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相关概念视频

Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

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Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
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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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Multi-species Conserved Sequences02:51

Multi-species Conserved Sequences

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Next-generation sequencing technologies have created large genomic databases of a variety of animals and plants. Ever since the human genome project was completed, scientists studied the genome of primates, mammals, and other phylogenetically distant living beings. Such large-scale  studies have provided new insights into the evolutionary relationship between organisms.
Although the genome of each species varies greatly from each other, a few sequences are highly conserved. Such conserved...
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Next-generation Sequencing03:00

Next-generation Sequencing

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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....
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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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Sanger Sequencing01:57

Sanger Sequencing

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DNA sequencing is a fundamental technique that is routinely used in the biological sciences. This method can be applied to a range of questions at different scales - from the sequencing of a cloned DNA fragment or the study of a mutation in a gene up to whole-genome sequencing. However, despite the widespread use of sequencing today, it was not until 1977 that Fredrick Sanger and his collaborators developed the chain-termination method to decode DNA sequences. It relies on the separation of a...
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Novel Sequence Discovery by Subtractive Genomics
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对基于序列的基因组学模型进行对比预训练.

Ksenia Sokolova, Kathleen M Chen, Olga Troyanskaya

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    概括
    此摘要是机器生成的。

    我们开发了cGen,这是一种新的无监督方法,用于预训练基因组学深度学习模型. 这种方法可以提高基因表达预测等任务的性能,特别是当数据有限时.

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    科学领域:

    • 基因组学就是基因组学.
    • 深度学习 (Deep Learning) 是一种深度学习.
    • 生物信息学是一种生物信息学.

    背景情况:

    • 深度学习在基因组学中越来越多地使用.
    • 复杂的模型需要大量的数据或战略初始化以获得最佳性能.

    研究的目的:

    • 介绍cGen,一个新的无监督的,模型不可知的对比性预训练方法,用于基于序列的模型.
    • 提高基因组学深度学习模型的性能,特别是在数据稀缺的情况下.

    主要方法:

    • cGen使用无监督的对比预训练来学习基因组内在特征.
    • 它初始化模型权重,减少对大型数据集的需求.
    • 该方法是无模型的,并且不对基因组结构做出任何假设.

    主要成果:

    • 来自无监督cGen的嵌入对于基因表达预测具有信息性.
    • 学习的序列特征可以实现有意义的集群.
    • cGen 提高了染色体分析预测和基因表达任务的性能.

    结论:

    • 在基因组学中,cGen 提高了深度学习模型的性能,而无需修改架构.
    • 这种方法对于数据可用性有限的应用特别有利.
    • 无监督的预训练为推进基因组深度学习提供了一个强大的策略.