Cython bindings and Python interface to Prodigal, an ORF finder for genomes and metagenomes. Now with SIMD!
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. The Benchmarks page of the documentation contains comprehensive comparisons. See the JOSS paper for details about how this is achieved.
same results: Pyrodigal is tested to make sure it produces exactly the same results as Prodigal
v2.6.3+31b300a. This was verified extensively by Julian Hahnfeld and can be checked with his comparison repository.
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 (
region masking: Prevent genes from being predicted across regions containing unknown nucleotides (
closed ends: Genes will be identified as running over edges if they are larger than a certain size, but this can be disabled (
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 (
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 (
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 (
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.
Several changes were done regarding memory management:
digitized sequences: 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 extracting said nodes.
node buffer growth: 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.
lightweight genes: 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.
pip install pyrodigal in a shell to download the latest release and all
its dependencies from PyPi, or have a look at the
Installation page to find other ways to install
Pyrodigal is scientific software, with a published paper in the Journal of Open-Source Software. Check the Publications page to see how to cite Pyrodigal properly.
- Output Formats
- API Reference
- v2.1.0 - 2023-02-20
- v2.0.4 - 2023-01-09
- v2.0.3 - 2022-12-20
- v2.0.2 - 2022-11-01
- v2.0.1 - 2022-11-01
- v2.0.0 - 2022-11-01
- v1.1.2 - 2022-08-31
- v1.1.1 - 2022-07-08
- v1.1.0 - 2022-06-09
- v1.0.2 - 2022-05-13
- v1.0.1 - 2022-04-28
- v1.0.0 - 2022-04-20
- v0.7.3 - 2022-04-06
- v0.7.2 - 2022-03-15
- v0.7.1 - 2022-03-14
- v0.7.0 - 2022-03-12
- v0.6.4 - 2021-12-23
- v0.6.3 - 2021-12-23
- v0.6.2 - 2021-09-25
- v0.6.1 - 2021-09-24
- v0.6.0 - 2021-09-23
- v0.5.4 - 2021-09-18
- v0.5.3 - 2021-09-12
- v0.5.2 - 2021-09-11
- v0.5.1 - 2021-09-04
- v0.5.0 - 2021-06-15
- v0.4.7 - 2021-04-09
- v0.4.6 - 2021-03-05
- v0.4.5 - 2021-03-03
- v0.4.4 - 2021-03-03
- v0.4.3 - 2021-03-01
- v0.4.2 - 2021-02-07
- v0.4.1 - 2021-01-07
- v0.4.0 - 2021-01-06
- v0.3.2 - 2020-11-27
- v0.3.1 - 2020-11-27
- v0.3.0 - 2020-09-07
- v0.2.4 - 2020-09-04
- v0.2.3 - 2020-08-09
- v0.2.2 - 2020-07-14
- v0.2.1 - 2020-05-29
- v0.2.0 - 2020-05-28
- v0.1.1 - 2020-04-30
- v0.1.0 - 2020-04-27
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. The
cpu_features library was written by
Guillaume Chatelet and is licensed under the terms of the
Apache License 2.0.
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.