Sequencing Error Rates Research Articles

Abstract LB063: Duplex Sequencing reveals ubiquitous clonal hematopoiesis and complex donor-recipient clonal dynamics following HSCT

Abstract Clonal hematopoiesis (CH aka CHIP) is the process whereby otherwise healthy individuals accumulate clones bearing low-frequency somatic mutations in hematologic malignancy (HM) driver genes. Historically thought to be a phenomenon of the elderly that is associated with increased risk of HM and cardiovascular disease, CH has been observed in increasingly younger cohorts as sequencing technology has advanced. CH in hematopoietic stem cell transplant (HSCT) donors has also been associated with adverse recipient outcomes. In this retrospective analysis, we asked whether CH mutations preferentially expanded in recipients vs. donors in paired samples collected a median of 34 years post-HSCT (range 6-45). 16 donor-recipient pairs were analyzed, 11 of which had a donor sample collected pre-HSCT. All individuals were healthy at the time of contemporary sampling. Median donor age was 26 years (range 12-84) at time of HSCT. We took advantage of the ultra-low error rate (<1-in-10-million) of Duplex Sequencing (DS) to comprehensively assess CH in genomic DNA from blood. We targeted 29 genes recurrently mutated in acute myeloid leukemia (AML) plus 48 kilobases of randomly chosen neutral genomic regions. The mean DS error-corrected molecular depth across all samples was 26,731x for AML-associated genes and 32,812x in the neutral regions. CH was detected in 100% of donors and recipients, with a trend toward more CH clones in older vs. younger donors. We defined CH as mutations observed in at least 2 unique DNA molecules, rather than by a threshold variant allele frequency (VAF). Among the 11 donors with matched historical and contemporary DNA samples, CH mutations in the latter had a higher average VAF and comprised more unique mutations than in the former, reflecting clonal expansions over time. The magnitude of CH expansions was greater for the AML-associated genes than for neutral loci, consistent with positive selection. Post-HSCT the overall number of unique mutations was similar between donors and recipients. Most donor CH variants did not preferentially expand in the recipient. A minority of recipients had a modestly increased overall average VAF relative to paired donors in AML genes. While the VAF of some likely pathogenic variants increased in the recipient vs the donor, this was not the case for most CH mutations in most recipients and the reciprocal was sometimes observed. DS represents a powerful tool for the high-resolution study of CH evolution. In a unique historical cohort comprising longitudinal samples from some of the earliest patients and donors to undergo HSCT, we observed limited instances of accelerated CH in recipients relative to donors. While this cohort is biased due to enriched inclusion of long-term survivors, our data exemplifies how healthy hematopoiesis, indistinguishable between donors and recipients, can be maintained almost half a century following transplant. Citation Format: Masumi Ueda, Jake Higgins, Thomas Smith, Elizabeth Schmidt, Fang Y. Lo, Charles C. Valentine, Jesse J. Salk, Rainer Storb, Jerald P. Radich. Duplex Sequencing reveals ubiquitous clonal hematopoiesis and complex donor-recipient clonal dynamics following HSCT [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr LB063.

