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相关概念视频

Ribosome Profiling02:24

Ribosome Profiling

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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
Ribosome profiling has many applications, including in vivo monitoring of translation inside a particular organ or tissue type and quantifying new protein synthesis levels.
The technique...
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From DNA to Protein03:06

From DNA to Protein

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The flow of genetic information in cells from DNA to mRNA to protein is described by the central dogma, which states that genes specify the sequence of mRNAs, which in turn specify the sequence of amino acids making up all proteins. The decoding of one molecule to another is performed by specific proteins and RNAs. Because the information stored in DNA is so central to cellular function, it makes intuitive sense that the cell would make mRNA copies of this information for protein synthesis...
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Leaky Scanning02:28

Leaky Scanning

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During most eukaryotic translation processes, the small 40S ribosome subunit scans an mRNA from its 5' end until it encounters the first start AUG codon. The large 60S ribosomal subunit then joins the smaller one to initiate protein synthesis. The location of the translation initiation is largely determined by the nucleotides near the start codon as there may be multiple translation initiation sites present on the mRNA.  Marilyn Kozak discovered that the sequence RCCAUGG (where R...
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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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Initiation of Translation02:33

Initiation of Translation

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Initiating translation is complex because it involves multiple molecules. Initiator tRNA, ribosomal subunits, and eukaryotic initiation factors (eIFs) are all required to assemble on the initiation codon of mRNA. This process consists of several steps that are mediated by different eIFs.
First, the initiator tRNA must be selected from the pool of elongator tRNAs by eukaryotic initiation factor 2 (eIF2). The initiator tRNA (Met-tRNAi) has conserved sequence elements including modified bases at...
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Structure of a Gene01:30

Structure of a Gene

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A gene is the fundamental unit of heredity. Every individual has two copies of each gene, one inherited from each parent. Although most people contain the same genes, there is a small fraction that is slightly different amongst people. A gene with a small difference in its sequence of DNA bases forms different alleles, contributing to different phenotypes.
However, only 1% of the DNA is composed of genes that encode proteins; the rest, 99% is non-coding DNA. This non-coding DNA performs...
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An Integrated Approach for Microprotein Identification and Sequence Analysis
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Ab initio 对蛋白质编码区域的基因预测

Lonnie Baker1, Charles David2, Donald J Jacobs3,4

  • 1Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, NC 28223, United States.

Bioinformatics advances
|August 28, 2023
PubMed
概括

一种新的神经网络方法改善了非模型生物的基因预测准确性. 这种方法克服了特定物种的局限性,比现有技术提供更高的灵敏度和特异性,培训数据较少.

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De novo Identification of Actively Translated Open Reading Frames with Ribosome Profiling Data

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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.
  • 计算生物学 计算生物学

背景情况:

  • 在非模型生物体中的基因预测是具有挑战性的,因为有特定物种的基因组模式.
  • 现有的方法通常在各种物种中实现低灵敏度和特异性 (约60%).
  • 需要方法来识别编码和非编码区域的通用遗传特征.

研究的目的:

  • 开发一种使用神经网络的新型基因预测方法.
  • 为了创建一种强大的方法,在全系遗传学上多样化的生物体.
  • 通过克服特定物种偏见来提高基因预测的准确性.

主要方法:

  • 一个神经网络 (NN) 模型,使用基于传感器的方法来提取特征.
  • 一个在核酸水平上应用的共识预测算法,以改进NN输出.
  • 一个数据驱动的程序,以优化编码序列 (CDS) /非CDS值.

主要成果:

  • 该NN方法实现了准确的基因预测,即使在类遗传学上遥远的训练和测试生物.
  • 共识算法通过优化CDS/非CDS值来提高预测准确度.
  • 与现有的ab initio方法相比,新的方法显示出更高的核酸水平准确性.
  • 与传统方法相比,所需的培训数据要少得多.

结论:

  • 开发的基于神经网络的方法为非模型生物的基因预测提供了显著的进步.
  • 这种方法为跨越多种物种的基因组分析提供了更普遍,更准确的工具.
  • 该方法在培训数据要求方面的效率使其成为基因组研究的宝贵资源.