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Updated: Feb 20, 2026

Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved Non-model Organisms

Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved Non-model Organisms

Published on: May 9, 2017

对长期读取的de novo转录组组件进行全面评估

Feng Yan^1,2, Pedro L Baldoni^3,4, James Lancaster^3,4,5

¹The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, 3052, Australia. yan.a@wehi.edu.au.

|February 19, 2026

概括

这项研究对长期阅读的转录组组装工具进行了基准分析,以进行无参考分析. 带有Corset的RNA-Bloom2为没有参考基因组的差异基因表达分析提供了最佳的准确性和效率.

科学领域:

基因组学就是基因组学.
生物信息学是一种生物信息学.
文字转录学 (Transcriptomics) 是一个学科.

背景情况:

使用长读序列的De novo转录组件对于缺乏参考基因组的非模型生物来说至关重要.
缺乏用于无引用长读汇编和差分表达式分析的既定协议.
这项研究解决了对长时间阅读的组装工具进行基准测试的需求.

研究的目的:

评估和比较长读新型转录组组装工具 (RATTLE,RNA-Bloom2,isONform) 与短读组装器 (Trinity) 的性能.
评估组合选择对差异基因和转录表达分析的影响.
为选择无参考长读转录组分析的最佳策略提供指导.

主要方法:

使用模拟和真实数据集 (人,) 评估RATTLE,RNA-Bloom2和isONform.
通过各种测序深度和技术 (ONT cDNA,ONT直接RNA,PacBio) 与Trinity (短读组装器) 的比较.
组装指标的评估和下游微分表达式分析.

主要成果:

长读数在无引用组合中产生比短读数更长的转录,尽管以引用为导向的方法仍然优越.
RNA-Bloom2与Corset相结合,在评估的de novo管道中表现出卓越的准确性和计算效率.
组装策略显著影响差异基因和转录表达检测.

结论:

在没有参考基因组的情况下,建议使用RNA-Bloom2 with Corset进行无参考的de novo转录组组合和差异表达分析.
长读序列为新组装提供了优势,但需要进一步优化以提高准确性和减少冗余性.
该研究为研究人员提供了与非模型生物工作的基本指导.

关键词:

不同表达式的差异表达式长时间阅读阅读非模型生物的非模型生物.没有引用,没有引用.转录组组件组件

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相关概念视频

Genome Annotation and Assembly

Genome Annotation and Assembly

The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.

RNA-seq

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases.
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...