Abstract 5066: Accurate detection of tumor sequence at low tumor content for MRD

Abstract Background Accurate detection of tumor sequence when the overall tumor content (TC) of the sample is low (<0.01%) is critical for tumor-informed minimal residual disease (MRD) monitoring. Current methodology generally relies on detection of multiple low-VAF (variant allele fraction) co-mutations. At low TC and low DNA input (<30ng, 9,000 genome equivalents), many variant sites are not expected to have any tumor-derived fragments or will be only marginally above background noise. A method that does not rely on the positive identification of mutations would be useful for detecting early disease recurrence. Methods We developed a maximum likelihood (ML) method for inferring the TC of MRD samples, rather than VAF at individual sites. This method considers the frequency of non-reference reads at each variant site along with estimated error rates. To evaluate our method at low TC, we carried out spike-in simulations of MRD samples across a range of sample TC from 0 to 0.1%. For each simulation, we randomly selected a single normal from a pool of sequenced normal samples to serve as background. Using this background, we randomly selected sites and mutations to represent somatic variants, sampled the existing noise at each site, and the number of ctDNA fragments and reads given the tumor content. Intrinsic error rates were modeled as either the mean error of the sample, the per-reference base error, or the mutation error. An additional randomly selected normal sample was used for extrinsic, per-site error estimates. We created contrived tumor-informed MRD samples by serial two-fold dilution of tumor DNA in a normal background (0.2%-0.0125% sample TC), followed by amplicon sequencing and characterization by our ML method. Results In our spike-in simulations, using 32 variants sites, we achieved 99% sensitivity in detecting tumor sequence at a TC of 0.01% (average ~1 tumor fragment per site), 73% sensitivity at 0.005% (average ~0.5 tumor fragments per site), and 99.9% specificity when using extrinsic error from a normal sample. At 16 sites we achieved 95% and 68% sensitivity respectively, and 99.6% specificity. Without the extrinsic error, specificity was greatly reduced to 71% (16 sites) and 77% (32 sites) at .005% TC. This highlights the advantage of extrinsic error in estimating low TC. In our contrived samples, we found that TC estimates from our ML approach correlated well with the expected values (r = 0.99). Conclusions With our approach, we were able to accurately quantify TC in both simulated and contrived samples. In simulated samples, we can detect TC well below 0.01% with high specificity and using only 16 sites. Importantly, the TC estimation does not rely on identifying individual positive sites. While modeling the intrinsic sequencing error rates in a sample does increase our ability to characterize TC, extrinsic, per-site error rates are needed to control false positives introduced by constitutively noisy sites. Citation Format: Andrew Conley, Huazhang Li, Alex V. Kotlar. Accurate detection of tumor sequence at low tumor content for MRD [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5066.

Assessing the impact of USER-treatment on hyRAD capture applied to ancient DNA.

Ancient DNA preservation in subfossil specimens provides a unique opportunity to retrieve genetic information from the past. As ancient DNA extracts are generally dominated by molecules originating from environmental microbes, capture techniques are often used to economically retrieve orthologous sequence data at the population scale. Post-mortem DNA damage, especially the deamination of cytosine residues into uracils, also considerably inflates sequence error rates unless ancient DNA extracts are treated with the USER enzymatic mix prior to library construction. While both approaches have recently gained popularity in ancient DNA research, the impact of USER-treatment on capture efficacy still remains untested. In this study, we applied hyRAD capture to eight ancient equine subfossil specimens from France (1st-17th century CE), including horses, donkeys and their first-generation mule hybrids. We found that USER-treatment could reduce capture efficacy and introduce significant experimental bias. It differentially affected the size distribution of on-target templates following capture with two distinct hyRAD probe sets in a manner that was not driven by differences in probe sizes and DNA methylation levels. Finally, we recovered unbalanced proportions of donkey-specific and horse-specific alleles in mule capture sequence data, due to the combined effects of USER-treatment, probe sets and reference bias. Our work demonstrates that while USER-treatment can improve the quality of ancient DNA sequence data, it can also significantly affect hyRAD capture outcomes, introducing bias in the sequence data that is difficult to predict based on simple molecular probe features. Such technical batch effects may prove easier to model and correct for using capture with synthetic probes of controlled sizes and diversity content.

JAFFAL: detecting fusion genes with long-read transcriptome sequencing

In cancer, fusions are important diagnostic markers and targets for therapy. Long-read transcriptome sequencing allows the discovery of fusions with their full-length isoform structure. However, due to higher sequencing error rates, fusion finding algorithms designed for short reads do not work. Here we present JAFFAL, to identify fusions from long-read transcriptome sequencing. We validate JAFFAL using simulations, cell lines, and patient data from Nanopore and PacBio. We apply JAFFAL to single-cell data and find fusions spanning three genes demonstrating transcripts detected from complex rearrangements. JAFFAL is available at https://github.com/Oshlack/JAFFA/wiki.

A Sequence-to-Sequence Framework Based on Transformer With Masked Language Model for Optical Music Recognition

Optical music recognition technology is of great significance in the development of the digital music. In recent years, the convolutional recurrent neural network framework with connectionist temporal classification has been used in music recognition. However, its loss function is calculated in serial mode, which leads to low efficiency in training and difficulty in convergence. Additionally, because of the gradient disappearance of excessive long music sequences, the existing music recognition models are hard to learn the relationships between musical symbols, resulting in high sequence error rate. Therefore, we propose a sequence-to-sequence framework based on transformer with masked language model to deal with these problems. The context representation between musical symbols can be captured further by the self-attention module in the transformer, which will reduce the sequence error rate. In addition, we refer to the masked language model and design a mask matrix to predict each musical symbol in a parallel way, so as to speed up the training process. Our experiments are carried out on the printed images of music stave dataset, and the results show that our proposed method is training-efficient and has great improvement in sequence accuracy rate.

