Detecting copy number variation in next generation sequencing data from diagnostic gene panels

Background Detection of copy number variation (CNV) in genes associated with disease is important in genetic diagnostics, and next generation sequencing (NGS) technology provides data that can be used for CNV detection. However, CNV detection based on NGS data is in general not often used in diagnostic labs as the data analysis is challenging, especially with data from targeted gene panels. Wet lab methods like MLPA (MRC Holland) are widely used, but are expensive, time consuming and have gene-specific limitations. Our aim has been to develop a bioinformatic tool for CNV detection from NGS data in medical genetic diagnostic samples. Results Our computational pipeline for detection of CNVs in NGS data from targeted gene panels utilizes coverage depth of the captured regions and calculates a copy number ratio score for each region. This is computed by comparing the mean coverage of the sample with the mean coverage of the same region in other samples, defined as a pool. The pipeline selects pools for comparison dynamically from previously sequenced samples, using the pool with an average coverage depth that is nearest to the one of the samples. A sliding window-based approach is used to analyze each region, where length of sliding window and sliding distance can be chosen dynamically to increase or decrease the resolution. This helps in detecting CNVs in small or partial exons. With this pipeline we have correctly identified the CNVs in 36 positive control samples, with sensitivity of 100% and specificity of 91%. We have detected whole gene level deletion/duplication, single/multi exonic level deletion/duplication, partial exonic deletion and mosaic deletion. Since its implementation in mid-2018 it has proven its diagnostic value with more than 45 CNV findings in routine tests. Conclusions With this pipeline as part of our diagnostic practices it is now possible to detect partial, single or multi-exonic, and intragenic CNVs in all genes in our target panel. This has helped our diagnostic lab to expand the portfolio of genes where we offer CNV detection, which previously was limited by the availability of MLPA kits.

Systematic Comparison of the Performances of De Novo Genome Assemblers for Oxford Nanopore Technology Reads From Piroplasm

BackgroundEmerging long reads sequencing technology has greatly changed the landscape of whole-genome sequencing, enabling scientists to contribute to decoding the genetic information of non-model species. The sequences generated by PacBio or Oxford Nanopore Technology (ONT) be assembled de novo before further analyses. Some genome de novo assemblers have been developed to assemble long reads generated by ONT. The performance of these assemblers has not been completely investigated. However, genome assembly is still a challenging task.Methods and ResultsWe systematically evaluated the performance of nine de novo assemblers for ONT on different coverage depth datasets. Several metrics were measured to determine the performance of these tools, including N50 length, sequence coverage, runtime, easy operation, accuracy of genome and genomic completeness in varying depths of coverage. Based on the results of our assessments, the performances of these tools are summarized as follows: 1) Coverage depth has a significant effect on genome quality; 2) The level of contiguity of the assembled genome varies dramatically among different de novo tools; 3) The correctness of an assembled genome is closely related to the completeness of the genome. More than 30× nanopore data can be assembled into a relatively complete genome, the quality of which is highly dependent on the polishing using next generation sequencing data.ConclusionConsidering the results of our investigation, the advantage and disadvantage of each tool are summarized and guidelines of selecting assembly tools are provided under specific conditions.

Whole human genome 5'-mC methylation analysis using long read nanopore sequencing

DNA methylation is a type of epigenetic modification that affects gene expression regulation and is associated with several human diseases. Microarray and short read sequencing technologies are often used to study 5'-methylcytosine (5'-mC) modification of CpG dinucleotides in the human genome. Although both technologies produce trustable results, the evaluation of the methylation status of CpG sites suffers from the potential side effects of DNA modification by bisulfite and the ambiguity of mapping short reads in repetitive and highly homologous genomic regions, respectively. Nanopore sequencing is an attractive alternative for the study of 5'-mC since the long reads produced by this technology allow to resolve those genomic regions more easily. Moreover, it allows direct sequencing of native DNA molecules using a fast library preparation procedure. In this work we show that 10X coverage depth nanopore sequencing, using DNA from a human cell line, produces 5'-mC methylation frequencies consistent with those obtained by methylation microarray and digital restriction enzyme analysis of methylation. In particular, the correlation of methylation values ranged from 0.73 to 0.90 using an average genome sequencing coverage depth <2X or a minimum read support of 17X for each CpG site, respectively. We also showed that a minimum of 5 reads per CpG yields strong correlations (>0.89) between sequencing runs and an almost uniform variation in methylation frequencies of CpGs across the entire value range. Furthermore, nanopore sequencing was able to correctly display methylation frequency patterns according to genomic annotations, including a majority of unmethylated and methylated sites in the CpG islands and inter-CpG island regions, respectively. These results demonstrate that low coverage depth nanopore sequencing is a fast, reliable and unbiased approach to the study of 5'-mC in the human genome.

