scholarly journals A computational toolset for rapid identification of SARS-CoV-2, other viruses and microorganisms from sequencing data

2020 ◽  
Author(s):  
Shifu Chen ◽  
Changshou He ◽  
Yingqiang Li ◽  
Zhicheng Li ◽  
Charles E Melançon
Keyword(s):  
Middle East ◽  
Severe Acute Respiratory Syndrome ◽  
Rapid Identification ◽  
Large Set ◽  
Sequencing Data ◽  
Short Read ◽  
Microbial Genomes ◽  
Extension Method ◽  
Long Read ◽  
Adapter Trimming

Abstract In this paper, we present a toolset and related resources for rapid identification of viruses and microorganisms from short-read or long-read sequencing data. We present fastv as an ultra-fast tool to detect microbial sequences present in sequencing data, identify target microorganisms and visualize coverage of microbial genomes. This tool is based on the k-mer mapping and extension method. K-mer sets are generated by UniqueKMER, another tool provided in this toolset. UniqueKMER can generate complete sets of unique k-mers for each genome within a large set of viral or microbial genomes. For convenience, unique k-mers for microorganisms and common viruses that afflict humans have been generated and are provided with the tools. As a lightweight tool, fastv accepts FASTQ data as input and directly outputs the results in both HTML and JSON formats. Prior to the k-mer analysis, fastv automatically performs adapter trimming, quality pruning, base correction and other preprocessing to ensure the accuracy of k-mer analysis. Specifically, fastv provides built-in support for rapid severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) identification and typing. Experimental results showed that fastv achieved 100% sensitivity and 100% specificity for detecting SARS-CoV-2 from sequencing data; and can distinguish SARS-CoV-2 from SARS, Middle East respiratory syndrome and other coronaviruses. This toolset is available at: https://github.com/OpenGene/fastv.

scholarly journals A Computational Toolset for Rapid Identification of SARS-CoV-2, other Viruses, and Microorganisms from Sequencing Data

2020 ◽  
Author(s):  
Shifu Chen ◽  
Changshou He ◽  
Yingqiang Li ◽  
Zhicheng Li ◽  
Charles E Melançon
Keyword(s):  
Rapid Identification ◽  
Experimental Results ◽  
Large Set ◽  
Sequencing Data ◽  
Short Read ◽  
Microbial Genomes ◽  
Extension Method ◽  
Link Type ◽  
Long Read ◽  
Adapter Trimming

ABSTRACTIn this paper, we present a toolset and related resources for rapid identification of viruses and microorganisms from short-read or long-read sequencing data. We present fastv as an ultra-fast tool to detect microbial sequences present in sequencing data, identify target microorganisms, and visualize coverage of microbial genomes. This tool is based on the k-mer mapping and extension method. K-mer sets are generated by UniqueKMER, another tool provided in this toolset. UniqueKMER can generate complete sets of unique k-mers for each genome within a large set of viral or microbial genomes. For convenience, unique k-mers for microorganisms and common viruses that afflict humans have been generated and are provided with the tools. As a lightweight tool, fastv accepts FASTQ data as input, and directly outputs the results in both HTML and JSON formats. Prior to the k-mer analysis, fastv automatically performs adapter trimming, quality pruning, base correction, and other pre-processing to ensure the accuracy of k-mer analysis. Specifically, fastv provides built-in support for rapid SARS-CoV-2 identification and typing. Experimental results showed that fastv achieved 100% sensitivity and 100% specificity for detecting SARS-CoV-2 from sequencing data; and can distinguish SARS-CoV-2 from SARS, MERS, and other coronaviruses. This toolset is available at: https://github.com/OpenGene/fastv.

scholarly journals Comprehensive identification of transposable element insertions using multiple sequencing technologies

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Chong Chu ◽  
Rebeca Borges-Monroy ◽  
Vinayak V. Viswanadham ◽  
Soohyun Lee ◽  
Heng Li ◽  
...  
Keyword(s):  
Transposable Element ◽  
Structure And Function ◽  
Endogenous Retroviruses ◽  
Whole Genome Sequencing Data ◽  
Whole Genome ◽  
Sequencing Data ◽  
Short Read ◽  
Sequencing Technologies ◽  
Long Read ◽  
And Function

