Tools overview - kundajelab/chrombpnet GitHub Wiki

A detailed overview of all the downstream tools available with this repo is provided as follows -

• Prediction bigwigs
• Generate contribution score bigwigs
• Denovo motif discovery
• Marginal footprinting
• Variant effect prediction
• Custom sequence prediction
• Custom sequence contributions

For a summary of all the command line tools available with this repo do chrombpnet -h

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

==================================================================================================
		Bias factorized, base-resolution deep learning models of chromatin accessibility reveal 
		cis-regulatory sequence syntax, transcription factor footprints and regulatory variants
		==================================================================================================

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