The unicellular green alga Chlamydomonas reinhardtii (hereafter Chlamydomonas) possesses both plant and animal attributes, and it is an ideal model organism for studying fundamental processes such as photosynthesis, sexual reproduction, and life cycle. N6-methyladenosine (m6A) is the most prevalent mRNA modification, and it plays important roles during sexual reproduction in animals and plants. However, the pattern and function of m6A modification during the sexual reproduction of Chlamydomonas remain unknown. Here, we performed transcriptome and methylated RNA immunoprecipitation sequencing (MeRIP-seq) analyses on six samples from different stages during sexual reproduction of the Chlamydomonas life cycle. The results show that m6A modification frequently occurs at the main motif of DRAC (D = G/A/U, R = A/G) in Chlamydomonas mRNAs. Moreover, m6A peaks in Chlamydomonas mRNAs are mainly enriched in the 3' untranslated regions (3'UTRs) and negatively correlated with the abundance of transcripts at each stage. In particular, there is a significant negative correlation between the expression levels and the m6A levels of genes involved in the microtubule-associated pathway, indicating that m6A modification influences the sexual reproduction and the life cycle of Chlamydomonas by regulating microtubule-based movement. In summary, our findings are the first to demonstrate the distribution and the functions of m6A modification in Chlamydomonas mRNAs and provide new evolutionary insights into m6A modification in the process of sexual reproduction in other plant organisms.
单细胞绿藻莱茵衣藻(Chlamydomonas reinhardtii，以下简称Chlamydomonas)具有植物和动物的双重属性，是研究光合作用、有性繁殖和生命周期等基本过程的理想模式生物。n6 -甲基腺苷(m6A)是最常见的mRNA修饰，在动植物有性生殖过程中起着重要作用。然而，衣藻有性生殖过程中m6A修饰的模式和功能尚不清楚。在这里，我们对衣藻生命周期有性繁殖不同阶段的6个样本进行了转录组和甲基化RNA免疫沉淀测序(MeRIP-seq)分析。结果表明，在衣藻mrna中，m6A修饰经常发生在DRAC的主基序(D = G/A/U, R = A/G)上。此外，衣藻mrna中的m6A峰主要富集在3′非翻译区(3′UTRs)，且与各阶段转录本丰度呈负相关。特别是微管相关通路相关基因的m6A表达水平与m6A表达水平呈显著负相关，说明m6A修饰通过调节微管为基础的运动影响衣藻的有性生殖和生命周期。综上所述，我们的研究结果首次揭示了m6A修饰在衣藻mrna中的分布和功能，并为其他植物生物有性生殖过程中m6A修饰的进化提供了新的见解。
{"title":"Characteristics of N<sup>6</sup>-methyladenosine Modification During Sexual Reproduction of Chlamydomonas reinhardtii.","authors":"Ying Lv, Fei Han, Mengxia Liu, Ting Zhang, Guanshen Cui, Jiaojiao Wang, Ying Yang, Yun-Gui Yang, Wenqiang Yang","doi":"10.1016/j.gpb.2022.04.004","DOIUrl":"10.1016/j.gpb.2022.04.004","url":null,"abstract":"<p><p>The unicellular green alga Chlamydomonas reinhardtii (hereafter Chlamydomonas) possesses both plant and animal attributes, and it is an ideal model organism for studying fundamental processes such as photosynthesis, sexual reproduction, and life cycle. N<sup>6</sup>-methyladenosine (m<sup>6</sup>A) is the most prevalent mRNA modification, and it plays important roles during sexual reproduction in animals and plants. However, the pattern and function of m<sup>6</sup>A modification during the sexual reproduction of Chlamydomonas remain unknown. Here, we performed transcriptome and methylated RNA immunoprecipitation sequencing (MeRIP-seq) analyses on six samples from different stages during sexual reproduction of the Chlamydomonas life cycle. The results show that m<sup>6</sup>A modification frequently occurs at the main motif of DRAC (D = G/A/U, R = A/G) in Chlamydomonas mRNAs. Moreover, m<sup>6</sup>A peaks in Chlamydomonas mRNAs are mainly enriched in the 3' untranslated regions (3'UTRs) and negatively correlated with the abundance of transcripts at each stage. In particular, there is a significant negative correlation between the expression levels and the m<sup>6</sup>A levels of genes involved in the microtubule-associated pathway, indicating that m<sup>6</sup>A modification influences the sexual reproduction and the life cycle of Chlamydomonas by regulating microtubule-based movement. In summary, our findings are the first to demonstrate the distribution and the functions of m<sup>6</sup>A modification in Chlamydomonas mRNAs and provide new evolutionary insights into m<sup>6</sup>A modification in the process of sexual reproduction in other plant organisms.</p>","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10787120/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138453356","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-06DOI: 10.1101/2022.01.05.475156
