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

Abstract Background With 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. Results In 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. Conclusions Based 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.

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Consensify: A Method for Generating Pseudohaploid Genome Sequences from Palaeogenomic Datasets with Reduced Error Rates

Genes ◽

10.3390/genes11010050 ◽

2020 ◽

Vol 11 (1) ◽

pp. 50

Author(s):

Axel Barlow ◽

Stefanie Hartmann ◽

Javier Gonzalez ◽

Michael Hofreiter ◽

Johanna L. A. Paijmans

Keyword(s):

Clustering Analysis ◽

Error Rates ◽

Sequencing Error ◽

Genome Sequences ◽

High Quality ◽

Short Read ◽

Future Studies ◽

Sequencing Coverage ◽

Simplifying Assumptions ◽

Population Clustering

A standard practise in palaeogenome analysis is the conversion of mapped short read data into pseudohaploid sequences, frequently by selecting a single high-quality nucleotide at random from the stack of mapped reads. This controls for biases due to differential sequencing coverage, but it does not control for differential rates and types of sequencing error, which are frequently large and variable in datasets obtained from ancient samples. These errors have the potential to distort phylogenetic and population clustering analyses, and to mislead tests of admixture using D statistics. We introduce Consensify, a method for generating pseudohaploid sequences, which controls for biases resulting from differential sequencing coverage while greatly reducing error rates. The error correction is derived directly from the data itself, without the requirement for additional genomic resources or simplifying assumptions such as contemporaneous sampling. For phylogenetic and population clustering analysis, we find that Consensify is less affected by artefacts than methods based on single read sampling. For D statistics, Consensify is more resistant to false positives and appears to be less affected by biases resulting from different laboratory protocols than other frequently used methods. Although Consensify is developed with palaeogenomic data in mind, it is applicable for any low to medium coverage short read datasets. We predict that Consensify will be a useful tool for future studies of palaeogenomes.

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Minimizer-space de Bruijn graphs

10.1101/2021.06.09.447586 ◽

2021 ◽

Author(s):

Barış Ekim ◽

Bonnie Berger ◽

Rayan Chikhi

Keyword(s):

Human Genome ◽

Dna Sequences ◽

Graphical Representation ◽

Error Rates ◽

Sequencing Error ◽

Sequencing Data ◽

De Bruijn Graphs ◽

Human Genome Assembly ◽

Long Read ◽

Metagenome Assembly

DNA sequencing data continues to progress towards longer reads with increasingly lower sequencing error rates. We focus on the problem of assembling such reads into genomes, which poses challenges in terms of accuracy and computational resources when using cutting-edge assembly approaches, e.g. those based on overlapping reads using minimizer sketches. Here, we introduce the concept of minimizer-space sequencing data analysis, where the minimizers rather than DNA nucleotides are the atomic tokens of the alphabet. By projecting DNA sequences into ordered lists of minimizers, our key idea is to enumerate what we call k-min-mers, that are k-mers over a larger alphabet consisting of minimizer tokens. Our approach, mdBG or minimizer-dBG, achieves orders-of magnitude improvement in both speed and memory usage over existing methods without much loss of accuracy. We demonstrate three uses cases of mdBG: human genome assembly, metagenome assembly, and the representation of large pangenomes. For assembly, we implemented mdBG in software we call rust-mdbg, resulting in ultra-fast, low memory and highly-contiguous assembly of PacBio HiFi reads. A human genome is assembled in under 10 minutes using 8 cores and 10 GB RAM, and 60 Gbp of metagenome reads are assembled in 4 minutes using 1 GB RAM. For pangenome graphs, we newly allow a graphical representation of a collection of 661,405 bacterial genomes as an mdBG and successfully search it (in minimizer-space) for anti-microbial resistance (AMR) genes. We expect our advances to be essential to sequence analysis, given the rise of long-read sequencing in genomics, metagenomics and pangenomics.

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ECNano: A Cost-Effective Workflow for Target Enrichment Sequencing and Accurate Variant Calling on 4,800 Clinically Significant Genes Using a Single MinION Flowcell

10.1101/2021.04.05.438455 ◽

2021 ◽

Author(s):

Amy Wing-Sze Leung ◽

Henry Chi-Ming Leung ◽

Chak-Lim Wong ◽

Zhen-Xian Zheng ◽

Wui-Wang Lui ◽

...

