ddRaptor

ddRaptor is a tool designed to help with ddRAD enzyme selection—it uses Aho–Corasick pattern matching to find cut sites for enzyme pairs, counts the fragments within a size range, and generates summary tables and a heatmap of fragment counts per chromosome.

Dependencies

• Python 3.6+
• Biopython
• pyahocorasick
• tqdm
• pandas
• matplotlib

Install them via:

pip install biopython pyahocorasick tqdm pandas matplotlib

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Installation

1. Clone or download this repository.

2. Make sure you have the dependencies installed (see above).

3. Ensure the script is executable:

   chmod +x ddraptor.py

4. (Optional) Copy into your PATH:

   cp ddraptor.py /usr/local/bin/ddraptor

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Input File Formats

1. enzymes.tsv

Tab-delimited list of enzymes and their IUPAC motifs, with a caret (^) marking the cut position:

# name      motif_with_caret
EcoRI       G^AATTC
BamHI       G^GATCC
HindIII     A^AGCTT
SphI        GCATG^C
PstI        CTGCA^G
CviQI       G^TAC
NsiI        ATGCA^T

2. combos.tsv

Tab-delimited list of ddRAD enzyme pairs (combos):

# combo_name    enzymeA,enzymeB
Combo1         EcoRI,BamHI
Combo2         EcoRI,HindIII
Combo3         BamHI,HindIII
Combo4         SphI,PstI
Combo5         EcoRI,PstI
Combo6         CviQI,NsiI

3. Reference FASTA

Any multi-FASTA file with chromosome or contig sequences:

>chr1
ACGTACGT...
>chr2
TTGACGTA...
...

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Usage

python ddraptor.py \
  <enzymes.tsv> <combos.tsv> <reference.fasta> \
  --min <MIN_LENGTH> --max <MAX_LENGTH> [options]

Required arguments

• enzymes.tsv – Path to enzyme definitions.
• combos.tsv – Path to combo definitions.
• reference.fasta – Path to your multi-FASTA reference.
• --min  Minimum fragment length (inclusive).
• --max  Maximum fragment length (inclusive).

Optional arguments

• --processes   Number of parallel worker processes (default: number of CPU cores).
• --totals-out   Path for combo totals TSV (default: ddrad_totals.tsv).
• --summary-out  Path for per-chromosome summary TSV (default: ddrad_summary.tsv).
• --heatmap-out  Path for heatmap PNG (default: ddrad_heatmap.png).

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Output

1. Combo totals TSV (--totals-out) Columns: combo | total_count

   • Sorted by total_count descending, so the enzyme combination producing the most fragments—and thus the best candidate for ddRAD—appears first.

   combo    total_count
   Combo3   12345
   Combo1    9876
   ...

2. Per-chromosome summary TSV (--summary-out) Columns: combo | chromosome | count

   combo    chromosome    count
   Combo3   chr1          2345
   Combo3   chr2          1987
   ...
   Combo1   chr1          1234
   ...

3. Heatmap PNG (--heatmap-out) A matrix plot of fragment counts (count) with:

   • Rows = enzyme combinations, sorted by total_count descending
   • Columns = chromosomes/contigs in the FASTA

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Examples

Default outputs

python ddraptor.py enzymes.tsv combos.tsv genome.fasta \
  --min 200 --max 600

Produces:

• ddrad_totals.tsv
• ddrad_summary.tsv
• ddrad_heatmap.png

Custom output paths

python ddraptor.py enzymes.tsv combos.tsv genome.fasta \
  --min 150 --max 600 --processes 8 \
  --totals-out=my_totals.tsv \
  --summary-out=my_summary.tsv \
  --heatmap-out=my_heatmap.png

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Tips & Notes

• IUPAC & reverse strands: The script auto-expands ambiguous IUPAC motifs and searches both forward and reverse complements.
• Performance: Uses Aho–Corasick plus multiprocessing; scales linearly with CPU cores and number of contigs.
• Error handling: Malformed lines in TSVs are skipped with a warning to stderr.
• Future goals: Rust Implementation (ddRustor)

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License: MIT
Author: Georgios Kousis Tsampazis
Contact: georgekousis6@gmail.com