S. Tong, Ke Fan, Zai-wei Zhou, Lin-Yun Liu, Shu-Qing Zhang, Yinghui Fu, Guangchao Wang, Ying Zhu, Yong-Chun Yu
Next generation sequencing technologies both boost the discovery of variants in the human genome and exacerbate the challenges of pathogenic variant identification. In this study, we developed mvPPT (Pathogenicity Prediction Tool for missense variants), a highly sensitive and accurate missense variant classifier based on gradient boosting. MvPPT adopts high-confidence training sets with a wide spectrum of variant profiles, and extracts three categories of features, including scores from existing prediction tools, allele, amino acid and genotype frequencies, and genomic context. Compared with established predictors, mvPPT achieved superior performance in all test sets, regardless of data source. In addition, our study also provides guidance for training set and feature selection strategies, as well as reveals highly relevant features, which may further provide biological insights of variant pathogenicity.
{"title":"mvPPT: A Highly Efficient and Sensitive Pathogenicity Prediction Tool for Missense Variants","authors":"S. Tong, Ke Fan, Zai-wei Zhou, Lin-Yun Liu, Shu-Qing Zhang, Yinghui Fu, Guangchao Wang, Ying Zhu, Yong-Chun Yu","doi":"10.1101/2022.01.05.475156","DOIUrl":"https://doi.org/10.1101/2022.01.05.475156","url":null,"abstract":"Next generation sequencing technologies both boost the discovery of variants in the human genome and exacerbate the challenges of pathogenic variant identification. In this study, we developed mvPPT (Pathogenicity Prediction Tool for missense variants), a highly sensitive and accurate missense variant classifier based on gradient boosting. MvPPT adopts high-confidence training sets with a wide spectrum of variant profiles, and extracts three categories of features, including scores from existing prediction tools, allele, amino acid and genotype frequencies, and genomic context. Compared with established predictors, mvPPT achieved superior performance in all test sets, regardless of data source. In addition, our study also provides guidance for training set and feature selection strategies, as well as reveals highly relevant features, which may further provide biological insights of variant pathogenicity.","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87455127","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-22DOI: 10.1101/2021.09.19.460993
Guangsheng Pei, F. Yan, L. Simon, Yulin Dai, P. Jia, Zhongming Zhao
Single-cell RNA sequencing (scRNA-seq) is revolutionizing the study of complex and dynamic cellular mechanisms. However, cell-type annotation remains a main challenge as it largely relies on a priori knowledge and manual curation, which is cumbersome and less accurate. The increasing number of scRNA-seq data sets, as well as numerous published genetic studies, motivated us to build a comprehensive human cell type reference atlas. Here, we present deCS (decoding Cell type-Specificity), an automatic cell type annotation method augmented by a comprehensive collection of human cell type expression profiles and marker genes. We used deCS to annotate scRNA-seq data from various tissue types and systematically evaluated the annotation accuracy under different conditions, including reference panels, sequencing depth and feature selection strategies. Our results demonstrated that expanding the references is critical for improving annotation accuracy. Compared to many existing state-of-the-art annotation tools, deCS significantly reduced computation time and increased accuracy. deCS can be integrated into the standard scRNA-seq analytical pipeline to enhance cell type annotation. Finally, we demonstrated the broad utility of deCS to identify trait-cell type associations in 51 human complex traits, providing deeper insights into the cellular mechanisms of disease pathogenesis. All documents, including source code, user manual, demo data, and tutorials, are freely available at https://github.com/bsml320/deCS.
