相关实验视频

Updated: Jun 3, 2025

Simultaneous Affinity Enrichment of Two Post-Translational Modifications for Quantification and Site Localization

Simultaneous Affinity Enrichment of Two Post-Translational Modifications for Quantification and Site Localization

Published on: February 27, 2020

LncSL:通过氨基酸增强特征和两阶段自动化选择策略,用于 lncRNA 亚细胞定位的新型堆叠合体计算工具

Lun Zhu¹, Hong Chen¹, Sen Yang¹

¹School of Computer Science and Artificial Intelligence Aliyun School of Big Data School of Software, Changzhou University, Changzhou 213164, China.

International journal of molecular sciences

|January 8, 2025

概括

我们开发了LncSL,这是一个新的计算模型,用于预测长非编码RNA (lncRNA) 细胞下定位. 通过结合编码潜在区域,LncSL提高了准确性,并且在基准数据集上优于现有的方法.

科学领域:

生物信息学是一种生物信息学.
分子生物学分子生物学
计算生物学计算生物学

背景情况:

长非编码RNAs (lncRNAs) 是细胞过程的关键调节者.
准确预测 lncRNA 亚细胞定位对于理解它们的生物功能至关重要.
当前的计算方法往往忽略了潜在区域的编码,从而限制了它们的有效性.

研究的目的:

开发一种新的计算模型,LncSL,用于预测 lncRNA 亚细胞定位.
通过整合序列信息来提高预测准确性,包括具有编码潜力的区域.
改进现有的基于机器学习的 lncRNA 定位方法.

主要方法:

特性编码使用 lncRNA 和开放读取框架 (ORF) 氨基酸序列.
使用CatBoost进行功能选择,并使用七个算法进行投票过程.
为元模型组装制定自动模型选择策略.
在基准数据集 (S1和S2) 上进行堆叠模型培训和评估.

主要成果:

与平衡测试数据集的现有方法相比,LncSL表现出优异的性能 (马修的相关系数有6.3%12.3%的改善).
在一个不平衡的独立测试数据集上,LncSL显示出显著的改善 (马修的相关系数增加了4.7%18.6%).
该模型有效地利用了被忽视的序列信息,并解决了元模型选择挑战.

结论:

LncSL提供了一种有效和准确的方法来预测lncRNA亚细胞定位.
该方法通过考虑编码潜力来增强对lncRNA功能的理解.
这项工作为未来的生物信息学研究和lncRNA本地化预测提供了宝贵的见解.

关键词:

功能融合功能融合功能长非编码RNA是什么意思序列分析分析的序列分析.堆叠策略的策略堆叠策略.

更多相关视频

Author Spotlight: RNA FISH for Locating lncRNA-SNHG6 in Osteosarcoma Cells

Author Spotlight: RNA FISH for Locating lncRNA-SNHG6 in Osteosarcoma Cells

Published on: June 16, 2023

Identification of RNAs Engaged in Direct RNA-RNA Interaction with a Long Non-Coding RNA

Identification of RNAs Engaged in Direct RNA-RNA Interaction with a Long Non-Coding RNA

Published on: July 9, 2021

相关概念视频

RNA-seq

RNA-seq

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...

Nuclear Localization Signals and Import

Nuclear Localization Signals and Import

Proteins targeted to the nucleus carry short stretches of amino acid sequences called the nuclear localization signal or NLS. Classical nuclear localization signals are of two types: monopartite and bipartite NLS. Monopartite classical NLS (cNLS) consists of a single cluster of 4-8 amino acids. Bipartite cNLS consists of two clusters of 2-3 amino acids and a 9-12 residue long proline-rich linker bridging the two clusters. Signal clusters are rich in positively charged amino acids such as...

Ribosome Profiling

Ribosome Profiling

Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
Applications of ribosome profiling
Ribosome profiling has many applications, including in vivo monitoring of translation inside a particular organ or tissue type and quantifying new protein synthesis levels.
The technique...

lncRNA - Long Non-coding RNAs

lncRNA - Long Non-coding RNAs

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...