Python port of TransDecoder - identify candidate coding regions within transcript sequences.

cd PyTransDecoder
pip install -e .

Use pyTransdecoder as the primary interface when you want the standard end-to-end pipeline:

pyTransdecoder -t transcripts.fasta

This runs LongOrfs followed by Predict and writes the usual *.transdecoder.{gff3,bed,pep,cds} outputs.

You can also have the pipeline run homology support searches directly:

pyTransdecoder -t transcripts.fasta \
    --blast-search-pep uniprot_sprot.pep \
    --pfam-search-db Pfam-A.hmm

If you want to run the phases separately, use the subcommand CLI:

pytransdecoder longorfs -t transcripts.fasta

This creates a directory transcripts.fasta.transdecoder_dir/ with:

• longest_orfs.pep - Protein sequences
• longest_orfs.cds - CDS sequences
• longest_orfs.gff3 - ORF annotations
• base_freqs.dat - Nucleotide frequencies

-t, --transcripts PATH          Input transcripts FASTA file [required]
-m, --min-protein-length INT    Minimum protein length (default: 100 aa)
-G, --genetic-code TEXT         Genetic code (default: Standard)
-S, --strand-specific           Only analyze top strand
-O, --output-dir PATH           Output directory
--gene-trans-map PATH           Gene-to-transcript mapping file
--complete-orfs-only            Only output complete ORFs
-v, --verbose                   Verbose output

PyTransDecoder's CLI accepts both underscore and dash formats for option names, ensuring compatibility with existing Perl TransDecoder workflows:

# Both of these work identically:
pytransdecoder predict -t transcripts.fasta --retain-pfam-hits pfam.domtblout
pytransdecoder predict -t transcripts.fasta --retain_pfam_hits pfam.domtblout

This means you can use existing scripts and Makefiles without modification.

Legacy compatibility wrappers for TransDecoder.LongOrfs and TransDecoder.Predict now live under util/ instead of the repository root.

• universal/standard
• vertebrate_mitochondrial
• yeast_mitochondrial
• invertebrate_mitochondrial
• ciliate/tetrahymena/dasycladacean
• euplotid
• bacterial
• candida
• And 15+ more...

pytransdecoder predict -t transcripts.fasta

This creates final output files in the same directory as your transcripts:

• transcripts.fasta.transdecoder.gff3 - Gene predictions
• transcripts.fasta.transdecoder.bed - BED format
• transcripts.fasta.transdecoder.pep - Protein sequences
• transcripts.fasta.transdecoder.cds - CDS sequences

# Run BLASTP against UniProt
blastp -query transcripts.fasta.transdecoder_dir/longest_orfs.pep \
       -db uniprot_sprot.pep -max_target_seqs 1 \
       -outfmt 6 -evalue 1e-5 -num_threads 4 > blastp.outfmt6

# Run Pfam domain search
hmmscan --cpu 4 --domtblout pfam.domtblout Pfam-A.hmm \
        transcripts.fasta.transdecoder_dir/longest_orfs.pep

# Run predict with homology data
pytransdecoder predict -t transcripts.fasta \
    --retain-blastp-hits blastp.outfmt6 \
    --retain-pfam-hits pfam.domtblout

For the full pipeline entrypoint, you can skip the separate Pfam step and let pyTransdecoder prepare and search the HMM database for you:

pyTransdecoder -t transcripts.fasta --pfam-search-db Pfam-A.hmm

-t, --transcripts PATH            Input transcripts FASTA file [required]
-O, --output-dir PATH             Output directory
-T, --top-orfs-train INT          Training ORFs (default: 500)
--retain-long-orfs-mode           'dynamic' or 'strict' (default: dynamic)
--retain-pfam-hits PATH           Pfam domain hits (domtblout format)
--retain-blastp-hits PATH         BLAST hits (outfmt 6 format)
--single-best-only                Only best ORF per transcript
--no-refine-starts                Skip start codon refinement
-G, --genetic-code TEXT           Genetic code (default: Standard)
-v, --verbose                     Verbose output

1. Training: Selects top longest unique ORFs to train hexamer scoring model
2. Scoring: Scores all ORFs using Markov chain model (hexamer composition)
3. Selection: Chooses best ORFs based on:
   • Homology support (BLAST/Pfam hits)
   • Coding potential score
   • ORF length
   • GC content-based thresholds
4. Output: Generates final predictions in multiple formats

• ✅ Phase 1 (LongOrfs): Implemented and validated
• ✅ Phase 2 (Predict): Implemented and tested
• ⏳ Performance benchmarking on large datasets

To validate output matches the Perl version:

# Run Perl version
cd ../TransDecoder
./TransDecoder.LongOrfs -t test.fasta

# Run Python version
cd ../PyTransDecoder  
pytransdecoder longorfs -t test.fasta

# Compare outputs
diff ../TransDecoder/test.fasta.transdecoder_dir/longest_orfs.pep \
     test.fasta.transdecoder_dir/longest_orfs.pep

• Python 3.8+
• BioPython >= 1.81
• tqdm >= 4.65

Note: Ported by Claude.io Sonnet 4.5 under guidance by bhaas. Jan 24, 2026.