Sequencing Depth Research Articles

Primer pairs, PCR conditions, and peptide nucleic acid clamps affect fungal diversity assessment from plant root tissues

ABSTRACT High-throughput sequencing has become a prominent tool to assess plant-associated microbial diversity. Still, some technical challenges remain in characterising these communities, notably due to plant and fungal DNA co-amplification. Fungal-specific primers, Peptide Nucleic Acid (PNA) clamps, or adjusting PCR conditions are approaches to limit plant DNA contamination. However, a systematic comparison of these factors and their interactions, which could limit plant DNA contamination in the study of plant mycobiota, is still lacking. Here, three primers targeting the ITS2 region were evaluated alone or in combination with PNA clamps both on nettle (Urtica dioica) root DNA and a mock community. PNA clamps did not improve the richness or diversity of the fungal communities but increased the number of fungal reads. Among the tested factors, the most significant was the primer pair. Specifically, the 5.8S-Fun/ITS4-Fun pair exhibited a higher OTU richness but fewer fungal reads. Our study demonstrates that the choice of primers is critical for limiting plant and fungal DNA co-amplification. PNA clamps increase the number of fungal reads when ITS2 is targeted but do not result in higher fungal diversity recovery at high sequencing depth. At lower read depths, PNA clamps might enhance microbial diversity quantification for primer pairs lacking fungal specificity.

Blood transcriptomics analysis offers insights into variant-specific immune response to SARS-CoV-2

Bulk RNA sequencing (RNA-seq) of blood is typically used for gene expression analysis in biomedical research but is still rarely used in clinical practice. In this study, we propose that RNA-seq should be considered a diagnostic tool, as it offers not only insights into aberrant gene expression and splicing but also delivers additional readouts on immune cell type composition as well as B-cell and T-cell receptor (BCR/TCR) repertoires. We demonstrate that RNA-seq offers insights into a patient’s immune status via integrative analysis of RNA-seq data from patients infected with various SARS-CoV-2 variants (in total 196 samples with up to 200 million reads sequencing depth). We compare the results of computational cell-type deconvolution methods (e.g., MCP-counter, xCell, EPIC, quanTIseq) to complete blood count data, the current gold standard in clinical practice. We observe varying levels of lymphocyte depletion and significant differences in neutrophil levels between SARS-CoV-2 variants. Additionally, we identify B and T cell receptor (BCR/TCR) sequences using the tools MiXCR and TRUST4 to show that—combined with sequence alignments and BLASTp—they could be used to classify a patient's disease. Finally, we investigated the sequencing depth required for such analyses and concluded that 10 million reads per sample is sufficient. In conclusion, our study reveals that computational cell-type deconvolution and BCR/TCR methods using bulk RNA-seq analyses can supplement missing CBC data and offer insights into immune responses, disease severity, and pathogen-specific immunity, all achievable with a sequencing depth of 10 million reads per sample.

Abstract A028: Quantifying resistance to targeted anticancer therapies at scale with duplex sequencing

