Evaluation of GRCh38 and de novo haploid genome assemblies demonstrates the enduring quality of the reference assembly

AbstractThe human reference genome assembly plays a central role in nearly all aspects of today’s basic and clinical research. GRCh38 is the first coordinate-changing assembly update since 2009 and reflects the resolution of roughly 1000 issues and encompasses modifications ranging from thousands of single base changes to megabase-scale path reorganizations, gap closures and localization of previously orphaned sequences. We developed a new approach to sequence generation for targeted base updates and used data from new genome mapping technologies and single haplotype resources to identify and resolve larger assembly issues. For the first time, the reference assembly contains sequence-based representations for the centromeres. We also expanded the number of alternate loci to create a reference that provides a more robust representation of human population variation. We demonstrate that the updates render the reference an improved annotation substrate, alter read alignments in unchanged regions and impact variant interpretation at clinically relevant loci. We additionally evaluated a collection of new de novo long-read haploid assemblies and conclude that while the new assemblies compare favorably to the reference with respect to continuity, error rate, and gene completeness, the reference still provides the best representation for complex genomic regions and coding sequences. We assert that the collected updates in GRCh38 make the newer assembly a more robust substrate for comprehensive analyses that will promote our understanding of human biology and advance our efforts to improve health.

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LongStitch: High-quality genome assembly correction and scaffolding using long reads

10.1101/2021.06.17.448848 ◽

2021 ◽

Author(s):

Lauren Coombe ◽

Janet X Li ◽

Theodora Lo ◽

Johnathan Wong ◽

Vladimir Nikolic ◽

...

Keyword(s):

Genome Assembly ◽

De Novo ◽

Draft Genome ◽

Model Organisms ◽

High Quality ◽

De Novo Genome Assembly ◽

Long Reads ◽

Long Read ◽

Genomic Regions ◽

Genome Assemblies

Background Generating high-quality de novo genome assemblies is foundational to the genomics study of model and non-model organisms. In recent years, long-read sequencing has greatly benefited genome assembly and scaffolding, a process by which assembled sequences are ordered and oriented through the use of long-range information. Long reads are better able to span repetitive genomic regions compared to short reads, and thus have tremendous utility for resolving problematic regions and helping generate more complete draft assemblies. Here, we present LongStitch, a scalable pipeline that corrects and scaffolds draft genome assemblies exclusively using long reads. Results LongStitch incorporates multiple tools developed by our group and runs in up to three stages, which includes initial assembly correction (Tigmint-long), followed by two incremental scaffolding stages (ntLink and ARKS-long). Tigmint-long and ARKS-long are misassembly correction and scaffolding utilities, respectively, previously developed for linked reads, that we adapted for long reads. Here, we describe the LongStitch pipeline and introduce our new long-read scaffolder, ntLink, which utilizes lightweight minimizer mappings to join contigs. LongStitch was tested on short and long-read assemblies of three different human individuals using corresponding nanopore long-read data, and improves the contiguity of each assembly from 2.0-fold up to 304.6-fold (as measured by NGA50 length). Furthermore, LongStitch generates more contiguous and correct assemblies compared to state-of-the-art long-read scaffolder LRScaf in most tests, and consistently runs in under five hours using less than 23GB of RAM. Conclusions Due to its effectiveness and efficiency in improving draft assemblies using long reads, we expect LongStitch to benefit a wide variety of de novo genome assembly projects. The LongStitch pipeline is freely available at https://github.com/bcgsc/longstitch.

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LongStitch: high-quality genome assembly correction and scaffolding using long reads

BMC Bioinformatics ◽

10.1186/s12859-021-04451-7 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Lauren Coombe ◽

Janet X. Li ◽

Theodora Lo ◽

Johnathan Wong ◽

Vladimir Nikolic ◽

...

