Highly accurate long-read HiFi sequencing data for five complex genomes

AbstractThe PacBio® HiFi sequencing method yields highly accurate long-read sequencing datasets with read lengths averaging 10–25 kb and accuracies greater than 99.5%. These accurate long reads can be used to improve results for complex applications such as single nucleotide and structural variant detection, genome assembly, assembly of difficult polyploid or highly repetitive genomes, and assembly of metagenomes. Currently, there is a need for sample data sets to both evaluate the benefits of these long accurate reads as well as for development of bioinformatic tools including genome assemblers, variant callers, and haplotyping algorithms. We present deep coverage HiFi datasets for five complex samples including the two inbred model genomes Mus musculus and Zea mays, as well as two complex genomes, octoploid Fragaria × ananassa and the diploid anuran Rana muscosa. Additionally, we release sequence data from a mock metagenome community. The datasets reported here can be used without restriction to develop new algorithms and explore complex genome structure and evolution. Data were generated on the PacBio Sequel II System.

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Highly accurate long-read HiFi sequencing data for five complex genomes

10.1101/2020.05.04.077180 ◽

2020 ◽

Author(s):

Ting Hon ◽

Kristin Mars ◽

Greg Young ◽

Yu-Chih Tsai ◽

Joseph W. Karalius ◽

...

Keyword(s):

Sequence Data ◽

Genome Structure ◽

Data Sets ◽

Sequencing Data ◽

Complex Samples ◽

Bioinformatic Tools ◽

Long Reads ◽

Sequencing Method ◽

Sample Data ◽

Long Read

AbstractThe PacBio® HiFi sequencing method yields highly accurate long-read sequencing datasets with read lengths averaging 10-25 kb and accuracies greater than 99.5%. These accurate long reads can be used to improve results for complex applications such as single nucleotide and structural variant detection, genome assembly, assembly of difficult polyploid or highly repetitive genomes, and assembly of metagenomes. Currently, there is a need for sample data sets to both evaluate the benefits of these long accurate reads as well as for development of bioinformatic tools including genome assemblers, variant callers, and haplotyping algorithms. We present deep coverage HiFi datasets for five complex samples including the two inbred model genomes Mus musculus and Zea mays, as well as two complex genomes, octoploid Fragaria × ananassa and the diploid anuran Rana muscosa. Additionally, we release sequence data from a mock metagenome community. The datasets reported here can be used without restriction to develop new algorithms and explore complex genome structure and evolution. Data were generated on the PacBio Sequel II System.

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Rapid Mycobacterium tuberculosis spoligotyping from uncorrected long reads using Galru

10.1101/2020.05.31.126490 ◽

2020 ◽

Author(s):

Andrew J. Page ◽

Nabil-Fareed Alikhan ◽

Michael Strinden ◽

Thanh Le Viet ◽

Timofey Skvortsov

Keyword(s):

Mycobacterium Tuberculosis ◽

State Of The Art ◽

Sequence Data ◽

Human Pathogen ◽

Sequencing Data ◽

Short Read ◽

Short Read Sequencing ◽

Long Reads ◽

Long Read

AbstractSpoligotyping of Mycobacterium tuberculosis provides a subspecies classification of this major human pathogen. Spoligotypes can be predicted from short read genome sequencing data; however, no methods exist for long read sequence data such as from Nanopore or PacBio. We present a novel software package Galru, which can rapidly detect the spoligotype of a Mycobacterium tuberculosis sample from as little as a single uncorrected long read. It allows for near real-time spoligotyping from long read data as it is being sequenced, giving rapid sample typing. We compare it to the existing state of the art software and find it performs identically to the results obtained from short read sequencing data. Galru is freely available from https://github.com/quadram-institute-bioscience/galru under the GPLv3 open source licence.

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poreCov-An Easy to Use, Fast, and Robust Workflow for SARS-CoV-2 Genome Reconstruction via Nanopore Sequencing

Frontiers in Genetics ◽

10.3389/fgene.2021.711437 ◽

2021 ◽

Vol 12 ◽

Author(s):

Christian Brandt ◽

Sebastian Krautwurst ◽

Riccardo Spott ◽

Mara Lohde ◽

Mateusz Jundzill ◽

...

