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Gamaarachchi H, Ferguson JM, Samarakoon H, Liyanage K, Deveson IW. Simulation of nanopore sequencing signal data with tunable parameters. Genome Res 2024; 34:778-783. [PMID: 38692839 PMCID: PMC11216307 DOI: 10.1101/gr.278730.123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Accepted: 04/24/2024] [Indexed: 05/03/2024]
Abstract
In silico simulation of high-throughput sequencing data is a technique used widely in the genomics field. However, there is currently a lack of effective tools for creating simulated data from nanopore sequencing devices, which measure DNA or RNA molecules in the form of time-series current signal data. Here, we introduce Squigulator, a fast and simple tool for simulation of realistic nanopore signal data. Squigulator takes a reference genome, a transcriptome, or read sequences, and generates corresponding raw nanopore signal data. This is compatible with basecalling software from Oxford Nanopore Technologies (ONT) and other third-party tools, thereby providing a useful substrate for development, testing, debugging, validation, and optimization at every stage of a nanopore analysis workflow. The user may generate data with preset parameters emulating specific ONT protocols or noise-free "ideal" data, or they may deterministically modify a range of experimental variables and/or noise parameters to shape the data to their needs. We present a brief example of Squigulator's use, creating simulated data to model the degree to which different parameters impact the accuracy of ONT basecalling and downstream variant detection. This analysis reveals new insights into the nature of ONT data and basecalling algorithms. We provide Squigulator as an open-source tool for the nanopore community.
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Affiliation(s)
- Hasindu Gamaarachchi
- School of Computer Science and Engineering, University of New South Wales, Sydney, New South Wales 2052, Australia;
- Genomics and Inherited Disease Program, Garvan Institute of Medical Research, Sydney, New South Wales 2010, Australia
- Centre for Population Genomics, Garvan Institute of Medical Research and Murdoch Children's Research Institute, New South Wales 2010, Australia Australia
| | - James M Ferguson
- Genomics and Inherited Disease Program, Garvan Institute of Medical Research, Sydney, New South Wales 2010, Australia
- Centre for Population Genomics, Garvan Institute of Medical Research and Murdoch Children's Research Institute, New South Wales 2010, Australia Australia
| | - Hiruna Samarakoon
- School of Computer Science and Engineering, University of New South Wales, Sydney, New South Wales 2052, Australia
- Genomics and Inherited Disease Program, Garvan Institute of Medical Research, Sydney, New South Wales 2010, Australia
- Centre for Population Genomics, Garvan Institute of Medical Research and Murdoch Children's Research Institute, New South Wales 2010, Australia Australia
| | - Kisaru Liyanage
- School of Computer Science and Engineering, University of New South Wales, Sydney, New South Wales 2052, Australia
- Genomics and Inherited Disease Program, Garvan Institute of Medical Research, Sydney, New South Wales 2010, Australia
- Centre for Population Genomics, Garvan Institute of Medical Research and Murdoch Children's Research Institute, New South Wales 2010, Australia Australia
| | - Ira W Deveson
- Genomics and Inherited Disease Program, Garvan Institute of Medical Research, Sydney, New South Wales 2010, Australia;
- Centre for Population Genomics, Garvan Institute of Medical Research and Murdoch Children's Research Institute, New South Wales 2010, Australia Australia
- St Vincent's Clinical School, Faculty of Medicine, University of New South Wales, Sydney, New South Wales 2052, Australia
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2
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Munro R, Wibowo S, Payne A, Loose M. Icarust, a real-time simulator for Oxford Nanopore adaptive sampling. Bioinformatics 2024; 40:btae141. [PMID: 38478392 PMCID: PMC10980563 DOI: 10.1093/bioinformatics/btae141] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Revised: 12/21/2023] [Accepted: 03/11/2024] [Indexed: 04/01/2024] Open
Abstract
MOTIVATION Oxford Nanopore Technologies (ONT) sequencers enable real-time generation of sequence data, which allows for concurrent analysis during a run. Adaptive sampling leverages this real-time capability in extremis, rejecting or accepting reads for sequencing based on assessment of the sequence from the start of each read. This functionality is provided by ONT's software, MinKNOW (Oxford Nanopore Technologies). Designing and developing software to take advantage of adaptive sampling can be costly in terms of sequencing consumables, using precious samples and preparing sequencing libraries. MinKNOW addresses this in part by allowing the replay of previously sequenced runs for testing. However, as we show, the sequencing output only partially changes in response to adaptive sampling instructions. Here we present Icarust, a tool enabling more accurate approximations of sequencing runs. Icarust recreates all the required endpoints of MinKNOW to perform adaptive sampling and writes output compatible with current base-callers and analysis pipelines. Icarust serves nanopore signal simulating a MinION or PromethION flow cell experiment from any reference genome using either R9 or R10 pore models. We show that simulating sequencing runs with Icarust provides a realistic testing and development environment for software exploiting the real-time nature of Nanopore sequencing. AVAILABILITY AND IMPLEMENTATION All code is open source and freely available here-https://github.com/LooseLab/Icarust. Icarust is implemented in Rust, with a docker container also available. The data underlying this article will be shared on reasonable request to the corresponding author.
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Affiliation(s)
- Rory Munro
- School of Life Sciences, Medical School, Queens Medical Centre, University of Nottingham, Nottingham NG72RD, United Kingdom
| | - Satrio Wibowo
- School of Life Sciences, Medical School, Queens Medical Centre, University of Nottingham, Nottingham NG72RD, United Kingdom
| | - Alexander Payne
- School of Life Sciences, Medical School, Queens Medical Centre, University of Nottingham, Nottingham NG72RD, United Kingdom
| | - Matthew Loose
- School of Life Sciences, Medical School, Queens Medical Centre, University of Nottingham, Nottingham NG72RD, United Kingdom
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3
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Karaoğlanoğlu F, Orabi B, Flannigan R, Chauve C, Hach F. TKSM: highly modular, user-customizable, and scalable transcriptomic sequencing long-read simulator. Bioinformatics 2024; 40:btae051. [PMID: 38273664 PMCID: PMC10868325 DOI: 10.1093/bioinformatics/btae051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Revised: 01/10/2024] [Accepted: 01/23/2024] [Indexed: 01/27/2024] Open
Abstract
MOTIVATION Transcriptomic long-read (LR) sequencing is an increasingly cost-effective technology for probing various RNA features. Numerous tools have been developed to tackle various transcriptomic sequencing tasks (e.g. isoform and gene fusion detection). However, the lack of abundant gold-standard datasets hinders the benchmarking of such tools. Therefore, the simulation of LR sequencing is an important and practical alternative. While the existing LR simulators aim to imitate the sequencing machine noise and to target specific library protocols, they lack some important library preparation steps (e.g. PCR) and are difficult to modify to new and changing library preparation techniques (e.g. single-cell LRs). RESULTS We present TKSM, a modular and scalable LR simulator, designed so that each RNA modification step is targeted explicitly by a specific module. This allows the user to assemble a simulation pipeline as a combination of TKSM modules to emulate a specific sequencing design. Additionally, the input/output of all the core modules of TKSM follows the same simple format (Molecule Description Format) allowing the user to easily extend TKSM with new modules targeting new library preparation steps. AVAILABILITY AND IMPLEMENTATION TKSM is available as an open source software at https://github.com/vpc-ccg/tksm.
