Cython bindings and Python interface to Prodigal, an ORF finder for genomes and metagenomes.

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🔥 Pyrodigal

Cython bindings and Python interface to Prodigal, an ORF finder for genomes and metagenomes. Now with SIMD!

🗺️ Overview

Pyrodigal is a Python module that provides bindings to Prodigal using Cython. It directly interacts with the Prodigal internals, which has the following advantages:

single dependency: Pyrodigal is distributed as a Python package, so you can add it as a dependency to your project, and stop worrying about the Prodigal binary being present on the end-user machine.
no intermediate files: Everything happens in memory, in a Python object you fully control, so you don't have to invoke the Prodigal CLI using a sub-process and temporary files. Sequences can be passed directly as strings or bytes, which avoids the overhead of formatting your input to FASTA for Prodigal.
lower memory usage: Pyrodigal is slightly more conservative when it comes to using memory, which can help process very large sequences. It also lets you save some more memory when running several meta-mode analyses
better performance: Pyrodigal uses SIMD instructions to compute which dynamic programming nodes can be ignored when scoring connections. This can save from a third to half the runtime depending on the sequence.

📋 Features

The library now features everything from the original Prodigal CLI:

run mode selection: Choose between single mode, using a training sequence to count nucleotide hexamers, or metagenomic mode, using pre-trained data from different organisms (prodigal -p).
region masking: Prevent genes from being predicted across regions containing unknown nucleotides (prodigal -m).
closed ends: Genes will be identified as running over edges if they are larger than a certain size, but this can be disabled (prodigal -c).
training configuration: During the training process, a custom translation table can be given (prodigal -g), and the Shine-Dalgarno motif search can be forcefully bypassed (prodigal -n)
output files: Output files can be written in a format mostly compatible with the Prodigal binary, including the protein translations in FASTA format (prodigal -a), the gene sequences in FASTA format (prodigal -d), or the potential gene scores in tabular format (prodigal -s).
training data persistence: Getting training data from a sequence and using it for other sequences is supported; in addition, a training data file can be saved and loaded transparently (prodigal -t).

In addition, the new features are available:

custom gene size threshold: While Prodigal uses a minimum gene size of 90 nucleotides (60 if on edge), Pyrodigal allows to customize this threshold, allowing for smaller ORFs to be identified if needed.

🐏 Memory

Pyrodigal makes several changes compared to the original Prodigal binary regarding memory management:

Sequences are stored as raw bytes instead of compressed bitmaps. This means that the sequence itself takes 3/8th more space, but since the memory used for storing the sequence is often negligible compared to the memory used to store dynamic programming nodes, this is an acceptable trade-off for better performance when finding the start and stop nodes.
Node arrays are dynamically allocated and grow exponentially instead of being pre-allocated with a large size. On small sequences, this leads to Pyrodigal using about 30% less memory.
Genes are stored in a more compact data structure than in Prodigal (which reserves a buffer to store string data), saving around 1KiB per gene.

🧶 Thread-safety

pyrodigal.OrfFinder instances are thread-safe. In addition, the find_genes method is re-entrant. This means you can train an OrfFinder instance once, and then use a pool to process sequences in parallel:

import pyrodigal

orf_finder = pyrodigal.OrfFinder()
orf_finder.train(training_sequence)

with multiprocessing.pool.ThreadPool() as pool:
    predictions = pool.map(orf_finder.find_genes, sequences)

🔧 Installing

Pyrodigal can be installed directly from PyPI, which hosts some pre-built wheels for the x86-64 architecture (Linux/OSX/Windows) and the Aarch64 architecture (Linux only), as well as the code required to compile from source with Cython:

$ pip install pyrodigal

Otherwise, Pyrodigal is also available as a Bioconda package:

$ conda install -c bioconda pyrodigal

💡 Example

Let's load a sequence from a GenBank file, use an OrfFinder to find all the genes it contains, and print the proteins in two-line FASTA format.