Keyword(s):

Genome Assembly ◽

De Novo ◽

Draft Genome ◽

Model Organisms ◽

High Quality ◽

De Novo Genome Assembly ◽

Long Reads ◽

Long Read ◽

Genomic Regions ◽

Genome Assemblies

Abstract Background Generating high-quality de novo genome assemblies is foundational to the genomics study of model and non-model organisms. In recent years, long-read sequencing has greatly benefited genome assembly and scaffolding, a process by which assembled sequences are ordered and oriented through the use of long-range information. Long reads are better able to span repetitive genomic regions compared to short reads, and thus have tremendous utility for resolving problematic regions and helping generate more complete draft assemblies. Here, we present LongStitch, a scalable pipeline that corrects and scaffolds draft genome assemblies exclusively using long reads. Results LongStitch incorporates multiple tools developed by our group and runs in up to three stages, which includes initial assembly correction (Tigmint-long), followed by two incremental scaffolding stages (ntLink and ARKS-long). Tigmint-long and ARKS-long are misassembly correction and scaffolding utilities, respectively, previously developed for linked reads, that we adapted for long reads. Here, we describe the LongStitch pipeline and introduce our new long-read scaffolder, ntLink, which utilizes lightweight minimizer mappings to join contigs. LongStitch was tested on short and long-read assemblies of Caenorhabditis elegans, Oryza sativa, and three different human individuals using corresponding nanopore long-read data, and improves the contiguity of each assembly from 1.2-fold up to 304.6-fold (as measured by NGA50 length). Furthermore, LongStitch generates more contiguous and correct assemblies compared to state-of-the-art long-read scaffolder LRScaf in most tests, and consistently improves upon human assemblies in under five hours using less than 23 GB of RAM. Conclusions Due to its effectiveness and efficiency in improving draft assemblies using long reads, we expect LongStitch to benefit a wide variety of de novo genome assembly projects. The LongStitch pipeline is freely available at https://github.com/bcgsc/longstitch.

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Evaluation of GRCh38 and de novo haploid genome assemblies demonstrates the enduring quality of the reference assembly

Genome Research ◽

10.1101/gr.213611.116 ◽

2017 ◽

Vol 27 (5) ◽

pp. 849-864 ◽

Cited By ~ 250

Author(s):

Valerie A. Schneider ◽

Tina Graves-Lindsay ◽

Kerstin Howe ◽

Nathan Bouk ◽

Hsiu-Chuan Chen ◽

...

Keyword(s):

De Novo ◽

Haploid Genome ◽

Reference Assembly ◽

Genome Assemblies

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Contiguity: Contig adjacency graph construction and visualisation

10.7287/peerj.preprints.1037v1 ◽

2015 ◽

Cited By ~ 8

Author(s):

Mitchell J Sullivan ◽

Nouri L Ben Zakour ◽

Brian M Forde ◽

Mitchell Stanton-Cook ◽

Scott A Beatson

Keyword(s):

De Novo ◽

Reference Sequence ◽

De Bruijn Graph ◽

Interactive Software ◽

Graph Exploration ◽

Adjacency Graph ◽

Highly Sensitive ◽

Long Read ◽

Genome Assemblies ◽

Adjacency Graphs

Contiguity is an interactive software for the visualization and manipulation of de novo genome assemblies. Contiguity creates and displays information on contig adjacency which is contextualized by the simultaneous display of a comparison between assembled contigs and reference sequence. Where scaffolders allow unambiguous connections between contigs to be resolved into a single scaffold, Contiguity allows the user to create all potential scaffolds in ambiguous regions of the genome. This enables the resolution of novel sequence or structural variants from the assembly. In addition, Contiguity provides a sequencing and assembly agnostic approach for the creation of contig adjacency graphs. To maximize the number of contig adjacencies determined, Contiguity combines information from read pair mappings, sequence overlap and De Bruijn graph exploration. We demonstrate how highly sensitive graphs can be achieved using this method. Contig adjacency graphs allow the user to visualize potential arrangements of contigs in unresolvable areas of the genome. By combining adjacency information with comparative genomics, Contiguity provides an intuitive approach for exploring and improving sequence assemblies. It is also useful in guiding manual closure of long read sequence assemblies. Contiguity is an open source application, implemented using Python and the Tkinter GUI package that can run on any Unix, OSX and Windows operating system. It has been designed and optimized for bacterial assemblies. Contiguity is available at http://mjsull.github.io/Contiguity .

