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相关概念视频

RNA-seq03:21

RNA-seq

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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...
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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.
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在基层分辨率和结合基层分辨率上对RNA-Seq Aligners进行基准测试,使用Arabidopsis thaliana基因组.

Tallon Coxe1, David J Burks1, Utkarsh Singh2

  • 1Department of Biological Sciences and BioDiscovery Institute, College of Science, University of North Texas, 1155 Union Circle #305220, Denton, TX 76203-5017, USA.

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概括
此摘要是机器生成的。

对植物数据的RNA-Seq对齐工具的基准测试对于准确的转录组分析至关重要. STAR在基层对齐准确度方面表现出色,而SubRead在工厂数据的交叉点准确度方面表现出前途.

关键词:
阿拉比多普西斯塔利亚纳.在RNA-Seqq.国家统一计划 (SNP) 是一个国家统一计划.调整工具是调整工具.基准测试 (benchmarking) 是一种比较的方法.读取对齐的读取方式翻译学 翻译学 翻译学 翻译学

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科学领域:

  • 生物信息学是一种生物信息学.
  • 计算生物学 计算生物学
  • 基因组学就是基因组学.

背景情况:

  • RNA-Seq对齐软件对于转录组分析至关重要.
  • 现有的工具通常是针对人类或 prokaryotic 数据进行优化,限制了植物应用.
  • 植物RNA-Seq数据库需要专门的工具评估和校准.

研究的目的:

  • 用模拟的植物数据对流行的RNA-Seq对齐工具进行基准测试.
  • 为了评估基层和交叉点级分辨率的对齐精度.
  • 用不同的参数和注释的SNP来评估工具性能.

主要方法:

  • 基于引用数的基准测试五个流行的RNA-Seq对齐工具.
  • 使用来自Arabidopsis thaliana的模拟数据.
  • 从TAIR引入注释的单核酸多态 (SNP).
  • 在基层和交叉点级分辨率上评估对齐准确度.
  • 测试默认设置和不同的参数,如信任值和SNP引入.

主要成果:

  • 在各种条件下,基层对齐精度在各种工具中是一致的.
  • 交叉口水平对齐准确度根据算法大大不同.
  • STAR显示出优越的总体基准准确度 (>90%).
  • 在大多数条件下,SubRead显示出有希望的连接级准确性 (>80%).

结论:

  • 在植物中,STAR是基层RNA-Seq读取对齐的顶级执行者.
  • 亚阅读显示了植物转录组学中准确的结点水平对齐的潜力.
  • 用植物特定数据对工具进行校准对于可靠的转录组学研究至关重要.