Multi-scale footprinting

Data associated with the multi-scale footprinting project. (1) Tn5_NN_model.h5 Pre-trained CNN-based Tn5 bias model implemented with Keras. Takes local DNA sequence context as input and predicts Tn5 insertion bias. See tutorial for how to use this model. (2) Tn5ModelTutorial.ipynb Tutorial showing how to use the pre-trained Tn5 bias model to score input sequences. (3) hg38Tn5Bias.tar.gz, hg19Tn5Bias.tar.gz, mm10Tn5Bias.tar.gz, panTro6Tn5Bias.tar.gz, sacCer3Tn5Bias.tar.gz, dm6Tn5Bias.tar.gz, danRer11Tn5Bias.tar.gz, ce11Tn5Bias.tar.gz h5 files containing the genome-wide Tn5 bias pre-computed using our convolutional neural net model. (4) dispModel.tar.gz Zipped folder containing Tn5 cutting dispersion models for each footprint window radius. The footprint window size in our paper refers to the diameter the footprint window, which is twice the number listed here. During footprinting, these models are loaded into the footprintingProject object and then used for footprinting. (5) cisBP_mouse_pwms_2021.rds, cisBP_human_pwms_2021.rds Motif PWMs used in our study. (6) TFBS_model.h5 Pre-trained footprint-to-TF binding prediction models. The models takes local multi-scale footprints as input and predict whether a genomic position is bound by a TF if the corresponding motif is present. This is obsolete. For the best performance of TF binding prediction, please use our seq2PRINT-based TF binding prediction. (7) clusterLabels.txt, clusterLabelsAllTFs.txt Cluster labels of TFs. clusterLabels.txt is the clustering result directly obtained from clustering multi-scale footprints of all TFs with ChIP data. clusterLabelsAllTFs.txt includes other TFs without ChIP data. The cluster membership of these TFs were assigned based on motif homology among TFs. (8) BMMCTutorial.tar.gz Data needed for our R version tutorial. Content of this foder can be put into the /data/BMMCTutorial folder. (9) PBMC_bulk_ATAC_tutorial fragments files. Files used by our PBMC bulk ATAC tutorial for scPrinter. See https://github.com/buenrostrolab/scPrinter for details. (10) PBMC_bulk_ATAC_tutorial example result TFBS bigwigs (Bcell_0_TFBS.bigwig, Bcell_1_TFBS.bigwig, Monocyte_0_TFBS.bigwig, Monocyte_1_TFBS.bigwig , Tcell_0_TFBS.bigwig, Tcell_1_TFBS.bigwig). Example result files generated by our PBMC bulk ATAC tutorial for scPrinter. See https://github.com/buenrostrolab/scPrinter for details. Here we filtered ATAC-seq peaks based on accessibility, keeping ~70k highly accessible peaks.本数据集关联于多尺度足迹分析（multi-scale footprinting）项目。 (1) Tn5_NN_model.h5 基于Keras实现的预训练卷积神经网络（Convolutional Neural Network, CNN）Tn5偏好模型。以局部DNA序列上下文作为输入，预测Tn5插入偏好。具体使用方法请参阅配套教程。 (2) Tn5ModelTutorial.ipynb 演示如何使用预训练Tn5偏好模型对输入序列进行评分的教程。 (3) hg38Tn5Bias.tar.gz、hg19Tn5Bias.tar.gz、mm10Tn5Bias.tar.gz、panTro6Tn5Bias.tar.gz、sacCer3Tn5Bias.tar.gz、dm6Tn5Bias.tar.gz、danRer11Tn5Bias.tar.gz、ce11Tn5Bias.tar.gz 包含全基因组Tn5偏好的h5格式文件，该类文件通过我们的卷积神经网络模型预计算得到。 (4) dispModel.tar.gz 压缩文件夹，内含对应各足迹窗口半径的Tn5切割离散度模型。本研究中足迹窗口尺寸指窗口直径，为此处标注数值的两倍。在足迹分析过程中，这些模型将被加载至footprintingProject对象中，用于后续足迹分析。 (5) cisBP_mouse_pwms_2021.rds、cisBP_human_pwms_2021.rds 本研究中使用的基序位置权重矩阵（Position Weight Matrices, PWMs）。 (6) TFBS_model.h5 预训练的足迹-转录因子结合预测模型。该模型以局部多尺度足迹作为输入，可在对应基序存在的前提下，预测基因组某一位置是否被转录因子结合。该模型已过时，如需获得最优的转录因子结合预测性能，请使用我们基于seq2PRINT的转录因子结合预测方法。 (7) clusterLabels.txt、clusterLabelsAllTFs.txt 转录因子聚类标签文件。其中clusterLabels.txt为直接对所有带有染色质免疫沉淀（ChIP）数据的转录因子的多尺度足迹进行聚类得到的结果；clusterLabelsAllTFs.txt则包含其余无ChIP数据的转录因子，这些转录因子的聚类归属基于转录因子间的基序同源性进行分配。 (8) BMMCTutorial.tar.gz 本研究R语言版本教程所需的数据。该文件夹内的内容可直接放置于/data/BMMCTutorial目录下。 (9) PBMC_bulk_ATAC_tutorial fragments files 我们用于scPrinter的PBMC批量ATAC教程的片段文件。详细信息请参阅https://github.com/buenrostrolab/scPrinter。 (10) PBMC_bulk_ATAC_tutorial example result TFBS bigwigs（Bcell_0_TFBS.bigwig、Bcell_1_TFBS.bigwig、Monocyte_0_TFBS.bigwig、Monocyte_1_TFBS.bigwig、Tcell_0_TFBS.bigwig、Tcell_1_TFBS.bigwig） 我们通过scPrinter的PBMC批量ATAC教程生成的示例结果文件。详细信息请参阅https://github.com/buenrostrolab/scPrinter。本次分析基于可及性对ATAC-seq峰进行过滤，保留了约7万个高可及性峰。