DNA-protein quasi-mapping for rapid differential gene expression analysis in non-model organisms.

IF 2.9 3区 生物学 Q2 BIOCHEMICAL RESEARCH METHODS BMC Bioinformatics Pub Date : 2024-10-24 DOI:10.1186/s12859-024-05924-1
Kyle Christian L Santiago, Anish M S Shrestha
{"title":"DNA-protein quasi-mapping for rapid differential gene expression analysis in non-model organisms.","authors":"Kyle Christian L Santiago, Anish M S Shrestha","doi":"10.1186/s12859-024-05924-1","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Conventional differential gene expression analysis pipelines for non-model organisms require computationally expensive transcriptome assembly. We recently proposed an alternative strategy of directly aligning RNA-seq reads to a protein database, and demonstrated drastic improvements in speed, memory usage, and accuracy in identifying differentially expressed genes.</p><p><strong>Result: </strong>Here we report a further speed-up by replacing DNA-protein alignment by quasi-mapping, making our pipeline > 1000× faster than assembly-based approach, and still more accurate. We also compare quasi-mapping to other mapping techniques, and show that it is faster but at the cost of sensitivity.</p><p><strong>Conclusion: </strong>We provide a quick-and-dirty differential gene expression analysis pipeline for non-model organisms without a reference transcriptome, which directly quasi-maps RNA-seq reads to a reference protein database, avoiding computationally expensive transcriptome assembly.</p>","PeriodicalId":8958,"journal":{"name":"BMC Bioinformatics","volume":null,"pages":null},"PeriodicalIF":2.9000,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11515663/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"BMC Bioinformatics","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1186/s12859-024-05924-1","RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"BIOCHEMICAL RESEARCH METHODS","Score":null,"Total":0}
引用次数: 0

Abstract

Background: Conventional differential gene expression analysis pipelines for non-model organisms require computationally expensive transcriptome assembly. We recently proposed an alternative strategy of directly aligning RNA-seq reads to a protein database, and demonstrated drastic improvements in speed, memory usage, and accuracy in identifying differentially expressed genes.

Result: Here we report a further speed-up by replacing DNA-protein alignment by quasi-mapping, making our pipeline > 1000× faster than assembly-based approach, and still more accurate. We also compare quasi-mapping to other mapping techniques, and show that it is faster but at the cost of sensitivity.

Conclusion: We provide a quick-and-dirty differential gene expression analysis pipeline for non-model organisms without a reference transcriptome, which directly quasi-maps RNA-seq reads to a reference protein database, avoiding computationally expensive transcriptome assembly.

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
用于非模式生物中快速差异基因表达分析的 DNA 蛋白质准图谱。
背景:传统的非模式生物差异基因表达分析管道需要计算昂贵的转录组组装。我们最近提出了一种替代策略,即直接将 RNA-seq 读数与蛋白质数据库进行比对,结果表明,这种方法在速度、内存使用和识别差异表达基因的准确性方面都有大幅提高:结果:在这里,我们报告了用准映射代替 DNA 蛋白配准的进一步提速,使我们的管道比基于组装的方法快 1000 倍以上,而且更准确。我们还将类映射与其他映射技术进行了比较,结果表明,类映射速度更快,但灵敏度却有所降低:我们为没有参考转录组的非模式生物提供了一种快速简便的差异基因表达分析管道,它能直接将 RNA-seq 读数准映射到参考蛋白质数据库,避免了计算成本高昂的转录组组装。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
BMC Bioinformatics
BMC Bioinformatics 生物-生化研究方法
CiteScore
5.70
自引率
3.30%
发文量
506
审稿时长
4.3 months
期刊介绍: BMC Bioinformatics is an open access, peer-reviewed journal that considers articles on all aspects of the development, testing and novel application of computational and statistical methods for the modeling and analysis of all kinds of biological data, as well as other areas of computational biology. BMC Bioinformatics is part of the BMC series which publishes subject-specific journals focused on the needs of individual research communities across all areas of biology and medicine. We offer an efficient, fair and friendly peer review service, and are committed to publishing all sound science, provided that there is some advance in knowledge presented by the work.
期刊最新文献
Rare copy number variant analysis in case-control studies using snp array data: a scalable and automated data analysis pipeline. Mining contextually meaningful subgraphs from a vertex-attributed graph. Robust double machine learning model with application to omics data. A mapping-free natural language processing-based technique for sequence search in nanopore long-reads. Closha 2.0: a bio-workflow design system for massive genome data analysis on high performance cluster infrastructure.
×
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