ecc_finder: A Robust and Accurate Tool for Detecting Extrachromosomal Circular DNA From Sequencing Data

Extrachromosomal circular DNA (eccDNA) has been observed in different species for decades, and more and more evidence shows that this specific type of DNA molecules may play an important role in rapid adaptation. Therefore, characterizing the full landscape of eccDNA has become critical, and there are several protocols for enriching eccDNAs and performing short-read or long-read sequencing. However, there is currently no available bioinformatic tool to identify eccDNAs from Nanopore reads. More importantly, the current tools based on Illumina short reads lack an efficient standardized pipeline notably to identify eccDNA originating from repeated loci and cannot be applied to very large genomes. Here, we introduce a comprehensive tool to solve both of these two issues.1 Applying ecc_finder to eccDNA-seq data (either mobilome-seq, Circle-Seq and CIDER-seq) from Arabidopsis, human, and wheat (with genome sizes ranging from 120Mb to 17 Gb), we document the improvement of computational time, sensitivity, and accuracy and demonstrate ecc_finder wide applicability and functionality.

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Optimizing source tracking of Listeria monocytogenes with quasimetagenomics and integrated long and short read sequencing

10.21203/rs.3.rs-119006/v1 ◽

2020 ◽

Author(s):

Seth Commichaux ◽

Kiran Javkar ◽

Padmini Ramachandran ◽

Niranjan Nagarajan ◽

Denis Bertrand ◽

...

Keyword(s):

Listeria Monocytogenes ◽

Core Gene ◽

Source Tracking ◽

Strain Typing ◽

Genome Length ◽

Sequencing Data ◽

Short Read ◽

Short Reads ◽

Gene Set ◽

Long Read

Abstract Background: Rapid pathogen detection is essential for an effective public health response. The Illumina MiSeq short read sequencer has been the workhorse of many whole genome sequencing source tracking programs. However, short reads cannot span many bacterial genomic repeats, resulting in highly fragmented assemblies. Long read sequencing can span the repeats resulting in the complete assembly of bacterial genomes. With the advancing throughput and accuracy of inexpensive long read sequencing data, it is important to evaluate this resource for potential inclusion in rapid response pathogen identification protocols.Results: Here we sought to maximize the utility of long read (GridIon, Oxford Nanopore) and short read (MiSeq, Illumina) sequencing data for the identification of Listeria monocytogenes from naturally contaminated ice cream. Aliquots from the temporal enrichment (quasimetagenomes) of L. monocytogenes were sequenced and assembled with 10 different assembly approaches. Long read assembly tools generated genome-length contigs and a circularized 71 kbp putative L. monocytogenes plasmid; however, the high sequencing error rate prevented strain typing at even 150X depth of coverage. Short read assemblies provided accurate core gene strain typing after 28 hours of enrichment but were too fragmented for typing the full gene set and reconstructing a circularized genome and plasmid. Hybrid approaches demonstrated the most promising results but were biased by assembly strategy. Short read assemblies scaffolded with long reads were able to accurately strain type L. monocytogenes after just 24 hours of enrichment, but were still too fragmented for sequence typing the full gene set. Long read assemblies polished with short reads recovered genome-length contigs, the full complement of genes, and a circularized plasmid after 24 hours enrichment; however they could not consistently achieve the core gene strain typing accuracy of short read or short read hybrid assembly approaches. Conclusion: These analyses demonstrate that strategic integration and optimization of microbiological (quasimetagenomic), molecular (integrated long and short reads), and bioinformatic approaches (diverse assembly strategies) can greatly improve our ability to quickly and accurately identify pathogens.

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Evaluating the accuracy of Listeria monocytogenes assemblies from quasimetagenomic samples using long and short reads

BMC Genomics ◽

10.1186/s12864-021-07702-2 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Seth Commichaux ◽

Kiran Javkar ◽

Padmini Ramachandran ◽

Niranjan Nagarajan ◽

Denis Bertrand ◽

...

Keyword(s):

Public Health ◽

Public Health Response ◽

High Quality ◽

Short Read ◽

Short Reads ◽

The Core ◽

Long Reads ◽

Health Response ◽

Long Read ◽

Core Genes

Abstract Background Whole genome sequencing of cultured pathogens is the state of the art public health response for the bioinformatic source tracking of illness outbreaks. Quasimetagenomics can substantially reduce the amount of culturing needed before a high quality genome can be recovered. Highly accurate short read data is analyzed for single nucleotide polymorphisms and multi-locus sequence types to differentiate strains but cannot span many genomic repeats, resulting in highly fragmented assemblies. Long reads can span repeats, resulting in much more contiguous assemblies, but have lower accuracy than short reads. Results We evaluated the accuracy of Listeria monocytogenes assemblies from enrichments (quasimetagenomes) of naturally-contaminated ice cream using long read (Oxford Nanopore) and short read (Illumina) sequencing data. Accuracy of ten assembly approaches, over a range of sequencing depths, was evaluated by comparing sequence similarity of genes in assemblies to a complete reference genome. Long read assemblies reconstructed a circularized genome as well as a 71 kbp plasmid after 24 h of enrichment; however, high error rates prevented high fidelity gene assembly, even at 150X depth of coverage. Short read assemblies accurately reconstructed the core genes after 28 h of enrichment but produced highly fragmented genomes. Hybrid approaches demonstrated promising results but had biases based upon the initial assembly strategy. Short read assemblies scaffolded with long reads accurately assembled the core genes after just 24 h of enrichment, but were highly fragmented. Long read assemblies polished with short reads reconstructed a circularized genome and plasmid and assembled all the genes after 24 h enrichment but with less fidelity for the core genes than the short read assemblies. Conclusion The integration of long and short read sequencing of quasimetagenomes expedited the reconstruction of a high quality pathogen genome compared to either platform alone. A new and more complete level of information about genome structure, gene order and mobile elements can be added to the public health response by incorporating long read analyses with the standard short read WGS outbreak response.

