在植物基因组组合中基于DNABERT的可解释的lncRNA识别
Monica F Danilevicz1, Mitchell Gill1, Cassandria G Tay Fernandez1
1School of Biological Sciences, University of Western Australia, Australia.
Computational and structural biotechnology journal
|December 7, 2023
概括
自然语言处理模型现在可以直接从植物基因组序列中识别长非编码RNA (lncRNAs). 这种方法为发现这些重要的基因调节分子提供了更准确,更少偏见的方法.
科学领域:
- 基因组学就是基因组学.
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
背景情况:
- 长非编码RNAs (lncRNAs) 是植物基因表达的关键调节者,影响表观遗传和转录水平.
- 目前用于lncRNA检测的机器学习方法通常依赖于转录组数据和手动定义的特征,导致代表性不足和偏差.
研究的目的:
- 开发和评估自然语言处理 (NLP) 模型,直接从基因组序列中识别植物 lncRNA.
- 评估NLP模型用于lncRNA发现的准确性和跨物种预测能力.
主要方法:
- 使用NLP模型训练了来自七种植物的基因组序列:Zea mays,Arabidopsis thaliana,Brassica napus,Brassica oleracea,Brassica rapa,Glycine max和Oryza sativa.
- 基于预测准确度和探索跨物种预测潜力的评估模型性能.
- 应用可解释的人工智能来识别对lncRNA预测至关重要的序列动机.
主要成果:
- 从基因组序列中获得lncRNA的高预测准确度,最高为Zea mays的83.4%,最低为Brassica rapa的57.9%.
- 证明成功的跨物种预测,未见物种的平均准确率为63.1%.
- 确定了对预测重要 lncRNA 区域侧面的序列动图,表明它们的功能相关性.
结论:
- NLP模型提供了一种有效和准确的方法,可以直接从基因组数据中识别植物 lncRNA,克服了转录组方法的局限性.
- 基因组组装质量似乎影响了ncRNA识别的准确性.
- NLP模型显示出在各种植物物种中广泛的lncRNA发现的前景,并且可以被解释为揭示关键序列特征.
更多相关视频
11:04RNA Next-Generation Sequencing and a Bioinformatics Pipeline to Identify Expressed LINE-1s at the Locus-Specific Level
Published on: May 19, 2019
10.0K
08:33Laser-Capture Microdissection RNA-Sequencing for Spatial and Temporal Tissue-Specific Gene Expression Analysis in Plants
Published on: August 5, 2020
8.1K
相关概念视频
02:39lncRNA - Long Non-coding RNAs
8.6K
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...
8.6K
03:36Genome Annotation and Assembly
18.9K
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.
18.9K
03:21RNA-seq
10.0K
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...
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...
10.0K