🔬 Biopython

To use the OrfFinder in single mode, you must explicitly call the train method with the sequence you want to use for training before trying to find genes, or you will get a RuntimeError:

orf_finder = pyrodigal.OrfFinder()
orf_finder.train(bytes(record.seq))
genes = orf_finder.find_genes(bytes(record.seq))

However, in meta mode, you can find genes directly:

record = Bio.SeqIO.read("sequence.gbk", "genbank")
orf_finder = pyrodigal.OrfFinder(meta=True)

for i, pred in enumerate(orf_finder.find_genes(bytes(record.seq))):
    print(f">{record.id}_{i+1}")
    print(pred.translate())

On older versions of Biopython (before 1.79) you will need to use record.seq.encode() instead of bytes(record.seq).

🧪 Scikit-bio

seq = next(skbio.io.read("sequence.gbk", "genbank"))
orf_finder = pyrodigal.OrfFinder(meta=True)

for i, pred in enumerate(orf_finder.find_genes(seq.values.view('B'))):
    print(f">{record.id}_{i+1}")
    print(pred.translate())

We need to use the view method to get the sequence viewable by Cython as an array of unsigned char.

💭 Feedback

⚠️ Issue Tracker

Found a bug ? Have an enhancement request ? Head over to the GitHub issue tracker if you need to report or ask something. If you are filing in on a bug, please include as much information as you can about the issue, and try to recreate the same bug in a simple, easily reproducible situation.

🏗️ Contributing

Contributions are more than welcome! See CONTRIBUTING.md for more details.

📋 Changelog

This project adheres to Semantic Versioning and provides a changelog in the Keep a Changelog format.

⚖️ License

This library is provided under the GNU General Public License v3.0. The Prodigal code was written by Doug Hyatt and is distributed under the terms of the GPLv3 as well. See vendor/Prodigal/LICENSE for more information. The cpu_features library was written by Guillaume Chatelet and is licensed under the terms of the Apache License 2.0. See vendor/cpu_features/LICENSE for more information.

This project is in no way not affiliated, sponsored, or otherwise endorsed by the original Prodigal authors. It was developed by Martin Larralde during his PhD project at the European Molecular Biology Laboratory in the Zeller team.

Project details

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3.7.1

Mar 7, 2026

3.7.0 yanked

Jan 7, 2026

Reason this release was yanked:

Degraded performance due to linetracing being enabled in Cython sources.

3.6.3.post1

Mar 4, 2025

3.6.3

Nov 4, 2024

3.6.2

Nov 3, 2024

3.6.1 yanked

Nov 3, 2024

Reason this release was yanked:

Broken CLI setup.

3.6.0 yanked

Nov 2, 2024

Reason this release was yanked:

Segmentation fault due to invalid instructions on old CPUs.

3.5.2

Sep 4, 2024

3.5.1

Jul 17, 2024

3.5.0 yanked

Jul 17, 2024

Reason this release was yanked:

