readily detecting clinically actionable structural variants.

scholarly journals Parliament2: Accurate structural variant calling at scale

GigaScience ◽  
2020 ◽  
Vol 9 (12) ◽  
Author(s):  
Samantha Zarate ◽  
Andrew Carroll ◽  
Medhat Mahmoud ◽  
Olga Krasheninina ◽  
Goo Jun ◽  
...  
Keyword(s):  
Dna Sequence ◽  
Sequence Data ◽  
Variant Calling ◽  
Ethnically Diverse ◽  
Current Standard ◽  
Short Read ◽  
Size Classes ◽  
Dna Sequence Data ◽  
1000 Genomes ◽  
Cost Efficient

Abstract Background Structural 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. Findings We 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. Conclusion Parliament2 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.

scholarly journals Prioritisation of structural variant calls in cancer genomes

PeerJ ◽  
2017 ◽  
Vol 5 ◽  
pp. e3166 ◽  
Author(s):  
Miika J. Ahdesmäki ◽  
Brad A. Chapman ◽  
Pablo Cingolani ◽  
Oliver Hofmann ◽  
Aleksandr Sidoruk ◽  
...  
Keyword(s):  
Dna Sequence ◽  
Gene Fusion ◽  
Sequence Data ◽  
High Impact ◽  
Structural Variants ◽  
Short Read ◽  
Structural Variant ◽  
Dna Sequence Data ◽  
Cancer Genomes ◽  
Fusion Detection

Sensitivity of short read DNA-sequencing for gene fusion detection is improving, but is hampered by the significant amount of noise composed of uninteresting or false positive hits in the data. In this paper we describe a tiered prioritisation approach to extract high impact gene fusion events from existing structural variant calls. Using cell line and patient DNA sequence data we improve the annotation and interpretation of structural variant calls to best highlight likely cancer driving fusions. We also considerably improve on the automated visualisation of the high impact structural variants to highlight the effects of the variants on the resulting transcripts. The resulting framework greatly improves on readily detecting clinically actionable structural variants.

scholarly journals Locality-sensitive hashing enables signal classification in high-throughput mass spectrometry raw data at scale

2021 ◽  
Author(s):  
Konstantin Bob ◽  
David Teschner ◽  
Thomas Kemmer ◽  
David Gomez-Zepeda ◽  
Stefan Tenzer ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
False Negative ◽  
Real Data ◽  
Data Locality ◽  
Locality Sensitive Hashing ◽  
Mass Spectrometry Data ◽  
Signal Classification ◽  
Superior Performance ◽  
Single Experiment ◽  
Raw Data

Mass spectrometry is an important experimental technique in the field of proteomics. However, analysis of certain mass spectrometry data faces a combination of two challenges: First, even a single experiment produces a large amount of multi-dimensional raw data and, second, signals of interest are not single peaks but patterns of peaks that span along the different dimensions. The rapidly growing amount of mass spectrometry data increases the demand for scalable solutions. Existing approaches for signal detection are usually not well suited for processing large amounts of data in parallel or rely on strong assumptions concerning the signals properties. In this study, it is shown that locality-sensitive hashing enables signal classification in mass spectrometry raw data at scale. Through appropriate choice of algorithm parameters it is possible to balance false-positive and false-negative rates. On synthetic data, a superior performance compared to an intensity thresholding approach was achieved. The implementation scaled out up to 88 threads on real data. Locality-sensitive hashing is a desirable approach for signal classification in mass spectrometry raw data. Generated data and code are available at https://github.com/hildebrandtlab/mzBucket. Raw data is available at \https://zenodo.org/record/5036526.

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