The high-throughput PAM determination assay (HT-PAMDA) is used to comprehensively profile the protospacer-adjacent motif (PAM) preferences of a large number of CRISPR-Cas variants.
The uploaded Python 2 scripts and documents will enable users to analyze HT-PAMDA data that has been generated using the HT-PAMDA method as described in Walton et al., Science 2020.

Briefly, the HT-PAMDA analysis pipeline is comprised of four scripts, described below. At the top of each file, input the appropriate input file and sample names. 
A comma separated values file is also required with the information shown in theÊexample .csv file provided (expRW086_pools_1-3_barcodes.csv). 
Barcodes for all samples from Walton et al., Science 2020Êare available (Table S7 - PAMDA data summary_final.xlsx) and can be used to analyze HT-PAMDA data uploaded to the NCBI sequence read archive (SRA) under BioProject ID: PRJNA605711 (http://www.ncbi.nlm.nih.gov/bioproject/605711).

The four HT-PAMDA Python 2 scripts to be run in order are:

HT_PAMDA_1_fastqs2counts.py Ð inputs fastqs and csv indicating sample barcodes as input, outputs raw read counts for each protein, spacer, PAM, timepoint

HT_PAMDA_2_rawcounts2normcounts.py Ð inputs raw read counts, outputs normalized read counts based on, read depth/unmodified library composition, adjusted for the increased fractional representation of uncleaved substrates as other substrates are depleted

HT_PAMDA_3_normcounts2rates.py Ð inputs normalized counts and outputs PAM depletion rates for each protein, spacer, PAM

HT_PAMDA_4_rates2heatmaps.py Ð inputs PAM depletion rates and sample barcode csv, outputs heatmap representations of PAM preference for each protein