Nuclease-Assisted, Multiplexed Minor-Allele Enrichment: Application in Liquid Biopsy of Cancer.

The use of next-generation sequencing (NGS) to profile genomic variation of individual cancer species is revolutionizing the practice of clinical oncology. In liquid biopsy of cancer, sequencing of circulating-free DNA (cfDNA) is gradually applied to all stages of cancer diagnosis and treatment, serving as complement or replacement of tissue biopsies. However, analysis of cfDNA obtained from blood draws still faces technical obstacles due in part to an excess of wild-type DNA originating from normal tissues and hematopoietic cells. The resulting low-level mutation abundance often falls below routine NGS detection sensitivity and limits reliable mutation identification that meets clinical sensitivity and specificity standards. Despite sample preparation advances that reduce sequencing error rates via use of unique molecular identifiers (molecular barcodes) and error-suppression algorithms, excessive amounts of sequencing are still required to detect mutations at allelic frequency levels below 1%. This requirement reduces throughput and increases cost.In this chapter, we describe a sensitive multiplex mutation detection method that enriches mutation-containing DNA during sample preparation, prior to sequencing, thereby increasing signal-to-noise ratios and providing low-level mutation detection without excessive sequencing depth. We couple targeted next-generation sequencing with wild-type DNA removal using Nuclease-assisted Minor-allele Enrichment using Probe Overlap, NaME-PrO, a recently developed method to eliminate wild-type sequences from multiple targets simultaneously. A step by step guide to library preparation and data analysis are provided as well as some precautions during the sample handling.

F-Statistics estimation and admixture graph construction with Pool-Seq or allele count data using the R package poolfstat.

By capturing various patterns of the structuring of genetic variation across populations, -statistics have proved highly effective for the inference of demographic history. Such statistics are defined as covariances of SNP allele frequency differences among sets of populations without requiring haplotype information and are hence particularly relevant for the analysis of pooled sequencing (Pool-Seq) data. We here propose a reinterpretation of the (and ) parameters in terms of probability of gene identity and derive from this unified definition unbiased estimators for both Pool-Seq data and standard allele count data obtained from individual genotypes. We implemented these estimators in a new version of the R package poolfstat, which now includes a wide range of inference methods: (i) three-population test of admixture; (ii) four-population test of treeness; (iii) -ratio estimation of admixture rates; and (iv) fitting, visualization and (semi-automatic) construction of admixture graphs. A comprehensive evaluation of the methods implemented in poolfstat on both simulated Pool-Seq (with various sequencing coverages and error rates) and allele count data confirmed the accuracy of these approaches, even for the most cost-effective Pool-Seq design involving relatively low sequencing coverages. We further analysed a real Pool-Seq data made of 14 populations of the invasive species Drosophila suzukii, which allowed refining both the demographic history of native populations and the invasion routes followed by this emblematic pest. Our new package poolfstat provides the community with a user-friendly and efficient all-in-one tool to unravel complex population genetic histories from large-size Pool-Seq or allele count SNP data.

LACE: Inference of cancer evolution models from longitudinal single-cell sequencing data

The rise of longitudinal single-cell sequencing experiments on patient-derived cell cultures, xenografts and organoids is opening new opportunities to track cancer evolution, assess the efficacy of therapies and identify resistant subclones.We introduce LACE, the first algorithmic framework that processes single-cell mutational profiles from samples collected at different time points to reconstruct longitudinal models of cancer evolution. The approach maximizes a weighted likelihood function computed on longitudinal data points to solve a Boolean matrix factorization problem, via Markov chain Monte Carlo sampling.On simulations, LACE outperforms state-of-the-art methods for both bulk and single-cell sequencing data with respect to the reconstruction of the ground-truth clonal phylogeny and dynamics, also in conditions of unbalanced datasets, significant rates of sequencing errors and sampling limitations. As the results are robust with respect to data-specific errors, LACE is effective with mutational profiles generated by calling variants from (full-length) scRNA-seq data, and this allows one to investigate the relation between genomic and phenotypic evolution of tumors at the single-cell level.Here, we apply LACE to a longitudinal scRNA-seq dataset of patient-derived xenografts of BRAFV600E/K mutant melanomas, dissecting the impact of BRAF/MEK-inhibition on clonal evolution, also in terms of clone-specific gene expression dynamics. Furthermore, the analysis of breast cancer PDXs from longitudinal targeted scDNA-sequencing experiments delivers a high-resolution temporal characterization of intra-tumor heterogeneity.