Abstract Drug resistance to targeted anticancer therapeutics is one of the most researched problems in cancer genetics. A fundamental limiting factor facing most drug resistance studies is that resistance is studied retrospectively, i.e., mutants are characterized for their drug resistance only once they are seen in the clinic. Deep mutational scanning (DMS) is a field of functional genomics that can enable a-priori identification of resistant mutants- long before they are seen in the clinic. Quantifying resistance prospectively can guide clinical treatment decision-making and drug discovery efforts. In order to guide clinical practice, a DMS screen would need to have the sensitivity to measure drug resistance well and be able to relate screen measurements to clinical pharmacology. However, current DMS screens are not adopted for drug resistance and face unique technical challenges that limit the overall interpretability of screen data. Here we demonstrate the first scalable duplex sequencing DMS screen and use it to study Imatinib resistance in BCR-ABL. We first studied the pooled growth of 20 known Imatinib resistant mutants at low allele frequencies. We observed significant depletion artifacts and dose-response variations when measuring mutant outgrowth using allele frequency alone. To correct for these artifacts, we developed an inference-based approach that identifies and addresses depletion and dose variation artifacts that are commonly seen in drug resistance screens. The described measurement methodology enables precise and accurate tracking of low allele-frequency mutants. We also describe the first error-corrected duplex sequencing workflow that can achieve sequencing depths of 3 × 105, which is 20x higher than commercially available duplex sequencing kits. Sequencing at these depths enabled us to detect and measure 96.7% of all possible amino acid substitutions in the ABL kinase (n=4,788 mutants). We used our measurements to characterize all treatment-emergent variants of uncertain drug resistance in CML patients. We find that 22% of the rare variants seen in Imatinib refractory CML patients drive imatinib resistance, while the rest are likely bystanders of another resistance mechanism. Furthermore, because we can relate our measurements to clinical pharmacology, we identify variants that are good candidates for dose escalation from 400mg to 800mg daily dose of imatinib. Here, we demonstrate the first scalable duplex sequencing DMS screen that achieves high levels of sensitivity and specificity in measuring mutant drug resistance. We demonstrate the utility of this workflow by characterizing all variants of uncertain drug resistance in the ABL kinase. Addressing technical key barriers that face DMS screens of drug resistance enables us to make recommendations for treatment decisions on variants of unknown drug resistance. This approach can be used by pharmaceutical companies and geneticists to prospectively identify drug resistant variants to investigational drugs in candidate druggable biomarkers. Citation Format: Haider Inam, Josh Reynolds, Marta Tomaszkiewicz, Scott Leighow, Yiyun Rao, Ivan Sokirniy, Justin Pritchard. Quantifying resistance to targeted anticancer therapies at scale with duplex sequencing [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Translating Cancer Evolution and Data Science: The Next Frontier; 2023 Dec 3-6; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(3 Suppl_2):Abstract nr A028.

Limited ability of increased sequencing depth in detecting cases missed by noninvasive prenatal testing: a comparative analysis of 3 clinical cases

Increased sequencing depth can improve the detection rate of noninvasive prenatal testing (NIPT) for chromosome aneuploidies and copy number variations (CNVs). However, due to the technical limitations of NIPT, false-positives and false-negatives are inevitable. False-positives for aneuploidy and CNVs have been widely reported, but few missed cases have been reported. In this study, we report 3 patients missed by NIPT, which were still missed after increasing the sequencing depth. To verify the detection efficiency of the platform, the results of NIPT in 32,796 patients treated in Yulin Women and Children Health Care Hospital from 2020 to 2022 were retrospectively analyzed. Data on false-negative cases found by postnatal follow-up or amniocentesis were collected, and the sequencing data, pregnancy examination data, and postnatal follow-up results of these missed patients were summarized. Five patients missed by NIPT were found, and they were missed again by retesting or increasing the sequencing depth. Except for hypospadias found in 1 patient, ultrasonography of the other 4 patients showed no obvious abnormalities during the whole pregnancy. Our results suggest that pregnant women should be fully informed of the benefits and limitations of NIPT before undergoing the examination to avoid unnecessary medical disputes.

YACHT: an ANI-based statistical test to detect microbial presence/absence in a metagenomic sample.

In metagenomics, the study of environmentally associated microbial communities from their sampled DNA, one of the most fundamental computational tasks is that of determining which genomes from a reference database are present or absent in a given sample metagenome. Existing tools generally return point estimates, with no associated confidence or uncertainty associated with it. This has led to practitioners experiencing difficulty when interpreting the results from these tools, particularly for low-abundance organisms as these often reside in the "noisy tail" of incorrect predictions. Furthermore, few tools account for the fact that reference databases are often incomplete and rarely, if ever, contain exact replicas of genomes present in an environmentally derived metagenome. We present solutions for these issues by introducing the algorithm YACHT: Yes/No Answers to Community membership via Hypothesis Testing. This approach introduces a statistical framework that accounts for sequence divergence between the reference and sample genomes, in terms of ANI, as well as incomplete sequencing depth, thus providing a hypothesis test for determining the presence or absence of a reference genome in a sample. After introducing our approach, we quantify its statistical power and how this changes with varying parameters. Subsequently, we perform extensive experiments using both simulated and real data to confirm the accuracy and scalability of this approach. The source code implementing this approach is available via Conda and at https://github.com/KoslickiLab/YACHT. We also provide the code for reproducing experiments at https://github.com/KoslickiLab/YACHT-reproducibles.

Kled: an ultra-fast and sensitive structural variant detection tool for long-read sequencing data.

