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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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DNA Microarrays02:34

DNA Microarrays

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Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...
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Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

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Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
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Genomics02:02

Genomics

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Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...
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Test for Homogeneity01:23

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The goodness–of–fit test can be used to decide whether a population fits a given distribution, but it will not suffice to decide whether two populations follow the same unknown distribution. A different test, called the test for homogeneity, can be used to conclude whether two populations have the same distribution. To calculate the test statistic for a test for homogeneity, follow the same procedure as with the test of independence. The hypotheses for the test for homogeneity can...
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Proteomics01:33

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A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
Proteomics is the study of proteomes' function. It involves the large-scale systematic study of the proteome to denote the protein complement expressed by a genome. Scientist Mark Wilkins coined the term...
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Updated: Jun 21, 2025

Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved Non-model Organisms
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奥马达:通过多次测试进行转录组的强大聚类.

Sokratis Kariotis1,2,3, Pei Fang Tan1,2, Haiping Lu4

  • 1Singapore Institute for Clinical Sciences, Agency for Science, Technology and Research (A*STAR), 30 Medical Dr, 117609, Singapore, Republic of Singapore.

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|July 11, 2024
PubMed
概括
此摘要是机器生成的。

通过机器学习,Omada自动化了无监督的转录组数据集群,使复杂的分析可访问. 该工具可靠地识别RNA测序数据中的子组,即使之前的专业知识有限.

关键词:
集群分析集群分析集群分析基因表达的基因表达方式软件工具包 软件工具包没有监督的学习学习.

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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:

  • 队列研究越来越多地收集生物样本用于分子分析,揭示了显著的分子异质性.
  • 高通量RNA测序产生大量数据集,对于了解疾病机制至关重要.
  • 分析复杂的转录数据需要在机器学习和广泛的计算实验方面的专业知识.

研究的目的:

  • 开发Omada,一套旨在自动化无监督转录基因数据集群的工具.
  • 通过自动化机器学习功能,使强大的转录基因数据分析更容易获得.
  • 帮助没有广泛机器学习专业知识的研究人员进行探索性集群分析.

主要方法:

  • 开发了Omada,这是一个工具包,具有基于机器学习的自动化功能,用于无监督的集群.
  • 使用7个不同的RNA测序数据集和不同表达信号强度测试了Omada的效率.
  • 评估了工具包在转录基因数据集中识别稳定的分区和生物区别的能力.

主要成果:

  • 奥马达准确地反映了数据集中的稳定分区的数量,其中有可辨别的子组.
  • 在数据集中,生物学区别不太清楚,Omada确定了具有明显表达特征和临床关联的稳定子组.
  • 该工具包还在具有挑战性的数据集中检测到有问题的数据迹象,例如有偏见的测量.

结论:

  • 奥马达成功地自动化了对转录基因数据的强大无监督集群.
  • 该工具包增强了对研究人员缺乏广泛的机器学习专业知识的高级转录组分析的可访问性和可靠性.
  • 欧马达可以在http://bioconductor.org/packages/omada/.上实现.