Mitochondrial DNA (mtDNA) is a compact, double-stranded circular genome of 16,569 bp with a cytosine-rich light (L) chain and a guanine-rich heavy (H) chain. mtDNA mutations have been increasingly recognized as important contributors to an array of human diseases such as Parkinson’s disease, Alzheimer’s disease, colorectal cancer and Kearns–Sayre syndrome. mtDNA mutations can affect all of the 1000-10,000 copies of the mitochondrial genome present in a cell (homoplasmic mutation) or only a subset of copies (heteroplasmic mutation). The ratio of normal to mutant mtDNAs within cells is a significant factor in whether mutations will result in disease, as well as the clinical presentation, penetrance, and severity of the phenotype. Over time, heteroplasmic mutations can become homoplastic due to differential replication and random assortment. Full characterization of the mitochondrial genome would involve detection of not only homoplastic but heteroplasmic mutations, as well as complete phasing. Previously, we sequenced human mtDNA on the PacBio RS II System with two partially overlapping amplicons. Here, we present amplification-free, full-length sequencing of linearized mtDNA using the Sequel System. Full-length sequencing allows variant phasing along the entire mitochondrial genome, identification of heteroplasmic variants, and detection of epigenetic modifications that are lost in amplicon-based methods.
Introduction: Long-read sequencing has been applied successfully to assemble genomes and detect structural variants. However, due to high raw-read error rates (10-15%), it has remained difficult to call small variants from long reads. Recent improvements in library preparation and sequencing chemistry have increased length, accuracy, and throughput of PacBio circular consensus sequencing (CCS) reads, resulting in 10-20kb reads with average read quality above 99%. Materials and Methods: We sequenced a 12kb library from human reference sample HG002 to 18-fold coverage on the PacBio Sequel II System with three SMRT Cells 8M. The CCS algorithm was used to generate highly-accurate (average 99.8%) 11.4kb reads, which were mapped to the hg19 reference with pbmm2. We detected small variants using Google DeepVariant with a model trained for CCS and phased the variants using WhatsHap. Structural variants were detected with pbsv. Variant calls were evaluated against Genome in a Bottle (GIAB) benchmarks. Results: With these reads, DeepVariant achieves SNP and Indel F1 scores of 99.82% and 96.70% against the GIAB truth set, and pbsv achieves 95.94% recall on structural variants longer than 50bp. Using WhatsHap, small variants were phased into haplotype blocks with 105kb N50. The improved mappability of long reads allows us to align to and detect variants in medically relevant genes such as CYP2D6 and PMS2 that have proven “difficult-to-map” with short reads. Conclusions: These highly-accurate long reads combine the mappability and ability to detect structural variants of long reads with the accuracy and ability to detect small variants of short reads.
NGS is commonly used for amplicon sequencing in clinical applications to study genetic disorders and detect disease-causing mutations. This approach can be plagued by limited ability to phase sequence variants and makes interpretation of sequence data difficult when pseudogenes are present. Long-read highly accurate amplicon sequencing can provide very accurate, efficient, high throughput (through multiplexing) sequences from single molecules, with read lengths largely limited by PCR. Data is easy to interpret; phased variants and breakpoints are present within high fidelity individual reads. Here we show SMRT Sequencing of the PMS2 and OPN1 (MW and LW) genes using the Sequel System. Homologous regions make NGS and MLPA results very difficult to interpret.
Introduction: Long-read sequencing has been applied successfully to assemble genomes and detect structural variants. However, due to high raw-read error rates (10-15%), it has remained difficult to call small variants from long reads. Recent improvements in library preparation and sequencing chemistry have increased length, accuracy, and throughput of PacBio circular consensus sequencing (CCS) reads, resulting in 15-20kb reads with average read quality above 99%. Materials and Methods: We sequenced a library from human reference sample HG002 to 18-fold coverage on the PacBio Sequel II with two SMRT Cells 8M. The CCS algorithm was used to generate highly accurate (average 99.9%) 12.9kb reads, which were mapped to the hg19 reference with pbmm2. We detected small variants using Google DeepVariant with a model trained for CCS and phased the variants using WhatsHap. Structural variants were detected with pbsv. Variant calls were evaluated against Genome in a Bottle (GIAB) benchmarks. Results: With these reads, DeepVariant achieves SNP and Indel F1 scores of 99.70% and 96.59% against the GIAB truth set, and pbsv achieves 97.72% recall on structural variants longer than 50bp. Using WhatsHap, small variants were phased into haplotype blocks with 145kb N50. The improved mappability of long reads allows us to align to and detect variants in medically relevant genes such as CYP2D6 and PMS2 that have proven “difficult-to-map” with short reads. Conclusions: These highly accurate long reads combine the mappability and ability to detect structural variants of long reads with the accuracy and ability to detect small variants of short reads.