Keyword(s):

Sequence Data ◽

Viral Evolution ◽

Relevant Information ◽

Data Sets ◽

Nanopore Sequencing ◽

Sequencing Data ◽

Genome Reconstruction ◽

Long Read ◽

Major Bottleneck ◽

Parallel Workflow

In response to the SARS-CoV-2 pandemic, a highly increased sequencing effort has been established worldwide to track and trace ongoing viral evolution. Technologies, such as nanopore sequencing via the ARTIC protocol are used to reliably generate genomes from raw sequencing data as a crucial base for molecular surveillance. However, for many labs that perform SARS-CoV-2 sequencing, bioinformatics is still a major bottleneck, especially if hundreds of samples need to be processed in a recurring fashion. Pipelines developed for short-read data cannot be applied to nanopore data. Therefore, specific long-read tools and parameter settings need to be orchestrated to enable accurate genotyping and robust reference-based genome reconstruction of SARS-CoV-2 genomes from nanopore data. Here we present poreCov, a highly parallel workflow written in Nextflow, using containers to wrap all the tools necessary for a routine SARS-CoV-2 sequencing lab into one program. The ease of installation, combined with concise summary reports that clearly highlight all relevant information, enables rapid and reliable analysis of hundreds of SARS-CoV-2 raw sequence data sets or genomes. poreCov is freely available on GitHub under the GNUv3 license: github.com/replikation/poreCov.

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poreCov - an easy to use, fast, and robust workflow for SARS-CoV-2 genome reconstruction via nanopore sequencing

10.1101/2021.05.07.443089 ◽

2021 ◽

Author(s):

Christian Brandt ◽

Sebastian Krautwurst ◽

Riccardo Spott ◽

Mara Lohde ◽

Mateusz Jundzill ◽

...

Keyword(s):

Sequence Data ◽

Viral Evolution ◽

Relevant Information ◽

Data Sets ◽

Nanopore Sequencing ◽

Sequencing Data ◽

Genome Reconstruction ◽

Long Read ◽

Major Bottleneck ◽

Parallel Workflow

In response to the SARS-CoV-2 pandemic, a highly increased sequencing effort has been established worldwide to track and trace ongoing viral evolution. Technologies such as nanopore sequencing via the ARTIC protocol are used to reliably generate genomes from raw sequencing data as a crucial base for molecular surveillance. However, for many labs that perform SARS-CoV-2 sequencing, bioinformatics is still a major bottleneck, especially if hundreds of samples need to be processed in a recurring fashion. Pipelines developed for short-read data cannot be applied to nanopore data. Therefore, specific long-read tools and parameter settings need to be orchestrated to enable accurate genotyping and robust reference-based genome reconstruction of SARS-CoV-2 genomes from nanopore data. Here we present poreCov, a highly parallel workflow written in Nextflow, using containers to wrap all the tools necessary for a routine SARS-CoV-2 sequencing lab into one program. The ease of installation, combined with concise summary reports that clearly highlight all relevant information, enables rapid and reliable analysis of hundreds of SARS-CoV-2 raw sequence data sets or genomes. poreCov is freely available on GitHub under the GNUv3 license: github.com/replikation/poreCov.

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Bayesian Classification of Microbial Communities Based on 16S rRNA Metagenomic Data

10.1101/340653 ◽

2018 ◽

Cited By ~ 1

Author(s):

Arghavan Bahadorinejad ◽

Ivan Ivanov ◽

Johanna W Lampe ◽

Meredith AJ Hullar ◽

Robert S Chapkin ◽

...

Keyword(s):

16S Rrna ◽

Sample Size ◽

Microbial Communities ◽

State Of The Art ◽

Metagenomic Data ◽

Data Sets ◽

Sequencing Data ◽

Sample Data

AbstractWe propose a Bayesian method for the classification of 16S rRNA metagenomic profiles of bacterial abundance, by introducing a Poisson-Dirichlet-Multinomial hierarchical model for the sequencing data, constructing a prior distribution from sample data, calculating the posterior distribution in closed form; and deriving an Optimal Bayesian Classifier (OBC). The proposed algorithm is compared to state-of-the-art classification methods for 16S rRNA metagenomic data, including Random Forests and the phylogeny-based Metaphyl algorithm, for varying sample size, classification difficulty, and dimensionality (number of OTUs), using both synthetic and real metagenomic data sets. The results demonstrate that the proposed OBC method, with either noninformative or constructed priors, is competitive or superior to the other methods. In particular, in the case where the ratio of sample size to dimensionality is small, it was observed that the proposed method can vastly outperform the others.Author summaryRecent studies have highlighted the interplay between host genetics, gut microbes, and colorectal tumor initiation/progression. The characterization of microbial communities using metagenomic profiling has therefore received renewed interest. In this paper, we propose a method for classification, i.e., prediction of different outcomes, based on 16S rRNA metagenomic data. The proposed method employs a Bayesian approach, which is suitable for data sets with small ration of number of available instances to the dimensionality. Results using both synthetic and real metagenomic data show that the proposed method can outperform other state-of-the-art metagenomic classification algorithms.