{"title":"deCS: A Tool for Systematic Cell Type Annotations of Single-cell RNA Sequencing Data among Human Tissues","authors":"Guangsheng Pei, F. Yan, L. Simon, Yulin Dai, P. Jia, Zhongming Zhao","doi":"10.1101/2021.09.19.460993","DOIUrl":"https://doi.org/10.1101/2021.09.19.460993","url":null,"abstract":"Single-cell RNA sequencing (scRNA-seq) is revolutionizing the study of complex and dynamic cellular mechanisms. However, cell-type annotation remains a main challenge as it largely relies on a priori knowledge and manual curation, which is cumbersome and less accurate. The increasing number of scRNA-seq data sets, as well as numerous published genetic studies, motivated us to build a comprehensive human cell type reference atlas. Here, we present deCS (decoding Cell type-Specificity), an automatic cell type annotation method augmented by a comprehensive collection of human cell type expression profiles and marker genes. We used deCS to annotate scRNA-seq data from various tissue types and systematically evaluated the annotation accuracy under different conditions, including reference panels, sequencing depth and feature selection strategies. Our results demonstrated that expanding the references is critical for improving annotation accuracy. Compared to many existing state-of-the-art annotation tools, deCS significantly reduced computation time and increased accuracy. deCS can be integrated into the standard scRNA-seq analytical pipeline to enhance cell type annotation. Finally, we demonstrated the broad utility of deCS to identify trait-cell type associations in 51 human complex traits, providing deeper insights into the cellular mechanisms of disease pathogenesis. All documents, including source code, user manual, demo data, and tutorials, are freely available at https://github.com/bsml320/deCS.","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86778324","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Noncoding genomic variants constitute the majority of trait-associated genome variations; however, identification of functional noncoding variants is still a challenge in human genetics, and a method systematically assessing the impact of regulatory variants on gene expression and linking them to potential target genes is still lacking. Here we introduce a deep neural network (DNN)-based computational framework, RegVar, that can accurately predict the tissue-specific impact of noncoding regulatory variants on target genes. We show that, by robustly learning the genomic characteristics of massive variant-gene expression associations in a variety of human tissues, RegVar vastly surpasses all current noncoding variants prioritization methods in predicting regulatory variants under different circumstances. The unique features of RegVar make it an excellent framework for assessing the regulatory impact of any variant on its putative target genes in a variety of tissues. RegVar is available as a webserver at http://regvar.cbportal.org/.
{"title":"RegVar: Tissue-specific Prioritization of Non-coding Regulatory Variants","authors":"Hao Lu, Luyu Ma, Cheng Quan, Lei Li, Yiming Lu, Gangqiao Zhou, Chenggang Zhang","doi":"10.1101/2021.04.17.440295","DOIUrl":"https://doi.org/10.1101/2021.04.17.440295","url":null,"abstract":"Noncoding genomic variants constitute the majority of trait-associated genome variations; however, identification of functional noncoding variants is still a challenge in human genetics, and a method systematically assessing the impact of regulatory variants on gene expression and linking them to potential target genes is still lacking. Here we introduce a deep neural network (DNN)-based computational framework, RegVar, that can accurately predict the tissue-specific impact of noncoding regulatory variants on target genes. We show that, by robustly learning the genomic characteristics of massive variant-gene expression associations in a variety of human tissues, RegVar vastly surpasses all current noncoding variants prioritization methods in predicting regulatory variants under different circumstances. The unique features of RegVar make it an excellent framework for assessing the regulatory impact of any variant on its putative target genes in a variety of tissues. RegVar is available as a webserver at http://regvar.cbportal.org/.","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91145552","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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