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人类 RNA-Seq 数据中 sRNA 分析方法:比较、基准测试。

Approaches for sRNA Analysis of Human RNA-Seq Data: Comparison, Benchmarking.

机构信息

Belozersky Institute of Physico-Chemical Biology, Lomonosov Moscow State University, 119992 Moscow, Russia.

Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, 119992 Moscow, Russia.

出版信息

Int J Mol Sci. 2023 Feb 20;24(4):4195. doi: 10.3390/ijms24044195.

Abstract

Expression analysis of small noncoding RNA (sRNA), including microRNA, piwi-interacting RNA, small rRNA-derived RNA, and tRNA-derived small RNA, is a novel and quickly developing field. Despite a range of proposed approaches, selecting and adapting a particular pipeline for transcriptomic analysis of sRNA remains a challenge. This paper focuses on the identification of the optimal pipeline configurations for each step of human sRNA analysis, including reads trimming, filtering, mapping, transcript abundance quantification and differential expression analysis. Based on our study, we suggest the following parameters for the analysis of human sRNA in relation to categorical analyses with two groups of biosamples: (1) trimming with the lower length bound = 15 and the upper length bound = Read length - 40% Adapter length; (2) mapping on a reference genome with bowtie aligner with one mismatch allowed (-v 1 parameter); (3) filtering by mean threshold > 5; (4) analyzing differential expression with DESeq2 with adjusted -value < 0.05 or limma with -value < 0.05 if there is very little signal and few transcripts.

摘要

小非编码 RNA(sRNA),包括 microRNA、piwi 相互作用 RNA、小 rRNA 衍生 RNA 和 tRNA 衍生小 RNA 的表达分析是一个新颖且快速发展的领域。尽管提出了多种方法,但选择和调整特定的 sRNA 转录组分析管道仍然是一个挑战。本文重点介绍了用于人类 sRNA 分析的每个步骤的最佳管道配置的识别,包括读取修剪、过滤、映射、转录丰度定量和差异表达分析。基于我们的研究,我们建议在涉及两组生物样本的分类分析时,使用以下参数来分析人类 sRNA:(1)修剪时下限长度=15,上限长度=Read 长度-40% Adapter 长度;(2)使用 bowtie 比对器在参考基因组上进行映射,允许一个错配(-v 1 参数);(3)通过平均阈值>5 进行过滤;(4)使用 DESeq2 进行差异表达分析,调整后的 P 值<0.05 或 limma 在信号非常少且转录本很少时 P 值<0.05。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6e32/9959513/afedf6157094/ijms-24-04195-g002.jpg

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