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Improved Genome Assembly and Annotation of the Soybean Aphid (Aphis glycines Matsumura)

G3 Genes|Genome|Genetics ◽

10.1534/g3.119.400954 ◽

2020 ◽

Vol 10 (3) ◽

pp. 899-906 ◽

Cited By ~ 8

Author(s):

Thomas C. Mathers

Keyword(s):

Genome Assembly ◽

Sequence Data ◽

Parasitoid Wasp ◽

Single Copy ◽

Aphid Species ◽

Soybean Aphid ◽

Aphis Glycines ◽

Data Sets ◽

Conserved Genes ◽

Long Read

Aphids are an economically important insect group due to their role as plant disease vectors. Despite this economic impact, genomic resources have only been generated for a small number of aphid species. The soybean aphid (Aphis glycines Matsumura) was the third aphid species to have its genome sequenced and the first to use long-read sequence data. However, version 1 of the soybean aphid genome assembly has low contiguity (contig N50 = 57 Kb, scaffold N50 = 174 Kb), poor representation of conserved genes and the presence of genomic scaffolds likely derived from parasitoid wasp contamination. Here, I use recently developed methods to reassemble the soybean aphid genome. The version 2 genome assembly is highly contiguous, containing half of the genome in only 40 scaffolds (contig N50 = 2.00 Mb, scaffold N50 = 2.51 Mb) and contains 11% more conserved single-copy arthropod genes than version 1. To demonstrate the utility of this improved assembly, I identify a region of conserved synteny between aphids and Drosophila containing members of the Osiris gene family that was split over multiple scaffolds in the original assembly. The improved genome assembly and annotation of A. glycines demonstrates the benefit of applying new methods to old data sets and will provide a useful resource for future comparative genome analysis of aphids.

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NanoGalaxy: Nanopore long-read sequencing data analysis in Galaxy

GigaScience ◽

10.1093/gigascience/giaa105 ◽

2020 ◽

Vol 9 (10) ◽

Cited By ~ 1

Author(s):

Willem de Koning ◽

Milad Miladi ◽

Saskia Hiltemann ◽

Astrid Heikema ◽

John P Hays ◽

...

Keyword(s):

Genome Assembly ◽

Bioinformatics Analysis ◽

De Novo ◽

Sequence Data ◽

Ease Of Use ◽

Easy Access ◽

Complex Data ◽

Sequencing Data ◽

Long Read ◽

Sequencing Platforms

Abstract Background Long-read sequencing can be applied to generate very long contigs and even completely assembled genomes at relatively low cost and with minimal sample preparation. As a result, long-read sequencing platforms are becoming more popular. In this respect, the Oxford Nanopore Technologies–based long-read sequencing “nanopore" platform is becoming a widely used tool with a broad range of applications and end-users. However, the need to explore and manipulate the complex data generated by long-read sequencing platforms necessitates accompanying specialized bioinformatics platforms and tools to process the long-read data correctly. Importantly, such tools should additionally help democratize bioinformatics analysis by enabling easy access and ease-of-use solutions for researchers. Results The Galaxy platform provides a user-friendly interface to computational command line–based tools, handles the software dependencies, and provides refined workflows. The users do not have to possess programming experience or extended computer skills. The interface enables researchers to perform powerful bioinformatics analysis, including the assembly and analysis of short- or long-read sequence data. The newly developed “NanoGalaxy" is a Galaxy-based toolkit for analysing long-read sequencing data, which is suitable for diverse applications, including de novo genome assembly from genomic, metagenomic, and plasmid sequence reads. Conclusions A range of best-practice tools and workflows for long-read sequence genome assembly has been integrated into a NanoGalaxy platform to facilitate easy access and use of bioinformatics tools for researchers. NanoGalaxy is freely available at the European Galaxy server https://nanopore.usegalaxy.eu with supporting self-learning training material available at https://training.galaxyproject.org.

