APARENT2 Training Data and Models

Processed training data for the APARENT2 model (measurements from the random MPRA and designed oligo pool originally published by Bogard et al., 2019; see https://doi.org/10.1016/j.cell.2019.04.046 for reference). This repository also contains the APARENT2 model file. For more information on the training procedure, see the Genome Biology article "Deciphering the impact of genetic variation on human polyadenylation using APARENT2" (https://genomebiology.biomedcentral.com/articles/10.1186/s13059-022-02799-4). Two versions of the model are available: (a) aparent_all_libs_resnet_no_clinvar_wt_ep_5.h5: The originally trained APARENT2 model. (b) aparent_all_libs_resnet_no_clinvar_wt_ep_5_var_batch_size_inference_mode_no_drop.h5: Identical weights and predictions as model (a), but the normalization layers have been set to inference mode and the dropout layers have been removed (thus making it compatible with the scrambler pipeline).

本数据集为APARENT2模型的预处理训练数据，其数据来自Bogard等人2019年发表的随机大规模平行报告基因测定法（Massively Parallel Reporter Assay，MPRA）实验及设计合成的寡核苷酸池；相关原始文献可参阅：https://doi.org/10.1016/j.cell.2019.04.046。本代码仓库同时包含APARENT2模型文件。有关模型训练流程的详细信息，请参阅《基因组生物学（Genome Biology）》期刊论文《利用APARENT2解析遗传变异对人类多聚腺苷酸化的影响》（链接：https://genomebiology.biomedcentral.com/articles/10.1186/s13059-022-02799-4）。目前提供两款模型：(a) `aparent_all_libs_resnet_no_clinvar_wt_ep_5.h5`：初始训练完成的APARENT2原始模型。(b) `aparent_all_libs_resnet_no_clinvar_wt_ep_5_var_batch_size_inference_mode_no_drop.h5`：与模型(a)的权重及预测结果完全一致，但将归一化层设置为推理模式并移除了Dropout层，因此可兼容scrambler流水线。

搜集汇总

背景与挑战

背景概述

该数据集包含APARENT2模型的训练数据，主要来源于随机MPRA和设计的寡核苷酸池的测量数据，同时提供了两个版本的模型文件。数据集与RNA多聚腺苷酸化（Polyadenylation）相关，适用于深度学习研究。