ABI Development: Improving transcriptome assembly from RNA-seq data

ABI 开发：改进 RNA-seq 数据的转录组组装

• 批准号: 1759518
• 负责人: Mihaela Pertea
• 金额: $ 99.34万
• 依托单位: Johns Hopkins University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-07-15 至 2023-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1759518&HistoricalAwards=false
• 关键词: ABI; Development; Improving; transcriptome; assembly

This project aims at improving the efficiency and accuracy of the computational method for transcript identification and quantification, StringTie. Due to its unparalleled speed and accuracy, StringTie has become one of the leading tools in the field and has a rapidly growing user base. Identifying the transcripts being expressed by a cell is a critical step in studying cell development, disease, the response to infection, specific gene pathways, and much more. By producing better models and expression levels for genes and transcripts, StringTie will, therefore, have an impact on many different areas of research that study biological diversity in our world. The software developed during this project will be made available under an open-source license, thereby enhancing the research infrastructure of the US by enabling the broad reuse of the code base by other scientists investigating similar research topics. The most significant result of this project extends StringTie's usability to a larger community of scientists interested in eukaryotic gene annotation by the addition of a de novo assembly method, which will incorporate genome assembly technology with sequencing coverage information and optimization techniques. By solving a maximum flow problem on a splicing graph built directly from uniquely assembled reads, this new assembly method has the potential to reduce false positives typically associated with methods that use a de Bruijn graph approach. Furthermore, two additional features will improve the accuracy of the transcriptome assembly: one will make StringTie efficiently handle long reads typically produced by third-generation sequencing technologies, and another one will incorporate annotation of open reading frames as information describing the assembled transcripts. Both additions have the potential to significantly improve the transcript structures inferred from short-read RNA-sequencing data. The results of this project will be disseminated via scientific publications and the StringTie website: http://ccb.jhu.edu/software/stringtie.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

本项目旨在提高成绩单识别和量化计算方法StringTie的效率和准确性。由于其无与伦比的速度和准确性，StringTie已成为该领域的领先工具之一，并拥有快速增长的用户基础。识别细胞表达的转录本是研究细胞发育、疾病、对感染的反应、特定基因途径等方面的关键步骤。因此，通过为基因和转录本制作更好的模型和表达水平，StringTie将对研究我们世界生物多样性的许多不同领域产生影响。在该项目期间开发的软件将在开放源码许可下提供，从而通过允许其他研究类似研究课题的科学家广泛重复使用代码库来增强美国的研究基础设施。该项目最重要的成果是通过增加从头组装方法，将StringTie的可用性扩展到对真核基因注释感兴趣的更大的科学家社区，该方法将把基因组组装技术与测序覆盖信息和优化技术相结合。通过解决直接从唯一组装的读取构建的拼接图上的最大流问题，这种新的组装方法有可能减少通常与使用De Bruijn图方法的方法相关的误报。此外，两个额外的功能将提高转录组组装的准确性：一个将使StringTie有效地处理通常由第三代测序技术产生的长阅读，另一个将包括开放阅读框架的注释，作为描述组装的转录的信息。这两个添加都有可能显著改善从短读RNA测序数据推断的转录本结构。该项目的结果将通过科学出版物和StringTie网站发布：http://ccb.jhu.edu/software/stringtie.This奖反映了美国国家科学基金会的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Transcriptome assembly from long-read RNA-seq alignments with StringTie2

• DOI: 10.1186/s13059-019-1910-1
• 发表时间: 2019-12-16
• 期刊: GENOME BIOLOGY
• 影响因子: 12.3
• 作者: Kovaka, Sam;Zimin, Aleksey, V;Pertea, Mihaela
• 通讯作者: Pertea, Mihaela

TieBrush: an efficient method for aggregating and summarizing mapped reads across large datasets

• DOI: 10.1093/bioinformatics/btab342
• 发表时间: 2021-05-08
• 期刊: BIOINFORMATICS
• 影响因子: 5.8
• 作者: Varabyou, Ales;Pertea, Geo;Pertea, Mihaela
• 通讯作者: Pertea, Mihaela

Effects of transcriptional noise on estimates of gene and transcript expression in RNA sequencing experiments.

• DOI: 10.1101/gr.266213.120
• 发表时间: 2021-03
• 期刊: Genome research
• 影响因子: 7
• 作者: Varabyou A;Salzberg SL;Pertea M
• 通讯作者: Pertea M

Investigating open reading frames in known and novel transcripts using ORFanage

使用 ORFanage 研究已知和新颖转录本中的开放阅读框

• DOI: 10.1038/s43588-023-00496-1
• 发表时间: 2023
• 期刊: Nature Computational Science
• 作者: Varabyou, Ales;Erdogdu, Beril;Salzberg, Steven L.;Pertea, Mihaela
• 通讯作者: Pertea, Mihaela