用Transformer从染色体交互图中精准定位着丝粒位置
$\textit{BlockFormer}$ : Transformer-based inference from interaction maps
- 基于Transformer架构处理实体数量和大小不一的交互图
- 在多种基因组大小的物种中准确恢复着丝粒位置
- 自研模拟器生成低成本合成数据，提升泛化能力
从交互图（如全基因组染色体构象捕获技术Hi-C）中进行推断可视为一个通用逆问题：给定描述实体间成对相互作用的块状地图，推断一组参数。本文提出一种数据驱动方法，利用地图间的共享结构（如局部模式的全局对齐），同时处理真实数据中实体数量与大小的可变性。方法基于可处理此类变异性得Transformer架构，并设计定制化模拟器生成大量且计算成本低的合成数据用于训练。该方法应用于着丝粒定位任务，在涵盖多种基因组大小的不同物种中均能准确恢复其基因组位置。
原文摘要 · Abstract (English)
Inference from interaction maps, such as centromere identification from genome-wide chromosome conformation capture techniques -- notably Hi-C -- can be formulated as a generic inverse problem: infer a set of parameters given a map summarizing pairwise interactions between entities through blocks of variable numbers and sizes. In this work, we introduce a data-driven approach that leverages shared structure between these maps, such as global alignment between localized patterns, while handling the variability in number and size of entities arising in real-world data. Our approach relies on a transformer architecture capable of handling such variability and a custom simulator to generate abundant, yet computationally cheap synthetic data for training. Applied to the problem of centromere localization, the method accurately recovers their genomic positions across a wide range of species of various genome sizes.
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