Keyword(s):

Variant Calling ◽

Cost Effective ◽

Turnaround Time ◽

Read Length ◽

Sequencing Error ◽

Target Enrichment ◽

Long Read ◽

Wet Lab ◽

Variant Detection ◽

Clinically Significant

Background: The application of long-read sequencing using the Oxford Nanopore Technologies (ONT) MinION sequencer is getting more diverse in the medical field. Having a high sequencing error of ONT and limited throughput from a single MinION flowcell, however, limits its applicability for accurate variant detection. Medical exome sequencing (MES) targets clinically significant exon regions, allowing rapid and comprehensive screening of pathogenic variants. By applying MES with MinION sequencing, the technology can achieve a more uniform capture of the target regions, shorter turnaround time, and lower sequencing cost per sample. Method: We introduced a cost-effective optimized workflow, ECNano, comprising a wet-lab protocol and bioinformatics analysis, for accurate variant detection at 4,800 clinically important genes and regions using a single MinION flowcell. The ECNano wet-lab protocol was optimized to perform long-read target enrichment and ONT library preparation to stably generate high-quality MES data with adequate coverage. The subsequent variant-calling workflow, Clair-ensemble, adopted a fast RNN-based variant caller, Clair, and was optimized for target enrichment data. To evaluate its performance and practicality, ECNano was tested on both reference DNA samples and patient samples. Results: ECNano achieved deep on-target depth of coverage (DoC) at average >100x and >98% uniformity using one MinION flowcell. For accurate ONT variant calling, the generated reads sufficiently covered 98.9% of pathogenic positions listed in ClinVar, with 98.96% having at least 30x DoC. ECNano obtained an average read length of 1,000 bp. The long reads of ECNano also covered the adjacent splice sites well, with 98.5% of positions having ≥ 30x DoC. Clair-ensemble achieved >99% recall and accuracy for SNV calling. The whole workflow from wet-lab protocol to variant detection was completed within three days. Conclusion: We presented ECNano, an out-of-the-box workflow comprising (1) a wet-lab protocol for ONT target enrichment sequencing and (2) a downstream variant detection workflow, Clair-ensemble. The workflow is cost-effective, with a short turnaround time for high accuracy variant calling in 4,800 clinically significant genes and regions using a single MinION flowcell. The long-read exon captured data has potential for further development, promoting the application of long-read sequencing in personalized disease treatment and risk prediction.

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rMETL: sensitive mobile element insertion detection with long read realignment

Bioinformatics ◽

10.1093/bioinformatics/btz106 ◽

2019 ◽

Vol 35 (18) ◽

pp. 3484-3486 ◽

Cited By ~ 3

Author(s):

Tao Jiang ◽

Bo Liu ◽

Junyi Li ◽

Yadong Wang

Keyword(s):

Rapid Development ◽

Mobile Element ◽

Error Rates ◽

Supplementary Information ◽

Sequencing Error ◽

Complex Signals ◽

Element Insertion ◽

Sequencing Technologies ◽

Long Read ◽

Mobile Element Insertion

Abstract Summary Mobile element insertion (MEI) is a major category of structure variations (SVs). The rapid development of long read sequencing technologies provides the opportunity to detect MEIs sensitively. However, the signals of MEI implied by noisy long reads are highly complex due to the repetitiveness of mobile elements as well as the high sequencing error rates. Herein, we propose the Realignment-based Mobile Element insertion detection Tool for Long read (rMETL). Benchmarking results of simulated and real datasets demonstrate that rMETL enables to handle the complex signals to discover MEIs sensitively. It is suited to produce high-quality MEI callsets in many genomics studies. Availability and implementation rMETL is available from https://github.com/hitbc/rMETL. Supplementary information Supplementary data are available at Bioinformatics online.

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SVNN: an efficient PacBio-specific pipeline for structural variations calling using neural networks

BMC Bioinformatics ◽

10.1186/s12859-021-04184-7 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Shaya Akbarinejad ◽

Mostafa Hadadian Nejad Yousefi ◽

Maziar Goudarzi

Keyword(s):

