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Using RNA-Sequencing Data to Examine Tissue-Specific Garlic Microbiomes
Garlic (Allium sativum) is a perennial bulbous plant. Due to its clonal propagation, various diseases threaten the yield and quality of garlic. In this study, we conducted in silico analysis to identify microorganisms, bacteria, fungi, and viruses in six different tissues using garlic RNA-sequencing...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8268838/ https://www.ncbi.nlm.nih.gov/pubmed/34202675 http://dx.doi.org/10.3390/ijms22136791 |
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author | Jo, Yeonhwa Back, Chang-Gi Kim, Kook-Hyung Chu, Hyosub Lee, Jeong Hun Moh, Sang Hyun Cho, Won Kyong |
author_facet | Jo, Yeonhwa Back, Chang-Gi Kim, Kook-Hyung Chu, Hyosub Lee, Jeong Hun Moh, Sang Hyun Cho, Won Kyong |
author_sort | Jo, Yeonhwa |
collection | PubMed |
description | Garlic (Allium sativum) is a perennial bulbous plant. Due to its clonal propagation, various diseases threaten the yield and quality of garlic. In this study, we conducted in silico analysis to identify microorganisms, bacteria, fungi, and viruses in six different tissues using garlic RNA-sequencing data. The number of identified microbial species was the highest in inflorescences, followed by flowers and bulb cloves. With the Kraken2 tool, 57% of identified microbial reads were assigned to bacteria and 41% were assigned to viruses. Fungi only made up 1% of microbial reads. At the species level, Streptomyces lividans was the most dominant bacteria while Fusarium pseudograminearum was the most abundant fungi. Several allexiviruses were identified. Of them, the most abundant virus was garlic virus C followed by shallot virus X. We obtained a total of 14 viral genome sequences for four allexiviruses. As we expected, the microbial community varied depending on the tissue types, although there was a dominant microorganism in each tissue. In addition, we found that Kraken2 was a very powerful and efficient tool for the bacteria using RNA-sequencing data with some limitations for virome study. |
format | Online Article Text |
id | pubmed-8268838 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-82688382021-07-10 Using RNA-Sequencing Data to Examine Tissue-Specific Garlic Microbiomes Jo, Yeonhwa Back, Chang-Gi Kim, Kook-Hyung Chu, Hyosub Lee, Jeong Hun Moh, Sang Hyun Cho, Won Kyong Int J Mol Sci Article Garlic (Allium sativum) is a perennial bulbous plant. Due to its clonal propagation, various diseases threaten the yield and quality of garlic. In this study, we conducted in silico analysis to identify microorganisms, bacteria, fungi, and viruses in six different tissues using garlic RNA-sequencing data. The number of identified microbial species was the highest in inflorescences, followed by flowers and bulb cloves. With the Kraken2 tool, 57% of identified microbial reads were assigned to bacteria and 41% were assigned to viruses. Fungi only made up 1% of microbial reads. At the species level, Streptomyces lividans was the most dominant bacteria while Fusarium pseudograminearum was the most abundant fungi. Several allexiviruses were identified. Of them, the most abundant virus was garlic virus C followed by shallot virus X. We obtained a total of 14 viral genome sequences for four allexiviruses. As we expected, the microbial community varied depending on the tissue types, although there was a dominant microorganism in each tissue. In addition, we found that Kraken2 was a very powerful and efficient tool for the bacteria using RNA-sequencing data with some limitations for virome study. MDPI 2021-06-24 /pmc/articles/PMC8268838/ /pubmed/34202675 http://dx.doi.org/10.3390/ijms22136791 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Jo, Yeonhwa Back, Chang-Gi Kim, Kook-Hyung Chu, Hyosub Lee, Jeong Hun Moh, Sang Hyun Cho, Won Kyong Using RNA-Sequencing Data to Examine Tissue-Specific Garlic Microbiomes |
title | Using RNA-Sequencing Data to Examine Tissue-Specific Garlic Microbiomes |
title_full | Using RNA-Sequencing Data to Examine Tissue-Specific Garlic Microbiomes |
title_fullStr | Using RNA-Sequencing Data to Examine Tissue-Specific Garlic Microbiomes |
title_full_unstemmed | Using RNA-Sequencing Data to Examine Tissue-Specific Garlic Microbiomes |
title_short | Using RNA-Sequencing Data to Examine Tissue-Specific Garlic Microbiomes |
title_sort | using rna-sequencing data to examine tissue-specific garlic microbiomes |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8268838/ https://www.ncbi.nlm.nih.gov/pubmed/34202675 http://dx.doi.org/10.3390/ijms22136791 |
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