{"title":"PIPETS:利用 3'-end 测序研究细菌终止的统计信息、基因注释不可知分析方法。","authors":"Quinlan Furumo, Michelle M Meyer","doi":"10.1186/s12859-024-05982-5","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Over the last decade the drop in short-read sequencing costs has allowed experimental techniques utilizing sequencing to address specific biological questions to proliferate, oftentimes outpacing standardized or effective analysis approaches for the data generated. There are growing amounts of bacterial 3'-end sequencing data, yet there is currently no commonly accepted analysis methodology for this datatype. Most data analysis approaches are somewhat ad hoc and, despite the presence of substantial signal within annotated genes, focus on genomic regions outside the annotated genes (e.g. 3' or 5' UTRs). Furthermore, the lack of consistent systematic analysis approaches, as well as the absence of genome-wide ground truth data, make it impossible to compare conclusions generated by different labs, using different organisms.</p><p><strong>Results: </strong>We present PIPETS, (Poisson Identification of PEaks from Term-Seq data), an R package available on Bioconductor that provides a novel analysis method for 3'-end sequencing data. PIPETS is a statistically informed, gene-annotation agnostic methodology. Across two different datasets from two different organisms, PIPETS identified significant 3'-end termination signal across a wider range of annotated genomic contexts than existing analysis approaches, suggesting that existing approaches may miss biologically relevant signal. Furthermore, assessment of the previously called 3'-end positions not captured by PIPETS showed that they were uniformly very low coverage.</p><p><strong>Conclusions: </strong>PIPETS provides a broadly applicable platform to explore and analyze 3'-end sequencing data sets from across different organisms. It requires only the 3'-end sequencing data, and is broadly accessible to non-expert users.</p>","PeriodicalId":8958,"journal":{"name":"BMC Bioinformatics","volume":"25 1","pages":"363"},"PeriodicalIF":2.9000,"publicationDate":"2024-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11585934/pdf/","citationCount":"0","resultStr":"{\"title\":\"PIPETS: a statistically informed, gene-annotation agnostic analysis method to study bacterial termination using 3'-end sequencing.\",\"authors\":\"Quinlan Furumo, Michelle M Meyer\",\"doi\":\"10.1186/s12859-024-05982-5\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Over the last decade the drop in short-read sequencing costs has allowed experimental techniques utilizing sequencing to address specific biological questions to proliferate, oftentimes outpacing standardized or effective analysis approaches for the data generated. 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引用次数: 0
摘要
背景:在过去十年中,短线程测序成本的下降使得利用测序解决特定生物学问题的实验技术激增,有时甚至超过了对所产生数据进行标准化或有效分析的方法。细菌 3'- 端测序数据的数量越来越多,但目前还没有针对这种数据类型的公认分析方法。大多数数据分析方法都是临时性的,尽管在已注释的基因中存在大量信号,但分析的重点是已注释基因以外的基因组区域(如 3' 或 5' UTR)。此外,由于缺乏一致的系统分析方法,也没有全基因组的基本真实数据,因此无法比较不同实验室使用不同生物体得出的结论:我们介绍了PIPETS(Poisson Identification of PEaks from Term-Seq data),它是Bioconductor上的一个R软件包,为3'端测序数据提供了一种新颖的分析方法。PIPETS 是一种不考虑基因注释的统计学方法。与现有的分析方法相比,PIPETS 在来自两种不同生物的两个不同数据集中,在更广泛的注释基因组上下文中发现了重要的 3'-end 终止信号,这表明现有的方法可能会遗漏与生物相关的信号。此外,对 PIPETS 未捕获的先前调用的 3'-end 位置进行的评估显示,这些位置的覆盖率都非常低:结论:PIPETS 为探索和分析不同生物的 3'-end 测序数据集提供了一个广泛适用的平台。它只需要 3'-end 测序数据,非专业用户也能广泛使用。
PIPETS: a statistically informed, gene-annotation agnostic analysis method to study bacterial termination using 3'-end sequencing.
Background: Over the last decade the drop in short-read sequencing costs has allowed experimental techniques utilizing sequencing to address specific biological questions to proliferate, oftentimes outpacing standardized or effective analysis approaches for the data generated. There are growing amounts of bacterial 3'-end sequencing data, yet there is currently no commonly accepted analysis methodology for this datatype. Most data analysis approaches are somewhat ad hoc and, despite the presence of substantial signal within annotated genes, focus on genomic regions outside the annotated genes (e.g. 3' or 5' UTRs). Furthermore, the lack of consistent systematic analysis approaches, as well as the absence of genome-wide ground truth data, make it impossible to compare conclusions generated by different labs, using different organisms.
Results: We present PIPETS, (Poisson Identification of PEaks from Term-Seq data), an R package available on Bioconductor that provides a novel analysis method for 3'-end sequencing data. PIPETS is a statistically informed, gene-annotation agnostic methodology. Across two different datasets from two different organisms, PIPETS identified significant 3'-end termination signal across a wider range of annotated genomic contexts than existing analysis approaches, suggesting that existing approaches may miss biologically relevant signal. Furthermore, assessment of the previously called 3'-end positions not captured by PIPETS showed that they were uniformly very low coverage.
Conclusions: PIPETS provides a broadly applicable platform to explore and analyze 3'-end sequencing data sets from across different organisms. It requires only the 3'-end sequencing data, and is broadly accessible to non-expert users.
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.