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Affiliation(s)
- Fatih Karaoğlanoğlu
- Computing Science Department, Simon Fraser University, Burnaby, BC V5A 1S6, Canada
| | - Baraa Orabi
- Department of Computer Science, the University of British Columbia, Vancouver, BC V6T 1Z4, Canada
| | - Ryan Flannigan
- Department of Urologic Sciences, the University of British Columbia, Vancouver, BC V5Z 1M9, Canada
- Vancouver Prostate Centre, Vancouver, BC V6H 3Z6, Canada
| | - Cedric Chauve
- Department of Mathematics, Simon Fraser University, Burnaby, BC V5A 1S6, Canada
| | - Faraz Hach
- Department of Computer Science, the University of British Columbia, Vancouver, BC V6T 1Z4, Canada
- Department of Urologic Sciences, the University of British Columbia, Vancouver, BC V5Z 1M9, Canada
- Vancouver Prostate Centre, Vancouver, BC V6H 3Z6, Canada
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4
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Mesloub Y, Beury D, Vandermeeren F, Caboche S. CuReSim-LoRM: A Tool to Simulate Metabarcoding Long Reads. Int J Mol Sci 2023; 24:14005. [PMID: 37762307 PMCID: PMC10531135 DOI: 10.3390/ijms241814005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 09/07/2023] [Accepted: 09/10/2023] [Indexed: 09/29/2023] Open
Abstract
Metabarcoding DNA sequencing has revolutionized the study of microbial communities. Third-generation sequencing producing long reads had opened up new perspectives. Obtaining the full-length ribosomal RNA gene would permit one to reach a better taxonomic resolution at the species or the strain level. However, Oxford Nanopore Technologies (ONT) sequencing produces reads with high error rates, which introduces biases in analysis. Understanding the biases introduced during the analysis allows one to better interpret the biological results and take care of conclusions drawn from metabarcoding experiments. To benchmark an analysis process, the ground truth, i.e., the real composition of the microbial community, has to be known. In addition to artificial mock communities, simulated data are often used to evaluate the biases and performances of the bioinformatics analysis step. Currently, no specific tool has been developed to simulate metabarcoding long reads, mimic the error rate and the length distribution, and allow one to benchmark the analysis process. Here, we introduce CuReSim-LoRM, for the customized read simulator to generate long reads for metabarcoding. We showed that CuReSim-LoRM is able to produce reads with varying error rates and length distributions by mimicking the real data very well.
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Affiliation(s)
| | | | | | - Ségolène Caboche
- Univ. Lille, CNRS, Inserm, CHU Lille, Institut Pasteur de Lille, US 41-UAR 2014-PLBS, F-59000 Lille, France
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5
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Mohamed M, Sabot F, Varoqui M, Mugat B, Audouin K, Pélisson A, Fiston-Lavier AS, Chambeyron S. TrEMOLO: accurate transposable element allele frequency estimation using long-read sequencing data combining assembly and mapping-based approaches. Genome Biol 2023; 24:63. [PMID: 37013657 PMCID: PMC10069131 DOI: 10.1186/s13059-023-02911-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Accepted: 03/23/2023] [Indexed: 04/05/2023] Open
Abstract
Transposable Element MOnitoring with LOng-reads (TrEMOLO) is a new software that combines assembly- and mapping-based approaches to robustly detect genetic elements called transposable elements (TEs). Using high- or low-quality genome assemblies, TrEMOLO can detect most TE insertions and deletions and estimate their allele frequency in populations. Benchmarking with simulated data revealed that TrEMOLO outperforms other state-of-the-art computational tools. TE detection and frequency estimation by TrEMOLO were validated using simulated and experimental datasets. Therefore, TrEMOLO is a comprehensive and suitable tool to accurately study TE dynamics. TrEMOLO is available under GNU GPL3.0 at https://github.com/DrosophilaGenomeEvolution/TrEMOLO .
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Affiliation(s)
- Mourdas Mohamed
- Institute of Human Genetics, UMR9002, CNRS and Université de Montpellier, Montpellier, France
| | - François Sabot
- DIADE, University of Montpellier, CIRAD, IRD, Montpellier, France
- IFB - Southgreen Bioversity, CIRAD, INRAE, IRD, Montpellier, France
| | - Marion Varoqui
- Institute of Human Genetics, UMR9002, CNRS and Université de Montpellier, Montpellier, France
| | - Bruno Mugat
- Institute of Human Genetics, UMR9002, CNRS and Université de Montpellier, Montpellier, France
| | | | - Alain Pélisson
- Institute of Human Genetics, UMR9002, CNRS and Université de Montpellier, Montpellier, France
| | - Anna-Sophie Fiston-Lavier
- ISEM, Université Montpellier, CNRS, IRD, CIRAD, EPHE, Montpellier, France.
- Institut Universitaire de France (IUF), Paris, France.
| | - Séverine Chambeyron
- Institute of Human Genetics, UMR9002, CNRS and Université de Montpellier, Montpellier, France.
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6
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Duncavage EJ, Coleman JF, de Baca ME, Kadri S, Leon A, Routbort M, Roy S, Suarez CJ, Vanderbilt C, Zook JM. Recommendations for the Use of in Silico Approaches for Next-Generation Sequencing Bioinformatic Pipeline Validation: A Joint Report of the Association for Molecular Pathology, Association for Pathology Informatics, and College of American Pathologists. J Mol Diagn 2023; 25:3-16. [PMID: 36244574 DOI: 10.1016/j.jmoldx.2022.09.007] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Revised: 09/14/2022] [Accepted: 09/28/2022] [Indexed: 11/21/2022] Open
Abstract
In silico approaches for next-generation sequencing (NGS) data modeling have utility in the clinical laboratory as a tool for clinical assay validation. In silico NGS data can take a variety of forms, including pure simulated data or manipulated data files in which variants are inserted into existing data files. In silico data enable simulation of a range of variants that may be difficult to obtain from a single physical sample. Such data allow laboratories to more accurately test the performance of clinical bioinformatics pipelines without sequencing additional cases. For example, clinical laboratories may use in silico data to simulate low variant allele fraction variants to test the analytical sensitivity of variant calling software or simulate a range of insertion/deletion sizes to determine the performance of insertion/deletion calling software. In this article, the Working Group reviews the different types of in silico data with their strengths and limitations, methods to generate in silico data, and how data can be used in the clinical molecular diagnostic laboratory. Survey data indicate how in silico NGS data are currently being used. Finally, potential applications for which in silico data may become useful in the future are presented.
