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基于 196 种原代细胞类型的精心挑选的人类细胞 microRNAome。

A curated human cellular microRNAome based on 196 primary cell types.

机构信息

Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA.

Department of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, NY 14642, USA.

出版信息

Gigascience. 2022 Aug 25;11. doi: 10.1093/gigascience/giac083.

Abstract

BACKGROUND

An incomplete picture of the expression distribution of microRNAs (miRNAs) across human cell types has long hindered our understanding of this important regulatory class of RNA. With the continued increase in available public small RNA sequencing datasets, there is an opportunity to more fully understand the general distribution of miRNAs at the cell level.

RESULTS

From the NCBI Sequence Read Archive, we obtained 6,054 human primary cell datasets and processed 4,184 of them through the miRge3.0 small RNA sequencing alignment software. This dataset was curated down, through shared miRNA expression patterns, to 2,077 samples from 196 unique cell types derived from 175 separate studies. Of 2,731 putative miRNAs listed in miRBase (v22.1), 2,452 (89.8%) were detected. Among reasonably expressed miRNAs, 108 were designated as cell specific/near specific, 59 as infrequent, 52 as frequent, 54 as near ubiquitous, and 50 as ubiquitous. The complexity of cellular microRNA expression estimates recapitulates tissue expression patterns and informs on the miRNA composition of plasma.

CONCLUSIONS

This study represents the most complete reference, to date, of miRNA expression patterns by primary cell type. The data are available through the human cellular microRNAome track at the UCSC Genome Browser (https://genome.ucsc.edu/cgi-bin/hgHubConnect) and an R/Bioconductor package (https://bioconductor.org/packages/microRNAome/).

摘要

背景

长期以来,人类细胞类型中 microRNAs(miRNAs)表达分布的不完整图像一直阻碍着我们对这一重要调控 RNA 类别的理解。随着可利用的公共小 RNA 测序数据集的持续增加,我们有机会更全面地了解 miRNA 在细胞水平上的一般分布。

结果

我们从 NCBI Sequence Read Archive 中获取了 6054 个人类原代细胞数据集,并通过 miRge3.0 小 RNA 测序比对软件处理了其中的 4184 个。通过共享 miRNA 表达模式,该数据集被精简为来自 175 项不同研究的 196 个独特细胞类型的 2077 个样本。在 miRBase(v22.1)中列出的 2731 个假定 miRNAs 中,有 2452 个(89.8%)被检测到。在表达合理的 miRNAs 中,有 108 个被指定为细胞特异性/近细胞特异性,59 个为罕见,52 个为常见,54 个为近普遍存在,50 个为普遍存在。细胞 miRNA 表达估计的复杂性再现了组织表达模式,并为血浆中的 miRNA 组成提供了信息。

结论

这项研究代表了迄今为止对原代细胞类型 miRNA 表达模式最完整的参考。这些数据可通过 UCSC 基因组浏览器(https://genome.ucsc.edu/cgi-bin/hgHubConnect)的人类细胞 microRNAome 轨迹和 R/Bioconductor 包(https://bioconductor.org/packages/microRNAome/)获得。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb70/9404528/d07f4aae9b9f/giac083fig1.jpg

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