AbstractTransposable elements (TEs) help shape the structure and function of the human genome. When inserted into some locations, TEs may disrupt gene regulation and cause diseases. Here, we present xTea (x-Transposable element analyzer), a tool for identifying TE insertions in whole-genome sequencing data. Whereas existing methods are mostly designed for short-read data, xTea can be applied to both short-read and long-read data. Our analysis shows that xTea outperforms other short read-based methods for both germline and somatic TE insertion discovery. With long-read data, we created a catalogue of polymorphic insertions with full assembly and annotation of insertional sequences for various types of retroelements, including pseudogenes and endogenous retroviruses. Notably, we find that individual genomes have an average of nine groups of full-length L1s in centromeres, suggesting that centromeres and other highly repetitive regions such as telomeres are a significant yet unexplored source of active L1s. xTea is available at https://github.com/parklab/xTea.

scholarly journals The long and the short of it: unlocking nanopore long-read RNA sequencing data with short-read differential expression analysis tools

2021 ◽  
Vol 3 (2) ◽  
Author(s):  
Xueyi Dong ◽  
Luyi Tian ◽  
Quentin Gouil ◽  
Hasaru Kariyawasam ◽  
Shian Su ◽  
...  
Keyword(s):  
Differential Expression ◽  
Expression Analysis ◽  
Differential Expression Analysis ◽  
Transcriptomic Analysis ◽  
Statistical Testing ◽  
Rna Seq ◽  
Sequencing Data ◽  
Short Read ◽  
Sequencing Platform ◽  
Long Read

Abstract Application of Oxford Nanopore Technologies’ long-read sequencing platform to transcriptomic analysis is increasing in popularity. However, such analysis can be challenging due to the high sequence error and small library sizes, which decreases quantification accuracy and reduces power for statistical testing. Here, we report the analysis of two nanopore RNA-seq datasets with the goal of obtaining gene- and isoform-level differential expression information. A dataset of synthetic, spliced, spike-in RNAs (‘sequins’) as well as a mouse neural stem cell dataset from samples with a null mutation of the epigenetic regulator Smchd1 was analysed using a mix of long-read specific tools for preprocessing together with established short-read RNA-seq methods for downstream analysis. We used limma-voom to perform differential gene expression analysis, and the novel FLAMES pipeline to perform isoform identification and quantification, followed by DRIMSeq and limma-diffSplice (with stageR) to perform differential transcript usage analysis. We compared results from the sequins dataset to the ground truth, and results of the mouse dataset to a previous short-read study on equivalent samples. Overall, our work shows that transcriptomic analysis of long-read nanopore data using long-read specific preprocessing methods together with short-read differential expression methods and software that are already in wide use can yield meaningful results.

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 Complete Genome Sequence of Rubrobacter xylanophilus Strain AA3-22, Isolated from Arima Onsen in Japan

2019 ◽  
Vol 8 (34) ◽  
Author(s):  
Natsuki Tomariguchi ◽  
Kentaro Miyazaki
Keyword(s):  
Genome Sequence ◽  
Complete Genome Sequence ◽  
Complete Genome ◽  
Hot Spring ◽  
Sequencing Data ◽  
Short Read ◽  
Content Type ◽  
Short Read Sequencing ◽  
Oxford Nanopore ◽  
Long Read

Rubrobacter xylanophilus strain AA3-22, belonging to the phylum Actinobacteria, was isolated from nonvolcanic Arima Onsen (hot spring) in Japan. Here, we report the complete genome sequence of this organism, which was obtained by combining Oxford Nanopore long-read and Illumina short-read sequencing data.

scholarly journals LinkedSV for detection of mosaic structural variants from linked-read exome and genome sequencing data

2018 ◽  
Author(s):  
Li Fang ◽  
Charlly Kao ◽  
Michael V Gonzalez ◽  
Fernanda A Mafra ◽  
Renata Pellegrino da Silva ◽  
...  
Keyword(s):  
Exome Sequencing ◽  
Read Depth ◽  
Structural Variants ◽  
Sequencing Data ◽  
High Coverage ◽  
Short Read ◽  
Short Read Sequencing ◽  
Sequencing Studies ◽  
Long Read ◽  
Local Assembly

AbstractLinked-read sequencing provides long-range information on short-read sequencing data by barcoding reads originating from the same DNA molecule, and can improve the detection and breakpoint identification for structural variants (SVs). We present LinkedSV for SV detection on linked-read sequencing data. LinkedSV considers barcode overlapping and enriched fragment endpoints as signals to detect large SVs, while it leverages read depth, paired-end signals and local assembly to detect small SVs. Benchmarking studies demonstrates that LinkedSV outperforms existing tools, especially on exome data and on somatic SVs with low variant allele frequencies. We demonstrate clinical cases where LinkedSV identifies disease causal SVs from linked-read exome sequencing data missed by conventional exome sequencing, and show examples where LinkedSV identifies SVs missed by high-coverage long-read sequencing. In summary, LinkedSV can detect SVs missed by conventional short-read and long-read sequencing approaches, and may resolve negative cases from clinical genome/exome sequencing studies.

