3t-seq:从 RNA-seq 数据中自动分析单拷贝基因、转座元件和 tRNA 的基因表达。

IF 6.8 2区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS Briefings in bioinformatics Pub Date : 2024-09-23 DOI:10.1093/bib/bbae467
Francesco Tabaro, Matthieu Boulard
{"title":"3t-seq:从 RNA-seq 数据中自动分析单拷贝基因、转座元件和 tRNA 的基因表达。","authors":"Francesco Tabaro, Matthieu Boulard","doi":"10.1093/bib/bbae467","DOIUrl":null,"url":null,"abstract":"<p><p>RNA sequencing is the gold-standard method to quantify transcriptomic changes between two conditions. The overwhelming majority of data analysis methods available are focused on polyadenylated RNA transcribed from single-copy genes and overlook transcripts from repeated sequences such as transposable elements (TEs). These self-autonomous genetic elements are increasingly studied, and specialized tools designed to handle multimapping sequencing reads are available. Transfer RNAs are transcribed by RNA polymerase III and are essential for protein translation. There is a need for integrated software that is able to analyze multiple types of RNA. Here, we present 3t-seq, a Snakemake pipeline for integrated differential expression analysis of transcripts from single-copy genes, TEs, and tRNA. 3t-seq produces an accessible report and easy-to-use results for downstream analysis starting from raw sequencing data and performing quality control, genome mapping, gene expression quantification, and statistical testing. It implements three methods to quantify TEs expression and one for tRNA genes. It provides an easy-to-configure method to manage software dependencies that lets the user focus on results. 3t-seq is released under MIT license and is available at https://github.com/boulardlab/3t-seq.</p>","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"25 6","pages":""},"PeriodicalIF":6.8000,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11424182/pdf/","citationCount":"0","resultStr":"{\"title\":\"3t-seq: automatic gene expression analysis of single-copy genes, transposable elements, and tRNAs from RNA-seq data.\",\"authors\":\"Francesco Tabaro, Matthieu Boulard\",\"doi\":\"10.1093/bib/bbae467\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>RNA sequencing is the gold-standard method to quantify transcriptomic changes between two conditions. The overwhelming majority of data analysis methods available are focused on polyadenylated RNA transcribed from single-copy genes and overlook transcripts from repeated sequences such as transposable elements (TEs). These self-autonomous genetic elements are increasingly studied, and specialized tools designed to handle multimapping sequencing reads are available. Transfer RNAs are transcribed by RNA polymerase III and are essential for protein translation. There is a need for integrated software that is able to analyze multiple types of RNA. Here, we present 3t-seq, a Snakemake pipeline for integrated differential expression analysis of transcripts from single-copy genes, TEs, and tRNA. 3t-seq produces an accessible report and easy-to-use results for downstream analysis starting from raw sequencing data and performing quality control, genome mapping, gene expression quantification, and statistical testing. It implements three methods to quantify TEs expression and one for tRNA genes. It provides an easy-to-configure method to manage software dependencies that lets the user focus on results. 3t-seq is released under MIT license and is available at https://github.com/boulardlab/3t-seq.</p>\",\"PeriodicalId\":9209,\"journal\":{\"name\":\"Briefings in bioinformatics\",\"volume\":\"25 6\",\"pages\":\"\"},\"PeriodicalIF\":6.8000,\"publicationDate\":\"2024-09-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11424182/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Briefings in bioinformatics\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://doi.org/10.1093/bib/bbae467\",\"RegionNum\":2,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"BIOCHEMICAL RESEARCH METHODS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Briefings in bioinformatics","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1093/bib/bbae467","RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BIOCHEMICAL RESEARCH METHODS","Score":null,"Total":0}
引用次数: 0

摘要

RNA 测序是量化两种情况下转录组变化的黄金标准方法。现有的绝大多数数据分析方法都侧重于从单拷贝基因转录的多聚腺苷酸 RNA,而忽略了从重复序列(如转座元素(TE))转录的转录本。对这些自发遗传元件的研究越来越多,也出现了专门处理多映射测序读数的工具。转运 RNA 由 RNA 聚合酶 III 转录,对蛋白质翻译至关重要。我们需要能够分析多种类型 RNA 的集成软件。在这里,我们介绍 3t-seq,这是一个 Snakemake 管道,用于对来自单拷贝基因、TE 和 tRNA 的转录本进行综合差异表达分析。3t-seq 可生成可访问的报告和易于使用的结果,以便从原始测序数据开始进行下游分析,并执行质量控制、基因组图谱、基因表达量化和统计测试。它采用三种方法量化 TEs 表达,一种方法量化 tRNA 基因。它提供了一种易于配置的方法来管理软件依赖性,让用户专注于结果。3t-seq 在 MIT 许可下发布,可从 https://github.com/boulardlab/3t-seq 获取。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
3t-seq: automatic gene expression analysis of single-copy genes, transposable elements, and tRNAs from RNA-seq data.

RNA sequencing is the gold-standard method to quantify transcriptomic changes between two conditions. The overwhelming majority of data analysis methods available are focused on polyadenylated RNA transcribed from single-copy genes and overlook transcripts from repeated sequences such as transposable elements (TEs). These self-autonomous genetic elements are increasingly studied, and specialized tools designed to handle multimapping sequencing reads are available. Transfer RNAs are transcribed by RNA polymerase III and are essential for protein translation. There is a need for integrated software that is able to analyze multiple types of RNA. Here, we present 3t-seq, a Snakemake pipeline for integrated differential expression analysis of transcripts from single-copy genes, TEs, and tRNA. 3t-seq produces an accessible report and easy-to-use results for downstream analysis starting from raw sequencing data and performing quality control, genome mapping, gene expression quantification, and statistical testing. It implements three methods to quantify TEs expression and one for tRNA genes. It provides an easy-to-configure method to manage software dependencies that lets the user focus on results. 3t-seq is released under MIT license and is available at https://github.com/boulardlab/3t-seq.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Briefings in bioinformatics
Briefings in bioinformatics 生物-生化研究方法
CiteScore
13.20
自引率
13.70%
发文量
549
审稿时长
6 months
期刊介绍: Briefings in Bioinformatics is an international journal serving as a platform for researchers and educators in the life sciences. It also appeals to mathematicians, statisticians, and computer scientists applying their expertise to biological challenges. The journal focuses on reviews tailored for users of databases and analytical tools in contemporary genetics, molecular and systems biology. It stands out by offering practical assistance and guidance to non-specialists in computerized methodologies. Covering a wide range from introductory concepts to specific protocols and analyses, the papers address bacterial, plant, fungal, animal, and human data. The journal's detailed subject areas include genetic studies of phenotypes and genotypes, mapping, DNA sequencing, expression profiling, gene expression studies, microarrays, alignment methods, protein profiles and HMMs, lipids, metabolic and signaling pathways, structure determination and function prediction, phylogenetic studies, and education and training.
期刊最新文献
Atomistic simulations reveal impacts of missense mutations on the structure and function of SynGAP1. COFFEE: consensus single cell-type specific inference for gene regulatory networks. DrugDoctor: enhancing drug recommendation in cold-start scenario via visit-level representation learning and training. 3t-seq: automatic gene expression analysis of single-copy genes, transposable elements, and tRNAs from RNA-seq data. AESurv: autoencoder survival analysis for accurate early prediction of coronary heart disease.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1