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Affiliation(s)
- Eric J Duncavage
- In Silico Pipeline Validation Working Group of the Clinical Practice Committee, Association for Molecular Pathology, Rockville, Maryland; Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, Missouri.
| | - Joshua F Coleman
- In Silico Pipeline Validation Working Group of the Clinical Practice Committee, Association for Molecular Pathology, Rockville, Maryland; Department of Pathology, University of Utah, Salt Lake City, Utah
| | - Monica E de Baca
- In Silico Pipeline Validation Working Group of the Clinical Practice Committee, Association for Molecular Pathology, Rockville, Maryland; Pacific Pathology Partners, Seattle, Washington
| | - Sabah Kadri
- In Silico Pipeline Validation Working Group of the Clinical Practice Committee, Association for Molecular Pathology, Rockville, Maryland; Department of Pathology, Anne and Robert H Lurie Children's Hospital of Chicago, Chicago, Illinois
| | - Annette Leon
- In Silico Pipeline Validation Working Group of the Clinical Practice Committee, Association for Molecular Pathology, Rockville, Maryland; Color Health, Burlingame, California
| | - Mark Routbort
- In Silico Pipeline Validation Working Group of the Clinical Practice Committee, Association for Molecular Pathology, Rockville, Maryland; Department of Hematopathology, MD Anderson Cancer Center, Houston, Texas
| | - Somak Roy
- In Silico Pipeline Validation Working Group of the Clinical Practice Committee, Association for Molecular Pathology, Rockville, Maryland; Department of Pathology and Laboratory Medicine, Cincinnati Children's Hospital, Cincinnati, Ohio
| | - Carlos J Suarez
- In Silico Pipeline Validation Working Group of the Clinical Practice Committee, Association for Molecular Pathology, Rockville, Maryland; Department of Pathology, Stanford University, Palo Alto, California
| | - Chad Vanderbilt
- In Silico Pipeline Validation Working Group of the Clinical Practice Committee, Association for Molecular Pathology, Rockville, Maryland; Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Justin M Zook
- In Silico Pipeline Validation Working Group of the Clinical Practice Committee, Association for Molecular Pathology, Rockville, Maryland; Biomarker and Genomic Sciences Group, National Institute of Standards and Technology, Gaithersburg, Maryland
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7
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Sheshanarayana R, Govind Rajan A. Tailoring Nanoporous Graphene via Machine Learning: Predicting Probabilities and Formation Times of Arbitrary Nanopore Shapes. J Chem Phys 2022; 156:204703. [DOI: 10.1063/5.0089469] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Nanopores in graphene, a 2D material, are currently being explored for various applications, such as gas separation, water desalination, and DNA sequencing. The shapes and sizes of nanopores play a major role in determining the performance of devices made out of graphene. However, given an arbitrary nanopore shape, anticipating its creation probability and formation time are challenging inverse problems, solving which could help develop theoretical models for nanoporous graphene and guide experiments in tailoring pore sizes/shapes. In this work, we develop a machine learning (ML) framework to predict these target variables, based on data generated using kinetic Monte Carlo simulations and chemical graph theory. Thereby, we enable the rapid quantification of the ease of formation of a given nanopore shape in graphene via silicon-catalyzed electron-beam etching and provide an experimental handle to realize it in practice. We use structural features such as the number of carbon atoms removed, the number of edge atoms, the diameter of the nanopore, and its shape factor, which can be readily extracted from the nanopore shape. We show that the trained models can accurately predict nanopore probabilities and formation times with R2 values on the test set of 0.97 and 0.95, respectively. Not only that, we obtain physical insight into the working of the model and discuss the role played by the various structural features in modulating nanopore formation. Overall, our work provides a solid foundation for experimental studies to manipulate nanopore sizes/shapes and for theoretical studies to consider realistic structures of nanopores in graphene.
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8
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Napieralski A, Nowak R. Basecalling Using Joint Raw and Event Nanopore Data Sequence-to-Sequence Processing. SENSORS (BASEL, SWITZERLAND) 2022; 22:2275. [PMID: 35336445 PMCID: PMC8954548 DOI: 10.3390/s22062275] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/29/2022] [Revised: 03/13/2022] [Accepted: 03/14/2022] [Indexed: 06/14/2023]
Abstract
Third-generation DNA sequencers provided by Oxford Nanopore Technologies (ONT) produce a series of samples of an electrical current in the nanopore. Such a time series is used to detect the sequence of nucleotides. The task of translation of current values into nucleotide symbols is called basecalling. Various solutions for basecalling have already been proposed. The earlier ones were based on Hidden Markov Models, but the best ones use neural networks or other machine learning models. Unfortunately, achieved accuracy scores are still lower than competitive sequencing techniques, like Illumina's. Basecallers differ in the input data type-currently, most of them work on a raw data straight from the sequencer (time series of current). Still, the approach of using event data is also explored. Event data is obtained by preprocessing of raw data and dividing it into segments described by several features computed from raw data values within each segment. We propose a novel basecaller that uses joint processing of raw and event data. We define basecalling as a sequence-to-sequence translation, and we use a machine learning model based on an encoder-decoder architecture of recurrent neural networks. Our model incorporates twin encoders and an attention mechanism. We tested our solution on simulated and real datasets. We compare the full model accuracy results with its components: processing only raw or event data. We compare our solution with the existing ONT basecaller-Guppy. Results of numerical experiments show that joint raw and event data processing provides better basecalling accuracy than processing each data type separately. We implement an application called Ravvent, freely available under MIT licence.
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9
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Abante J, Kambhampati S, Feinberg AP, Goutsias J. Estimating DNA methylation potential energy landscapes from nanopore sequencing data. Sci Rep 2021; 11:21619. [PMID: 34732768 PMCID: PMC8566571 DOI: 10.1038/s41598-021-00781-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Accepted: 10/18/2021] [Indexed: 11/23/2022] Open
Abstract
High-throughput third-generation nanopore sequencing devices have enormous potential for simultaneously observing epigenetic modifications in human cells over large regions of the genome. However, signals generated by these devices are subject to considerable noise that can lead to unsatisfactory detection performance and hamper downstream analysis. Here we develop a statistical method, CpelNano, for the quantification and analysis of 5mC methylation landscapes using nanopore data. CpelNano takes into account nanopore noise by means of a hidden Markov model (HMM) in which the true but unknown (“hidden”) methylation state is modeled through an Ising probability distribution that is consistent with methylation means and pairwise correlations, whereas nanopore current signals constitute the observed state. It then estimates the associated methylation potential energy function by employing the expectation-maximization (EM) algorithm and performs differential methylation analysis via permutation-based hypothesis testing. Using simulations and analysis of published data obtained from three human cell lines (GM12878, MCF-10A, and MDA-MB-231), we show that CpelNano can faithfully estimate DNA methylation potential energy landscapes, substantially improving current methods and leading to a powerful tool for the modeling and analysis of epigenetic landscapes using nanopore sequencing data.