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Comprehensive identification of transposable element insertions using multiple sequencing technologies

Nature Communications ◽

10.1038/s41467-021-24041-8 ◽

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.

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

NAR Genomics and Bioinformatics ◽

10.1093/nargab/lqab028 ◽

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.

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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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PERHAPS: Paired-End short Reads-based HAPlotyping from next-generation Sequencing data

Briefings in Bioinformatics ◽

10.1093/bib/bbaa320 ◽

2020 ◽

Author(s):

Jie Huang ◽

Stefano Pallotti ◽

Qianling Zhou ◽

Marcus Kleber ◽

Xiaomeng Xin ◽

...

Keyword(s):

Next Generation Sequencing ◽

Snp Array ◽

Simple Approach ◽

Next Generation Sequencing Data ◽

Next Generation ◽

Sequencing Data ◽

Short Read ◽

Array Data ◽

Short Reads ◽

Generation Sequencing

Abstract The identification of rare haplotypes may greatly expand our knowledge in the genetic architecture of both complex and monogenic traits. To this aim, we developed PERHAPS (Paired-End short Reads-based HAPlotyping from next-generation Sequencing data), a new and simple approach to directly call haplotypes from short-read, paired-end Next Generation Sequencing (NGS) data. To benchmark this method, we considered the APOE classic polymorphism (*1/*2/*3/*4), since it represents one of the best examples of functional polymorphism arising from the haplotype combination of two Single Nucleotide Polymorphisms (SNPs). We leveraged the big Whole Exome Sequencing (WES) and SNP-array data obtained from the multi-ethnic UK BioBank (UKBB, N=48,855). By applying PERHAPS, based on piecing together the paired-end reads according to their FASTQ-labels, we extracted the haplotype data, along with their frequencies and the individual diplotype. Concordance rates between WES directly called diplotypes and the ones generated through statistical pre-phasing and imputation of SNP-array data are extremely high (>99%), either when stratifying the sample by SNP-array genotyping batch or self-reported ethnic group. Hardy-Weinberg Equilibrium tests and the comparison of obtained haplotype frequencies with the ones available from the 1000 Genome Project further supported the reliability of PERHAPS. Notably, we were able to determine the existence of the rare APOE*1 haplotype in two unrelated African subjects from UKBB, supporting its presence at appreciable frequency (approximatively 0.5%) in the African Yoruba population. Despite acknowledging some technical shortcomings, PERHAPS represents a novel and simple approach that will partly overcome the limitations in direct haplotype calling from short read-based sequencing.

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

Microbiology Resource Announcements ◽

10.1128/mra.00818-19 ◽

2019 ◽

Vol 8 (34) ◽

Cited By ~ 1

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.

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

Briefings in Bioinformatics ◽

10.1093/bib/bbaa231 ◽

2020 ◽

Cited By ~ 1

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.

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

10.1101/409789 ◽

2018 ◽

Cited By ~ 2

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.

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Assembling reads improves taxonomic classification of species

10.21203/rs.3.rs-22309/v1 ◽

2020 ◽

Author(s):

Quang Tran ◽

Vinhthuy Phan

Keyword(s):

Classification Performance ◽

Performance Characteristics ◽

Metagenomic Data ◽

Species Classification ◽

Short Read ◽

Short Reads ◽

Sequencing Errors ◽

Trade Offs ◽

Long Reads ◽

Long Read

Abstract Background: Most current metagenomic classifiers and profilers employ short reads to classify, bin and profile microbial genomes that are present in metagenomic samples. Many of these methods adopt techniques that aim to identify unique genomic regions of genomes so as to differentiate them. Because of this, short-read lengths might be suboptimal. Longer read lengths might improve the performance of classification and profiling. However, longer reads produced by current technology tend to have a higher rate of sequencing errors, compared to short reads. It is not clear if the trade-off between longer length versus higher sequencing errors will increase or decrease classification and profiling performance.Results: We compared performance of popular metagenomic classifiers on short reads and longer reads, which are assembled from the same short reads. When using a number of popular assemblers to assemble long reads from the short reads, we discovered that most classifiers made fewer predictions with longer reads and that they achieved higher classification performance on synthetic metagenomic data. Specifically, across most classifiers, we observed a significant increase in precision, while recall remained the same, resulting in higher overall classification performance. On real metagenomic data, we observed a similar trend that classifiers made fewer predictions. This suggested that they might have the same performance characteristics of having higher precision while maintaining the same recall with longer reads.Conclusions: This finding has two main implications. First, it suggests that classifying species in metagenomic environments can be achieved with higher overall performance simply by assembling short reads. This suggested that they might have the same performance characteristics of having higher precision while maintaining the same recall as shorter reads. Second, this finding suggests that it might be a good idea to consider utilizing long-read technologies in species classification for metagenomic applications. Current long-read technologies tend to have higher sequencing errors and are more expensive compared to short-read technologies. The trade-offs between the pros and cons should be investigated.

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