Structural Variants (SVs) are a crucial type of genetic variant that can significantly impact phenotypes. Therefore, the identification of SVs is an essential part of modern genomic analysis. In this article, we present kled, an ultra-fast and sensitive SV caller for long-read sequencing data given the specially designed approach with a novel signature-merging algorithm, custom refinement strategies and a high-performance program structure. The evaluation results demonstrate that kled can achieve optimal SV calling compared to several state-of-the-art methods on simulated and real long-read data for different platforms and sequencing depths. Furthermore, kled excels at rapid SV calling and can efficiently utilize multiple Central Processing Unit (CPU) cores while maintaining low memory usage. The source code for kled can be obtained from https://github.com/CoREse/kled.

On the core segmentation algorithms of copy number variation detection tools.

Shotgun sequencing is a high-throughput method used to detect copy number variants (CNVs). Although there are numerous CNV detection tools based on shotgun sequencing, their quality varies significantly, leading to performance discrepancies. Therefore, we conducted a comprehensive analysis of next-generation sequencing-based CNV detection tools over the past decade. Our findings revealed that the majority of mainstream tools employ similar detection rationale: calculates the so-called read depth signal from aligned sequencing reads and then segments the signal by utilizing either circular binary segmentation (CBS) or hidden Markov model (HMM). Hence, we compared the performance of those two core segmentation algorithms in CNV detection, considering varying sequencing depths, segment lengths and complex types of CNVs. To ensure a fair comparison, we designed a parametrical model using mainstream statistical distributions, which allows for pre-excluding bias correction such as guanine-cytosine (GC) content during the preprocessing step. The results indicate the following key points: (1) Under ideal conditions, CBS demonstrates high precision, while HMM exhibits a high recall rate. (2) For practical conditions, HMM is advantageous at lower sequencing depths, while CBS is more competitive in detecting small variant segments compared to HMM. (3) In case involving complex CNVs resembling real sequencing, HMM demonstrates more robustness compared with CBS. (4) When facing large-scale sequencing data, HMM costs less time compared with the CBS, while their memory usage is approximately equal. This can provide an important guidance and reference for researchers to develop new tools for CNV detection.

Rarefaction is currently the best approach to control for uneven sequencing effort in amplicon sequence analyses.

Sequencing 16S rRNA gene fragments has become a fundamental tool for understanding the diversity of microbial communities and the factors that affect their diversity. Due to technical challenges, it is common to observe wide variation in the number of sequences that are collected from different samples within the same study. However, the diversity metrics used by microbial ecologists are sensitive to differences in sequencing effort. Therefore, tools are needed to control for the uneven levels of sequencing. This simulation-based analysis shows that despite a longstanding controversy, rarefaction is the most robust approach to control for uneven sequencing effort. The controversy started because of confusion over the definition of rarefaction and violation of assumptions that are made by methods that have been borrowed from other fields. Microbial ecologists should use rarefaction.

Benchmarking of computational methods for m6A profiling with Nanopore direct RNA sequencing.

N6-methyladenosine (m6A) is the most abundant internal eukaryotic mRNA modification, and is involved in the regulation of various biological processes. Direct Nanopore sequencing of native RNA (dRNA-seq) emerged as a leading approach for its identification. Several software were published for m6A detection and there is a strong need for independent studies benchmarking their performance on data from different species, and against various reference datasets. Moreover, a computational workflow is needed to streamline the execution of tools whose installation and execution remains complicated. We developed NanOlympicsMod, a Nextflow pipeline exploiting containerized technology for comparing 14 tools for m6A detection on dRNA-seq data. NanOlympicsMod was tested on dRNA-seq data generated from in vitro (un)modified synthetic oligos. The m6A hits returned by each tool were compared to the m6A position known by design of the oligos. In addition, NanOlympicsMod was used on dRNA-seq datasets from wild-type and m6A-depleted yeast, mouse and human, and each tool's hits were compared to reference m6A sets generated by leading orthogonal methods. The performance of the tools markedly differed across datasets, and methods adopting different approaches showed different preferences in terms of precision and recall. Changing the stringency cut-offs allowed for tuning the precision-recall trade-off towards user preferences. Finally, we determined that precision and recall of tools are markedly influenced by sequencing depth, and that additional sequencing would likely reveal additional m6A sites. Thanks to the possibility of including novel tools, NanOlympicsMod will streamline the benchmarking of m6A detection tools on dRNA-seq data, improving future RNA modification characterization.