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VGEA: an RNA viral assembly toolkit

PeerJ ◽

10.7717/peerj.12129 ◽

2021 ◽

Vol 9 ◽

pp. e12129

Author(s):

Paul E. Oluniyi ◽

Fehintola Ajogbasile ◽

Judith Oguzie ◽

Jessica Uwanibe ◽

Adeyemi Kayode ◽

...

Keyword(s):

De Novo ◽

Sequence Data ◽

Workflow Management ◽

Viral Population ◽

Lassa Virus ◽

Viral Genomes ◽

Bioinformatics Tools ◽

Reference Sequences ◽

Genome Assemblies

Next generation sequencing (NGS)-based studies have vastly increased our understanding of viral diversity. Viral sequence data obtained from NGS experiments are a rich source of information, these data can be used to study their epidemiology, evolution, transmission patterns, and can also inform drug and vaccine design. Viral genomes, however, represent a great challenge to bioinformatics due to their high mutation rate and forming quasispecies in the same infected host, bringing about the need to implement advanced bioinformatics tools to assemble consensus genomes well-representative of the viral population circulating in individual patients. Many tools have been developed to preprocess sequencing reads, carry-out de novo or reference-assisted assembly of viral genomes and assess the quality of the genomes obtained. Most of these tools however exist as standalone workflows and usually require huge computational resources. Here we present (Viral Genomes Easily Analyzed), a Snakemake workflow for analyzing RNA viral genomes. VGEA enables users to map sequencing reads to the human genome to remove human contaminants, split bam files into forward and reverse reads, carry out de novo assembly of forward and reverse reads to generate contigs, pre-process reads for quality and contamination, map reads to a reference tailored to the sample using corrected contigs supplemented by the user’s choice of reference sequences and evaluate/compare genome assemblies. We designed a project with the aim of creating a flexible, easy-to-use and all-in-one pipeline from existing/stand-alone bioinformatics tools for viral genome analysis that can be deployed on a personal computer. VGEA was built on the Snakemake workflow management system and utilizes existing tools for each step: fastp (Chen et al., 2018) for read trimming and read-level quality control, BWA (Li & Durbin, 2009) for mapping sequencing reads to the human reference genome, SAMtools (Li et al., 2009) for extracting unmapped reads and also for splitting bam files into fastq files, IVA (Hunt et al., 2015) for de novo assembly to generate contigs, shiver (Wymant et al., 2018) to pre-process reads for quality and contamination, then map to a reference tailored to the sample using corrected contigs supplemented with the user’s choice of existing reference sequences, SeqKit (Shen et al., 2016) for cleaning shiver assembly for QUAST, QUAST (Gurevich et al., 2013) to evaluate/assess the quality of genome assemblies and MultiQC (Ewels et al., 2016) for aggregation of the results from fastp, BWA and QUAST. Our pipeline was successfully tested and validated with SARS-CoV-2 (n = 20), HIV-1 (n = 20) and Lassa Virus (n = 20) datasets all of which have been made publicly available. VGEA is freely available on GitHub at: https://github.com/pauloluniyi/VGEA under the GNU General Public License.

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dnAQET: a framework to compute a consolidated metric for benchmarking quality of de novo assemblies

BMC Genomics ◽

10.1186/s12864-019-6070-x ◽

2019 ◽

Vol 20 (1) ◽

Author(s):

Gokhan Yavas ◽

Huixiao Hong ◽

Wenming Xiao

Keyword(s):

