scholarly journals Comparative Analysis and Data Provenance for 1,113 Bacterial Genome Assemblies

2021 ◽  
Author(s):  
David A Yarmosh ◽  
Juan G Lopera ◽  
Nikhita P Puthuveetil ◽  
Patrick Ford Combs ◽  
Amy L Reese ◽  
...  
Keyword(s):  
Bacterial Genome ◽  
Data Provenance ◽  
Microbial Genomics ◽  
Refseq Database ◽  
Biological Source ◽  
Genome Assemblies ◽  
Public Health Epidemiology ◽  
Source Materials ◽  
Reference Genomes

The quality and traceability of microbial genomics data in public databases is deteriorating as they rapidly expand and struggle to cope with data curation challenges. While the availability of public genomic data has become essential for modern life sciences research, the curation of the data is a growing area of concern that has significant real-world impacts on public health epidemiology, drug discovery, and environmental biosurveillance research. While public microbial genome databases such as NCBI's RefSeq database leverage the scalability of crowd sourcing for growth, they do not require data provenance to the original biological source materials or accurate descriptions of how the data was produced. Here, we describe the de novo assembly of 1,113 bacterial genome references produced from authenticated materials sourced from the American Type Culture Collection (ATCC), each with full data provenance. Over 98% of these ATCC Standard Reference Genomes (ASRGs) are superior to assemblies for comparable strains found in NCBI's RefSeq database. Comparative genomics analysis revealed significant issues in RefSeq bacterial genome assemblies related to genome completeness, mutations, structural differences, metadata errors, and gaps in traceability to the original biological source materials. For example, nearly half of RefSeq assemblies lack details on sample source information, sequencing technology, or bioinformatics methods. We suggest there is an intrinsic connection between the quality of genomic metadata, the traceability of the data, and the methods used to produce them with the quality of the resulting genome assemblies themselves. Our results highlight common problems with "reference genomes" and underscore the importance of data provenance for precision science and reproducibility. These gaps in metadata accuracy and data provenance represent an "elephant in the room" for microbial genomics research, but addressing these issues would require raising the level of accountability for data depositors and our own expectations of data quality.

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

BMC Genomics ◽  
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.

scholarly journals Assembly of chromosome-scale contigs by efficiently resolving repetitive sequences with long reads

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Huilong Du ◽  
Chengzhi Liang
Keyword(s):  
Single Molecule ◽  
Repetitive Sequences ◽  
Tartary Buckwheat ◽  
Gene Sequences ◽  
Assembly Method ◽  
Long Reads ◽  
A Genome ◽  
Genome Assemblies ◽  
Reference Genomes

AbstractThe abundant repetitive sequences in complex eukaryotic genomes cause fragmented assemblies, which lose value as reference genomes, often due to incomplete gene sequences and unanchored or mispositioned contigs on chromosomes. Here we report a genome assembly method HERA, which resolves repeats efficiently by constructing a connection graph from an overlap graph. We test HERA on the genomes of rice, maize, human, and Tartary buckwheat with single-molecule sequencing and mapping data. HERA correctly assembles most of the previously unassembled regions, resulting in dramatically improved, highly contiguous genome assemblies with newly assembled gene sequences. For example, the maize contig N50 size reaches 61.2 Mb and the Tartary buckwheat genome comprises only 20 contigs. HERA can also be used to fill gaps and fix errors in reference genomes. The application of HERA will greatly improve the quality of new or existing assemblies of complex genomes.

scholarly journals Trycycler: consensus long-read assemblies for bacterial genomes

2021 ◽  
Author(s):  
Ryan R Wick ◽  
Louise M Judd ◽  
Louise T Cerdeira ◽  
Jane Hawkey ◽  
Guillaume Meric ◽  
...  
Keyword(s):  
Bacterial Genome ◽  
Bacterial Genomes ◽  
Manual Intervention ◽  
Long Reads ◽  
Oxford Nanopore ◽  
Multiple Input ◽  
Long Read ◽  
Complete Bacterial Genome ◽  
Genome Assemblies ◽  
Reference Genomes