In this AGBT 2017 talk, PacBio CSO Jonas Korlach provided a technology roadmap for the Sequel System, including plans the continue performance and throughput increases through early 2019. Per SMRT…
SMRT Sequencing is a DNA sequencing technology characterized by long read lengths and high consensus accuracy, regardless of the sequence complexity or GC content of the DNA sample. These characteristics…
Haplotype phasing of genetic variants is important for interpretation of the maize genome, population genetic analysis, and functional genomic analysis of allelic activity. Accordingly, accurate methods for phasing full-length isoforms are essential for functional genomics study. In this study, we performed an isoform-level phasing study in maize, using two inbred lines and their reciprocal crosses, based on single-molecule full-length cDNA sequencing. To phase and analyze full-length transcripts between hybrids and parents, we developed a tool called IsoPhase. Using this tool, we validated the majority of SNPs called against matching short read data and identified cases of allele-specific, gene-level, and isoform-level expression. Our results revealed that maize parental and hybrid lines exhibit different splicing activities. After phasing 6,847 genes in two reciprocal hybrids using embryo, endosperm and root tissues, we annotated the SNPs and identified large-effect genes. In addition, based on single-molecule sequencing, we identified parent-of-origin isoforms in maize hybrids, different novel isoforms between maize parent and hybrid lines, and imprinted genes from different tissues. Finally, we characterized variation in cis- and trans-regulatory effects. Our study provides measures of haplotypic expression that could increase power and accuracy in studies of allelic expression.
Long-read sequencing has substantial advantages for structural variant discovery and phasing of vari- ants compared to short-read technologies, but the required and optimal read length has not been as- sessed. In this work, we used long reads simulated from human genomes and evaluated structural vari- ant discovery and variant phasing using current best practicebioinformaticsmethods.Wedeterminedthatoptimal discovery of structural variants from human genomes can be obtained with reads of minimally 20 kb. Haplotyping variants across genes only reaches its optimum from reads of 100 kb. These findings are important for the design of future long-read sequenc- ing projects.
Targeted Long-Read RNA Sequencing Demonstrates Transcriptional Diversity Driven by Splice-Site Variation in MYBPC3.
To date, clinical sequencing has focused on genomic DNA using targeted panels and exome sequencing. Sequencing of a large hypertrophic cardiomyopathy (HCM) cohort revealed that positive identification of a disease-associated variant was returned in only 32% of patients, with an additional 15% receiving inconclusive results. When genome sequencing fails to reveal causative variants, the transcriptome may provide additional diagnostic clarity. A recent study examining patients with genetically undiagnosed muscle disorders found that RNA sequencing, when used as a complement to exome and whole genome sequencing, had an overall diagnosis rate of 35%.
In the past several years, single-molecule sequencing platforms, such as those by Pacific Biosciences and Oxford Nanopore Technologies, have become available to researchers and are currently being tested for clinical applications. They offer exceptionally long reads that permit direct sequencing through regions of the genome inaccessible or difficult to analyze by short-read platforms. This includes disease-causing long repetitive elements, extreme GC content regions, and complex gene loci. Similarly, these platforms enable structural variation characterization at previously unparalleled resolution and direct detection of epigenetic marks in native DNA. Here, we review how these technologies are opening up new clinical avenues that are being applied to pathogenic microorganisms and viruses, constitutional disorders, pharmacogenomics, cancer, and more.Copyright © 2018 Elsevier Ltd. All rights reserved.
Copy-number variants in clinical genome sequencing: deployment and interpretation for rare and undiagnosed disease.
Current diagnostic testing for genetic disorders involves serial use of specialized assays spanning multiple technologies. In principle, genome sequencing (GS) can detect all genomic pathogenic variant types on a single platform. Here we evaluate copy-number variant (CNV) calling as part of a clinically accredited GS test.We performed analytical validation of CNV calling on 17 reference samples, compared the sensitivity of GS-based variants with those from a clinical microarray, and set a bound on precision using orthogonal technologies. We developed a protocol for family-based analysis of GS-based CNV calls, and deployed this across a clinical cohort of 79 rare and undiagnosed cases.We found that CNV calls from GS are at least as sensitive as those from microarrays, while only creating a modest increase in the number of variants interpreted (~10 CNVs per case). We identified clinically significant CNVs in 15% of the first 79 cases analyzed, all of which were confirmed by an orthogonal approach. The pipeline also enabled discovery of a uniparental disomy (UPD) and a 50% mosaic trisomy 14. Directed analysis of select CNVs enabled breakpoint level resolution of genomic rearrangements and phasing of de novo CNVs.Robust identification of CNVs by GS is possible within a clinical testing environment.
The wide implementation of next-generation sequencing (NGS) technologies has revolutionized the field of medical genetics. However, the short read lengths of currently used sequencing approaches pose a limitation for identification of structural variants, sequencing repetitive regions, phasing alleles and distinguishing highly homologous genomic regions. These limitations may significantly contribute to the diagnostic gap in patients with genetic disorders who have undergone standard NGS, like whole exome or even genome sequencing. Now, the emerging long-read sequencing (LRS) technologies may offer improvements in the characterization of genetic variation and regions that are difficult to assess with the currently prevailing NGS approaches. LRS has so far mainly been used to investigate genetic disorders with previously known or strongly suspected disease loci. While these targeted approaches already show the potential of LRS, it remains to be seen whether LRS technologies can soon enable true whole genome sequencing routinely. Ultimately, this could allow the de novo assembly of individual whole genomes used as a generic test for genetic disorders. In this article, we summarize the current LRS-based research on human genetic disorders and discuss the potential of these technologies to facilitate the next major advancements in medical genetics.