Deqformer: high-definition and scalable deep learning probe design method.

Target enrichment sequencing techniques are gaining widespread use in the field of genomics, prized for their economic efficiency and swift processing times. However, their success depends on the performance of probes and the evenness of sequencing depth among each probe. To accurately predict probe coverage depth, a model called Deqformer is proposed in this study. Deqformer utilizes the oligonucleotides sequence of each probe, drawing inspiration from Watson-Crick base pairing and incorporating two BERT encoders to capture the underlying information from the forward and reverse probe strands, respectively. The encoded data are combined with a feed-forward network to make precise predictions of sequencing depth. The performance of Deqformer is evaluated on four different datasets: SNP panel with 38200 probes, lncRNA panel with 2000 probes, synthetic panel with 5899 probes and HD-Marker panel for Yesso scallop with 11000 probes. The SNP and synthetic panels achieve impressive factor 3 of accuracy (F3acc) of 96.24% and 99.66% in 5-fold cross-validation. F3acc rates of over 87.33% and 72.56% are obtained when training on the SNP panel and evaluating performance on the lncRNA and HD-Marker datasets, respectively. Our analysis reveals that Deqformer effectively captures hybridization patterns, making it robust for accurate predictions in various scenarios. Deqformer leads to a novel perspective for probe design pipeline, aiming to enhance efficiency and effectiveness in probe design tasks.

Development and application of an ultra-sensitive ctDNA mutation profiling assay in monitoring therapy response and drug resistance.

264 Background: Next-generation sequencing (NGS) assays offer efficient high-throughput profiling methods for analyzing circulating tumor DNA (ctDNA) in blood or other liquid biopsy specimens. The ctDNA mutant allele fractions of patients after treatment is often very low and below the detection limit of regular cfDNA assays. Here we report the development and clinical application of a CLIA-certified ultra-sensitive NGS cfDNA assay in detecting the low-frequency ctDNA mutations and monitoring therapy response and drug resistance in patients with metastatic gastroesophageal adenocarcinoma under combined blockade treatments. Methods: PredicineCARE ULTRA cfDNA assay with 100,000X sequencing depth was validated under CLIA settings. Limit of blank (LoB) and limit of detection (LoD) were established by profiling 38 healthy donor plasma samples, 23 contrived human cfDNAs samples from patients with various cancers, and 30 replicates of well-established ctDNA reference standards. In silico down-sampling of FASTQ files was carried out to test the sensitivity of this assay with a lower coverage (50,000X). This assay was then adopted for longitudinal sequencing of 57 serial plasmas from 6 patients with HER2+ gastroesophageal adenocarcinomas (GEC) who underwent combined treatments (capecitabine, oxaliplatin, bevacizumab and trastuzumab) at different time points (baseline, response, and progression). Results: PredicineCARE ULTRA cfDNA assay exhibited exceptional sensitivity in calling single-nucleotide variants and indels, with a 98.0% detection rate at a LoD of 0.075% mutant allele frequency (MAF) for 60 ng cfDNA. This is significantly lower than the LoD (0.25% MAF) of a standard PredicineCARE cfDNA assay. The ultra-sensitive assay also displayed decent analytical specificity (>99.99%) and precision (intra-assay 100%; inter-assay 99.41%). In 6 GEC patients’ longitudinal samples, PredicineCARE ULTRA cfDNA assay detected 36 additional somatic mutations with the MAFs spanning in a range from 0.024% to 0.1% beyond 124 baseline mutations identified using standard PredicineCARE. In two representative cases presented here, all baseline mutations (genes TP53 and PIK3CA etc.) were consistently reported in the follow-up time points with a noticeable MAF reduction (lowest MAFs from 0.046% to 0.024%) during response. Subsequent elevations were observed in more genes, including low-frequency mutations (KRAS and ERBB2) not detected at baseline or only detected in PredicineCARE ULTRA. Conclusions: This study demonstrated the promise and clinical utility of the ultra-sensitive cfDNA molecular profiling at 100,000X sequencing depth in treatment selection, therapy monitoring, and drug resistance studies.