Randomly primed, strand-switching, MinION-based sequencing for the detection and characterization of cultured RNA viruses

RNA viruses rapidly mutate, which can result in increased virulence, increased escape from vaccine protection, and false-negative detection results. Targeted detection methods have a limited ability to detect unknown viruses and often provide insufficient data to detect coinfections or identify antigenic variants. Random, deep sequencing is a method that can more fully detect and characterize RNA viruses and is often coupled with molecular techniques or culture methods for viral enrichment. We tested viral culture coupled with third-generation sequencing for the ability to detect and characterize RNA viruses. Cultures of bovine viral diarrhea virus, canine distemper virus (CDV), epizootic hemorrhagic disease virus, infectious bronchitis virus, 2 influenza A viruses, and porcine respiratory and reproductive syndrome virus were sequenced on the MinION platform using a random, reverse primer in a strand-switching reaction, coupled with PCR-based barcoding. Reads were taxonomically classified and used for reference-based sequence building using a stock personal computer. This method accurately detected and identified complete coding sequence genomes with a minimum of 20× coverage depth for all 7 viruses, including a sample containing 2 viruses. Each lineage-typing region had at least 26× coverage depth for all viruses. Furthermore, analyzing the CDV sample through a pipeline devoid of CDV reference sequences modeled the ability of this protocol to detect unknown viruses. Our results show the ability of this technique to detect and characterize dsRNA, negative- and positive-sense ssRNA, and nonsegmented and segmented RNA viruses.

Comparison of Deep Whole Exome Versus Targeted Gene Sequencing for Assessment of Persistent Molecular Disease in Acute Myeloid Leukemia Samples

Background There is growing evidence that genomics-based assessment of persistent molecular disease (PMD) may be a useful risk stratification tool for patients with acute myeloid leukemia (AML). However, there is no consensus about the optimal approach for detection and monitoring of leukemia-associated mutations (LAMs) for PMD testing. One approach is whole exome sequencing (WES), which provides a comprehensive assessment of the clonal architecture by identifying LAMs across the exome at diagnosis, which can then be measured in follow-up samples. Another approach is to use highly sensitive targeted gene sequencing (TGS) to detect persistent LAMs even at very low levels, although detection is limited to genes interrogated by the panel. Although both of approaches have been shown to predict outcome in retrospective studies, there are substantial differences in the number of LAMs identified and the limit of detection, and a head-to-head comparison has not yet been reported. Here, we use a defined cohort enrolled in a prospective clinical trial to compare PMD results from deep WES to error-corrected TGS. Methods Cohort: Patients were age 18-60 with de novo AML classified as intermediate risk by European Leukemia Net criteria, who achieved a morphologic remission after undergoing standard induction therapy. All patients were enrolled in a prospective clinical trial (NCT02756962). Sequencing: Deep WES was performed using DNA from normal tissue (buccal swab or skin) and pre- and post-induction (~day 30) bone marrow (BM) samples to achieve an average coverage depth of ~600x. LAMs identified via paired tumor/normal analysis of the pre-induction sample were queried in the post-induction sample for assessment of PMD. PMD testing by TGS was performed using error-corrected sequencing of 40 genes recurrently mutated AML genes to an average error-corrected coverage depth of ~4500x. Definitions: Based on previously published work, we used two separate variant allele frequency (VAF) cutoffs to define PMD. For WES, PMD+ was defined as ≥1 LAM with a VAF &gt;2.5% (Klco JAMA 2015). For TGS, PMD+ was defined as having ≥1 LAM with a VAF &gt;0.5% (Duncavage NEJM 2018). LAMs were sub-classified per Table 1. TGS mutations not identified as LAMs by WES at diagnosis were excluded from the primary analysis. Results 31 patients were studied. LAMs are summarized in Table 1. 20 patients (65%) were PMD+ by WES after induction, and 22 patients (71%) were PMD+ by TGS. The concordance between WES and TGS was 81% (25/31) (Table 2). Two patients were PMD+ by WES only, due to the persistence of LAMs in the exome space, but not in the targeted panel. Four patients were PMD+ by TGS only, due to the presence of recurrent mutations at VAFs below the detection limit of WES (range: 0.59 - 1.90%). Two patients were PMD+ by both assays, but due to different mutations. All other patients who were PMD+ by both assays had at least one overlapping mutation. Analysis of the mutations that persisted after therapy in both assays showed that 26% of patients (8/31) were PMD+ by TGS because of mutations in DNMT3A, TET2, or ASXL1 (i.e., "DTA" mutations). All of these patients were also PMD+ by WES, with 7 of the 8 patients having ≥1 additional non-DTA mutation (median: 3, range: 1 - 7). In an exploratory analysis, 22 additional mutations in 13 patients were identified by TGS that were not detected by WES on the diagnostic sample. This included two patients who were PMD-, but who had new mutations in TET2 and DNMT3A, respectively, likely representing selection for ancestral clones that were unrelated to the AML founding clone. Conclusion Concordance between WES and TGS-based PMD assessment was high. Discordant results were generally driven by non-recurrent mutations detected by WES, and low-level mutations detected by the high coverage, error-corrected TGS. Although isolated DTA mutations were common on TGS, WES analysis showed additional LAMs accompanied these variants in most cases, indicating the persistence of an ancestral leukemic clone that may provide useful prognostic information. We also observed new, low-level mutations that emerged after therapy in 42% of patients, some of which were not part of the leukemic clone identified at diagnosis. This indicates that use of highly sensitive PMD approaches may be challenging without pre-induction mutation testing, which is required to understand the relevance of markers of persistent molecular disease. Disclosures Jacoby: AbbVie: Research Funding; Takeda: Consultancy; Jazz Pharmaceuticals: Research Funding.

