cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.14164466
title: Multi-scale footprinting

description: 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 This is obsolete.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.
DOI: 10.5281/zenodo.14194242
title: Multi-scale footprinting

description: 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.
DOI: 10.5281/zenodo.14866808
title: Multi-scale footprinting

description: 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.
DOI: 10.5281/zenodo.14901416
title: Multi-scale footprinting

description: 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.
DOI: 10.5281/zenodo.15058588
title: Multi-scale footprinting

description: 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.

(11) CTCF_degron.tar.gz.

Input data used for the CTCF degron analysis. See https://github.com/buenrostrolab/PRINT/blob/main/analyses/degron/ENCODE_CTCF_degron.ipynb for details.
DOI: 10.5281/zenodo.15085256
title: Multi-scale footprinting

description: 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.

(11) CTCF_degron.tar.gz.

Input data used for the CTCF degron analysis. See https://github.com/buenrostrolab/PRINT/blob/main/analyses/degron/ENCODE_CTCF_degron.ipynb for details.
DOI: 10.5281/zenodo.15085406
title: Multi-scale footprinting

description: 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.

(11) CTCF_degron.tar.gz.

Input data used for the CTCF degron analysis. See https://github.com/buenrostrolab/PRINT/blob/main/analyses/degron/ENCODE_CTCF_degron.ipynb for details.
DOI: 10.5281/zenodo.15085417
title: Multi-scale footprinting

description: 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.

(11) CTCF_degron.tar.gz.

Input data used for the CTCF degron analysis. See https://github.com/buenrostrolab/PRINT/blob/main/analyses/degron/ENCODE_CTCF_degron.ipynb for details.
DOI: 10.5281/zenodo.15103252
title: Multi-scale footprinting

description: 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.

(11) CTCF_degron.tar.gz.

Input data used for the CTCF degron analysis. See https://github.com/buenrostrolab/PRINT/blob/main/analyses/degron/ENCODE_CTCF_degron.ipynb for details.