Quality Assessment ◽

Genome Assembly ◽

Reference Genome ◽

De Novo ◽

Quality Score ◽

De Novo Genome Assembly ◽

Genome Assemblies ◽

Reference Genomes ◽

Better Than

Abstract Background Accurate de novo genome assembly has become reality with the advancements in sequencing technology. With the ever-increasing number of de novo genome assembly tools, assessing the quality of assemblies has become of great importance in genome research. Although many quality metrics have been proposed and software tools for calculating those metrics have been developed, the existing tools do not produce a unified measure to reflect the overall quality of an assembly. Results To address this issue, we developed the de novo Assembly Quality Evaluation Tool (dnAQET) that generates a unified metric for benchmarking the quality assessment of assemblies. Our framework first calculates individual quality scores for the scaffolds/contigs of an assembly by aligning them to a reference genome. Next, it computes a quality score for the assembly using its overall reference genome coverage, the quality score distribution of its scaffolds and the redundancy identified in it. Using synthetic assemblies randomly generated from the latest human genome build, various builds of the reference genomes for five organisms and six de novo assemblies for sample NA24385, we tested dnAQET to assess its capability for benchmarking quality evaluation of genome assemblies. For synthetic data, our quality score increased with decreasing number of misassemblies and redundancy and increasing average contig length and coverage, as expected. For genome builds, dnAQET quality score calculated for a more recent reference genome was better than the score for an older version. To compare with some of the most frequently used measures, 13 other quality measures were calculated. The quality score from dnAQET was found to be better than all other measures in terms of consistency with the known quality of the reference genomes, indicating that dnAQET is reliable for benchmarking quality assessment of de novo genome assemblies. Conclusions The dnAQET is a scalable framework designed to evaluate a de novo genome assembly based on the aggregated quality of its scaffolds (or contigs). Our results demonstrated that dnAQET quality score is reliable for benchmarking quality assessment of genome assemblies. The dnQAET can help researchers to identify the most suitable assembly tools and to select high quality assemblies generated.

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A high-quality genome assembly from a single, field-collected spotted lanternfly (Lycorma delicatula) using the PacBio Sequel II system

GigaScience ◽

10.1093/gigascience/giz122 ◽

2019 ◽

Vol 8 (10) ◽

Cited By ~ 12

Author(s):

Sarah B Kingan ◽

Julie Urban ◽

Christine C Lambert ◽

Primo Baybayan ◽

Anna K Childers ◽

...

Keyword(s):

Invasive Species ◽

Genome Assembly ◽

De Novo ◽

Fragment Size ◽

High Quality ◽

De Novo Genome Assembly ◽

Lycorma Delicatula ◽

Long Read ◽

Genome Assemblies ◽

High Quality Genome

ABSTRACT Background A high-quality reference genome is an essential tool for applied and basic research on arthropods. Long-read sequencing technologies may be used to generate more complete and contiguous genome assemblies than alternate technologies; however, long-read methods have historically had greater input DNA requirements and higher costs than next-generation sequencing, which are barriers to their use on many samples. Here, we present a 2.3 Gb de novo genome assembly of a field-collected adult female spotted lanternfly (Lycorma delicatula) using a single Pacific Biosciences SMRT Cell. The spotted lanternfly is an invasive species recently discovered in the northeastern United States that threatens to damage economically important crop plants in the region. Results The DNA from 1 individual was used to make 1 standard, size-selected library with an average DNA fragment size of ∼20 kb. The library was run on 1 Sequel II SMRT Cell 8M, generating a total of 132 Gb of long-read sequences, of which 82 Gb were from unique library molecules, representing ∼36× coverage of the genome. The assembly had high contiguity (contig N50 length = 1.5 Mb), completeness, and sequence level accuracy as estimated by conserved gene set analysis (96.8% of conserved genes both complete and without frame shift errors). Furthermore, it was possible to segregate more than half of the diploid genome into the 2 separate haplotypes. The assembly also recovered 2 microbial symbiont genomes known to be associated with L. delicatula, each microbial genome being assembled into a single contig. Conclusions We demonstrate that field-collected arthropods can be used for the rapid generation of high-quality genome assemblies, an attractive approach for projects on emerging invasive species, disease vectors, or conservation efforts of endangered species.

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High contiguity long read assembly of Brassica nigra allows localization of active centromeres and provides insights into the ancestral Brassica genome

10.1101/2020.02.03.932665 ◽

2020 ◽

Cited By ~ 5

Author(s):

Sampath Perumal ◽

Chu Shin Koh ◽

Lingling Jin ◽

Miles Buchwaldt ◽

Erin Higgins ◽

...

