These scripts provide the different steps for identifying overrepresented motifs within the provided sequences. Script are written for Python3.

1. ProcessingScript.py - Accepts pretrimmed fastq containing only cleavage target sequences (nt). Outputs a fasta file with cleavage target sequences converted to AA and sequences not meeting quality minimums filtered out. Options available through --help allow the user to set filter conditions. Examples include removal of all sequences with a stop codon, and removal of sequences containing other improper codons.
2. Kmer_Analysis.py - Accepts fasta/fastq sequence files as input, such as those output from the ProcessingScript.py. Outputs are diverse and represent different stages of processing. Options can be listed with --help. Key options include -k to indicate the kmer size to use for decomposing the input sequences. -c, -n, -m, -d and -e to filter the resultant kmers. This will return the primary kmers of interest. Also responsible for generating randomized datasets with --random flag.

• Extract_Fullseqs.py - Pull all the full sequences that contain a specific kmer.
• ImageComposite.py - Accepts a directory path to a folder of WebLogo images. Makes a composite image from these individual images.

• Entropy_calc.txt - Pre computed table of entropy values.
• Hydro_tab.txt - Hydrophobicity table for filtering peptides for mass spec analysis.
• PAM250wX.txt - PAM250 matrix for use as a distance matrix in the kmer analysis. Xs were added to allow for special treatment fixed sequences flanking the peptides.

• compare_peptides.py - Library of functions called by other scripts.

• Weblogo2 - Weblogo is used to visualize motifs
• Python3 - The provided scripts were developed and tested in the following python3 environment. The scripts are likely to work in other environments but were not tested for such.
  • Python 3.7.1
  • GCC 7.3.0, Anaconda on linux (CentOS 7)
  • Packages include: Matplotlib and NetworkX
• Shell scipts for testing are compatible with sh/bash
• System requirements are highly dependent on kmer count and complexity. Analysis from the associated paper used 4 cores, 200gb RAM and 72 hours max walltime to generate kmer networks 4 at a time

To console:

• Input path resolved to:
  • Resolved path to listed input file
• Total Seq:
  • Number of sequences in input file
• Total background seq:
  • Number of sequences in the background seq file
• Total Nodes:
  • Number of unique kmers identified. Covers both input and background
• Total kmers:
  • Sum of all input kmer weights. This includes frequency of repeat kmers and downweighting due to fixed flanking sequences.
• Total background kmers:
  • Sum of all background kmer weights. This includes frequency of repeat kmers and downweighting due to fixed flanking sequences.
• First Pass Kmers:
  • Sum of all input kmer weights after filtering on Enrichment vs background and minimum frequency. This includes frequency of repeat kmers and downweighting due to fixed flanking sequences.
• First Pass Nodes:
  • Number of unique kmers identified after filtering on Enrichment vs background and minimum frequency. Covers both input and background
• Second Pass Kmers:
  • Sum of all input kmer weights after filtering on network degree (based on minimum edge weight). This includes frequency of repeat kmers and downweighting due to fixed flanking sequences.
• Second Pass Nodes:
  • Number of unique kmers identified after filtering on network degree (based on minimum edge weight). Covers both input and background
• Output Name
  • root used for naming all output files.

File outputs Square bracket denote variable fields based on parameters and script.

• Kmer_Analysis.py
  • kmer_delete_[File1]vs[File2][kSize]kmer_size[edge_filter]min_edge[min_nodes]min_node[min_deg]min_degree[min_enrich]fold_enrichment[random_num][real or rand]_[dist_mat].txt
    • Kmers filtered out based on parameters supplied
  • kmer_frequency_histo_[File1]vs[File2][kSize]kmer_size[edge_filter]min_edge[min_nodes]min_node[min_deg]min_degree[min_enrich]fold_enrichment[random_num][real or rand]_[dist_mat].png
    • Frequency histogram showing the distribution of frequencies for the kmers.
  • kmer_output_[File1]vs[File2][kSize]kmer_size[edge_filter]min_edge[min_nodes]min_node[min_deg]min_degree[min_enrich]fold_enrichment[random_num][real or rand]_[dist_mat].txt
    • Kmers that passed all filtering based on parameters supplied
  • node_info_[File1]vs[File2][kSize]kmer_size[edge_filter]min_edge[min_nodes]min_node[min_deg]min_degree[min_enrich]fold_enrichment[random_num][real or rand]_[dist_mat].txt
    • More in depth information about each kmer that passed filtering. Columns are: Kmer sequence, Weighted frequency of occurence, Enrichment fraction, Node Degree, Weighted Node Degree
  • network_[File1]vs[File2][kSize]kmer_size[edge_filter]min_edge[min_nodes]min_node[min_deg]min_degree[min_enrich]fold_enrichment[random_num][real or rand]_[dist_mat].png
    • A plot of kmers in the network after filtering on all paramters. Line width denotes edge weight.
  • [File1]vs[File2][kSize]kmer_size[edge_filter]min_edge[min_nodes]min_node[min_deg]min_degree[min_enrich]fold_enrichment[random_num][real or rand]_[dist_mat].log
    • A copy of the run log for each sample

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