ScKWARN: Kernel-weighted-average robust normalization for single-cell RNA-seq data.

Single-cell RNA-seq normalization is an essential step to correct unwanted biases caused by sequencing depth, capture efficiency, dropout, and other technical factors. Existing normalization methods primarily reduce biases arising from sequencing depth by modeling count-depth relationship and/or assuming a specific distribution for read counts. However, these methods may lead to over or under-correction due to presence of technical biases beyond sequencing depth and the restrictive assumption on models and distributions. We present scKWARN, a Kernel Weighted Average Robust Normalization designed to correct known or hidden technical confounders without assuming specific data distributions or count-depth relationships. scKWARN generates a pseudo expression profile for each cell by borrowing information from its fuzzy technical neighbors through a kernel smoother. It then compares this profile against the reference derived from cells with the same bimodality patterns to determine the normalization factor. As demonstrated in both simulated and real datasets, scKWARN outperforms existing methods in removing a variety of technical biases while preserving true biological heterogeneity. scKWARN is freely available at https://github.com/cyhsuTN/scKWARN.

Development of a targeted genotyping platform for reproducible results within tetraploid and hexaploid blueberry

Blueberry (Vaccinium spp.) is one of the most economically important berry crops worldwide. Validation of genetic mapping studies is often hindered by asynchronous marker technology. The development of a standardized genotyping platform that targets a specific set of polymorphic loci can be a practical solution to unify the scientific and breeding community toward blueberry improvement. The objective of this study was to develop and evaluate a targeted genotyping platform for cultivated blueberries that is affordable, reproducible, and sufficiently high density to warrant large-scale adoption for genomic studies. The Flex-Seq platform was developed in a two-step procedure that resulted in 22,000 loci that yielded 194,365 single nucleotide polymorphisms when assessed in a diversity set of 192 samples including cultivated and other related wild Vaccinium species. Locus recovery averaged 89.4% in the cultivated polyploid blueberry (northern highbush [NHB], southern highbush [SHB], and rabbiteye [RE]) and on average 88.8% were polymorphic. While recovery of these loci was lower in the other Vaccinium species assayed, recovery remained high and ranged between 60.8% and 70.4% depending on the taxonomic distance to the cultivated blueberry targeted in this platform. NHB had the highest mean number of variants per locus at 9.7, followed by RE with 9.1, SHB with 8.5, and a range between 7.7 and 8.5 in other species. As expected, the total number of unique-in-state haplotypes exceeded the total number of variants in the domesticated blueberries. Phylogenetic analysis using a subset of the SNPs and haplotypes mostly conformed to known relationships. The platform also offers flexibility about the number of loci, depth of sequencing for accurate dosage calling, loci and haplotype reconstruction from increased fragment length. This genotyping platform will accelerate the development and improvement of blueberry cultivars through genomic-assisted breeding tools.

A cautionary tale of low-pass sequencing and imputation with respect to haplotype accuracy

BackgroundLow-pass whole-genome sequencing and imputation offer significant cost savings, enabling substantial increases in sample size and statistical power. This approach is particularly promising in livestock breeding, providing an affordable means of screening individuals for deleterious alleles or calculating genomic breeding values. Consequently, it may also be of value in companion animal genomics to support pedigree breeding. We sought to evaluate in dogs the impact of low coverage sequencing and reference-guided imputation on genotype concordance and association analyses.ResultsDNA isolated from saliva of 30 Labrador retrievers was sequenced at low (0.9X and 3.8X) and high (43.5X) coverage, and down-sampled from 43.5X to 9.6X and 17.4X. Genotype imputation was performed using a diverse reference panel (1021 dogs), and two subsets of the former panel (256 dogs each) where one had an excess of Labrador retrievers relative to other breeds. We observed little difference in imputed genotype concordance between reference panels. Association analyses for a locus acting as a disease proxy were performed using single-marker (GEMMA) and haplotype-based (XP-EHH) tests. GEMMA results were highly correlated (r ≥ 0.97) between 43.5X and ≥ 3.8X depths of coverage, while for 0.9X the correlation was lower (r ≤ 0.8). XP-EHH results were less well correlated, with r ranging from 0.58 (0.9X) to 0.88 (17.4X). Across a random sample of 10,000 genomic regions averaging 17 kb in size, we observed a median of three haplotypes per dog across the sequencing depths, with 5% of the regions returning more than eight haplotypes. Inspection of one such region revealed genotype and phasing inconsistencies across sequencing depths.ConclusionsWe demonstrate that saliva-derived canine DNA is suitable for whole-genome sequencing, highlighting the feasibility of client-based sampling. Low-pass sequencing and imputation require caution as incorrect allele assignments result when the subject possesses alleles that are absent in the reference panel. Larger panels have the capacity for greater allelic diversity, which should reduce the potential for imputation error. Although low-pass sequencing can accurately impute allele dosage, we highlight issues with phasing accuracy that impact haplotype-based analyses. Consequently, if accurately phased genotypes are required for analyses, we advocate sequencing at high depth (> 20X).