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A comprehensive evaluation of long read error correction methods

BMC Genomics ◽

10.1186/s12864-020-07227-0 ◽

2020 ◽

Vol 21 (S6) ◽

Author(s):

Haowen Zhang ◽

Chirag Jain ◽

Srinivas Aluru

Keyword(s):

Error Correction ◽

Hybrid Methods ◽

Comprehensive Evaluation ◽

Data Sets ◽

Short Reads ◽

Long Reads ◽

Long Read ◽

Read Error Correction ◽

Downstream Analysis ◽

The Impact

Abstract Background Third-generation single molecule sequencing technologies can sequence long reads, which is advancing the frontiers of genomics research. However, their high error rates prohibit accurate and efficient downstream analysis. This difficulty has motivated the development of many long read error correction tools, which tackle this problem through sampling redundancy and/or leveraging accurate short reads of the same biological samples. Existing studies to asses these tools use simulated data sets, and are not sufficiently comprehensive in the range of software covered or diversity of evaluation measures used. Results In this paper, we present a categorization and review of long read error correction methods, and provide a comprehensive evaluation of the corresponding long read error correction tools. Leveraging recent real sequencing data, we establish benchmark data sets and set up evaluation criteria for a comparative assessment which includes quality of error correction as well as run-time and memory usage. We study how trimming and long read sequencing depth affect error correction in terms of length distribution and genome coverage post-correction, and the impact of error correction performance on an important application of long reads, genome assembly. We provide guidelines for practitioners for choosing among the available error correction tools and identify directions for future research. Conclusions Despite the high error rate of long reads, the state-of-the-art correction tools can achieve high correction quality. When short reads are available, the best hybrid methods outperform non-hybrid methods in terms of correction quality and computing resource usage. When choosing tools for use, practitioners are suggested to be careful with a few correction tools that discard reads, and check the effect of error correction tools on downstream analysis. Our evaluation code is available as open-source at https://github.com/haowenz/LRECE.

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LRSDAY: Long-read Sequencing Data Analysis for Yeasts

10.1101/184572 ◽

2017 ◽

Author(s):

Jia-Xing Yue ◽

Gianni Liti

Keyword(s):

Genome Assembly ◽

Model Organism ◽

Sequencing Data ◽

Protein Coding ◽

Sequencing Technologies ◽

Long Reads ◽

Long Read ◽

Downstream Analysis ◽

Eukaryotic Organisms ◽

Genomic Regions

AbstractLong-read sequencing technologies have become increasingly popular in genome projects due to their strengths in resolving complex genomic regions. As a leading model organism with small genome size and great biotechnological importance, the budding yeast, Saccharomyces cerevisiae, has many isolates currently being sequenced with long reads. However, analyzing long-read sequencing data to produce high-quality genome assembly and annotation remains challenging. Here we present LRSDAY, the first one-stop solution to streamline this process. LRSDAY can produce chromosome-level end-to-end genome assembly and comprehensive annotations for various genomic features (including centromeres, protein-coding genes, tRNAs, transposable elements and telomere-associated elements) that are ready for downstream analysis. Although tailored for S. cerevisiae, we designed LRSDAY to be highly modular and customizable, making it adaptable for virtually any eukaryotic organisms. Applying LRSDAY to a S. cerevisiae strain takes ∼43 hrs to generate a complete and well-annotated genome from ∼100X Pacific Biosciences (PacBio) reads using four threads.

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Evaluation of Germline Structural Variant Calling Methods for Nanopore Sequencing Data

Frontiers in Genetics ◽

10.3389/fgene.2021.761791 ◽

2021 ◽

Vol 12 ◽

Author(s):

Davide Bolognini ◽

Alberto Magi

Keyword(s):

Variant Calling ◽

Research Report ◽

Nanopore Sequencing ◽

Sequencing Data ◽

Factors Affecting ◽

Sequencing Technologies ◽

Long Reads ◽

Oxford Nanopore ◽

Sequencing Studies ◽

Long Read

Structural variants (SVs) are genomic rearrangements that involve at least 50 nucleotides and are known to have a serious impact on human health. While prior short-read sequencing technologies have often proved inadequate for a comprehensive assessment of structural variation, more recent long reads from Oxford Nanopore Technologies have already been proven invaluable for the discovery of large SVs and hold the potential to facilitate the resolution of the full SV spectrum. With many long-read sequencing studies to follow, it is crucial to assess factors affecting current SV calling pipelines for nanopore sequencing data. In this brief research report, we evaluate and compare the performances of five long-read SV callers across four long-read aligners using both real and synthetic nanopore datasets. In particular, we focus on the effects of read alignment, sequencing coverage, and variant allele depth on the detection and genotyping of SVs of different types and size ranges and provide insights into precision and recall of SV callsets generated by integrating the various long-read aligners and SV callers. The computational pipeline we propose is publicly available at https://github.com/davidebolo1993/EViNCe and can be adjusted to further evaluate future nanopore sequencing datasets.

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