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Uncultured prokaryotic genomes in the spotlight: An examination of publicly available data from metagenomics and single-cell genomics

Owing to the ineffectiveness of traditional culture techniques for the vast majority of microbial species, culture-independent analyses utilizing next-generation sequencing and bioinformatics have become essential for gaining insight into microbial ecology and function. This mini-review focuses on t...

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Detalles Bibliográficos
Autores principales: Arikawa, Koji, Hosokawa, Masahito
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Research Network of Computational and Structural Biotechnology 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10523443/
https://www.ncbi.nlm.nih.gov/pubmed/37771751
http://dx.doi.org/10.1016/j.csbj.2023.09.010
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author Arikawa, Koji
Hosokawa, Masahito
author_facet Arikawa, Koji
Hosokawa, Masahito
author_sort Arikawa, Koji
collection PubMed
description Owing to the ineffectiveness of traditional culture techniques for the vast majority of microbial species, culture-independent analyses utilizing next-generation sequencing and bioinformatics have become essential for gaining insight into microbial ecology and function. This mini-review focuses on two essential methods for obtaining genetic information from uncultured prokaryotes, metagenomics and single-cell genomics. We analyzed the registration status of uncultured prokaryotic genome data from major public databases and assessed the advantages and limitations of both the methods. Metagenomics generates a significant quantity of sequence data and multiple prokaryotic genomes using straightforward experimental procedures. However, in ecosystems with high microbial diversity, such as soil, most genes are presented as brief, disconnected contigs, and lack association of highly conserved genes and mobile genetic elements with individual species genomes. Although technically more challenging, single-cell genomics offers valuable insights into complex ecosystems by providing strain-resolved genomes, addressing issues in metagenomics. Recent technological advancements, such as long-read sequencing, machine learning algorithms, and in silico protein structure prediction, in combination with vast genomic data, have the potential to overcome the current technical challenges and facilitate a deeper understanding of uncultured microbial ecosystems and microbial dark matter genes and proteins. In light of this, it is imperative that continued innovation in both methods and technologies take place to create high-quality reference genome databases that will support future microbial research and industrial applications.
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spelling pubmed-105234432023-09-28 Uncultured prokaryotic genomes in the spotlight: An examination of publicly available data from metagenomics and single-cell genomics Arikawa, Koji Hosokawa, Masahito Comput Struct Biotechnol J Mini-Review Owing to the ineffectiveness of traditional culture techniques for the vast majority of microbial species, culture-independent analyses utilizing next-generation sequencing and bioinformatics have become essential for gaining insight into microbial ecology and function. This mini-review focuses on two essential methods for obtaining genetic information from uncultured prokaryotes, metagenomics and single-cell genomics. We analyzed the registration status of uncultured prokaryotic genome data from major public databases and assessed the advantages and limitations of both the methods. Metagenomics generates a significant quantity of sequence data and multiple prokaryotic genomes using straightforward experimental procedures. However, in ecosystems with high microbial diversity, such as soil, most genes are presented as brief, disconnected contigs, and lack association of highly conserved genes and mobile genetic elements with individual species genomes. Although technically more challenging, single-cell genomics offers valuable insights into complex ecosystems by providing strain-resolved genomes, addressing issues in metagenomics. Recent technological advancements, such as long-read sequencing, machine learning algorithms, and in silico protein structure prediction, in combination with vast genomic data, have the potential to overcome the current technical challenges and facilitate a deeper understanding of uncultured microbial ecosystems and microbial dark matter genes and proteins. In light of this, it is imperative that continued innovation in both methods and technologies take place to create high-quality reference genome databases that will support future microbial research and industrial applications. Research Network of Computational and Structural Biotechnology 2023-09-12 /pmc/articles/PMC10523443/ /pubmed/37771751 http://dx.doi.org/10.1016/j.csbj.2023.09.010 Text en © 2023 The Authors https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Mini-Review
Arikawa, Koji
Hosokawa, Masahito
Uncultured prokaryotic genomes in the spotlight: An examination of publicly available data from metagenomics and single-cell genomics
title Uncultured prokaryotic genomes in the spotlight: An examination of publicly available data from metagenomics and single-cell genomics
title_full Uncultured prokaryotic genomes in the spotlight: An examination of publicly available data from metagenomics and single-cell genomics
title_fullStr Uncultured prokaryotic genomes in the spotlight: An examination of publicly available data from metagenomics and single-cell genomics
title_full_unstemmed Uncultured prokaryotic genomes in the spotlight: An examination of publicly available data from metagenomics and single-cell genomics
title_short Uncultured prokaryotic genomes in the spotlight: An examination of publicly available data from metagenomics and single-cell genomics
title_sort uncultured prokaryotic genomes in the spotlight: an examination of publicly available data from metagenomics and single-cell genomics
topic Mini-Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10523443/
https://www.ncbi.nlm.nih.gov/pubmed/37771751
http://dx.doi.org/10.1016/j.csbj.2023.09.010
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