将基因组区间转化为标准词汇，让深度学习直接处理基因组数据。
Fast, memory-efficient genomic interval tokenizers for modern machine learning
- 用类似文本分词的思路，把基因组区间映射到统一词汇表。
- 支持大规模数据，速度比传统方法快数倍，兼容PyTorch/TensorFlow。
- 适合做基因组数据分析的机器学习研究者快速上手使用。
高通量测序产生的表观基因组数据通常以基因组区间形式呈现。随着数据量增长，深度学习分析需求上升，但不同数据集定义的区域不一致，阻碍了机器学习应用。本文提出gtars-tokenizers，一个高性能库，将基因组区间映射到预定义的区域词汇表中，类比自然语言处理中的分词。该库基于Rust实现，提供Python、R、CLI和WebAssembly接口，支持两种重叠检测方法（BITS和AIList），并通过与Hugging Face兼容的API无缝集成现代机器学习框架。实验表明，该工具在大规模数据下表现最优，使基因组区间可直接使用PyTorch和TensorFlow等标准流程处理，无需定制预处理。该方法推动了基因组学与机器学习的融合，支持跨平台、可扩展的标准化分析。
原文摘要 · Abstract (English)
Introduction: Epigenomic datasets from high-throughput sequencing experiments are commonly summarized as genomic intervals. As the volume of this data grows, so does interest in analyzing it through deep learning. However, the heterogeneity of genomic interval data, where each dataset defines its own regions, creates barriers for machine learning methods that require consistent, discrete vocabularies. Methods: We introduce gtars-tokenizers, a high-performance library that maps genomic intervals to a predefined universe or vocabulary of regions, analogous to text tokenization in natural language processing. Built in Rust with bindings for Python, R, CLI, and WebAssembly, gtars-tokenizers implements two overlap methods (BITS and AIList) and integrates seamlessly with modern ML frameworks through Hugging Face-compatible APIs. Results: The gtars-tokenizers package achieves top efficiency for large-scale datasets, while enabling genomic intervals to be processed using standard ML workflows in PyTorch and TensorFlow without ad hoc preprocessing. This token-based approach bridges genomics and machine learning, supporting scalable and standardized analysis of interval data across diverse computational environments. Availability: PyPI and GitHub: https://github.com/databio/gtars.
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