Structural Variation ◽

State Of The Art ◽

Detection Methods ◽

Sequencing Error ◽

Structural Variations ◽

High Coverage ◽

Long Reads ◽

Long Read ◽

Sensitivity Improvement ◽

Low Coverage

Abstract Background Once aligned, long-reads can be a useful source of information to identify the type and position of structural variations. However, due to the high sequencing error of long reads, long-read structural variation detection methods are far from precise in low-coverage cases. To be accurate, they need to use high-coverage data, which in turn, results in an extremely time-consuming pipeline, especially in the alignment phase. Therefore, it is of utmost importance to have a structural variation calling pipeline which is both fast and precise for low-coverage data. Results In this paper, we present SVNN, a fast yet accurate, structural variation calling pipeline for PacBio long-reads that takes raw reads as the input and detects structural variants of size larger than 50 bp. Our pipeline utilizes state-of-the-art long-read aligners, namely NGMLR and Minimap2, and structural variation callers, videlicet Sniffle and SVIM. We found that by using a neural network, we can extract features from Minimap2 output to detect a subset of reads that provide useful information for structural variation detection. By only mapping this subset with NGMLR, which is far slower than Minimap2 but better serves downstream structural variation detection, we can increase the sensitivity in an efficient way. As a result of using multiple tools intelligently, SVNN achieves up to 20 percentage points of sensitivity improvement in comparison with state-of-the-art methods and is three times faster than a naive combination of state-of-the-art tools to achieve almost the same accuracy. Conclusion Since prohibitive costs of using high-coverage data have impeded long-read applications, with SVNN, we provide the users with a much faster structural variation detection platform for PacBio reads with high precision and sensitivity in low-coverage scenarios.

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Long-read amplicon denoising

10.1101/383794 ◽

2018 ◽

Cited By ~ 3

Author(s):

Venkatesh Kumar ◽

Thomas Vollbrecht ◽

Mark Chernyshev ◽

Sanjay Mohan ◽

Brian Hanst ◽

...

Keyword(s):

Ground Truth ◽

Amplicon Sequencing ◽

Error Rates ◽

Sequencing Error ◽

Short Read Sequencing ◽

Sequencing Technologies ◽

Medium Length ◽

Long Reads ◽

Long Read ◽

Error Profiles

Long-read next generation amplicon sequencing shows promise for studying complete genes or genomes from complex and diverse populations. Current long-read sequencing technologies have challenging error profiles, hindering data processing and incorporation into downstream analyses. Here we consider the problem of how to reconstruct, free of sequencing error, the true sequence variants and their associated frequencies. Called “amplicon denoising”, this problem has been extensively studied for short-read sequencing technologies, but current solutions do not appear to generalize well to long reads with high indel error rates. We introduce two methods: one that runs nearly instantly and is very accurate for medium length reads (here ~2.6kb) and high template coverage, and another, slower method that is more robust when reads are very long or coverage is lower.On one real dataset with ground truth, and on a number of simulated datasets, we compare our two approaches to each other and to existing algorithms. We outperform all tested methods in accuracy, with competitive run times even for our slower method.Fast Amplicon Denoising (FAD) and Robust Amplicon Denoising (RAD) are implemented purely in the Julia scientific computing language, and are hereby released along with a complete toolkit of functions that allow long-read amplicon sequence analysis pipelines to be constructed in pure Julia. Further, we make available a webserver to dramatically simplify the processing of long-read PacBio sequences.

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Long-read DNA metabarcoding of ribosomal rRNA in the analysis of fungi from aquatic environments

10.1101/283127 ◽

2018 ◽

Cited By ~ 4

Author(s):

Felix Heeger ◽

Elizabeth C. Bourne ◽

Christiane Baschien ◽

Andrey Yurkov ◽

Boyke Bunk ◽

...

Keyword(s):

De Novo ◽

Error Rates ◽

Rrna Genes ◽

Taxonomic Resolution ◽

Sequencing Error ◽

Rrna Gene ◽

Dna Metabarcoding ◽

Long Read ◽

Reference Sequences ◽

Ribosomal Rrna

ABSTRACTDNA metabarcoding is now widely used to study prokaryotic and eukaryotic microbial diversity. Technological constraints have limited most studies to marker lengths of ca. 300-600 bp. Longer sequencing reads of several 5 thousand bp are now possible with third-generation sequencing. The increased marker lengths provide greater taxonomic resolution and enable the use of phylogenetic methods of classifcation, but longer reads may be subject to higher rates of sequencing error and chimera formation. In addition, most well-established bioinformatics tools for DNA metabarcoding were originally 10 designed for short reads and are therefore not suitable. Here we used Pacifc Biosciences circular consensus sequencing (CCS) to DNA-metabarcode environmental samples using a ca. 4,500 bp marker that included most of the eukaryote ribosomal SSU and LSU rRNA genes and the ITS spacer region. We developed a long-read analysis pipeline that reduced error rates to levels 15 comparable to short-read platforms. Validation using fungal isolates and a mock community indicated that our pipeline detected 98% of chimeras de novo i.e., even in the absence of reference sequences. We recovered 947 OTUs from water and sediment samples in a natural lake, 848 of which could be classifed to phylum, 486 to family, 397 to genus and 330 to species. By 20 allowing for the simultaneous use of three global databases (Unite, SILVA, RDP LSU), long-read DNA metabarcoding provided better taxonomic resolution than any single marker. We foresee the use of long reads enabling the cross-validation of reference sequences and the synthesis of ribosomal rRNA gene databases. The universal nature of the rRNA operon and our recovery of >100 25 non-fungal OTUs indicate that long-read DNA metabarcoding holds promise for the study of eukaryotic diversity more broadly.