scholarly journals Systematic analysis of dark and camouflaged genes: disease-relevant genes hiding in plain sight

2019 ◽  
Author(s):  
Mark T. W. Ebbert ◽  
Tanner D. Jensen ◽  
Karen Jansen-West ◽  
Jonathon P. Sens ◽  
Joseph S. Reddy ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Sequencing Data ◽  
Systematic Analysis ◽  
Protein Coding ◽  
Short Read ◽  
Sequencing Project ◽  
Short Read Sequencing ◽  
Sequencing Technologies ◽  
Long Read

AbstractBackgroundThe human genome contains ‘dark’ gene regions that cannot be adequately assembled or aligned using standard short-read sequencing technologies, preventing researchers from identifying mutations within these gene regions that may be relevant to human disease. Here, we identify regions that are ‘dark by depth’ (few mappable reads) and others that are ‘camouflaged’ (ambiguous alignment), and we assess how well long-read technologies resolve these regions. We further present an algorithm to resolve most camouflaged regions (including in short-read data) and apply it to the Alzheimer’s Disease Sequencing Project (ADSP; 13142 samples), as a proof of principle.ResultsBased on standard whole-genome lllumina sequencing data, we identified 37873 dark regions in 5857 gene bodies (3635 protein-coding) from pathways important to human health, development, and reproduction. Of the 5857 gene bodies, 494 (8.4%) were 100% dark (142 protein-coding) and 2046 (34.9%) were ≥5% dark (628 protein-coding). Exactly 2757 dark regions were in protein-coding exons (CDS) across 744 genes. Long-read sequencing technologies from 10x Genomics, PacBio, and Oxford Nanopore Technologies reduced dark CDS regions to approximately 45.1%, 33.3%, and 18.2% respectively. Applying our algorithm to the ADSP, we rescued 4622 exonic variants from 501 camouflaged genes, including a rare, ten-nucleotide frameshift deletion in CR1, a top Alzheimer’s disease gene, found in only five ADSP cases and zero controls.ConclusionsWhile we could not formally assess the CR1 frameshift mutation in Alzheimer’s disease (insufficient sample-size), we believe it merits investigating in a larger cohort. There remain thousands of potentially important genomic regions overlooked by short-read sequencing that are largely resolved by long-read technologies.

scholarly journals High conservation combined with high plasticity: genomics and evolution of Borrelia bavariensis

2020 ◽  
Author(s):  
Noémie S Becker ◽  
Robert Ethan Rollins ◽  
Kateryna Nosenko ◽  
Alexander Paulus ◽  
Samantha Martin ◽  
...  
Keyword(s):  
Lyme Borreliosis ◽  
Next Generation Sequencing Data ◽  
Borrelia Burgdorferi Sensu Lato ◽  
High Plasticity ◽  
Sequencing Data ◽  
Short Read ◽  
Genome Reconstruction ◽  
A Genome ◽  
Long Read ◽  
Genomic Studies

Abstract BackgroundBorrelia bavariensis is one of the agents of Lyme Borreliosis (or Lyme disease) in Eurasia. The genome of the Borrelia burgdorferi sensu lato species complex, that includes B. bavariensis , is known to be very complex and fragmented making the assembly of whole genomes with next-generation sequencing data a challenge. ResultsWe present a genome reconstruction for 33 B. bavariensis isolates from Eurasia based on long-read (Pacific Bioscience, for three isolates) and short-read (Illumina) data. We show that the combination of both sequencing techniques allows proper genome reconstruction of all plasmids in most cases but us e of a very close reference is necessary when only short-read sequencing data is available. B. bavariensis genomes combine a high degree of genetic conservation with high plasticity: all isolates share the main chromosome and five plasmids, but the repertoire of other plasmids is highly variable. In addition to plasmid losses and gains through horizontal transfer, we also observe several fusions between plasmids. Although European isolates of B. bavariensis have little diversity in genome content, there is some geographic structure to this variation. In contrast, each Asian isolate has a unique plasmid repertoire and we observe no geographically based differences between Japanese and Russian isolates. Comparing the genomes of Asian and European populations of B. bavariensis suggest s that some genes which are markedly different between the two populations may be good candidates for adaptation to the tick vector, ( Ixodes ricinus in Europe and I. persulcatus in Asia) . ConclusionsWe present the characterization of genomes of a large sample of B. bavariensis isolates and show that their plasmid content is highly variable. This study opens the way for genomic studies seeking to understand host and vector adaptation as well as human pathogenicity in Eurasian Lyme borreliosis agents.