Keyword(s):

De Novo ◽

Low Complexity ◽

Error Rates ◽

Brassica Nigra ◽

Genome Integrity ◽

Ancestral Genome ◽

Genomic Distance ◽

Long Read ◽

Genome Assemblies ◽

Technology Comparison

AbstractHigh-quality nanopore genome assemblies were generated for two Brassica nigra genotypes (Ni100 and CN115125); a member of the agronomically important Brassica species. The N50 contig length for the two assemblies were 17.1 Mb (58 contigs) and 0.29 Mb (963 contigs), respectively, reflecting recent improvements in the technology. Comparison with a de novo short read assembly for Ni100 corroborated genome integrity and quantified sequence related error rates (0.002%). The contiguity and coverage allowed unprecedented access to low complexity regions of the genome. Pericentromeric regions and coincidence of hypo-methylation enabled localization of active centromeres and identified a novel centromere-associated ALE class I element which appears to have proliferated through relatively recent nested transposition events (<1 million years ago). Computational abstraction was used to define a post-triplication Brassica specific ancestral genome and to calculate the extensive rearrangements that define the genomic distance separating B. nigra from its diploid relatives.

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Merqury: reference-free quality, completeness, and phasing assessment for genome assemblies

10.1101/2020.03.15.992941 ◽

2020 ◽

Cited By ~ 15

Author(s):

Arang Rhie ◽

Brian P. Walenz ◽

Sergey Koren ◽

Adam M. Phillippy

Keyword(s):

De Novo ◽

High Accuracy ◽

Link Type ◽

Base Level ◽

Project Home Page ◽

Set Operations ◽

Assembly Evaluation ◽

Long Read ◽

Genome Assemblies ◽

Reference Genomes

AbstractRecent long-read assemblies often exceed the quality and completeness of available reference genomes, making validation challenging. Here we present Merqury, a novel tool for reference-free assembly evaluation based on efficient k-mer set operations. By comparing k-mers in a de novo assembly to those found in unassembled high-accuracy reads, Merqury estimates base-level accuracy and completeness. For trios, Merqury can also evaluate haplotype-specific accuracy, completeness, phase block continuity, and switch errors. Multiple visualizations, such as k-mer spectrum plots, can be generated for evaluation. We demonstrate on both human and plant genomes that Merqury is a fast and robust method for assembly validation.Availability of data and materialProject name: MerquryProject home page: https://github.com/marbl/merqury, https://github.com/marbl/merylArchived version: https://github.com/marbl/merqury/releases/tag/v1.0Operating system(s): Platform independentProgramming language: C++, Java, PerlOther requirements: gcc 4.8 or higher, java 1.6 or higherLicense: Public domain (see https://github.com/marbl/merqury/blob/master/README.license) Any restrictions to use by non-academics: No restrictions applied

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Long-read genome sequencing for the diagnosis of neurodevelopmental disorders

10.1101/2020.07.02.185447 ◽

2020 ◽

Author(s):

Susan M. Hiatt ◽

James M.J. Lawlor ◽

Lori H. Handley ◽

Ryne C. Ramaker ◽

Brianne B. Rogers ◽

...

Keyword(s):

Genome Sequencing ◽

Neurodevelopmental Disorders ◽

De Novo ◽

Genomic Analysis ◽

Data Sets ◽

Short Read ◽

Long Read ◽

Variant Detection ◽

Circular Consensus Sequencing

AbstractPurposeExome and genome sequencing have proven to be effective tools for the diagnosis of neurodevelopmental disorders (NDDs), but large fractions of NDDs cannot be attributed to currently detectable genetic variation. This is likely, at least in part, a result of the fact that many genetic variants are difficult or impossible to detect through typical short-read sequencing approaches.MethodsHere, we describe a genomic analysis using Pacific Biosciences circular consensus sequencing (CCS) reads, which are both long (>10 kb) and accurate (>99% bp accuracy). We used CCS on six proband-parent trios with NDDs that were unexplained despite extensive testing, including genome sequencing with short reads.ResultsWe identified variants and created de novo assemblies in each trio, with global metrics indicating these data sets are more accurate and comprehensive than those provided by short-read data. In one proband, we identified a likely pathogenic (LP), de novo L1-mediated insertion in CDKL5 that results in duplication of exon 3, leading to a frameshift. In a second proband, we identified multiple large de novo structural variants, including insertion-translocations affecting DGKB and MLLT3, which we show disrupt MLLT3 transcript levels. We consider this extensive structural variation likely pathogenic.ConclusionThe breadth and quality of variant detection, coupled to finding variants of clinical and research interest in two of six probands with unexplained NDDs strongly support the value of long-read genome sequencing for understanding rare disease.

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