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Parameter exploration improves the accuracy of long-read genome assembly

10.1101/2021.05.28.446135 ◽

2021 ◽

Author(s):

Anurag Priyam ◽

Alicja Witwicka ◽

Anindita Brahma ◽

Eckart Stolle ◽

Yannick Wurm

Keyword(s):

Genome Assembly ◽

Reference Genome ◽

Error Rates ◽

Fine Tuning ◽

Sequencing Error ◽

High Quality ◽

Long Read ◽

Genome Assemblies ◽

Error Profiles

Long-molecule sequencing is now routinely applied to generate high-quality reference genome assemblies. However, datasets differ in repeat composition, heterozygosity, read lengths and error profiles. The assembly parameters that provide the best results could thus differ across datasets. By integrating four complementary and biologically meaningful metrics, we show that simple fine-tuning of assembly parameters can substantially improve the quality of long-read genome assemblies. In particular, modifying estimates of sequencing error rates improves some metrics more than two-fold. We provide a flexible software, CompareGenomeQualities, that automates comparisons of assembly qualities for researchers wanting a straightforward mechanism for choosing among multiple assemblies.

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Using de novo assembly to identify structural variation of complex immune system gene regions

10.1101/2021.02.03.429586 ◽

2021 ◽

Author(s):

Jia-Yuan Zhang ◽

Hannah Roberts ◽

David S. C. Flores ◽

Antony J. Cutler ◽

Andrew C. Brown ◽

...

Keyword(s):

Immune System ◽

T Cell ◽

Structural Variation ◽

De Novo ◽

Optical Mapping ◽

Association Studies ◽

Killer Cell ◽

Error Rates ◽

Single Individual ◽

Long Read

AbstractDriven by the necessity to survive environmental pathogens, the human immune system has evolved exceptional diversity and plasticity, to which several factors contribute including inheritable structural polymorphism of the underlying genes. Characterizing this variation is challenging due to the complexity of these loci, which contain extensive regions of paralogy, segmental duplication and high copy-number repeats, but recent progress in long-read sequencing and optical mapping techniques suggests this problem may now be tractable. Here we assess this by using long-read sequencing platforms from PacBio and Oxford Nanopore, supplemented with short-read sequencing and Bionano optical mapping, to sequence DNA extracted from CD14+ monocytes and peripheral blood mononuclear cells from a single European individual identified as HV31. We use this data to build a de novo assembly of eight genomic regions encoding four key components of the immune system, namely the human leukocyte antigen, immunoglobulins, T cell receptors, and killer-cell immunoglobulin-like receptors. Validation of our assembly using k-mer based and alignment approaches suggests that it has high accuracy, with estimated base-level error rates below 1 in 10 kb, although we identify a small number of remaining structural errors. We use the assembly to identify heterozygous and homozygous structural variation in comparison to GRCh38. Despite analyzing only a single individual, we find multiple large structural variants affecting core genes at all three immunoglobulin regions and at two of the three T cell receptor regions. Several of these variants are not accurately callable using current algorithms, implying that further methodological improvements are needed. Our results demonstrate that assessing haplotype variation in these regions is possible given sufficiently accurate long-read and associated data; application of these methods to larger samples would provide a broader catalogue of germline structural variation at these loci, an important step toward making these regions accessible to large-scale genetic association studies.

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MBG: Minimizer-based Sparse de Bruijn Graph Construction

10.1101/2020.09.18.303156 ◽

2020 ◽

Author(s):

Mikko Rautiainen ◽

Tobias Marschall

Keyword(s):

Source Code ◽

Error Rates ◽

Read Length ◽

De Bruijn Graph ◽

De Bruijn Graphs ◽

E Coli ◽

Link Type ◽

Sequencing Technologies ◽

Long Read ◽

De Bruijn

MotivationDe Bruijn graphs can be constructed from short reads efficiently and have been used for many purposes. Traditionally long read sequencing technologies have had too high error rates for de Bruijn graph-based methods. Recently, HiFi reads have provided a combination of long read length and low error rate, which enables de Bruijn graphs to be used with HiFi reads.ResultsWe have implemented MBG, a tool for building sparse de Bruijn graphs from HiFi reads. MBG outperforms existing tools for building dense de Bruijn graphs, and can build a graph of 50x coverage whole human genome HiFi reads in four hours on a single core. MBG also assembles the bacterial E. coli genome into a single contig in 8 seconds.AvailabilityPackage manager: https://anaconda.org/bioconda/mbg and source code: https://github.com/maickrau/MBG

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