Raw data for <i>Training Data Diversity Enhances the Basecalling of Novel RNA Modification-Induced Nanopore Sequencing Readouts</i>

Raw data, R scripts for the paper titled "Training Data Diversity Enhances the Basecalling of Novel RNA Modification-Induced Nanopore Sequencing Readouts"Accurately basecalling sequence backbones in the presence of nucleotide modifications remains a substantial challenge in nanopore sequencing bioinformatics. It has been extensively demonstrated that state-of-the-art basecallers are less compatible with modification-induced sequencing signals. A precise basecalling, on the other hand, serves as the prerequisite for virtually all the downstream analyses. Here, we report that basecallers exposed to diverse training modifications gain the generalizability to analyze novel modifications. With synthesized oligos as the model system, we precisely basecall various out-of-sample RNA modifications. From the representation learning perspective, we attribute this generalizability to basecaller representation space expanded by diverse training modifications. Taken together, we conclude increasing the training data diversity as a novel paradigm for building modification-tolerant nanopore sequencing basecallers.<br><br><i>For inquiries regarding the contents of this dataset, please contact the Corresponding Author listed in the README.txt file. Administrative inquiries (e.g., removal requests, trouble downloading, etc.) can be directed to [e-mail]</i>

本数据集包含题为《训练数据多样性提升新型RNA修饰诱导的纳米孔测序读出信号碱基识别性能》的论文配套原始数据与R脚本。在存在核苷酸修饰的情况下精准识别序列骨架，仍是纳米孔测序生物信息学领域的重大挑战。现有研究已广泛证实，当前最先进的碱基识别工具（basecaller）对修饰诱导的测序信号兼容性欠佳。而精准的碱基识别，几乎是所有下游分析的先决条件。本研究表明，经多样化修饰训练的碱基识别工具可获得分析新型修饰的泛化能力。本研究以合成寡核苷酸为模型系统，精准识别了多种样本外RNA修饰。从表征学习（representation learning）视角来看，本研究将该泛化能力归因于多样化训练修饰拓展了碱基识别工具的表征空间。综上，本研究提出：提升训练数据多样性，可作为构建耐受修饰的纳米孔测序碱基识别工具的全新范式。<br><br><i>若对本数据集内容存在疑问，请联系README.txt文件中列出的通讯作者。有关行政管理类事宜（例如删除请求、下载故障等），请发送邮件至[e-mail]</i>

搜集汇总

背景与挑战

背景概述

该数据集提供了用于研究“训练数据多样性增强新型RNA修饰诱导的纳米孔测序读数的碱基识别”的原始数据和R脚本。它支持通过增加训练数据多样性来提高纳米孔测序中RNA修饰碱基识别通用性的研究，涉及生物信息学、序列分析和统计方法开发。数据集发布于2024年12月10日，使用MIT许可证，涵盖纳米孔测序和RNA修饰等关键词。