相关实验视频
Updated: Jan 14, 2026

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Detection of Copy Number Alterations Using Single Cell Sequencing
Published on: February 17, 2017
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LncRNACNVIntegrateR:一种用于将长非编码RNA与副本数变异异常和疾病进展相关的新框架
Neetu Tyagi1,2, Shikha Roy2, Dinesh Gupta2
1Regional Centre for Biotechnology, Faridabad, Haryana, India.
PeerJ
|October 21, 2025
概括
本研究介绍了IncRNACNVIntegrateR,这是一个用于多omics数据集成的R包. 它分析长非编码RNA (lncRNA) 和副本数变异 (CNV) 的相互作用,以确定预后癌症特征并构建预测模型.
科学领域:
- 生物信息学是一种生物信息学.
- 基因组学就是基因组学.
- 癌症研究 癌症研究
背景情况:
- 多学科数据集成对于理解复杂的生物系统和疾病机制至关重要.
- 样本一致性和分析框架的挑战限制了多omics数据的潜力.
- 识别分子关系和生物标志物是推进精准医学的关键.
研究的目的:
- 开发一个R包, lncRNACNVIntegrateR,用于整合多omics数据.
- 探索长非编码RNAs (lncRNAs) 和副本数变异 (CNVs) 之间的相互作用.
- 为了识别CNV驱动的预后特征,并建立癌症的预测模型.
主要方法:
- 该 lncRNACNVIntegrateR 包整合了转录组数据,CNV 资料和临床信息.
- 它为数据预处理,lncRNA-CNV相关性分析和签名识别提供了一个管道.
- 风险评分模型和功能丰富分析被用来评估生物学意义.
主要成果:
- 该包使用癌症基因组图谱 (TCGA) 质母细胞瘤 (GBM) 和结肠直肠腺癌 (COAD) 数据集进行了验证.
- 预测模型实现了GBM的AUC为0.80,COAD的AUC为0.71.
- 功能丰富分析揭示了已识别的预后特征的生物学意义.
结论:
- lncRNACNVIntegrateR促进了多omics数据集成,以发现 lncRNA-CNV相互作用.
- 鉴定的特征和预测模型为疾病进展和风险分层提供了洞察力.
- 该套餐支持发现个性化医学的潜在治疗点.
相关概念视频
Comparing Copy Number Variations and SNPs
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Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...
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In humans, more than 80% of the genome gets transcribed. However, only around 2% of the genome codes for proteins. The remaining part produces non-coding RNAs which includes ribosomal RNAs, transfer RNAs, telomerase RNAs, and regulatory RNAs, among other types. A large number of regulatory non-coding RNAs have been classified into two groups depending upon their length – small non-coding RNAs, such as microRNA, which are less than 200 nucleotides in length, and long non-coding RNA...
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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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