Computational Analysis Predicts Hundreds of Coding lncRNAs in Zebrafish.
Computational Analysis Predicts Hundreds of Coding lncRNAs in Zebrafish.
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计算分析预测斑马鱼中数百个编码 lncRNA
DOI：
10.3390/biology10050371
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发表时间：
2021-04-26
期刊：
影响因子：
4.2
通讯作者：
Wang H
中科院分区：
文献类型：
--
作者：
Mishra SK;Wang H
非编码RNA（ncRNA）调节多种基本生命过程，如发育、生理、代谢和昼夜节律。RNA测序（RNA-seq）技术促进了整个转录组的测序，从而捕获和量化转录组范围的RNA表达谱的动态。然而，在庞大的非编码RNA数据集中，还有许多尚未揭示的信息需要进一步的生物信息学分析。在这项研究中，我们应用了六种生物信息学工具来研究大约21，000种lncRNA的编码潜力。预测总共有313个lncRNA被所有六个工具编码。我们的研究结果提供了对lncRNA的调控作用的见解，并为这些lncRNA及其编码的微肽的功能研究奠定了基础。摘要最近的研究表明，许多长的非编码RNA（ncRNA具有超过200个核苷酸碱基对（lncRNA））实际上编码的功能性微肽，这可能是下一个调控生物学前沿。因此，从不断增加的lncRNA数据库中识别编码lncRNA将是生物信息学的挑战。在这里，我们使用编码潜力比对工具（CPAT）、编码潜力计算器2（CPC 2）、LGC网络服务器、编码-非编码识别工具（CNIT）、RNAsamba和微肽识别工具（MiPepid）分析了大约21，000个斑马鱼lncRNA，并通过计算识别出2730-6676个具有高编码潜力的斑马鱼lncRNA，包括所有六种生物信息学工具预测的313种编码lncRNA。我们还比较了这六种生物信息学工具识别具有编码潜力的lncRNA的灵敏度和特异性，并总结了它们的优点和缺点。这些预测的斑马鱼编码lncRNA为进一步的实验研究奠定了基础。
Simple Summary Noncoding RNAs (ncRNAs) regulate a variety of fundamental life processes such as development, physiology, metabolism and circadian rhythmicity. RNA-sequencing (RNA-seq) technology has facilitated the sequencing of the whole transcriptome, thereby capturing and quantifying the dynamism of transcriptome-wide RNA expression profiles. However, much remains unrevealed in the huge noncoding RNA datasets that require further bioinformatic analysis. In this study, we applied six bioinformatic tools to investigate coding potentials of approximately 21,000 lncRNAs. A total of 313 lncRNAs are predicted to be coded by all the six tools. Our findings provide insights into the regulatory roles of lncRNAs and set the stage for the functional investigation of these lncRNAs and their encoded micropeptides. Abstract Recent studies have demonstrated that numerous long noncoding RNAs (ncRNAs having more than 200 nucleotide base pairs (lncRNAs)) actually encode functional micropeptides, which likely represents the next regulatory biology frontier. Thus, identification of coding lncRNAs from ever-increasing lncRNA databases would be a bioinformatic challenge. Here we employed the Coding Potential Alignment Tool (CPAT), Coding Potential Calculator 2 (CPC2), LGC web server, Coding-Non-Coding Identifying Tool (CNIT), RNAsamba, and MicroPeptide identification tool (MiPepid) to analyze approximately 21,000 zebrafish lncRNAs and computationally to identify 2730–6676 zebrafish lncRNAs with high coding potentials, including 313 coding lncRNAs predicted by all the six bioinformatic tools. We also compared the sensitivity and specificity of these six bioinformatic tools for identifying lncRNAs with coding potentials and summarized their strengths and weaknesses. These predicted zebrafish coding lncRNAs set the stage for further experimental studies.
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