用图结构分析突变体的生物适应度景观，提升预测模型评估精度
Augmenting Biological Fitness Prediction Benchmarks with Landscapes Features from GraphFLA
- 基于突变数据构建多模态适应度景观图，计算20个拓扑特征
- 在超5300个景观上验证模型性能，揭示不同模型优劣因素
- 开源2.2万+序列的完整组合景观，助力精准评估与可复现研究
机器学习模型正日益用于映射生物序列-适应度景观以预测突变影响。有效评估这些模型需依赖实证数据构建的基准测试集。尽管现有基准规模庞大，却缺乏底层适应度景观的拓扑信息，限制了对模型性能的深入理解与比较。本文提出GraphFLA，一个Python框架，可从多种模态（如DNA、RNA、蛋白质等）的突变数据中构建并分析适应度景观，支持高达数百万突变体。GraphFLA计算20个生物学相关的特征，刻画景观拓扑的4个核心方面。我们将其应用于ProteinGym、RNAGym和CIS-BP中的超过5,300个景观，展示了其在解释和比较数十种适应度预测模型表现方面的实用性，揭示影响模型准确性的关键因素，并凸显不同模型的相对优势。此外，我们发布了155个组合完备的实证适应度景观，涵盖超过220万条序列。所有代码与数据均可在https://github.com/COLA-Laboratory/GraphFLA获取。
原文摘要 · Abstract (English)
Machine learning models increasingly map biological sequence-fitness landscapes to predict mutational effects. Effective evaluation of these models requires benchmarks curated from empirical data. Despite their impressive scales, existing benchmarks lack topographical information regarding the underlying fitness landscapes, which hampers interpretation and comparison of model performance beyond averaged scores. Here, we introduce GraphFLA, a Python framework that constructs and analyzes fitness landscapes from mutagensis data in diverse modalities (e.g., DNA, RNA, protein, and beyond) with up to millions of mutants. GraphFLA calculates 20 biologically relevant features that characterize 4 fundamental aspects of landscape topography. By applying GraphFLA to over 5,300 landscapes from ProteinGym, RNAGym, and CIS-BP, we demonstrate its utility in interpreting and comparing the performance of dozens of fitness prediction models, highlighting factors influencing model accuracy and respective advantages of different models. In addition, we release 155 combinatorially complete empirical fitness landscapes, encompassing over 2.2 million sequences across various modalities. All the codes and datasets are available at https://github.com/COLA-Laboratory/GraphFLA.
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