807. Same-day Transmission Analysis of Nosocomial Transmission Using Nanopore Whole Genome Sequencing

Abstract Background Healthcare associated infections (HAIs) are a major contributor to patient morbidity and mortality worldwide. HAIs are increasingly important due to the rise of multidrug resistant pathogens which can lead to deadly nosocomial outbreaks. Current methods for investigating transmissions are slow, costly, or have poor detection resolution. A rapid, cost-effective and high-resolution method to identify transmission events is imperative to guide infection control. Whole genome sequencing of infecting pathogens paired with a single nucleotide polymorphism (SNP) analysis can provide high-resolution clonality determination, yet these methods typically have long turnaround times. Here we examined the utility of the Oxford Nanopore Technologies (ONT) platform, a rapid sequencing technology, for whole genome sequencing based transmission analysis. Methods We developed a SNP calling pipeline customized for ONT data, which exhibit higher sequencing error rates and can therefore be challenging for transmission analysis. The pipeline leverages the latest basecalling tools as well as a suite of custom variant calling and filtering algorithms to achieve highest accuracy in clonality calls compared to Illumina-based sequencing. We also capitalize on ONT long reads by assembling outbreak-specific genomes in order to overcome the need for an external reference genome. Results We examined 20 bacterial isolates from 5 HAI investigations previously performed at Day Zero Diagnostics as part of epiXact®, our commercialized Illumina-based HAI sequencing and analysis service. Using the ONT data and pipeline, we achieved greater than 90% SNP-calling sensitivity and precision, allowing 100% accuracy of clonality classification compared to Illumina-based results across common HAI species. We demonstrate the validity and increased resolution of our SNP analysis pipeline using assembled genomes from each outbreak. We also demonstrate that this ONT-based workflow can produce isolate to transmission determination (i.e. including WGS and analysis) in less than 24 hours. SNP calling performance ONT-based SNP calling sensitivity and precision compared to Illumina-based pipeline Conclusion We demonstrate the utility of ONT for HAI investigation, establishing the potential to transform healthcare epidemiology with same-day high-resolution transmission determination. Disclosures Mohamad Sater, PhD, Day Zero Diagnostics (Employee, Shareholder) Remy Schwab, MSc, Day Zero Diagnostics (Employee, Shareholder) Ian Herriott, BS, Day Zero Diagnostics (Employee, Shareholder) Tim Farrell, MS, Day Zero Diagnostics, Inc. (Employee, Shareholder) Miriam Huntley, PhD, Day Zero Diagnostics (Employee, Shareholder)

High-Throughput Nucleotide Resolution Predictions of Assay Limitations Increase the Reliability and Concordance of Clinical Tests.

The ability of next-generation sequencing (NGS) assays to interrogate thousands of genomic loci has revolutionized genetic testing. However, translation to the clinic is impeded by false-negative results that pose a risk to patients. In response, regulatory bodies are calling for reliability measures to be reported alongside NGS results. Existing methods to estimate reliability do not account for sample- and position-specific variability, which can be significant. Here, we report an approach that computes reliability metrics for every genomic position and sample interrogated by an NGS assay. Our approach predicts the limit of detection (LOD), the lowest reliably detectable variant fraction, by taking technical factors into account. We initially explored how LOD is affected by input material amount, library conversion rate, sequencing coverage, and sequencing error rate. This revealed that LOD depends heavily on genomic context and sample properties. Using these insights, we developed a computational approach to predict LOD on the basis of a biophysical model of the NGS workflow. We focused on targeted assays for cell-free DNA, but, in principle, this approach applies to any NGS assay. We validated our approach by showing that it accurately predicts LOD and distinguishes reliable from unreliable results when screening 580 lung cancer samples for actionable mutations. Compared with a standard variant calling workflow, our approach avoided most false negatives and improved interassay concordance from 94% to 99%. Our approach, which we name LAVA (LOD-aware variant analysis), reports the LOD for every position and sample interrogated by an NGS assay. This enables reliable results to be identified and improves the transparency and safety of genetic tests.