Multi-Omics Analyses Unravel Genetic Relationship of Chinese Coffee Germplasm Resources

The genetic relationships between Coffea arabica resources were analyzed via specific length amplified fragment sequencing (SLAF-seq) and transcriptome sequencing to provide the theoretical basis for breeding new varieties. Twenty C. arabica accessions were used to analyze genetic diversity on the basis of SNPs identified in SLAFs and the transcriptome data. For the SLAF-seq analysis of 20 C. arabica accessions, two Coffea canephora accessions, one Coffea liberica accession, and one Coffea racemosa accession, the number of reads ranged from 2,665,424 to 7,210,310, with a GC content of 38.49%–40.91% and a Q30 value of 94.99%–96.36%. A total of 3,347,069 SLAF tags were obtained, with an average sequencing depth of 13.90×. Moreover, the 1,048,575 SNPs identified in the polymorphic SLAFs were filtered, then the remaining 198,955 SNPs were used to construct a phylogenetic tree, perform a principal component analysis, and characterize the population structure. For the transcriptome analysis, 128.50 Gb clean reads were generated for the 20 C. arabica accessions, with a GC content of 44.36%–51.09% and a Q30 value of 94.55%–95.40%. Furthermore, 25,872 genes’ expression levels were used for the correlation analysis. The phylogenetic relationships as well as the results of the principal component analysis, population structure analysis, and correlation analysis clearly distinguished C. arabica Typica-type accessions from the C. arabica Bourbon-type accessions. Notably, several C. arabica local selections with unknown genetic backgrounds were classified according to all four clustering results.

Selecting autotetraploids from a facultatively apomictic tree of the nonapomictic ‘Licheng’ sweet orange (Citrus sinensis) variety

Tetraploid plays major roles in the polyploid breeding and production of citrus plants. Autotetraploids of nonapomictic citrus varieties are desirable in citrus polyploid breeding due to their high frequencies of hybrid polyploid production, certain genotypes and some predictable phenotypes. However, it is difficult to obtain autotetraploids of nonapomictic citrus varieties. In the present study, the nonapomictic sweet orange variety ‘Licheng’, which is native to China, was used as the main material. A ‘Licheng’ tree, LCOYC-04, that produced polyembryonic seeds at a frequency of 34.96±2.88 % was selected. Plants that grew from self-pollinated polyembryonic seeds of LCOYC-04 were investigated by flow cytometry. Four tetraploids were selected from 550 plants and identified according to chromosome observation. Exonic single-nucleotide polymorphism (SNP) locus genotyping based on genome resequencing showed that 22.18 %-70.49 % of heterozygous loci in the genome of LCOYC-04 were homozygous in the genomes of plants grown from self-pollinated monoembryonic seeds. The proportions of homozygous loci were 2.07 %-2.45 % in the 4 selected tetraploids. These values were close to those in 7 asexual ‘Licheng’ relatives (1.97 %-2.71 %). Therefore, the 4 tetraploids were confirmed to be autotetraploids of LCOYC-04. Genome sequencing revealed a region of low sequencing depth (approximately 855 kb) on chromosome 4 of all ‘Licheng’ trees. The CitRWP gene, which is responsible for the occurrence of nucellar embryos in citrus plants, was located in the low sequencing depth region. It was confirmed by quantitative PCR that one of two allelic CitRWP gene was defective in genome of ‘Licheng’. The current study indicates that it is possible to induce nonapomictic citrus to mutate into facultatively apomictic citrus through chromosome segment deletion, and provides a reference for excavating autotetraploids which produce polyembryonic and monoembryonic seeds.