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Affiliation(s)
- Jordi Abante
- Whitaker Biomedical Engineering Institute, Johns Hopkins University, Baltimore, MD, 21218, USA. .,Department of Electrical & Computer Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA. .,Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA, 94305, USA.
| | - Sandeep Kambhampati
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, 21205, USA.,Department of Biomedical Informatics, Harvard Medical School, Boston, MA, 02115, USA
| | - Andrew P Feinberg
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, 21205, USA.,Center for Epigenetics, Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA.,Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA
| | - John Goutsias
- Whitaker Biomedical Engineering Institute, Johns Hopkins University, Baltimore, MD, 21218, USA. .,Department of Electrical & Computer Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA.
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10
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Sikolenko MA, Valentovich LN. Barapost: Binning of Nucleotide Sequences According to Taxonomic Annotation. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021; 18:2766-2767. [PMID: 32750890 DOI: 10.1109/tcbb.2020.3009780] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Contemporary sequencing technologies, Oxford Nanopore in particular, provide a way to sequence multiple samples during single run using molecular barcodes. Specific circumstances, however, can make barcoding undesirable or unaffordable. Here, we introduce Barapost: a command-line toolkit that demultiplexes long nucleotide sequences relying on taxonomic annotation instead of barcoding.
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11
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Comprehensive Wet-Bench and Bioinformatics Workflow for Complex Microbiota Using Oxford Nanopore Technologies. mSystems 2021; 6:e0075021. [PMID: 34427527 PMCID: PMC8407471 DOI: 10.1128/msystems.00750-21] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022] Open
Abstract
The advent of high-throughput sequencing techniques has recently provided an astonishing insight into the composition and function of the human microbiome. Next-generation sequencing (NGS) has become the gold standard for advanced microbiome analysis; however, 3rd generation real-time sequencing, such as Oxford Nanopore Technologies (ONT), enables rapid sequencing from several kilobases to >2 Mb with high resolution. Despite the wide availability and the enormous potential for clinical and translational applications, ONT is poorly standardized in terms of sampling and storage conditions, DNA extraction, library creation, and bioinformatic classification. Here, we present a comprehensive analysis pipeline with sampling, storage, DNA extraction, library preparation, and bioinformatic evaluation for complex microbiomes sequenced with ONT. Our findings from buccal and rectal swabs and DNA extraction experiments indicate that methods that were approved for NGS microbiome analysis cannot be simply adapted to ONT. We recommend using swabs and DNA extractions protocols with extended washing steps. Both 16S rRNA and metagenomic sequencing achieved reliable and reproducible results. Our benchmarking experiments reveal thresholds for analysis parameters that achieved excellent precision, recall, and area under the precision recall values and is superior to existing classifiers (Kraken2, Kaiju, and MetaMaps). Hence, our workflow provides an experimental and bioinformatic pipeline to perform a highly accurate analysis of complex microbial structures from buccal and rectal swabs. IMPORTANCE Advanced microbiome analysis relies on sequencing of short DNA fragments from microorganisms like bacteria, fungi, and viruses. More recently, long fragment DNA sequencing of 3rd generation sequencing has gained increasing importance and can be rapidly conducted within a few hours due to its potential real-time sequencing. However, the analysis and correct identification of the microbiome relies on a multitude of factors, such as the method of sampling, DNA extraction, sequencing, and bioinformatic analysis. Scientists have used different protocols in the past that do not allow us to compare results across different studies and research fields. Here, we provide a comprehensive workflow from DNA extraction, sequencing, and bioinformatic workflow that allows rapid and accurate analysis of human buccal and rectal swabs with reproducible protocols. This workflow can be readily applied by many scientists from various research fields that aim to use long-fragment microbiome sequencing.
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12
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Bartoszewicz JM, Genske U, Renard BY. Deep learning-based real-time detection of novel pathogens during sequencing. Brief Bioinform 2021; 22:6326527. [PMID: 34297793 DOI: 10.1093/bib/bbab269] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Revised: 06/09/2021] [Accepted: 06/23/2021] [Indexed: 11/12/2022] Open
Abstract
Novel pathogens evolve quickly and may emerge rapidly, causing dangerous outbreaks or even global pandemics. Next-generation sequencing is the state of the art in open-view pathogen detection, and one of the few methods available at the earliest stages of an epidemic, even when the biological threat is unknown. Analyzing the samples as the sequencer is running can greatly reduce the turnaround time, but existing tools rely on close matches to lists of known pathogens and perform poorly on novel species. Machine learning approaches can predict if single reads originate from more distant, unknown pathogens but require relatively long input sequences and processed data from a finished sequencing run. Incomplete sequences contain less information, leading to a trade-off between sequencing time and detection accuracy. Using a workflow for real-time pathogenic potential prediction, we investigate which subsequences already allow accurate inference. We train deep neural networks to classify Illumina and Nanopore reads and integrate the models with HiLive2, a real-time Illumina mapper. This approach outperforms alternatives based on machine learning and sequence alignment on simulated and real data, including SARS-CoV-2 sequencing runs. After just 50 Illumina cycles, we observe an 80-fold sensitivity increase compared to real-time mapping. The first 250 bp of Nanopore reads, corresponding to 0.5 s of sequencing time, are enough to yield predictions more accurate than mapping the finished long reads. The approach could also be used for screening synthetic sequences against biosecurity threats.