RDAClone: Deciphering Tumor Heterozygosity through Single-Cell Genomics Data Analysis with Robust Deep Autoencoder.

Rapid advances in single-cell genomics sequencing (SCGS) have allowed researchers to characterize tumor heterozygosity with unprecedented resolution and reveal the phylogenetic relationships between tumor cells or clones. However, high sequencing error rates of current SCGS data, i.e., false positives, false negatives, and missing bases, severely limit its application. Here, we present a deep learning framework, RDAClone, to recover genotype matrices from noisy data with an extended robust deep autoencoder, cluster cells into subclones by the Louvain-Jaccard method, and further infer evolutionary relationships between subclones by the minimum spanning tree. Studies on both simulated and real datasets demonstrate its robustness and superiority in data denoising, cell clustering, and evolutionary tree reconstruction, particularly for large datasets.

Long-read sequencing settings for efficient structural variation detection based on comprehensive evaluation

BackgroundWith the rapid development of long-read sequencing technologies, it is possible to reveal the full spectrum of genetic structural variation (SV). However, the expensive cost, finite read length and high sequencing error for long-read data greatly limit the widespread adoption of SV calling. Therefore, it is urgent to establish guidance concerning sequencing coverage, read length, and error rate to maintain high SV yields and to achieve the lowest cost simultaneously.ResultsIn this study, we generated a full range of simulated error-prone long-read datasets containing various sequencing settings and comprehensively evaluated the performance of SV calling with state-of-the-art long-read SV detection methods. The benchmark results demonstrate that almost all SV callers perform better when the long-read data reach 20× coverage, 20 kbp average read length, and approximately 10–7.5% or below 1% error rates. Furthermore, high sequencing coverage is the most influential factor in promoting SV calling, while it also directly determines the expensive costs.ConclusionsBased on the comprehensive evaluation results, we provide important guidelines for selecting long-read sequencing settings for efficient SV calling. We believe these recommended settings of long-read sequencing will have extraordinary guiding significance in cutting-edge genomic studies and clinical practices.

A First Genome Survey and Genomic SSR Marker Analysis of Trematomus loennbergii Regan, 1913.

Simple SummaryThe scaly rockcod (Trematomus loennbergii) is distributed in the Antarctic Ocean and this area is isolated by the Antarctic Circumpolar Current. It is important region to study evolutionary diversity. Trematomus is the main genus, having 11 species, and their habit distribution is well known. However, their genetic and genomic information is not studied. In addition, some species have similar morphology. In this study, a genome survey of T. loennbergii and microsatellite motif analysis were conducted to obtain genomic profile. The fundamental data such as genome size, heterozygosity ratio, duplication ration and microsatellite motifs were obtained. These data will provide a foundation for further whole-genome sequencing and the development of new molecular markers of T. loennbergii.Trematomus loennbergii Regan, 1913, is an evolutionarily important marine fish species distributed in the Antarctic Ocean. However, its genome has not been studied to date. In the present study, whole genome sequencing was performed using next-generation sequencing (NGS) technology to characterize its genome and develop genomic microsatellite markers. The 25-mer frequency distribution was estimated to be the best, and the genome size was predicted to be 815,042,992 bp. The heterozygosity, average rate of read duplication, and sequencing error rates were 0.536%, 0.724%, and 0.292%, respectively. These data were used to analyze microsatellite markers, and a total of 2,264,647 repeat motifs were identified. The most frequent repeat motif was di-nucleotide with 87.00% frequency, followed by tri-nucleotide (10.45%), tetra-nucleotide (1.94%), penta-nucleotide (0.34%), and hexa-nucleotide (0.27%). The AC repeat motif was the most abundant motif among di-nucleotides and among all repeat motifs. Among microsatellite markers, 181 markers were selected and PCR technology was used to validate several markers. A total of 15 markers produced only one band. In summary, these results provide a good basis for further studies, including evolutionary biology studies and population genetics of Antarctic fish species.

