• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

RNA测序:基础生物信息学分析

RNA-seq: Basic Bioinformatics Analysis.

作者信息

Ji Fei, Sadreyev Ruslan I

机构信息

Department of Molecular Biology, Massachusetts General Hospital, Boston, Massachusetts.

Department of Genetics, Harvard Medical School, Boston, Massachusetts.

出版信息

Curr Protoc Mol Biol. 2018 Oct;124(1):e68. doi: 10.1002/cpmb.68. Epub 2018 Sep 17.

DOI:10.1002/cpmb.68
PMID:30222249
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6168365/
Abstract

Quantitative analysis of gene expression is crucial for understanding the molecular mechanisms underlying genome regulation. RNA-seq is a powerful platform for comprehensive investigation of the transcriptome. In this unit, we present a general bioinformatics workflow for the quantitative analysis of RNA-seq data and describe a few current publicly available computational tools applicable at various steps of this workflow. These tools comprise a pipeline for quality assessment and quantitation of RNA-seq data that starts from raw sequencing files and is focused on the identification and analysis of genes that are differentially expressed between biological conditions. © 2018 by John Wiley & Sons, Inc.

摘要

基因表达的定量分析对于理解基因组调控背后的分子机制至关重要。RNA测序是全面研究转录组的强大平台。在本单元中,我们展示了一个用于RNA测序数据定量分析的通用生物信息学工作流程,并描述了一些当前可公开获取的适用于此工作流程各个步骤的计算工具。这些工具包括一个从原始测序文件开始的RNA测序数据质量评估和定量分析流程,重点是识别和分析在生物学条件之间差异表达的基因。© 2018约翰威立父子公司。

相似文献

1
RNA-seq: Basic Bioinformatics Analysis.RNA测序:基础生物信息学分析
Curr Protoc Mol Biol. 2018 Oct;124(1):e68. doi: 10.1002/cpmb.68. Epub 2018 Sep 17.
2
Single-Cell RNA-seq: Introduction to Bioinformatics Analysis.单细胞RNA测序:生物信息学分析导论。
Curr Protoc Mol Biol. 2019 Jun;127(1):e92. doi: 10.1002/cpmb.92.
3
SPARTA: Simple Program for Automated reference-based bacterial RNA-seq Transcriptome Analysis.SPARTA:用于基于参考的细菌RNA测序转录组自动分析的简单程序。
BMC Bioinformatics. 2016 Feb 4;17:66. doi: 10.1186/s12859-016-0923-y.
4
A Guide for Designing and Analyzing RNA-Seq Data.RNA测序数据的设计与分析指南
Methods Mol Biol. 2018;1783:35-80. doi: 10.1007/978-1-4939-7834-2_3.
5
A Bioinformatics Pipeline for the Identification of CHO Cell Differential Gene Expression from RNA-Seq Data.一种用于从RNA测序数据中鉴定CHO细胞差异基因表达的生物信息学流程。
Methods Mol Biol. 2017;1603:169-186. doi: 10.1007/978-1-4939-6972-2_11.
6
Bioinformatics Pipeline for Transcriptome Sequencing Analysis.转录组测序分析的生物信息学流程
Methods Mol Biol. 2017;1468:201-19. doi: 10.1007/978-1-4939-4035-6_14.
7
A pipeline for RNA-seq data processing and quality assessment.RNA-seq 数据处理和质量评估的流水线。
Bioinformatics. 2011 Mar 15;27(6):867-9. doi: 10.1093/bioinformatics/btr012. Epub 2011 Jan 13.
8
RNA-Seq in Nonmodel Organisms.非模式生物的 RNA-Seq。
Methods Mol Biol. 2021;2243:143-167. doi: 10.1007/978-1-0716-1103-6_8.
9
UTAP: User-friendly Transcriptome Analysis Pipeline.UTAP:用户友好型转录组分析流程。
BMC Bioinformatics. 2019 Mar 25;20(1):154. doi: 10.1186/s12859-019-2728-2.
10
RNA-Seq Analysis Pipeline Based on Oshell Environment.基于Oshell环境的RNA测序分析流程
IEEE/ACM Trans Comput Biol Bioinform. 2014 Sep-Oct;11(5):973-8. doi: 10.1109/TCBB.2014.2321156.

引用本文的文献

1
Screening and identification analysis of core markers for leukemia and cervical cancer: Calmodulin 3 as a core target.白血病和宫颈癌核心标志物的筛选与鉴定分析:钙调蛋白3作为核心靶点
Medicine (Baltimore). 2025 Apr 4;104(14):e41665. doi: 10.1097/MD.0000000000041665.
2
Advances in actinobacteria-based bioremediation: mechanistic insights, genetic regulation, and emerging technologies.基于放线菌的生物修复研究进展:作用机制、基因调控及新兴技术
Biodegradation. 2025 Mar 14;36(2):24. doi: 10.1007/s10532-025-10118-4.
3
Closha 2.0: a bio-workflow design system for massive genome data analysis on high performance cluster infrastructure.

