Tutorial

In this section, we will provide a step-by-step guide on how to train and evaluate ChromBPNet models using the K562 bulk ATAC-seq data (ENCSR868FGK).

• Download test data
• Preprocessing
• Training bias-factorized ChromBPNet model
• Training BPNet bias model
• Output format

To view a summary of all the command line tools available with the ChromBPNet repository, simply run the command chrombpnet -h in the terminal. This will display a list of all the available command line options and their functions.

usage: chrombpnet [-h] {pipeline,train,qc,bias,prep,pred_bw,contribs_bw,modisco_motifs,footprints,snp_score} ...

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		Bias factorized, base-resolution deep learning models of chromatin accessibility reveal 
		cis-regulatory sequence syntax, transcription factor footprints and regulatory variants
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positional arguments:
  {pipeline,train,qc,bias,prep,pred_bw,contribs_bw,modisco_motifs,footprints,snp_score}
                        Must be eithier 'pipeline', 'train', 'qc', 'bias', 'prep', 'pred_bw', 'contribs_bw', 'modisco_motifs' ,'footprints', or 'snp_score'.
    pipeline            End-to-end pipline with train, quality check and test for bias factorized ChromBPNet model
    train               Train bias factorized ChromBPNet model
    qc                  Do quality checks and test for bias factorized ChromBPNet model
    bias                Tools to train, quality check and test bias model
    prep                Tools to generate preprocessing data for chrombpnet
    pred_bw             Get model prediction bigwigs (Metrics calculated if observed bigwig provided)
    contribs_bw         Get contribution score bigwigs
    modisco_motifs      Summarize motifs from contribution scores with TFModisco
    footprints          Get marginal footprinting for given model and given motifs
    snp_score           Score SNPs with model