Assembly of bacterial genomes from long-read data (generated by Oxford Nanopore or Pacific Biosciences platforms) can often be complete: a single contig for each chromosome or plasmid in the genome. However, even complete bacterial genome assemblies constructed solely from long reads still contain a variety of errors, and different assemblies of the same genome often contain different errors. Here, we present Trycycler, a tool which produces a consensus assembly from multiple input assemblies of the same genome. Benchmarking using both simulated and real sequencing reads showed that Trycycler consensus assemblies contained fewer errors than any of those constructed with a single long-read assembler. Post-assembly polishing with Medaka and Pilon further reduced errors and yielded the most accurate genome assemblies in our study. As Trycycler can require human judgement and manual intervention, its output is not deterministic, and different users can produce different Trycycler assemblies from the same input data. However, we demonstrated that multiple users with minimal training converge on similar assemblies that are consistently more accurate than those produced by automated assembly tools. We therefore recommend Trycycler+Medaka+Pilon as an ideal approach for generating high-quality bacterial reference genomes.

scholarly journals Hawkeye and AMOS: visualizing and assessing the quality of genome assemblies

2011 ◽  
Vol 14 (2) ◽  
pp. 213-224 ◽  
Author(s):  
M. C. Schatz ◽  
A. M. Phillippy ◽  
D. D. Sommer ◽  
A. L. Delcher ◽  
D. Puiu ◽  
...  
Keyword(s):  
Genome Assemblies

scholarly journals DeepMAsED: evaluating the quality of metagenomic assemblies

2020 ◽  
Vol 36 (10) ◽  
pp. 3011-3017 ◽  
Author(s):  
Olga Mineeva ◽  
Mateo Rojas-Carulla ◽  
Ruth E Ley ◽  
Bernhard Schölkopf ◽  
Nicholas D Youngblut
Keyword(s):  
Large Scale ◽  
State Of The Art ◽  
Ground Truth ◽  
Supplementary Information ◽  
Learning Approach ◽  
Wide Range ◽  
Metagenome Assembly ◽  
Model Training ◽  
Reference Genomes

Abstract Motivation Methodological advances in metagenome assembly are rapidly increasing in the number of published metagenome assemblies. However, identifying misassemblies is challenging due to a lack of closely related reference genomes that can act as pseudo ground truth. Existing reference-free methods are no longer maintained, can make strong assumptions that may not hold across a diversity of research projects, and have not been validated on large-scale metagenome assemblies. Results We present DeepMAsED, a deep learning approach for identifying misassembled contigs without the need for reference genomes. Moreover, we provide an in silico pipeline for generating large-scale, realistic metagenome assemblies for comprehensive model training and testing. DeepMAsED accuracy substantially exceeds the state-of-the-art when applied to large and complex metagenome assemblies. Our model estimates a 1% contig misassembly rate in two recent large-scale metagenome assembly publications. Conclusions DeepMAsED accurately identifies misassemblies in metagenome-assembled contigs from a broad diversity of bacteria and archaea without the need for reference genomes or strong modeling assumptions. Running DeepMAsED is straight-forward, as well as is model re-training with our dataset generation pipeline. Therefore, DeepMAsED is a flexible misassembly classifier that can be applied to a wide range of metagenome assembly projects. Availability and implementation DeepMAsED is available from GitHub at https://github.com/leylabmpi/DeepMAsED. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Closed and High-Quality Bacterial Genome Sequences of the Oligo-Mouse-Microbiota Community

2021 ◽  
Vol 10 (17) ◽  
Author(s):  
Quentin Lamy-Besnier ◽  
Romain Koszul ◽  
Laurent Debarbieux ◽  
Martial Marbouty
Keyword(s):  
Murine Model ◽  
Bacterial Genome ◽  
Chromosome Conformation Capture ◽  
Genome Sequences ◽  
Pacbio Sequencing ◽  
High Quality ◽  
Chromosome Conformation

ABSTRACT The Oligo-Mouse-Microbiota (OMM12) gnotobiotic murine model is an increasingly popular model in microbiota studies. However, following Illumina and PacBio sequencing, the genomes of the 12 strains could not be closed. Here, we used genomic chromosome conformation capture (Hi-C) data to reorganize, close, and improve the quality of these 12 genomes.

scholarly journals VGEA: an RNA viral assembly toolkit

PeerJ ◽  
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.

scholarly journals Assessing species coverage and assembly quality of rapidly accumulating sequenced genomes