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Affiliation(s)
- Jakub M Bartoszewicz
- Digital Engineering Faculty, Hasso Plattner Institute, University of Postdam, Prof.-Dr.-Helmert-Straße 2-3, 14482 Brandenburg, Germany
| | - Ulrich Genske
- Digital Engineering Faculty, Hasso Plattner Institute, University of Postdam, Prof.-Dr.-Helmert-Straße 2-3, 14482 Brandenburg, Germany
| | - Bernhard Y Renard
- Digital Engineering Faculty, Hasso Plattner Institute, University of Postdam, Prof.-Dr.-Helmert-Straße 2-3, 14482 Brandenburg, Germany
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13
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Zhang H, Li H, Jain C, Cheng H, Au KF, Li H, Aluru S. Real-time mapping of nanopore raw signals. Bioinformatics 2021; 37:i477-i483. [PMID: 34252938 PMCID: PMC8336444 DOI: 10.1093/bioinformatics/btab264] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/04/2022] Open
Abstract
Motivation Oxford Nanopore Technologies sequencing devices support adaptive sequencing, in which undesired reads can be ejected from a pore in real time. This feature allows targeted sequencing aided by computational methods for mapping partial reads, rather than complex library preparation protocols. However, existing mapping methods either require a computationally expensive base-calling procedure before using aligners to map partial reads or work well only on small genomes. Results In this work, we present a new streaming method that can map nanopore raw signals for real-time selective sequencing. Rather than converting read signals to bases, we propose to convert reference genomes to signals and fully operate in the signal space. Our method features a new way to index reference genomes using k-d trees, a novel seed selection strategy and a seed chaining algorithm tailored toward the current signal characteristics. We implemented the method as a tool Sigmap. Then we evaluated it on both simulated and real data and compared it to the state-of-the-art nanopore raw signal mapper Uncalled. Our results show that Sigmap yields comparable performance on mapping yeast simulated raw signals, and better mapping accuracy on mapping yeast real raw signals with a 4.4× speedup. Moreover, our method performed well on mapping raw signals to genomes of size >100 Mbp and correctly mapped 11.49% more real raw signals of green algae, which leads to a significantly higher F1-score (0.9354 versus 0.8660). Availability and implementation Sigmap code is accessible at https://github.com/haowenz/sigmap. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Haowen Zhang
- School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA
| | - Haoran Li
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH, 43210, USA
| | - Chirag Jain
- Department of Computational and Data Sciences, Indian Institute of Science, Bangalore KA, 560012, India
| | - Haoyu Cheng
- Department of Data Science, Dana-Faber Cancer Institute, Boston, MA, 02215, USA.,Department of Biomedical Informatics, Harvard Medical School, Boston, MA, 02215, USA
| | - Kin Fai Au
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH, 43210, USA
| | - Heng Li
- Department of Data Science, Dana-Faber Cancer Institute, Boston, MA, 02215, USA.,Department of Biomedical Informatics, Harvard Medical School, Boston, MA, 02215, USA
| | - Srinivas Aluru
- School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA.,Institute for Data Engineering and Science, Georgia Institute of Technology, Atlanta, GA, 30332, USA
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14
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Bi C, Ramos-Mandujano G, Tian Y, Hala S, Xu J, Mfarrej S, Esteban CR, Delicado EN, Alofi FS, Khogeer A, Hashem AM, Almontashiri NAM, Pain A, Izpisua Belmonte JC, Li M. Simultaneous detection and mutation surveillance of SARS-CoV-2 and multiple respiratory viruses by rapid field-deployable sequencing. MED 2021; 2:689-700.e4. [PMID: 33821249 PMCID: PMC8011639 DOI: 10.1016/j.medj.2021.03.015] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Revised: 03/03/2021] [Accepted: 03/24/2021] [Indexed: 12/26/2022]
Abstract
BACKGROUND Strategies for monitoring the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection are crucial for combating the pandemic. Detection and mutation surveillance of SARS-CoV-2 and other respiratory viruses require separate and complex workflows that rely on highly specialized facilities, personnel, and reagents. To date, no method can rapidly diagnose multiple viral infections and determine variants in a high-throughput manner. METHODS We describe a method for multiplex isothermal amplification-based sequencing and real-time analysis of multiple viral genomes, termed nanopore sequencing of isothermal rapid viral amplification for near real-time analysis (NIRVANA). It can simultaneously detect SARS-CoV-2, influenza A, human adenovirus, and human coronavirus and monitor mutations for up to 96 samples in real time. FINDINGS NIRVANA showed high sensitivity and specificity for SARS-CoV-2 in 70 clinical samples with a detection limit of 20 viral RNA copies per μL of extracted nucleic acid. It also detected the influenza A co-infection in two samples. The variant analysis results of SARS-CoV-2-positive samples mirror the epidemiology of coronavirus disease 2019 (COVID-19). Additionally, NIRVANA could simultaneously detect SARS-CoV-2 and pepper mild mottle virus (PMMoV) (an omnipresent virus and water-quality indicator) in municipal wastewater samples. CONCLUSIONS NIRVANA provides high-confidence detection of both SARS-CoV-2 and other respiratory viruses and mutation surveillance of SARS-CoV-2 on the fly. We expect it to offer a promising solution for rapid field-deployable detection and mutational surveillance of pandemic viruses. FUNDING M.L. is supported by KAUST Office of Sponsored Research (BAS/1/1080-01). This work is supported by KAUST Competitive Research Grant (URF/1/3412-01-01; M.L. and J.C.I.B.) and Universidad Catolica San Antonio de Murcia (J.C.I.B.). A.M.H. is supported by Saudi Ministry of Education (project 436).
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Affiliation(s)
- Chongwei Bi
- Biological and Environmental Science and Engineering Division (BESE), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
| | - Gerardo Ramos-Mandujano
- Biological and Environmental Science and Engineering Division (BESE), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
| | - Yeteng Tian
- Biological and Environmental Science and Engineering Division (BESE), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
| | - Sharif Hala
- Biological and Environmental Science and Engineering Division (BESE), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
- King Saud bin Abdulaziz University for Health Sciences, Jeddah, Saudi Arabia
- King Abdullah International Medical Research Centre, Ministry of National Guard Health Affairs, Jeddah, Makkah, Saudi Arabia
| | - Jinna Xu
- Biological and Environmental Science and Engineering Division (BESE), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
| | - Sara Mfarrej
- Biological and Environmental Science and Engineering Division (BESE), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
| | - Concepcion Rodriguez Esteban
- Gene Expression Laboratory, Salk Institute for Biological Studies, 10010 North Torrey Pines Road, La Jolla, CA 92037, USA
| | - Estrella Nuñez Delicado
- Universidad Católica San Antonio de Murcia (UCAM), Campus de los Jerónimos, No. 135 12, Guadalupe 30107, Spain
| | - Fadwa S Alofi
- Infectious Diseases Department, King Fahad Hospital, Madinah, Saudi Arabia
| | - Asim Khogeer
- Plan and Research Department, General Directorate of Health Affairs Makkah Region, MOH, Saudi Arabia
| | - Anwar M Hashem
- Vaccines and Immunotherapy Unit, King Fahd Medical Research Center, King Abdulaziz University, Jeddah, Saudi Arabia
- Department of Medical Microbiology and Parasitology, Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Naif A M Almontashiri
- College of Applied Medical Sciences, Taibah University, Madinah, Saudi Arabia
- Center for Genetics and Inherited Diseases, Taibah University, Madinah, Saudi Arabia
| | - Arnab Pain
- Biological and Environmental Science and Engineering Division (BESE), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
| | - Juan Carlos Izpisua Belmonte
- Gene Expression Laboratory, Salk Institute for Biological Studies, 10010 North Torrey Pines Road, La Jolla, CA 92037, USA
| | - Mo Li
- Biological and Environmental Science and Engineering Division (BESE), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
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15
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Ono Y, Asai K, Hamada M. PBSIM2: a simulator for long-read sequencers with a novel generative model of quality scores. Bioinformatics 2021; 37:589-595. [PMID: 32976553 PMCID: PMC8097687 DOI: 10.1093/bioinformatics/btaa835] [Citation(s) in RCA: 46] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2020] [Revised: 08/20/2020] [Accepted: 09/11/2020] [Indexed: 12/21/2022] Open
