标题： RawBench：一种用于原始纳米孔信号分析技术的全面基准测试框架 显示英文标题

标题： RawBench: A Comprehensive Benchmarking Framework for Raw Nanopore Signal Analysis Techniques

摘要： 纳米孔测序技术持续迅速发展，提供了实时分析、能够测序极长的DNA片段（单次读取可达数百万个碱基）以及在分子完成前选择性停止测序等关键优势。 传统上，测序过程中生成的原始电信号通过称为碱基识别的过程转换为DNA序列，这通常依赖于大型神经网络模型。 原始信号分析已成为这些资源密集型方法的有前途的替代方案。 虽然已经尝试对传统碱基识别方法进行基准测试，但现有的评估框架存在以下问题：1）忽略了原始信号分析技术，2）缺乏灵活容纳新原始信号分析工具的能力，3）未包含纳米孔数据集的最新改进。 我们的目标是提供一个可扩展的基准测试框架，以设计和比较新的原始信号分析方法。 为此，我们引入了RawBench，这是第一个用于评估原始纳米孔信号分析技术的灵活框架。 RawBench提供了对三个核心流程组件的模块化评估：1）参考基因组编码（使用不同的孔模型），2）信号编码（通过各种分割方法），3）表示匹配（通过不同的数据结构）。 我们从三个方面对原始信号分析技术进行了广泛评估：1）读取映射的质量和性能，2）读取分类的质量和性能，3）原始信号分析辅助的碱基识别质量。 我们的评估结果表明，原始信号分析可以在显著降低资源需求的同时实现具有竞争力的质量，特别是在需要实时处理或边缘部署的环境中。 显示英文摘要

摘要： Nanopore sequencing technologies continue to advance rapidly, offering critical benefits such as real-time analysis, the ability to sequence extremely long DNA fragments (up to millions of bases in a single read), and the option to selectively stop sequencing a molecule before completion. Traditionally, the raw electrical signals generated during sequencing are converted into DNA sequences through a process called basecalling, which typically relies on large neural network models. Raw signal analysis has emerged as a promising alternative to these resource-intensive approaches. While attempts have been made to benchmark conventional basecalling methods, existing evaluation frameworks 1) overlook raw signal analysis techniques, 2) lack the flexibility to accommodate new raw signal analysis tools easily, and 3) fail to include the latest improvements in nanopore datasets. Our goal is to provide an extensible benchmarking framework that enables designing and comparing new methods for raw signal analysis. To this end, we introduce RawBench, the first flexible framework for evaluating raw nanopore signal analysis techniques. RawBench provides modular evaluation of three core pipeline components: 1) reference genome encoding (using different pore models), 2) signal encoding (through various segmentation methods), and 3) representation matching (via different data structures). We extensively evaluate raw signal analysis techniques in terms of 1) quality and performance for read mapping, 2) quality and performance for read classification, and 3) quality of raw signal analysis-assisted basecalling. Our evaluations show that raw signal analysis can achieve competitive quality while significantly reducing resource requirements, particularly in settings where real-time processing or edge deployment is necessary.