本文引用的文献

1
Ensembl 2018.Ensembl 2018.
Nucleic Acids Res. 2018 Jan 4;46(D1):D754-D761. doi: 10.1093/nar/gkx1098.
2
Salmon provides fast and bias-aware quantification of transcript expression.鲑鱼提供快速且无偏倚的转录本表达定量。
Nat Methods. 2017 Apr;14(4):417-419. doi: 10.1038/nmeth.4197. Epub 2017 Mar 6.
3
Enrichr: a comprehensive gene set enrichment analysis web server 2016 update.Enrichr:一个全面的基因集富集分析网络服务器2016年更新版。
Closha 2.0:一个用于高性能集群基础设施上大规模基因组数据分析的生物工作流设计系统。
BMC Bioinformatics. 2024 Nov 12;25(1):353. doi: 10.1186/s12859-024-05963-8.
4
GUCA2A dysregulation as a promising biomarker for accurate diagnosis and prognosis of colorectal cancer.GUCA2A 失调作为一种有前途的生物标志物,可用于结直肠癌的准确诊断和预后。
Clin Exp Med. 2024 Nov 1;24(1):251. doi: 10.1007/s10238-024-01512-y.
5
Targeting GLI1 and BAX by nanonoscapine could impede prostate adenocarcinoma progression.纳诺斯卡品通过靶向 GLI1 和 BAX 抑制前列腺腺癌进展。
Sci Rep. 2024 Aug 16;14(1):18977. doi: 10.1038/s41598-024-65968-4.
6
Single-cell combined with transcriptome sequencing to explore the molecular mechanism of cell communication in idiopathic pulmonary fibrosis.单细胞联合转录组测序探索特发性肺纤维化细胞通讯的分子机制。
J Cell Mol Med. 2024 Jun;28(12):e18499. doi: 10.1111/jcmm.18499.
7
Single cell RNA-seq: a novel tool to unravel virus-host interplay.单细胞RNA测序:揭示病毒与宿主相互作用的新型工具。
Virusdisease. 2024 Mar;35(1):41-54. doi: 10.1007/s13337-024-00859-w. Epub 2024 Mar 9.
8
Benchmarking RNA-Seq Aligners at Base-Level and Junction Base-Level Resolution Using the Genome.使用基因组在碱基水平和连接点碱基水平分辨率下对RNA序列比对工具进行基准测试。
Plants (Basel). 2024 Feb 21;13(5):582. doi: 10.3390/plants13050582.
9
Transcriptome analysis of skeletal muscle in dermatomyositis, polymyositis, and dysferlinopathy, using a bioinformatics approach.采用生物信息学方法对皮肌炎、多发性肌炎和dysferlinopathy患者的骨骼肌进行转录组分析。
Front Neurol. 2023 Dec 6;14:1328547. doi: 10.3389/fneur.2023.1328547. eCollection 2023.
10
Multi-Omic, Histopathologic, and Clinicopathologic Effects of Once-Weekly Oral Rapamycin in a Naturally Occurring Feline Model of Hypertrophic Cardiomyopathy: A Pilot Study.每周一次口服雷帕霉素对自然发生的肥厚型心肌病猫模型的多组学、组织病理学和临床病理学影响:一项初步研究
Animals (Basel). 2023 Oct 12;13(20):3184. doi: 10.3390/ani13203184.
Nucleic Acids Res. 2016 Jul 8;44(W1):W90-7. doi: 10.1093/nar/gkw377. Epub 2016 May 3.
4
Near-optimal probabilistic RNA-seq quantification.近乎最优的概率 RNA-seq 定量。
Nat Biotechnol. 2016 May;34(5):525-7. doi: 10.1038/nbt.3519. Epub 2016 Apr 4.
5
Qualimap 2: advanced multi-sample quality control for high-throughput sequencing data.Qualimap 2:用于高通量测序数据的高级多样本质量控制
Bioinformatics. 2016 Jan 15;32(2):292-4. doi: 10.1093/bioinformatics/btv566. Epub 2015 Oct 1.
6
Mapping RNA-seq Reads with STAR.使用STAR对RNA测序读数进行比对
Curr Protoc Bioinformatics. 2015 Sep 3;51:11.14.1-11.14.19. doi: 10.1002/0471250953.bi1114s51.
7
RNA-Seq Analysis of Differential Splice Junction Usage and Intron Retentions by DEXSeq.使用DEXSeq对差异剪接连接使用情况和内含子保留进行RNA测序分析
PLoS One. 2015 Sep 1;10(9):e0136653. doi: 10.1371/journal.pone.0136653. eCollection 2015.
8
Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2.使用DESeq2对RNA测序数据的倍数变化和离散度进行适度估计。
Genome Biol. 2014;15(12):550. doi: 10.1186/s13059-014-0550-8.
9
HTSeq--a Python framework to work with high-throughput sequencing data.HTSeq——一个用于处理高通量测序数据的Python框架。
Bioinformatics. 2015 Jan 15;31(2):166-9. doi: 10.1093/bioinformatics/btu638. Epub 2014 Sep 25.
10
Sailfish enables alignment-free isoform quantification from RNA-seq reads using lightweight algorithms.旗鱼能够使用轻量级算法从RNA测序读段中进行无比对的异构体定量分析。
Nat Biotechnol. 2014 May;32(5):462-4. doi: 10.1038/nbt.2862. Epub 2014 Apr 20.