Transcriptomic Analysis of Triticum aestivum under Salt Stress Reveals Change of Gene Expression (RNA sequencing을 이용한 염 스트레스 처리 밀(Triticum aestivum)의 유전자 발현 차이 확인 및 후보 유전자 선발)
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- KOREAN JOURNAL OF CROP SCIENCE
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- v.67 no.1
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- pp.41-52
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- 2022
As a cultivar of Korean wheat, 'Keumgang' wheat variety has a fast growth period and can be grown stably. Hexaploid wheat (Triticum aestivum) has moderately high salt tolerance compared to tetraploid wheat (Triticum turgidum L.). However, the molecular mechanisms related to salt tolerance of hexaploid wheat have not been elucidated yet. In this study, the candidate genes related to salt tolerance were identified by investigating the genes that are differently expressed in Keumgang variety and examining salt tolerant mutation '2020-s1340.'. A total of 85,771,537 reads were obtained after quality filtering using NextSeq 500 Illumina sequencing technology. A total of 23,634,438 reads were aligned with the NCBI Campala Lr22a pseudomolecule v5 reference genome (Triticum aestivum). A total of 282 differentially expressed genes (DEGs) were identified in the two Triticum aestivum materials. These DEGs have functions, including salt tolerance related traits such as 'wall-associated receptor kinase-like 8', 'cytochrome P450', '6-phosphofructokinase 2'. In addition, the identified DEGs were classified into three categories, including biological process, molecular function, cellular component using gene ontology analysis. These DEGs were enriched significantly for terms such as the 'copper ion transport', 'oxidation-reduction process', 'alternative oxidase activity'. These results, which were obtained using RNA-seq analysis, will improve our understanding of salt tolerance of wheat. Moreover, this study will be a useful resource for breeding wheat varieties with improved salt tolerance using molecular breeding technology.
From January 2020 to October 2021, more than 500,000 academic studies related to COVID-19 (Coronavirus-2, a fatal respiratory syndrome) have been published. The rapid increase in the number of papers related to COVID-19 is putting time and technical constraints on healthcare professionals and policy makers to quickly find important research. Therefore, in this study, we propose a method of extracting useful information from text data of extensive literature using LDA and Word2vec algorithm. Papers related to keywords to be searched were extracted from papers related to COVID-19, and detailed topics were identified. The data used the CORD-19 data set on Kaggle, a free academic resource prepared by major research groups and the White House to respond to the COVID-19 pandemic, updated weekly. The research methods are divided into two main categories. First, 41,062 articles were collected through data filtering and pre-processing of the abstracts of 47,110 academic papers including full text. For this purpose, the number of publications related to COVID-19 by year was analyzed through exploratory data analysis using a Python program, and the top 10 journals under active research were identified. LDA and Word2vec algorithm were used to derive research topics related to COVID-19, and after analyzing related words, similarity was measured. Second, papers containing 'vaccine' and 'treatment' were extracted from among the topics derived from all papers, and a total of 4,555 papers related to 'vaccine' and 5,971 papers related to 'treatment' were extracted. did For each collected paper, detailed topics were analyzed using LDA and Word2vec algorithms, and a clustering method through PCA dimension reduction was applied to visualize groups of papers with similar themes using the t-SNE algorithm. A noteworthy point from the results of this study is that the topics that were not derived from the topics derived for all papers being researched in relation to COVID-19 (