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相关概念视频

Downsampling01:20

Downsampling

194
When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...
194
Upsampling01:22

Upsampling

266
Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
266
Next-generation Sequencing03:00

Next-generation Sequencing

91.6K
The first human genome sequencing project cost $2.7 billion and was declared complete in 2003, after 15 years of international cooperation and collaboration between several research teams and funding agencies. Today, with the advent of next-generation sequencing technologies, the cost and time of sequencing a human genome have dropped over 100 fold.
Next-Generation Sequencing Methods
Although all next-generation methods use different technologies, they all share a set of standard features....
91.6K
RNA-seq03:21

RNA-seq

10.1K
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...
10.1K

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相关实验视频

Updated: Jul 25, 2025

Author Spotlight: Cost-Effective Transcriptomic Drug Screening - Unlocking New Targets
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Author Spotlight: Cost-Effective Transcriptomic Drug Screening - Unlocking New Targets

Published on: February 23, 2024

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S-leaping:一种高效的下方采样方法,用于大规模的高通量测序数据.

Hiroyuki Kuwahara1, Xin Gao1

  • 1Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia.

Bioinformatics (Oxford, England)
|June 24, 2023
PubMed
概括
此摘要是机器生成的。

缩小样本的大型omics数据集在计算上具有挑战性. 我们开发了s-leaping,一种高效的方法,以及fadso,一种用于FASTQ文件的工具,可以提高速度并减少对omics数据分析的内存使用.

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Author Spotlight: Cost-Effective Transcriptomic Drug Screening - Unlocking New Targets

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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:

  • 测序覆盖面对OMIC研究设计至关重要.
  • 低采样用于估计具有成本效益的测序覆盖范围.
  • 大数据集使传统的下方采样计算密集.

研究的目的:

  • 为了开发一个高效和准确的下方采样方法,大omics数据.
  • 创建一个用户友好的工具,用于将该方法应用于FASTQ文件.

主要方法:

  • 开发了一种近似的下方采样算法,称为s-leaping.
  • 创建了一个名为fadso的基于C的工具,用于处理FASTQ数据.
  • 对比了s-leaping和fadso与现有的下方采样方法.

主要成果:

  • s-leaping 显示的性能高达比现有准确度可比的方法快39%.
  • 在大型数据集上,fadso显示速度增加了12%,内存使用量降低了21%.
  • fadso在并行计算设置中实现了高达40%的更高吞吐量.

结论:

  • s-leaping提供了一种高效且准确的解决方案,用于减少大omics数据集的样本.
  • fadso为FASTQ文件下采样提供了一种实用且高吞吐量的工具.
  • 开发的方法有助于成本效益的设计和分析omics研究.