Man pages for HarteminkLab/TOP
Predict transcription factor occupancy using DNase- or ATAC-seq data

add_chip_peak_labels_to_sitesAdds ChIP-seq peak labels to the candidate sites
add_chip_signals_to_sitesAdds ChIP-seq signals to the candidate sites
assemble_training_dataAssembles TOP training data for all TF x cell type...
bin_transform_countsBins and transforms count matrix
combine_TOP_samplesCombines and takes the average of TOP posterior samples from...
count_genome_cutsCounts DNase-seq or ATAC-seq cuts along the genome
count_normalize_chipCounts and normalizes (and transforms) ChIP-seq read coverage
extract_tf_cell_combosCreates a table listing the indices and names of TF and cell...
extract_TOP_coef_samplesExtracts regression coefficients from TOP posterior samples
extract_TOP_mean_coefExtracts the posterior mean of regression coefficients for...
fimo_motif_matchesRuns 'FIMO' to scan for motif matches
fit_TOP_M5_modelFits TOP model with M5 bins
get_sites_countsExtracts count matrices around candidate binding sites
get_total_readsGets total number of mapped reads from the 'idxstats' file
index_faIndexes the FASTA file, and generates a 'chrom.sizes' file
merge_normalize_bin_transform_countsMerges DNase or ATAC-seq counts from multiple replicates,...
merge_normalize_countsMerges DNase or ATAC-seq counts from multiple replicates,...
millipede_binningPerforms 'MILLIPEDE' binning on count matrix
normalize_bin_transform_countsNormalizes, bins and transforms counts
normalize_chipNormalizes (and transforms) ChIP-seq read coverage
normalize_countsNormalizes read counts
plot_profilePlots DNase or ATAC profiles
plot_profile_strandsPlots DNase or ATAC profiles by strands of motif matches
predict_TOPPredicts quantitative TF occupancy or TF binding probability
process_candidate_sitesObtains and filters candidate sites from FIMO result
scatterplot_predictionsScatter plot of measured and predicted occupancy
sort_index_idxstats_bamSorts, indexes the BAM file, and retrieves the 'idxstats'.
HarteminkLab/TOP documentation built on July 27, 2023, 6:14 p.m.