Abstract
Motivation Recent advances in high-throughput long-read sequencers, such as PacBio and Oxford Nanopore sequencers, produce longer reads with more errors than short-read sequencers. In addition to the high error rates of reads, non-uniformity of errors leads to difficulties in various downstream analyses using long reads. Many useful simulators, which characterize long-read error patterns and simulate them, have been developed. However, there is still room for improvement in the simulation of the non-uniformity of errors. Results To capture characteristics of errors in reads for long-read sequencers, here, we introduce a generative model for quality scores, in which a hidden Markov Model with a latest model selection method, called factorized information criteria, is utilized. We evaluated our developed simulator from various points, indicating that our simulator successfully simulates reads that are consistent with real reads. Availability and implementation The source codes of PBSIM2 are freely available from https://github.com/yukiteruono/pbsim2. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Yukiteru Ono
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, University of Tokyo, Kashiwa 277-8561, Japan
| | - Kiyoshi Asai
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, University of Tokyo, Kashiwa 277-8561, Japan.,Artificial Intelligence Research Center (AIRC), National Institute of Advanced Industrial Science and Technology (AIST), Tokyo 135-0064, Japan
| | - Michiaki Hamada
- Department of Electrical Engineering and Bioscience, Faculty of Science and Engineering, Waseda University, Tokyo 169-8555, Japan.,Computational Bio Big-Data Open Innovation Laboratory (CBBD-OIL), National Institute of Advanced Industrial Science and Technology (AIST), Tokyo 169-8555, Japan.,Institute for Medical-oriented Structural Biology, Waseda University, Tokyo 162-8480, Japan.,Graduate School of Medicine, Nippon Medical School, Tokyo 113-8602, Japan
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16
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Li Y, Xu Z, Han W, Cao H, Umarov R, Yan A, Fan M, Chen H, Duarte CM, Li L, Ho PL, Gao X. HMD-ARG: hierarchical multi-task deep learning for annotating antibiotic resistance genes. MICROBIOME 2021; 9:40. [PMID: 33557954 PMCID: PMC7871585 DOI: 10.1186/s40168-021-01002-3] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/11/2020] [Accepted: 01/08/2021] [Indexed: 05/07/2023]
Abstract
BACKGROUND The spread of antibiotic resistance has become one of the most urgent threats to global health, which is estimated to cause 700,000 deaths each year globally. Its surrogates, antibiotic resistance genes (ARGs), are highly transmittable between food, water, animal, and human to mitigate the efficacy of antibiotics. Accurately identifying ARGs is thus an indispensable step to understanding the ecology, and transmission of ARGs between environmental and human-associated reservoirs. Unfortunately, the previous computational methods for identifying ARGs are mostly based on sequence alignment, which cannot identify novel ARGs, and their applications are limited by currently incomplete knowledge about ARGs. RESULTS Here, we propose an end-to-end Hierarchical Multi-task Deep learning framework for ARG annotation (HMD-ARG). Taking raw sequence encoding as input, HMD-ARG can identify, without querying against existing sequence databases, multiple ARG properties simultaneously, including if the input protein sequence is an ARG, and if so, what antibiotic family it is resistant to, what resistant mechanism the ARG takes, and if the ARG is an intrinsic one or acquired one. In addition, if the predicted antibiotic family is beta-lactamase, HMD-ARG further predicts the subclass of beta-lactamase that the ARG is resistant to. Comprehensive experiments, including cross-fold validation, third-party dataset validation in human gut microbiota, wet-experimental functional validation, and structural investigation of predicted conserved sites, demonstrate not only the superior performance of our method over the state-of-art methods, but also the effectiveness and robustness of the proposed method. CONCLUSIONS We propose a hierarchical multi-task method, HMD-ARG, which is based on deep learning and can provide detailed annotations of ARGs from three important aspects: resistant antibiotic class, resistant mechanism, and gene mobility. We believe that HMD-ARG can serve as a powerful tool to identify antibiotic resistance genes and, therefore mitigate their global threat. Our method and the constructed database are available at http://www.cbrc.kaust.edu.sa/HMDARG/ . Video abstract (MP4 50984 kb).
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Affiliation(s)
- Yu Li
- Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955, Saudi Arabia
- Department of Computer Science and Engineering (CSE), The Chinese University of Hong Kong (CUHK), Hong Kong, People's Republic of China
| | - Zeling Xu
- School of Biological Sciences, The University of Hong Kong, Hong Kong, People's Republic of China
| | - Wenkai Han
- Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955, Saudi Arabia
| | - Huiluo Cao
- Carol Yu Center for Infection and Department of Microbiology, The University of Hong Kong, Hong Kong, People's Republic of China
| | - Ramzan Umarov
- Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955, Saudi Arabia
| | - Aixin Yan
- School of Biological Sciences, The University of Hong Kong, Hong Kong, People's Republic of China
| | - Ming Fan
- Institute of Biomedical Engineering and Instrumentation, Hangzhou Dianzi University, Hangzhou, People's Republic of China
| | - Huan Chen
- Key Laboratory of Microbial Technology and Bioinformatics of Zhejiang Province, Zhejiang Institute of Microbiology, Hangzhou, People's Republic of China
| | - Carlos M Duarte
- Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955, Saudi Arabia
- Biological and Environmental Sciences and Engineering (BESE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955, Saudi Arabia
| | - Lihua Li
- Institute of Biomedical Engineering and Instrumentation, Hangzhou Dianzi University, Hangzhou, People's Republic of China
| | - Pak-Leung Ho
- Carol Yu Center for Infection and Department of Microbiology, The University of Hong Kong, Hong Kong, People's Republic of China
| | - Xin Gao
- Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955, Saudi Arabia.
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17
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Chen W, Zhang P, Song L, Yang J, Han C. Simulation of Nanopore Sequencing Signals Based on BiGRU. SENSORS 2020; 20:s20247244. [PMID: 33348876 PMCID: PMC7766754 DOI: 10.3390/s20247244] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/05/2020] [Revised: 12/14/2020] [Accepted: 12/15/2020] [Indexed: 01/02/2023]
Abstract
Oxford Nanopore sequencing is an important sequencing technology, which reads the nucleotide sequence by detecting the electrical current signal changes when DNA molecule is forced to pass through a biological nanopore. The research on signal simulation of nanopore sequencing is highly desirable for method developments of nanopore sequencing applications. To improve the simulation accuracy, we propose a novel signal simulation method based on Bi-directional Gated Recurrent Units (BiGRU). In this method, the signal processing model based on BiGRU is built to replace the traditional low-pass filter to post-process the ground-truth signal calculated by the input nucleotide sequence and nanopore sequencing pore model. Gaussian noise is then added to the filtered signal to generate the final simulated signal. This method can accurately model the relation between ground-truth signal and real-world sequencing signal through experimental sequencing data. The simulation results reveal that the proposed method utilizing the powerful learning ability of the neural network can generate the simulated signal that is closer to the real-world sequencing signal in the time and frequency domains than the existing simulation method.
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Affiliation(s)
- Weigang Chen
- School of Microelectronics, Tianjin University, Tianjin 300072, China; (W.C.); (P.Z.); (J.Y.)
- Frontier Science Center for Synthetic Biology (Ministry of Education), Tianjin University, Tianjin 300072, China;
| | - Peng Zhang
- School of Microelectronics, Tianjin University, Tianjin 300072, China; (W.C.); (P.Z.); (J.Y.)
| | - Lifu Song
- Frontier Science Center for Synthetic Biology (Ministry of Education), Tianjin University, Tianjin 300072, China;
- School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
| | - Jinsheng Yang
- School of Microelectronics, Tianjin University, Tianjin 300072, China; (W.C.); (P.Z.); (J.Y.)
| | - Changcai Han
- School of Microelectronics, Tianjin University, Tianjin 300072, China; (W.C.); (P.Z.); (J.Y.)
