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相关概念视频

Gene Evolution - Fast or Slow?02:05

Gene Evolution - Fast or Slow?

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The genomes of eukaryotes are punctuated by long stretches of sequence which do not code for proteins or RNAs. Although some of these regions do contain crucial regulatory sequences, the vast majority of this DNA serves no known function. Typically, these regions of the genome are the ones in which the fastest change, in evolutionary terms, is observed, because there is typically little to no selection pressure acting on these regions to preserve their sequences.
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Parallel Processing01:20

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The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...
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Combinatorial gene control is the synergistic action of several transcriptional factors to regulate the expression of a single gene. The absence of one or more of these factors may lead to a significant difference in the level of gene expression or repression.
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Cis-regulatory sequences are short fragments of non-coding DNA that are present on the same chromosomes as the genes that they regulate. These fragments serve as binding sites for transcriptional regulators, proteins that are responsible for controlling gene transcription and differential gene expression across cell types in eukaryotes. Cis-regulatory sequences can be close to the gene of interest or thousands of bases away in the DNA sequence; however, those sequences that are further away are...
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相关实验视频

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FastSCODE:一种加速的SCODE算法,用于在多核处理器上推断基因调节网络.

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  • 1Department of Applied Art and Technology, College of Art and Technology, Chung-Ang University, Anseong 17546, Republic of Korea.

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FastSCODE从单细胞RNA测序 (scRNA-seq) 数据显著加速基因调控网络的重建. 这种新方法在使用图形处理单元 (GPU) 的大型数据集上提高了计算性能.

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科学领域:

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

背景情况:

  • 单细胞RNA测序 (scRNA-seq) 可实现高分辨率的基因表达分析.
  • 重建基因调节网络 (GRNs) 对于理解细胞机制至关重要.
  • 像SCODE这样的现有方法面临着大型scRNA-seq数据集的计算限制.

研究的目的:

  • 从scRNA-seq数据开发一个计算效率高的GRN重建算法.
  • 为了优化 SCODE 在多核心架构上进行并行处理,例如 GPU.
  • 显著减少对大型数据集的GRN分析所需的时间.

主要方法:

  • 开发了FastSCODE,这是SCODE算法的批量计算版本.
  • 在多个基因表达特征上实施批量计算.
  • 利用多核计算,特别是GPU,用于对线性普通微分方程 (ODE) 模型的参数优化.

主要成果:

  • 与最初的SCODE实现相比,FastSCODE实现了显著的性能改进.
  • 在使用多个GPU的CeNGEN scRNA-seq数据集上演示了高达6000倍的加速度.
  • 大数据集的处理时间从大约一个月减少到10分钟.

结论:

  • 对于大型scRNA-seq数据集,FastSCODE克服了SCODE的计算瓶.
  • 优化的算法可以实现更快,更可扩展的GRN重建.
  • FastSCODE是公开可用的,促进其在系统生物学研究中的应用.