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

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.

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

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.

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

2020 ◽  
Vol 7 (1) ◽  
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.

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

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.

scholarly journals Rapid Mycobacterium tuberculosis spoligotyping from uncorrected long reads using Galru

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.

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

2020 ◽  
Vol 10 (3) ◽  
pp. 899-906 ◽  
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.

scholarly journals NanoGalaxy: Nanopore long-read sequencing data analysis in Galaxy

GigaScience ◽  
2020 ◽  
Vol 9 (10) ◽  
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.

scholarly journals Evaluation of Germline Structural Variant Calling Methods for Nanopore Sequencing Data

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.

scholarly journals Cas9-Assisted Targeting of CHromosome segments (CATCH) for targeted nanopore sequencing and optical genome mapping

2017 ◽  
Author(s):  
Tslil Gabrieli ◽  
Hila Sharim ◽  
Yael Michaeli ◽  
Yuval Ebenstein
Keyword(s):  
Single Molecule ◽  
Genome Mapping ◽  
Single Point ◽  
Read Length ◽  
Whole Genome ◽  
Sequencing Analysis ◽  
Nanopore Sequencing ◽  
Sequencing Data ◽  
Whole Genome Analysis ◽  
Long Read

ABSTRACTVariations in the genetic code, from single point mutations to large structural or copy number alterations, influence susceptibility, onset, and progression of genetic diseases and tumor transformation. Next-generation sequencing analysis is unable to reliably capture aberrations larger than the typical sequencing read length of several hundred bases. Long-read, single-molecule sequencing methods such as SMRT and nanopore sequencing can address larger variations, but require costly whole genome analysis. Here we describe a method for isolation and enrichment of a large genomic region of interest for targeted analysis based on Cas9 excision of two sites flanking the target region and isolation of the excised DNA segment by pulsed field gel electrophoresis. The isolated target remains intact and is ideally suited for optical genome mapping and long-read sequencing at high coverage. In addition, analysis is performed directly on native genomic DNA that retains genetic and epigenetic composition without amplification bias. This method enables detection of mutations and structural variants as well as detailed analysis by generation of hybrid scaffolds composed of optical maps and sequencing data at a fraction of the cost of whole genome sequencing.

scholarly journals Rapid incidence estimation from SARS-CoV-2 genomes reveals decreased case detection in Europe during summer 2020

2021 ◽  
Author(s):  
Maureen Rebecca Smith ◽  
Maria Trofimova ◽  
Ariane Weber ◽  
Yannick Duport ◽  
Denise Kuhnert ◽  
...  
Keyword(s):  
Sequence Data ◽  
Large Data ◽  
Relative Magnitude ◽  
Large Data Sets ◽  
Data Sets ◽  
Sequencing Data ◽  
A Genome ◽  
Incidence Estimation ◽  
Automated Pipeline ◽  
Testing Policies

In May 2021, over 160 million SARS-CoV-2 infections have been reported worldwide. Yet, the true amount of infections is unknown and believed to exceed the reported numbers by several fold, depending on national testing policies that can strongly affect the proportion of undetected cases. To overcome this testing bias and better assess SARS-CoV-2 transmission dynamics, we propose a genome-based computational pipeline, GInPipe, to reconstruct the SARS-CoV-2 incidence dynamics through time. After validating GInPipe against in silico generated outbreak data, as well as more complex phylodynamic analyses, we use the pipeline to reconstruct incidence histories in Denmark, Scotland, Switzerland, and Victoria (Australia) solely from viral sequence data. The proposed method robustly reconstructs the different pandemic waves in the investigated countries and regions, does not require phylodynamic reconstruction, and can be directly applied to publicly deposited SARS-CoV-2 sequencing data sets. We observe differences in the relative magnitude of reconstructed versus reported incidences during times with sparse availability of diagnostic tests. Using the reconstructed incidence dynamics, we assess how testing policies may have affected the probability to diagnose and report infected individuals. We find that under-reporting was highest in mid 2020 in all analysed countries, coinciding with liberal testing policies at times of low test capacities. Due to the increased use of real-time sequencing, it is envisaged that GInPipe can complement established surveillance tools to monitor the SARS-CoV-2 pandemic and evaluate testing policies. The method executes within minutes on very large data sets and is freely available as a fully automated pipeline from https://github.com/KleistLab/GInPipe.

scholarly journals Metannot: A succinct data structure for compression of colors in dynamic de Bruijn graphs

2017 ◽  
Author(s):  
Harun Mustafa ◽  
André Kahles ◽  
Mikhail Karasikov ◽  
Gunnar Rätsch
Keyword(s):  
Data Structure ◽  
High Throughput Sequencing ◽  
Sequence Data ◽  
Data Sets ◽  
Sequencing Data ◽  
Dynamic Data ◽  
De Bruijn Graphs ◽  
Dna And Rna ◽  
Succinct Data Structure ◽  
Dynamic Data Structure

AbstractMuch of the DNA and RNA sequencing data available is in the form of high-throughput sequencing (HTS) reads and is currently unindexed by established sequence search databases. Recent succinct data structures for indexing both reference sequences and HTS data, along with associated metadata, have been based on either hashing or graph models, but many of these structures are static in nature, and thus, not well-suited as backends for dynamic databases.We propose a parallel construction method for and novel application of the wavelet trie as a dynamic data structure for compressing and indexing graph metadata. By developing an algorithm for merging wavelet tries, we are able to construct large tries in parallel by merging smaller tries constructed concurrently from batches of data.When compared against general compression algorithms and those developed specifically for graph colors (VARI and Rainbowfish), our method achieves compression ratios superior to gzip and VARI, converging to compression ratios of 6.5% to 2% on data sets constructed from over 600 virus genomes.While marginally worse than compression by bzip2 or Rainbowfish, this structure allows for both fast extension and query. We also found that additionally encoding graph topology metadata improved compression ratios, particularly on data sets consisting of several mutually-exclusive reference genomes.It was also observed that the compression ratio of wavelet tries grew sublinearly with the density of the annotation matrices.This work is a significant step towards implementing a dynamic data structure for indexing large annotated sequence data sets that supports fast query and update operations. At the time of writing, no established standard tool has filled this niche.

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