Minimizer-space de Bruijn graphs: Whole-genome assembly of long reads in minutes on a personal computer.

DNA sequencing data continue to progress toward longer reads with increasingly lower sequencing error rates. Here, we define an algorithmic approach, mdBG, that makes use of minimizer-space de Bruijn graphs to enable long-read genome assembly. mdBG achieves orders-of-magnitude improvement in both speed and memory usage over existing methods without compromising accuracy. A human genome is assembled in under 10min using 8 cores and 10 GB RAM, and 60 Gbp of metagenome reads are assembled in 4min using 1 GB RAM. In addition, we constructed a minimizer-space de Bruijn graph-based representation of 661,405 bacterial genomes, comprising 16 million nodes and 45 million edges, and successfully search it for anti-microbial resistance (AMR) genes in 12min. We expect our advances to be essential to sequence analysis, given the rise of long-read sequencing in genomics, metagenomics, and pangenomics. Code for constructing mdBGs is freely available for download at https://github.com/ekimb/rust-mdbg/.

Handling of targeted amplicon sequencing data focusing on index hopping and demultiplexing using a nested metabarcoding approach in ecology

High-throughput sequencing platforms are increasingly being used for targeted amplicon sequencing because they enable cost-effective sequencing of large sample sets. For meaningful interpretation of targeted amplicon sequencing data and comparison between studies, it is critical that bioinformatic analyses do not introduce artefacts and rely on detailed protocols to ensure that all methods are properly performed and documented. The analysis of large sample sets and the use of predefined indexes create challenges, such as adjusting the sequencing depth across samples and taking sequencing errors or index hopping into account. However, the potential biases these factors introduce to high-throughput amplicon sequencing data sets and how they may be overcome have rarely been addressed. On the example of a nested metabarcoding analysis of 1920 carabid beetle regurgitates to assess plant feeding, we investigated: (i) the variation in sequencing depth of individually tagged samples and the effect of library preparation on the data output; (ii) the influence of sequencing errors within index regions and its consequences for demultiplexing; and (iii) the effect of index hopping. Our results demonstrate that despite library quantification, large variation in read counts and sequencing depth occurred among samples and that the sequencing error rate in bioinformatic software is essential for accurate adapter/primer trimming and demultiplexing. Moreover, setting an index hopping threshold to avoid incorrect assignment of samples is highly recommended.

Nanopore sequencing approach for immunoglobulin gene analysis in chronic lymphocytic leukemia

The evaluation of the somatic hypermutation of the clonotypic immunoglobulin heavy variable gene has become essential in the therapeutic management in chronic lymphocytic leukemia patients. European Research Initiative on Chronic Lymphocytic Leukemia promotes good practices and standardized approaches to this assay but often they are labor-intensive, technically complex, with limited in scalability. The use of next-generation sequencing in this analysis has been widely tested, showing comparable accuracy and distinct advantages. However, the adoption of the next generation sequencing requires a high sample number (run batching) to be economically convenient, which could lead to a longer turnaround time. Here we present data from nanopore sequencing for the somatic hypermutation evaluation compared to the standard method. Our results show that nanopore sequencing is suitable for immunoglobulin heavy variable gene mutational analysis in terms of sensitivity, accuracy, simplicity of analysis and is less time-consuming. Moreover, our work showed that the development of an appropriate data analysis pipeline could lower the nanopore sequencing error rate attitude.

Performance assessment of DNA sequencing platforms in the ABRF Next-Generation Sequencing Study.

Assessing the reproducibility, accuracy and utility of massively parallel DNA sequencing platforms remains an ongoing challenge. Here the Association of Biomolecular Resource Facilities (ABRF) Next-Generation Sequencing Study benchmarks the performance of a set of sequencing instruments (HiSeq/NovaSeq/paired-end 2 × 250-bp chemistry, Ion S5/Proton, PacBio circular consensus sequencing (CCS), Oxford Nanopore Technologies PromethION/MinION, BGISEQ-500/MGISEQ-2000 and GS111) on human and bacterial reference DNA samples. Among short-read instruments, HiSeq 4000 and X10 provided the most consistent, highest genome coverage, while BGI/MGISEQ provided the lowest sequencing error rates. The long-read instrument PacBio CCS had the highest reference-based mapping rate and lowest non-mapping rate. The two long-read platforms PacBio CCS and PromethION/MinION showed the best sequence mapping in repeat-rich areas and across homopolymers. NovaSeq 6000 using 2 × 250-bp read chemistry was the most robust instrument for capturing known insertion/deletion events. This study serves as a benchmark for current genomics technologies, as well as a resource to inform experimental design and next-generation sequencing variant calling.

