Center for Artificial Intelligence Biology, Hubei Bioinformatics & Molecular Imaging Key Laboratory, Key Laboratory of Molecular Biophysics of the Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology; Wuhan, 430074, China.
Shenzhen Huazhong University of Science and Technology Research Institute, Shenzhen, China.
Brief Bioinform. 2022 Sep 20;23(5). doi: 10.1093/bib/bbac310.
Extracellular vesicles (EVs) carrying various small non-coding RNAs (sncRNAs) play a vital roles in cell communication and diseases. Correct quantification of multiple sncRNA biotypes simultaneously in EVs is a challenge due to the short reads (<30 bp) could be mapped to multiple sncRNA types. To address this question, we developed an optimized reads assignment algorithm (ORAA) to dynamically map multi-mapping reads to the sncRNA type with a higher proportion. We integrated ORAA with reads processing steps into EVAtool Python-package (http://bioinfo.life.hust.edu.cn/EVAtool) to quantify sncRNAs, especially for sncRNA-seq from EV samples. EVAtool allows users to specify interested sncRNA types in advanced mode or use default seven sncRNAs (microRNA, small nucleolar RNA, PIWI-interacting RNAs, small nuclear RNA, ribosomal RNA, transfer RNA and Y RNA). To prove the utilities of EVAtool, we quantified the sncRNA expression profiles for 200 samples from cognitive decline and multiple sclerosis. We found that more than 20% of short reads on average were mapped to multiple sncRNA biotypes in multiple sclerosis. In cognitive decline, the proportion of Y RNA is significantly higher than other sncRNA types. EVAtool is a flexible and extensible tool that would benefit to mine potential biomarkers and functional molecules in EVs.
细胞外囊泡 (EVs) 携带各种小非编码 RNA (sncRNAs)，在细胞通讯和疾病中发挥着重要作用。由于短读长 (<30 bp) 可能映射到多种 sncRNA 类型，因此正确定量 EVs 中的多种 sncRNA 生物型是一个挑战。为了解决这个问题，我们开发了一种优化的读段分配算法 (ORAA)，以动态地将多映射读段映射到具有更高比例的 sncRNA 类型。我们将 ORAA 与读段处理步骤集成到 EVAtool Python 包中（http://bioinfo.life.hust.edu.cn/EVAtool），以定量 sncRNAs，特别是来自 EV 样本的 sncRNA-seq。EVAtool 允许用户在高级模式下指定感兴趣的 sncRNA 类型，或使用默认的七种 sncRNAs（microRNA、小核仁 RNA、PIWI 相互作用 RNA、小核 RNA、核糖体 RNA、转移 RNA 和 Y RNA）。为了证明 EVAtool 的实用性，我们对 200 个认知能力下降和多发性硬化症的样本进行了 sncRNA 表达谱定量。我们发现，平均有超过 20%的短读段被映射到多发性硬化症中的多种 sncRNA 生物型。在认知能力下降中，Y RNA 的比例明显高于其他 sncRNA 类型。EVAtool 是一个灵活且可扩展的工具，将有助于挖掘 EVs 中的潜在生物标志物和功能分子。
