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      Unraveling metagenomics through long-read sequencing: a comprehensive review

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          Abstract

          The study of microbial communities has undergone significant advancements, starting from the initial use of 16S rRNA sequencing to the adoption of shotgun metagenomics. However, a new era has emerged with the advent of long-read sequencing (LRS), which offers substantial improvements over its predecessor, short-read sequencing (SRS). LRS produces reads that are several kilobases long, enabling researchers to obtain more complete and contiguous genomic information, characterize structural variations, and study epigenetic modifications. The current leaders in LRS technologies are Pacific Biotechnologies (PacBio) and Oxford Nanopore Technologies (ONT), each offering a distinct set of advantages. This review covers the workflow of long-read metagenomics sequencing, including sample preparation (sample collection, sample extraction, and library preparation), sequencing, processing (quality control, assembly, and binning), and analysis (taxonomic annotation and functional annotation). Each section provides a concise outline of the key concept of the methodology, presenting the original concept as well as how it is challenged or modified in the context of LRS. Additionally, the section introduces a range of tools that are compatible with LRS and can be utilized to execute the LRS process. This review aims to present the workflow of metagenomics, highlight the transformative impact of LRS, and provide researchers with a selection of tools suitable for this task.

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          Minimap2: pairwise alignment for nucleotide sequences

          Heng Li (2018)
          Recent advances in sequencing technologies promise ultra-long reads of ∼100 kb in average, full-length mRNA or cDNA reads in high throughput and genomic contigs over 100 Mb in length. Existing alignment programs are unable or inefficient to process such data at scale, which presses for the development of new alignment algorithms.
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            Canu: scalable and accurate long-read assembly via adaptive k-mer weighting and repeat separation

            Long-read single-molecule sequencing has revolutionized de novo genome assembly and enabled the automated reconstruction of reference-quality genomes. However, given the relatively high error rates of such technologies, efficient and accurate assembly of large repeats and closely related haplotypes remains challenging. We address these issues with Canu, a successor of Celera Assembler that is specifically designed for noisy single-molecule sequences. Canu introduces support for nanopore sequencing, halves depth-of-coverage requirements, and improves assembly continuity while simultaneously reducing runtime by an order of magnitude on large genomes versus Celera Assembler 8.2. These advances result from new overlapping and assembly algorithms, including an adaptive overlapping strategy based on tf-idf weighted MinHash and a sparse assembly graph construction that avoids collapsing diverged repeats and haplotypes. We demonstrate that Canu can reliably assemble complete microbial genomes and near-complete eukaryotic chromosomes using either Pacific Biosciences (PacBio) or Oxford Nanopore technologies and achieves a contig NG50 of >21 Mbp on both human and Drosophila melanogaster PacBio data sets. For assembly structures that cannot be linearly represented, Canu provides graph-based assembly outputs in graphical fragment assembly (GFA) format for analysis or integration with complementary phasing and scaffolding techniques. The combination of such highly resolved assembly graphs with long-range scaffolding information promises the complete and automated assembly of complex genomes.
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              Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2’s q2-feature-classifier plugin

              Background Taxonomic classification of marker-gene sequences is an important step in microbiome analysis. Results We present q2-feature-classifier (https://github.com/qiime2/q2-feature-classifier), a QIIME 2 plugin containing several novel machine-learning and alignment-based methods for taxonomy classification. We evaluated and optimized several commonly used classification methods implemented in QIIME 1 (RDP, BLAST, UCLUST, and SortMeRNA) and several new methods implemented in QIIME 2 (a scikit-learn naive Bayes machine-learning classifier, and alignment-based taxonomy consensus methods based on VSEARCH, and BLAST+) for classification of bacterial 16S rRNA and fungal ITS marker-gene amplicon sequence data. The naive-Bayes, BLAST+-based, and VSEARCH-based classifiers implemented in QIIME 2 meet or exceed the species-level accuracy of other commonly used methods designed for classification of marker gene sequences that were evaluated in this work. These evaluations, based on 19 mock communities and error-free sequence simulations, including classification of simulated “novel” marker-gene sequences, are available in our extensible benchmarking framework, tax-credit (https://github.com/caporaso-lab/tax-credit-data). Conclusions Our results illustrate the importance of parameter tuning for optimizing classifier performance, and we make recommendations regarding parameter choices for these classifiers under a range of standard operating conditions. q2-feature-classifier and tax-credit are both free, open-source, BSD-licensed packages available on GitHub.
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                Author and article information

                Contributors
                thantrira.p@chula.ac.th
                Journal
                J Transl Med
                J Transl Med
                Journal of Translational Medicine
                BioMed Central (London )
                1479-5876
                28 January 2024
                28 January 2024
                2024
                : 22
                : 111
                Affiliations
                [1 ]Center of Excellence in Genomics and Precision Dentistry, Department of Physiology, Faculty of Dentistry, Chulalongkorn University, ( https://ror.org/028wp3y58) Bangkok, Thailand
                [2 ]Graduate Program in Bioinformatics and Computational Biology, Faculty of Science, Chulalongkorn University, ( https://ror.org/028wp3y58) Bangkok, Thailand
                [3 ]Department of Mathematics and Computer Science, Faculty of Science, Chulalongkorn University, ( https://ror.org/028wp3y58) Bangkok, Thailand
                [4 ]Center of Excellence for Cancer and Inflammation, Chulalongkorn University, ( https://ror.org/028wp3y58) Bangkok, Thailand
                [5 ]Graduate Program in Geriatric and Special Patients Care, Faculty of Dentistry, Chulalongkorn University, ( https://ror.org/028wp3y58) Bangkok, Thailand
                Author information
                http://orcid.org/0000-0003-0145-9801
                Article
                4917
                10.1186/s12967-024-04917-1
                10823668
                38282030
                82398cdd-6b39-4cf0-9efe-0625ee90f1c9
                © The Author(s) 2024

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 15 October 2023
                : 21 January 2024
                Funding
                Funded by: Health systems research institute
                Award ID: 66-101
                Award Recipient :
                Funded by: National Research Council of Thailand
                Award ID: N42A650229
                Award Recipient :
                Funded by: Thailand science research and innovation fund
                Funded by: FundRef http://dx.doi.org/10.13039/501100023915, Faculty of Dentistry, Chulalongkorn University;
                Award ID: DRF67_012
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100002873, Chulalongkorn University;
                Award ID: Meta_66_005_3200_002
                Award Recipient :
                Categories
                Review
                Custom metadata
                © BioMed Central Ltd., part of Springer Nature 2024

                Medicine
                hifi,microbiome,nanopore microbes,ont,pacbio
                Medicine
                hifi, microbiome, nanopore microbes, ont, pacbio

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