PB-Motif—A Method for Identifying Gene/Pseudogene Rearrangements With Long Reads: An Application to CYP21A2 Genotyping

Long read sequencing technologies have the potential to accurately detect and phase variation in genomic regions that are difficult to fully characterize with conventional short read methods. These difficult to sequence regions include several clinically relevant genes with highly homologous pseudogenes, many of which are prone to gene conversions or other types of complex structural rearrangements. We present PB-Motif, a new method for identifying rearrangements between two highly homologous genomic regions using PacBio long reads. PB-Motif leverages clustering and filtering techniques to efficiently report rearrangements in the presence of sequencing errors and other systematic artifacts. Supporting reads for each high-confidence rearrangement can then be used for copy number estimation and phased variant calling. First, we demonstrate PB-Motif's accuracy with simulated sequence rearrangements of PMS2 and its pseudogene PMS2CL using simulated reads sweeping over a range of sequencing error rates. We then apply PB-Motif to 26 clinical samples, characterizing CYP21A2 and its pseudogene CYP21A1P as part of a diagnostic assay for congenital adrenal hyperplasia. We successfully identify damaging variation and patient carrier status concordant with clinical diagnosis obtained from multiplex ligation-dependent amplification (MLPA) and Sanger sequencing. The source code is available at: github.com/zstephens/pb-motif.

Low-coverage whole-genome sequencing in livestock species for individual traceability and parentage testing

Procedures for genetic traceability of animal products and parentage testing mainly focus on microsatellites or SNPs panels. Nevertheless, current availability of high-throughput sequencing technologies must be considered as an appealing alternative. This research focused on the evaluation of low-coverage whole-genome sequencing for traceability and paternity testing purposes, within a context of evidential statistics. Analyses were performed on a simulation basis and assumed individuals with 30 100-Mb/100-cM chromosome pairs and ~1,000,000 polymorphic SNPs per chromosome. Ten independent populations were simulated under recombination and mutation with effective populations size 100 (generations 1–1000), 10,000 (generation 1001) and 25,000 (generation 1002), and this last generation was retained for analytical purposes. Appropriate both traceability and paternity tests were developed and evaluated on different high-throughput sequencing scenarios accounting for genome coverage depth (0.01×, 0.05×, 0.1× and 0.5×), length of base-pair reads (100, 1000 and 10,000 bp), and sequencing error rate (0%, 1% and 10%). Assuming true sequencing error rates and genotypic frequencies, 0.05× genome coverage depth guaranteed 100% sensitivity and specificity for traceability and paternity tests (n = 1000). Same results were obtained when sequencing error rate was arbitrarily set to 0, or the maximum value assumed during simulation (i.e., 1%). In a similar way, uncertainly about genotypic frecuencies did not impair sensitivity under 0.05× genome coverage, although it reduced specificity for paternity tests up to 85.2%. These results highlighted low-coverage whole-genome sequencing as a promising tool for the livestock and food industry with both technological and (maybe) economic advantages.

Accurate assembly of minority viral haplotypes from next-generation sequencing through efficient noise reduction.

Rapidly evolving RNA viruses continuously produce minority haplotypes that can become dominant if they are drug-resistant or can better evade the immune system. Therefore, early detection and identification of minority viral haplotypes may help to promptly adjust the patient’s treatment plan preventing potential disease complications. Minority haplotypes can be identified using next-generation sequencing, but sequencing noise hinders accurate identification. The elimination of sequencing noise is a non-trivial task that still remains open. Here we propose CliqueSNV based on extracting pairs of statistically linked mutations from noisy reads. This effectively reduces sequencing noise and enables identifying minority haplotypes with the frequency below the sequencing error rate. We comparatively assess the performance of CliqueSNV using an in vitro mixture of nine haplotypes that were derived from the mutation profile of an existing HIV patient. We show that CliqueSNV can accurately assemble viral haplotypes with frequencies as low as 0.1% and maintains consistent performance across short and long bases sequencing platforms.