scholarly journals NextSV: a meta-caller for structural variants from low-coverage long-read sequencing data

2016 ◽  
Author(s):  
Li Fang ◽  
Jiang Hu ◽  
Depeng Wang ◽  
Kai Wang
Keyword(s):  
Whole Genome ◽  
Ashkenazi Jewish ◽  
Structural Variants ◽  
Sequencing Data ◽  
Short Read ◽  
Short Read Sequencing ◽  
Human Genomes ◽  
Long Read ◽  
Personal Genomes ◽  
Low Coverage

AbstractBackgroundStructural variants (SVs) in human genomes are implicated in a variety of human diseases. Long-read sequencing delivers much longer read lengths than short-read sequencing and may greatly improve SV detection. However, due to the relatively high cost of long-read sequencing, it is unclear what coverage is needed and how to optimally use the aligners and SV callers.ResultsIn this study, we developed NextSV, a meta-caller to perform SV calling from low coverage long-read sequencing data. NextSV integrates three aligners and three SV callers and generates two integrated call sets (sensitive/stringent) for different analysis purposes. We evaluated SV calling performance of NextSV under different PacBio coverages on two personal genomes, NA12878 and HX1. Our results showed that, compared with running any single SV caller, NextSV stringent call set had higher precision and balanced accuracy (F1 score) while NextSV sensitive call set had a higher recall. At 10X coverage, the recall of NextSV sensitive call set was 93.5% to 94.1% for deletions and 87.9% to 93.2% for insertions, indicating that ~10X coverage might be an optimal coverage to use in practice, considering the balance between the sequencing costs and the recall rates. We further evaluated the Mendelian errors on an Ashkenazi Jewish trio dataset.ConclusionsOur results provide useful guidelines for SV detection from low coverage whole-genome PacBio data and we expect that NextSV will facilitate the analysis of SVs on long-read sequencing data.

scholarly journals Local adaptation and archaic introgression shape global diversity at human structural variant loci

2021 ◽  
Author(s):  
Stephanie M. Yan ◽  
Rachel M. Sherman ◽  
Dylan J. Taylor ◽  
Divya R. Nair ◽  
Andrew N. Bortvin ◽  
...  
Keyword(s):  
Local Adaptation ◽  
Human Populations ◽  
Sequencing Data ◽  
High Coverage ◽  
Short Read ◽  
Asian Populations ◽  
Long Read ◽  
Archaic Introgression ◽  
Immune Related Genes

AbstractLarge genomic insertions, deletions, and inversions are a potent source of functional and fitness-altering variation, but are challenging to resolve with short-read DNA sequencing alone. While recent long-read sequencing technologies have greatly expanded the catalog of structural variants (SVs), their costs have so far precluded their application at population scales. Given these limitations, the role of SVs in human adaptation remains poorly characterized. Here, we used a graph-based approach to genotype 107,866 long-read-discovered SVs in short-read sequencing data from diverse human populations. We then applied an admixture-aware method to scan these SVs for patterns of population-specific frequency differentiation—a signature of local adaptation. We identified 220 SVs exhibiting extreme frequency differentiation, including several SVs that were among the lead variants at their corresponding loci. The top two signatures traced to separate insertion and deletion polymorphisms at the immunoglobulin heavy chain locus, together tagging a 325 Kbp haplotype that swept to high frequency and was subsequently fragmented by recombination. Alleles defining this haplotype are nearly fixed (60-95%) in certain Southeast Asian populations, but are rare or absent from other global populations composing the 1000 Genomes Project. Further investigation revealed that the haplotype closely matches with sequences observed in two of three high-coverage Neanderthal genomes, providing strong evidence of a Neanderthal-introgressed origin. This extraordinary episode of positive selection, which we infer to have occurred between 1700 and 8400 years ago, corroborates the role of immune-related genes as prominent targets of adaptive archaic introgression. Our study demonstrates how combining recent advances in genome sequencing, genotyping algorithms, and population genetic methods can reveal signatures of key evolutionary events that remained hidden within poorly resolved regions of the genome.

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