- Correspondence:
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18
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Armstrong RE, Horáček M, Zijlstra P. Plasmonic Assemblies for Real-Time Single-Molecule Biosensing. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2020; 16:e2003934. [PMID: 33258287 DOI: 10.1002/smll.202003934] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/29/2020] [Revised: 10/09/2020] [Indexed: 05/11/2023]
Abstract
Their tunable optical properties and versatile surface functionalization have sparked applications of plasmonic assemblies in the fields of biosensing, nonlinear optics, and photonics. Particularly, in the field of biosensing, rapid advances have occurred in the use of plasmonic assemblies for real-time single-molecule sensing. Compared to individual particles, the use of assemblies as sensors provides stronger signals, more control over the optical properties, and access to a broader range of timescales. In the past years, they have been used to directly reveal single-molecule interactions, mechanical properties, and conformational dynamics. This review summarizes the development of real-time single-molecule sensors built around plasmonic assemblies. First, a brief overview of their optical properties is given, and then recent applications are described. The current challenges in the field and suggestions to overcome those challenges are discussed in detail. Their stability, specificity, and sensitivity as sensors provide a complementary approach to other single-molecule techniques like force spectroscopy and single-molecule fluorescence. In future applications, the impact in real-time sensing on ultralong timescales (hours) and ultrashort timescales (sub-millisecond), time windows that are difficult to access using other techniques, is particularly foreseen.
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Affiliation(s)
- Rachel E Armstrong
- Department of Applied Physics & Institute for Complex Molecular Systems, Eindhoven University of Technology, Postbus 513, Eindhoven, MB, 5600, the Netherlands
| | - Matěj Horáček
- Department of Applied Physics & Institute for Complex Molecular Systems, Eindhoven University of Technology, Postbus 513, Eindhoven, MB, 5600, the Netherlands
| | - Peter Zijlstra
- Department of Applied Physics & Institute for Complex Molecular Systems, Eindhoven University of Technology, Postbus 513, Eindhoven, MB, 5600, the Netherlands
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19
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Vereecke N, Bokma J, Haesebrouck F, Nauwynck H, Boyen F, Pardon B, Theuns S. High quality genome assemblies of Mycoplasma bovis using a taxon-specific Bonito basecaller for MinION and Flongle long-read nanopore sequencing. BMC Bioinformatics 2020; 21:517. [PMID: 33176691 PMCID: PMC7661149 DOI: 10.1186/s12859-020-03856-0] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2020] [Accepted: 10/30/2020] [Indexed: 01/23/2023] Open
Abstract
BACKGROUND Implementation of Third-Generation Sequencing approaches for Whole Genome Sequencing (WGS) all-in-one diagnostics in human and veterinary medicine, requires the rapid and accurate generation of consensus genomes. Over the last years, Oxford Nanopore Technologies (ONT) released various new devices (e.g. the Flongle R9.4.1 flow cell) and bioinformatics tools (e.g. the in 2019-released Bonito basecaller), allowing cheap and user-friendly cost-efficient introduction in various NGS workflows. While single read, overall consensus accuracies, and completeness of genome sequences has been improved dramatically, further improvements are required when working with non-frequently sequenced organisms like Mycoplasma bovis. As an important primary respiratory pathogen in cattle, rapid M. bovis diagnostics is crucial to allow timely and targeted disease control and prevention. Current complete diagnostics (including identification, strain typing, and antimicrobial resistance (AMR) detection) require combined culture-based and molecular approaches, of which the first can take 1-2 weeks. At present, cheap and quick long read all-in-one WGS approaches can only be implemented if increased accuracies and genome completeness can be obtained. RESULTS Here, a taxon-specific custom-trained Bonito v.0.1.3 basecalling model (custom-pg45) was implemented in various WGS assembly bioinformatics pipelines. Using MinION sequencing data, we showed improved consensus accuracies up to Q45.2 and Q46.7 for reference-based and Canu de novo assembled M. bovis genomes, respectively. Furthermore, the custom-pg45 model resulted in mean consensus accuracies of Q45.0 and genome completeness of 94.6% for nine M. bovis field strains. Improvements were also observed for the single-use Flongle sequencer (mean Q36.0 accuracies and 80.3% genome completeness). CONCLUSIONS These results implicate that taxon-specific basecalling of MinION and single-use Flongle Nanopore long reads are of great value to be implemented in rapid all-in-one WGS tools as evidenced for Mycoplasma bovis as an example.
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Affiliation(s)
- Nick Vereecke
- Faculty of Veterinary Medicine, Department of Virology, Parasitology and Immunology, Ghent University, Salisburylaan 133, 9820, Merelbeke, Belgium.
- PathoSense, Merelbeke, Belgium.
| | - Jade Bokma
- Faculty of Veterinary Medicine, Department of Large Animal Internal Medicine, Ghent University, Salisburylaan 133, 9820, Merelbeke, Belgium
| | - Freddy Haesebrouck
- Faculty of Veterinary Medicine, Department of Pathology, Bacteriology and Avian Diseases, Ghent University, Salisburylaan 133, 9820, Merelbeke, Belgium
| | - Hans Nauwynck
- Faculty of Veterinary Medicine, Department of Virology, Parasitology and Immunology, Ghent University, Salisburylaan 133, 9820, Merelbeke, Belgium
- PathoSense, Merelbeke, Belgium
| | - Filip Boyen
- Faculty of Veterinary Medicine, Department of Pathology, Bacteriology and Avian Diseases, Ghent University, Salisburylaan 133, 9820, Merelbeke, Belgium
| | - Bart Pardon
- Faculty of Veterinary Medicine, Department of Large Animal Internal Medicine, Ghent University, Salisburylaan 133, 9820, Merelbeke, Belgium
| | - Sebastiaan Theuns
- Faculty of Veterinary Medicine, Department of Virology, Parasitology and Immunology, Ghent University, Salisburylaan 133, 9820, Merelbeke, Belgium
- PathoSense, Merelbeke, Belgium
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20
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Zhou L, Li Z, Zhou J, Li H, Chen Y, Huang Y, Xie D, Zhao L, Fan M, Hashmi S, Abdelkareem F, Eiada R, Xiao X, Li L, Qiu Z, Gao X. A Rapid, Accurate and Machine-Agnostic Segmentation and Quantification Method for CT-Based COVID-19 Diagnosis. IEEE TRANSACTIONS ON MEDICAL IMAGING 2020; 39:2638-2652. [PMID: 32730214 PMCID: PMC8769013 DOI: 10.1109/tmi.2020.3001810] [Citation(s) in RCA: 85] [Impact Index Per Article: 21.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/24/2020] [Revised: 06/06/2020] [Accepted: 06/07/2020] [Indexed: 05/03/2023]
Abstract
COVID-19 has caused a global pandemic and become the most urgent threat to the entire world. Tremendous efforts and resources have been invested in developing diagnosis, prognosis and treatment strategies to combat the disease. Although nucleic acid detection has been mainly used as the gold standard to confirm this RNA virus-based disease, it has been shown that such a strategy has a high false negative rate, especially for patients in the early stage, and thus CT imaging has been applied as a major diagnostic modality in confirming positive COVID-19. Despite the various, urgent advances in developing artificial intelligence (AI)-based computer-aided systems for CT-based COVID-19 diagnosis, most of the existing methods can only perform classification, whereas the state-of-the-art segmentation method requires a high level of human intervention. In this paper, we propose a fully-automatic, rapid, accurate, and machine-agnostic method that can segment and quantify the infection regions on CT scans from different sources. Our method is founded upon two innovations: 1) the first CT scan simulator for COVID-19, by fitting the dynamic change of real patients' data measured at different time points, which greatly alleviates the data scarcity issue; and 2) a novel deep learning algorithm to solve the large-scene-small-object problem, which decomposes the 3D segmentation problem into three 2D ones, and thus reduces the model complexity by an order of magnitude and, at the same time, significantly improves the segmentation accuracy. Comprehensive experimental results over multi-country, multi-hospital, and multi-machine datasets demonstrate the superior performance of our method over the existing ones and suggest its important application value in combating the disease.