The first clinical validation of whole-genome screening on standard trophectoderm biopsies of preimplantation embryos

The first clinical validation of whole-genome screening on standard trophectoderm biopsies of preimplantation embryos

An overlooked phenomenon: complex interactions of potential error sources on the quality of bacterial de novo genome assemblies

BackgroundParameters adversely affecting the contiguity and accuracy of the assemblies from Illumina next-generation sequencing (NGS) are well described. However, past studies generally focused on their additive effects, overlooking their potential interactions possibly exacerbating one another’s effects in a multiplicative manner. To investigate whether or not they act interactively on de novo genome assembly quality, we simulated sequencing data for 13 bacterial reference genomes, with varying levels of error rate, sequencing depth, PCR and optical duplicate ratios.ResultsWe assessed the quality of assemblies from the simulated sequencing data with a number of contiguity and accuracy metrics, which we used to quantify both additive and multiplicative effects of the four parameters. We found that the tested parameters are engaged in complex interactions, exerting multiplicative, rather than additive, effects on assembly quality. Also, the ratio of non-repeated regions and GC% of the original genomes can shape how the four parameters affect assembly quality.ConclusionsWe provide a framework for consideration in future studies using de novo genome assembly of bacterial genomes, e.g. in choosing the optimal sequencing depth, balancing between its positive effect on contiguity and negative effect on accuracy due to its interaction with error rate. Furthermore, the properties of the genomes to be sequenced also should be taken into account, as they might influence the effects of error sources themselves.

Large-scale single-virus genomics uncovers hidden diversity of river water viruses and diversified gene profiles.

Environmental viruses (primarily bacteriophages) are widely recognized as playing an important role in ecosystem homeostasis through the infection of host cells. However, the majority of environmental viruses are still unknown as their mosaic structure and frequent mutations in their sequences hinder genome construction in current metagenomics. To enable the large-scale acquisition of environmental viral genomes, we developed a new single-viral genome sequencing platform with microfluidic-generated gel beads. Amplification of individual DNA viral genomes in mass-produced gel beads allows high-throughput genome sequencing compared to conventional single-virus genomics. The sequencing analysis of river water samples yielded 1431 diverse viral single-amplified genomes, whereas viral metagenomics recovered 100 viral metagenome-assembled genomes at the comparable sequence depth. The 99.5% of viral single-amplified genomes were determined novel at the species level, most of which could not be recovered by a metagenomic assembly. The large-scale acquisition of diverse viral genomes identified protein clusters commonly detected in different viral strains, allowing the gene transfer to be tracked. Moreover, comparative genomics within the same viral species revealed that the profiles of various methyltransferase subtypes were diverse, suggesting an enhanced escape from host bacterial internal defense mechanisms. Our use of gel bead-based single-virus genomics will contribute to exploring the nature of viruses by accelerating the accumulation of draft genomes of environmental DNA viruses.

Genome stability assessment of PRRS vaccine strain with new ARTIC-style sequencing protocol.

A tiling amplicon sequencing protocol was developed to analyse the genome sequence stability of the modified live PRRSV vaccine strain, Porcilis MLV. The backbone of the ARTIC-style protocol was formed by 34 individual primer pairs, which were divided into two primer pools. Primer pairs were designed to amplify 532 to 588 bp fragments of the corresponding genomic region. The amplicons are suitable for sequencing on Illumina DNA sequencers with available 600-cycle sequencing kits. The concentration of primer pairs in the pools was optimized to obtain a balanced sequencing depth along the genome. Deep sequencing data of three vaccine batches were also analysed. All three vaccine batches were very similar to each other, although they also showed single nucleotide variations (SNVs) affecting less than 1 % of the genome. In the three vaccine strains, 113 to 122 SNV sites were identified; at these sites, the minority variants represented a frequency range of 1 to 48.7 percent. Additionally, the strains within the batches contained well-known length polymorphisms; the genomes of these minority deletion mutants were 135 to 222 bp shorter than the variant with the complete genome. Our results show the usefulness of ARTIC-style protocols in the evaluation of the genomic stability of PRRS MLV strains.