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相关概念视频

Genome Annotation and Assembly03:36

Genome Annotation and Assembly

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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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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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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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Ribosome Profiling02:24

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Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
Applications of ribosome profiling
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Master Transcription Regulators02:23

Master Transcription Regulators

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Master transcription regulators are regulatory proteins that are predominantly responsible for regulating the expression of multiple genes. Often these genes work in concert to drive a  complex process. Activation of a master transcription regulator can lead to a cascade of transcriptional activation necessary for that outcome. These regulators can directly bind to the regulatory sequences of the various genes involved, or they can indirectly regulate transcription by binding to regulatory...
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Alternative RNA Splicing02:18

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Alternative RNA splicing is the regulated splicing of exons and introns to produce different mature mRNAs from a single pre-mRNA. Unlike in constitutive splicing where a single gene produces a single type of mRNA, alternative splicing allows an organism to produce multiple proteins from a single gene and plays an important role in protein diversity.
There are five types of alternative RNA splicing that vary in the ways the pre-mRNA segments are removed or retained in the mature mRNA. The first...
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相关实验视频

Updated: Jul 6, 2025

Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved Non-model Organisms
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烤:用于对超转录组组合的无参考优化工具.

Madiha Shabbir1, Aziz Mithani2

  • 1Department of Life Sciences, Syed Babar Ali School of Science and Engineering, Lahore University of Management Sciences (LUMS), DHA, Lahore, 54792, Pakistan.

BMC bioinformatics
|January 3, 2024
PubMed
概括

ROAST使用RNA-seq错误签名优化了de novo超转录组组件. 这种无引用的工具可以提高非模型生物的组装精度,而无需依赖BLAST搜索.

关键词:
组装错误 组装错误 组装错误组装的改进 组装的改进在RNA-seqqq.没有引用的优化优化.超级转录 超级转录 超级转录超转录组组合组件的组合.

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科学领域:

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

背景情况:

  • 对于缺乏参考基因组的生物来说,de novo转录组和超转录组组装至关重要.
  • 超转录组组装的挑战包括转录丰度变化,替代拼接和测序错误.
  • 现有的组装工具往往会产生碎片化或不准确的结合,改进方法取决于相关物种数据.

研究的目的:

  • 开发一种新的计算工具ROAST,用于优化新的超转录组件.
  • 为提高超转录组组件的准确性提供无参考方法.
  • 解决依赖BLAST搜索的现有组装改进工具的局限性.

主要方法:

  • ROAST利用来自Illumina平台的配对末端RNA-seq数据.
  • 该工具通过分析RNA-seq对齐签名 (如软剪贴和意外覆盖) 来识别和纠正组装错误.
  • 它特别针对像碎片化contigs,假模拟器和局部错误组装等错误,而不使用BLAST.

主要成果:

  • ROAST有效地识别和修复各种超转录组组装错误.
  • 使用模拟和真实数据集的评估表明组装质量的显著改善.
  • 该工具成功地改进了新的组件,提高了组件的准确性和完整性.

结论:

  • ROAST提供了一种强大的,无引用的方法来优化超转录组组件.
  • 该工具对于精制非模型生物组件特别有价值.
  • 在没有参考基因组的情况下,ROAST提高了转录基因组数据分析的可靠性和准确性.