Metaviral SPAdes: assembly of viruses from metagenomic data

Motivation Although the set of currently known viruses has been steadily expanding, only a tiny fraction of the Earth’s virome has been sequenced so far. Shotgun metagenomic sequencing provides an opportunity to reveal novel viruses but faces the computational challenge of identifying viral genomes that are often difficult to detect in metagenomic assemblies. Results We describe a MetaviralSPAdes tool for identifying viral genomes in metagenomic assembly graphs that is based on analyzing variations in the coverage depth between viruses and bacterial chromosomes. We benchmarked MetaviralSPAdes on diverse metagenomic datasets, verified our predictions using a set of virus-specific Hidden Markov Models and demonstrated that it improves on the state-of-the-art viral identification pipelines. Availability and implementation Metaviral SPAdes includes ViralAssembly, ViralVerify and ViralComplete modules that are available as standalone packages: https://github.com/ablab/spades/tree/metaviral_publication, https://github.com/ablab/viralVerify/ and https://github.com/ablab/viralComplete/. Contact [email protected] Supplementary information Supplementary data are available at Bioinformatics online.

Probabilistic model based on circular statistics for quantifying coverage depth dynamics originating from DNA replication

Background With the development of DNA sequencing technology, static omics profiling in microbial communities, such as taxonomic and functional gene composition determination, has become possible. Additionally, the recently proposed in situ growth rate estimation method allows the applicable range of current comparative metagenomics to be extended to dynamic profiling. However, with this method, the applicable target range is presently limited. Furthermore, the characteristics of coverage depth during replication have not been sufficiently investigated. Results We developed a probabilistic model that mimics coverage depth dynamics. This statistical model explains the bias that occurs in the coverage depth due to DNA replication and errors that arise from coverage depth observation. Although our method requires a complete genome sequence, it involves a stable to low coverage depth (>0.01×). We also evaluated the estimation using real whole-genome sequence datasets and reproduced the growth dynamics observed in previous studies. By utilizing a circular distribution in the model, our method facilitates the quantification of unmeasured coverage depth features, including peakedness, skewness, and degree of density, around the replication origin. When we applied the model to time-series culture samples, the skewness parameter, which indicates the asymmetry, was stable over time; however, the peakedness and degree of density parameters, which indicate the concentration level at the replication origin, changed dynamically. Furthermore, we demonstrated the activity measurement of multiple replication origins in a single chromosome. Conclusions We devised a novel framework for quantifying coverage depth dynamics. Our study is expected to serve as a basis for replication activity estimation from a broader perspective using the statistical model.