Broken CLI

3.4.1

May 23, 2024

3.4.0

May 19, 2024

3.3.0

Jan 24, 2024

3.2.2

Jan 21, 2024

3.2.1

Nov 27, 2023

3.2.0

Nov 27, 2023

3.1.1

Nov 6, 2023

3.1.0

Oct 22, 2023

3.0.1

Sep 27, 2023

3.0.0

Sep 17, 2023

2.3.0

Jul 20, 2023

2.2.0

Jun 19, 2023

2.1.0

Feb 21, 2023

2.0.4

Jan 9, 2023

2.0.3

Dec 20, 2022

2.0.2

Nov 1, 2022

2.0.1

Nov 1, 2022

2.0.0

Nov 1, 2022

1.1.2

Aug 31, 2022

1.1.1

Jul 8, 2022

1.1.0

Jun 9, 2022

1.0.2

May 13, 2022

1.0.1

Apr 28, 2022

1.0.0

Apr 20, 2022

0.7.3

Apr 6, 2022

This version

0.7.2

Mar 15, 2022

0.7.1

Mar 14, 2022

0.7.0

Mar 12, 2022

0.6.4

Dec 23, 2021

0.6.3

Dec 23, 2021

0.6.2

Sep 25, 2021

0.6.1

Sep 24, 2021

0.6.0

Sep 23, 2021

0.5.4

Sep 18, 2021

0.5.3

Sep 11, 2021

0.5.2

Sep 11, 2021

0.5.1

Sep 5, 2021

0.5.0

Jun 15, 2021

0.4.7

Apr 9, 2021

0.4.6

Mar 5, 2021

0.4.5

Mar 3, 2021

0.4.4

Mar 3, 2021

0.4.3

Mar 1, 2021

0.4.2

Feb 7, 2021

0.4.1

Jan 7, 2021

0.4.0

Jan 6, 2021

0.3.2

Nov 27, 2020

0.3.1

Nov 27, 2020

0.3.0

Sep 7, 2020

0.2.4

Sep 4, 2020

0.2.3

Aug 9, 2020

0.2.2

Jul 14, 2020

0.2.1

May 28, 2020

0.2.0

May 28, 2020

0.1.1

Apr 30, 2020

0.1.0

Apr 27, 2020

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pyrodigal 0.7.2

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Algorithm	Hash digest
SHA256	`9ea7ad4c2ab9e787acaf63b826486d7c8b8f37c32fa0051d46eaa58ed3184d7b`
MD5	`0cb72a7c7df3588d04a353de8128b9de`
BLAKE2b-256	`8e808f806d33f164f0def099f6a9132eab67d8410d031b5ed230963b5ae68151`

Algorithm	Hash digest
SHA256	`5ce93ce58885c467d9f396503d76cc5890c5f730a0136d86c14a56740ba2dfef`
MD5	`200e01f1b8dcb963f9eb3101689ab768`
BLAKE2b-256	`0461105fc6f621007878a81daa7cb50dbeea1a93bd70c86c51876aa587e832c9`

Algorithm	Hash digest
SHA256	`7e5fb9dda3dbf8fd51e2cf654aaa1c3faf6b7ec2a5e39259cbed2e3559e5289b`
MD5	`8ab66ad4937275cca09c6a236da19a72`
BLAKE2b-256	`5387d824ef38acbed3f033285be0f8e77569661b40425c23a8255a3d47fd5348`

Algorithm	Hash digest
SHA256	`3f62a7f534fc4ac5cc7f0e922f317f7d9f0058a049dc5992f47cc455a0dd2401`
MD5	`c622e9d442dcc619cd535cbd07685bd3`
BLAKE2b-256	`8f13e014d3a878fb9a695fb9b1ad3c32bb736d351a2cf1eb6fc56901730d3a3d`

Algorithm	Hash digest
SHA256	`fdf24ac29aaa5ce106ce6fde905684ff1d3066ef7cd2a3aa8d761545fc2bc778`
MD5	`568edc6511dfa5694f71ec2a220604ed`
BLAKE2b-256	`5072040193c1de37e95e3ce5494bc76c717fc7406d8288fec33a316d3267e861`

Algorithm	Hash digest
SHA256	`811e50a5c65394e5939a5c07eaf55bf9f16722bb9e33410b4f2f992e3aabf731`
MD5	`38085cb3525606e8fff73a7aa06bfcbc`
BLAKE2b-256	`dec90984642e7626c6f4bbaff3adeca5ea07f1f294317e6b7967e5fc6a6ea5a9`

Algorithm	Hash digest
SHA256	`48424d9753ad4588861eedb46eb06a407215944fceb5236f7038aefd58aaa01d`
MD5	`05bf162ef918d370d287fa40217f92ee`
BLAKE2b-256	`d52d08f12ecad3055137de144944ac84c25c8ef08dcc2d9e000aabe6e8f2a719`

Algorithm	Hash digest
SHA256	`57050b7b484ecb338875e09e244cc06b812c05db1b514fce3e416e985faf1ff8`
MD5	`7b5772fb32f8214157e4acfb00f0908e`
BLAKE2b-256	`8437667fb981ea0d16a90f2c7d30e2ed71c3e4bfc394a8ec76880b9ef01d9803`

Algorithm	Hash digest
SHA256	`2b3292fd2becfa60be219dc4963f6a64fd592ceb68252cb7ee84f7a5ce41f75b`
MD5	`599fca4b4eb38f0be41171a9aa4b5d8f`
BLAKE2b-256	`31ffcb9023ad8098a264e6342ca01cad3baf4ab8572f68796cd6fc923b32ad99`

Algorithm	Hash digest
SHA256	`09f90beade4421652e300f7446214cb685359cff9880349913015191ffa91c02`
MD5	`cb7dc98352be716f347316e3ec75d134`
BLAKE2b-256	`06b0d7351ddfe7f5fc5a9f21ecca856a3d778e42d28c93f7427883cc7b629107`