2021 ◽  
Author(s):  
Romain Feron ◽  
Robert Michael Waterhouse
Keyword(s):  
Large Scale ◽  
Automated Analysis ◽  
Single Copy ◽  
The United States ◽  
Community Resource ◽  
Assembly Quality ◽  
Sequencing Technologies ◽  
Analysis Workflow ◽  
Genome Assemblies

Ambitious initiatives to coordinate genome sequencing of Earth's biodiversity mean that the accumulation of genomic data is growing rapidly. In addition to cataloguing biodiversity, these data provide the basis for understanding biological function and evolution. Accurate and complete genome assemblies offer a comprehensive and reliable foundation upon which to advance our understanding of organismal biology at genetic, species, and ecosystem levels. However, ever-changing sequencing technologies and analysis methods mean that available data are often heterogeneous in quality. In order to guide forthcoming genome generation efforts and promote efficient prioritisation of resources, it is thus essential to define and monitor taxonomic coverage and quality of the data. Here we present an automated analysis workflow that surveys genome assemblies from the United States National Center for Biotechnology Information (NCBI), assesses their completeness using the relevant Benchmarking Universal Single-Copy Orthologue (BUSCO) datasets, and collates the results into an interactively browsable resource. We apply our workflow to produce a community resource of available assemblies from the phylum Arthropoda, the Arthropoda Assembly Assessment Catalogue. Using this resource, we survey current taxonomic coverage and assembly quality at the NCBI, we examine how key assembly metrics relate to gene content completeness, and we compare results from using different BUSCO lineage datasets. These results demonstrate how the workflow can be used to build a community resource that enables large-scale assessments to survey species coverage and data quality of available genome assemblies, and to guide prioritisations for ongoing and future sampling, sequencing, and genome generation initiatives.

scholarly journals Gene Ontology Meta Annotator for Plants

2019 ◽  
Author(s):  
Kokulapalan Wimalanathan ◽  
Carolyn J. Lawrence-Dill
Keyword(s):  
Gene Ontology ◽  
Go Annotation ◽  
Plant Genomes ◽  
Link Type ◽  
Gene Structures ◽  
And Performance ◽  
Genome Assemblies ◽  
Genome Scale ◽  
Per Gene

AbstractAnnotating gene structures and functions to genome assemblies is a must to make assembly resources useful for biological inference. Gene Ontology (GO) term assignment is the most pervasively used functional annotation system, and new methods for GO assignment have improved the quality of GO-based function predictions. GOMAP, the Gene Ontology Meta Annotator for Plants (GOMAP) is an optimized, high-throughput, and reproducible pipeline for genome-scale GO annotation for plant genomes. GOMAP’s methods have been shown to expand and improve the number of genes annotated and annotations assigned per gene as well as the quality (based on F-score) of GO assignments in maize. Here we report on the pipeline’s availability and performance for annotating large, repetitive plant genomes and describe how to deploy GOMAP to annotate additional plant genomes. We containerized GOMAP to increase portability and reproducibility, and optimized its performance for HPC environments. GOMAP has been used to annotate multiple maize lines, and is currently being deployed to annotate other species including wheat, rice, barley, cotton, soy, and others. Instructions along with access to the GOMAP Singularity container are freely available online at https://gomap-singularity.readthedocs.io/en/latest/. A list of annotated genomes and links to data is maintained at https://dill-picl.org/projects/gomap/gomap-datasets/.

Variation of Vanadium Incorporation in Semi-Insulating SiC Single Crystals Grown by PVT Method

2019 ◽  
Vol 963 ◽  
pp. 30-33
Author(s):  
Chae Young Lee ◽  
Jeong Min Choi ◽  
Dae Sung Kim ◽  
Mi Seon Park ◽  
Yeon Suk Jang ◽  
...  
Keyword(s):  
Electrical Properties ◽  
Single Crystals ◽  
High Purity ◽  
Growth Direction ◽  
Source Materials ◽  
Better Than

Two SiC crystals were grown using SiC source powder with different level of purity and then the effect of the purity of SiC source materials on the final electrical properties has been systematically observed. Furthermore, the variation of vanadium amount according to the growth direction of vanadium doped semi-insulated SiC single crystals has been investigated. The quality of SiC crystal grown using SiC source powder with higher purity was definitely better than SiC crystal with lower purity. SiC crystals having an average resistivity value of about 1×1010 Ωcm were successfully obtained. In the result of COREMA measurement, the use of high purity SiC powder was revealed to obtain wafers with better uniformity in resistivity value.

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