IncRna: The R Package for Optimizing lncRNA Identification Processes.

IF 1.4 4区 生物学 Q4 BIOCHEMICAL RESEARCH METHODS
Journal of Computational Biology Pub Date : 2023-12-01 Epub Date: 2023-10-25 DOI:10.1089/cmb.2023.0091
Jan Pawel Jastrzebski, Stefano Pascarella, Aleksandra Lipka, Slawomir Dorocki
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引用次数: 0

Abstract

In silico identification of long noncoding RNAs (lncRNAs) is a multistage process including filtering of transcripts according to their physical characteristics (e.g., length, exon-intron structure) and determination of the coding potential of the sequence. A common issue within this process is the choice of the most suitable method of coding potential analysis for the conducted research. Selection of tools on the sole basis of their single performance may not provide the most effective choice for a specific problem. To overcome these limitations, we developed the R library lncRna, which provides functions to easily carry out the entire lncRNA identification process. For example, the package prepares the data files for coding potential analysis to perform error analysis. Moreover, the package gives the opportunity to analyze the effectiveness of various combinations of the lncRNA prediction methods to select the optimal configuration of the entire process.

IncRna:优化lncRNA鉴定过程的R包。
长非编码RNA(lncRNA)的计算机识别是一个多阶段的过程,包括根据转录物的物理特征(如长度、外显子-内含子结构)过滤转录物和确定序列的编码潜力。这个过程中的一个常见问题是为所进行的研究选择最合适的潜在分析编码方法。仅根据工具的单一性能来选择工具可能无法为特定问题提供最有效的选择。为了克服这些限制,我们开发了lncRna R文库,该文库提供了轻松进行整个lncRna鉴定过程的功能。例如,包准备用于编码潜在分析的数据文件以执行错误分析。此外,该软件包还提供了分析lncRNA预测方法各种组合的有效性的机会,以选择整个过程的最佳配置。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Computational Biology
Journal of Computational Biology 生物-计算机:跨学科应用
CiteScore
3.60
自引率
5.90%
发文量
113
审稿时长
6-12 weeks
期刊介绍: Journal of Computational Biology is the leading peer-reviewed journal in computational biology and bioinformatics, publishing in-depth statistical, mathematical, and computational analysis of methods, as well as their practical impact. Available only online, this is an essential journal for scientists and students who want to keep abreast of developments in bioinformatics. Journal of Computational Biology coverage includes: -Genomics -Mathematical modeling and simulation -Distributed and parallel biological computing -Designing biological databases -Pattern matching and pattern detection -Linking disparate databases and data -New tools for computational biology -Relational and object-oriented database technology for bioinformatics -Biological expert system design and use -Reasoning by analogy, hypothesis formation, and testing by machine -Management of biological databases
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