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Affiliation(s)
- Longxi Zhou
- Computer, Electrical, and Mathematical Sciences and Engineering (CEMSE) Division, Computational Bioscience Research Center (CBRC)King Abdullah University of Science and Technology (KAUST)Thuwal23955Saudi Arabia
| | - Zhongxiao Li
- Computer, Electrical, and Mathematical Sciences and Engineering (CEMSE) Division, Computational Bioscience Research Center (CBRC)King Abdullah University of Science and Technology (KAUST)Thuwal23955Saudi Arabia
| | - Juexiao Zhou
- Department of BiologySouthern University of Science and TechnologyShenzhen518055China
| | - Haoyang Li
- Cancer Systems Biology Center, China–Japan Union HospitalJilin UniversityChangchun130031China
| | | | - Yuxin Huang
- Heilongjiang Tuomeng Technology Company Ltd.Harbin150040China
| | - Dexuan Xie
- Department of Computer TomographyThe First Affiliated Hospital of Harbin Medical UniversityHarbin150001China
| | - Lintao Zhao
- Department of Computer TomographyThe First Hospital of Harbin Medical UniversityHarbin150010China
| | - Ming Fan
- Institute of Biomedical Engineering and Instrumentation, Hangzhou Dianzi UniversityHangzhou310018China
| | - Shahrukh Hashmi
- Oncology CenterKing Faisal Specialist Hospital and Research CenterRiyadh11211Saudi Arabia
| | - Faisal Abdelkareem
- Department Medical ImagingKing Faisal Specialist Hospital and Research CenterRiyadh11211Saudi Arabia
| | - Riham Eiada
- Department Medical ImagingKing Faisal Specialist Hospital and Research CenterRiyadh11211Saudi Arabia
| | - Xigang Xiao
- Department of Computer TomographyThe First Affiliated Hospital of Harbin Medical UniversityHarbin150001China
| | - Lihua Li
- Institute of Biomedical Engineering and Instrumentation, Hangzhou Dianzi UniversityHangzhou310018China
| | - Zhaowen Qiu
- Institute of Information and Computer Engineering, Northeast Forestry UniversityHarbin150040China
| | - Xin Gao
- Computer, Electrical, and Mathematical Sciences and Engineering (CEMSE) Division, Computational Bioscience Research Center (CBRC)King Abdullah University of Science and Technology (KAUST)Thuwal23955Saudi Arabia
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21
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Hafezqorani S, Yang C, Lo T, Nip KM, Warren RL, Birol I. Trans-NanoSim characterizes and simulates nanopore RNA-sequencing data. Gigascience 2020; 9:5855462. [PMID: 32520350 PMCID: PMC7285873 DOI: 10.1093/gigascience/giaa061] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2020] [Revised: 04/14/2020] [Accepted: 05/12/2020] [Indexed: 01/08/2023] Open
Abstract
Background Compared with second-generation sequencing technologies, third-generation single-molecule RNA sequencing has unprecedented advantages; the long reads it generates facilitate isoform-level transcript characterization. In particular, the Oxford Nanopore Technology sequencing platforms have become more popular in recent years owing to their relatively high affordability and portability compared with other third-generation sequencing technologies. To aid the development of analytical tools that leverage the power of this technology, simulated data provide a cost-effective solution with ground truth. However, a nanopore sequence simulator targeting transcriptomic data is not available yet. Findings We introduce Trans-NanoSim, a tool that simulates reads with technical and transcriptome-specific features learnt from nanopore RNA-sequncing data. We comprehensively benchmarked Trans-NanoSim on direct RNA and complementary DNA datasets describing human and mouse transcriptomes. Through comparison against other nanopore read simulators, we show the unique advantage and robustness of Trans-NanoSim in capturing the characteristics of nanopore complementary DNA and direct RNA reads. Conclusions As a cost-effective alternative to sequencing real transcriptomes, Trans-NanoSim will facilitate the rapid development of analytical tools for nanopore RNA-sequencing data. Trans-NanoSim and its pre-trained models are freely accessible at https://github.com/bcgsc/NanoSim.
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Affiliation(s)
- Saber Hafezqorani
- Canada's Michael Smith Genome Sciences Centre, 100 - 570 W 7th Ave, Vancouver, BC Cancer, BC V5Z 4S6 Canada.,Bioinformatics Graduate Program, University of British Columbia, 100 - 570 W 7th Ave, Vancouver, BC Cancer, BC V5Z 4S6 Canada
| | - Chen Yang
- Canada's Michael Smith Genome Sciences Centre, 100 - 570 W 7th Ave, Vancouver, BC Cancer, BC V5Z 4S6 Canada.,Bioinformatics Graduate Program, University of British Columbia, 100 - 570 W 7th Ave, Vancouver, BC Cancer, BC V5Z 4S6 Canada
| | - Theodora Lo
- Canada's Michael Smith Genome Sciences Centre, 100 - 570 W 7th Ave, Vancouver, BC Cancer, BC V5Z 4S6 Canada
| | - Ka Ming Nip
- Canada's Michael Smith Genome Sciences Centre, 100 - 570 W 7th Ave, Vancouver, BC Cancer, BC V5Z 4S6 Canada.,Bioinformatics Graduate Program, University of British Columbia, 100 - 570 W 7th Ave, Vancouver, BC Cancer, BC V5Z 4S6 Canada
| | - René L Warren
- Canada's Michael Smith Genome Sciences Centre, 100 - 570 W 7th Ave, Vancouver, BC Cancer, BC V5Z 4S6 Canada
| | - Inanc Birol
- Canada's Michael Smith Genome Sciences Centre, 100 - 570 W 7th Ave, Vancouver, BC Cancer, BC V5Z 4S6 Canada.,Department of Medical Genetics, University of British Columbia, 2350 Health Science Mall, Vancouver, BC V6T 1Z3, Canada
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