# 🔥 Pyrodigal [](https://github.com/althonos/pyrodigal/stargazers)
*Cython bindings and Python interface to [Prodigal](https://github.com/hyattpd/Prodigal/), an ORF
finder for genomes and metagenomes. **Now with SIMD!***
[](https://github.com/althonos/pyrodigal/actions)
[](https://codecov.io/gh/althonos/pyrodigal/)
[](https://choosealicense.com/licenses/gpl-3.0/)
[](https://pypi.org/project/pyrodigal)
[](https://anaconda.org/bioconda/pyrodigal)
[](https://aur.archlinux.org/packages/python-pyrodigal)
[](https://pypi.org/project/pyrodigal/#files)
[](https://pypi.org/project/pyrodigal/#files)
[](https://pypi.org/project/pyrodigal/#files)
[](https://github.com/althonos/pyrodigal/)
[](https://github.com/althonos/pyrodigal/issues)
[](https://pyrodigal.readthedocs.io)
[](https://github.com/althonos/pyrodigal/blob/main/CHANGELOG.md)
[](https://pepy.tech/project/pyrodigal)
[](https://doi.org/10.21105/joss.04296)
[](https://badge.dimensions.ai/details/id/pub.1147419140)
## 🗺️ Overview
Pyrodigal is a Python module that provides bindings to Prodigal using
[Cython](https://cython.org/). 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.
- **better memory usage**: Pyrodigal uses more compact data structures compared
to the original Prodigal implementation, allowing to save memory to store
the same information. A heuristic is used to estimate the number of nodes
to allocate based on the sequence GC% in order to minimize reallocations.
- **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](https://pyrodigal.readthedocs.io/en/stable/benchmarks.html) page of the documentation contains comprehensive comparisons. See the [JOSS paper](https://doi.org/10.21105/joss.04296)
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](https://github.com/jhahnfeld) and can be
checked with his [comparison repository](https://github.com/jhahnfeld/prodigal-pyrodigal-comparison).*
### 📋 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.
- **custom metagenomic models**: Since `v3.0.0`, you can use your own
metagenomic models to run Pyrodigal in *meta*-mode. *Check for instance
[`pyrodigal-gv`](https://github.com/althonos/pyrodigal-gv), which
provides additional models for giant viruses and gut phages.*
### 🐏 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 extracting said nodes.
* Node fields use smaller data types to fit into 128 bytes, compared to the
176 bytes of the original Prodigal data structure.
* Node arrays are pre-allocated based on the sequence GC% to extrapolate the
probability to find a start or stop codon.
* 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.GeneFinder`](https://pyrodigal.readthedocs.io/en/stable/api/orf_finder.html#pyrodigal.GeneFinder)
instances are thread-safe. In addition, the
[`find_genes`](https://pyrodigal.readthedocs.io/en/stable/api/gene_finder.html#pyrodigal.GeneFinder.find_genes)
method is re-entrant. This means you can train an
[`GeneFinder`](https://pyrodigal.readthedocs.io/en/stable/api/gene_finder.html#pyrodigal.GeneFinder)
instance once, and then use a pool to process sequences in parallel:
```python
import multiprocessing.pool
import pyrodigal
gene_finder = pyrodigal.GeneFinder()
gene_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](https://pypi.org/project/pyrodigal/),
which hosts some pre-built wheels for the x86-64 architecture (Linux/MacOS/Windows)
and the Aarch64 architecture (Linux/MacOS), as well as the code required to compile
from source with Cython:
```console
$ pip install pyrodigal
```
Otherwise, Pyrodigal is also available as a [Bioconda](https://bioconda.github.io/)
package:
```console
$ conda install -c bioconda pyrodigal
```
Check the [*install* page](https://pyrodigal.readthedocs.io/en/stable/install.html)
of the documentation for other ways to install Pyrodigal on your machine.
## 💡 Example
Let's load a sequence from a
[GenBank](http://www.insdc.org/files/feature_table.html) file, use an `GeneFinder`
to find all the genes it contains, and print the proteins in two-line FASTA
format.
### 🔬 [Biopython](https://github.com/biopython/biopython)
To use the [`GeneFinder`](https://pyrodigal.readthedocs.io/en/stable/api/orf_finder.html#pyrodigal.GeneFinder)
in single mode (corresponding to `prodigal -p single`, the default operation mode of Prodigal),
you must explicitly call the
[`train`](https://pyrodigal.readthedocs.io/en/stable/api/orf_finder.html#pyrodigal.GeneFinder.train) method
with the sequence you want to use for training before trying to find genes,
or you will get a [`RuntimeError`](https://docs.python.org/3/library/exceptions.html#RuntimeError):
```python
import Bio.SeqIO
import pyrodigal
record = Bio.SeqIO.read("sequence.gbk", "genbank")
orf_finder = pyrodigal.GeneFinder()
orf_finder.train(bytes(record.seq))
genes = orf_finder.find_genes(bytes(record.seq))
```
However, in `meta` mode (corresponding to `prodigal -p meta`), you can find genes directly:
```python
import Bio.SeqIO
import pyrodigal
record = Bio.SeqIO.read("sequence.gbk", "genbank")
orf_finder = pyrodigal.GeneFinder(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](https://github.com/biocore/scikit-bio)
```python
import skbio.io
import pyrodigal
seq = next(skbio.io.read("sequence.gbk", "genbank"))
orf_finder = pyrodigal.GeneFinder(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`](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.view.html)
method to get the sequence viewable by Cython as an array of `unsigned char`.*
## 🔖 Citation
Pyrodigal is scientific software, with a
[published paper](https://doi.org/10.21105/joss.04296)
in the [Journal of Open-Source Software](https://joss.theoj.org/). Please
cite both [Pyrodigal](https://doi.org/10.21105/joss.04296)
and [Prodigal](https://doi.org/10.1186/1471-2105-11-119) if you are using it in
an academic work, for instance as:
> Pyrodigal (Larralde, 2022), a Python library binding to Prodigal (Hyatt *et al.*, 2010).
Detailed references are available on the [Publications page](https://pyrodigal.readthedocs.io/en/stable/publications.html) of the
[online documentation](https://pyrodigal.readthedocs.io/).
## 💭 Feedback
### ⚠️ Issue Tracker
Found a bug ? Have an enhancement request ? Head over to the [GitHub issue
tracker](https://github.com/althonos/pyrodigal/issues) 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`](https://github.com/althonos/pyrodigal/blob/main/CONTRIBUTING.md)
for more details.
## 📋 Changelog
This project adheres to [Semantic Versioning](http://semver.org/spec/v2.0.0.html)
and provides a [changelog](https://github.com/althonos/pyrodigal/blob/main/CHANGELOG.md)
in the [Keep a Changelog](http://keepachangelog.com/en/1.0.0/) format.
## ⚖️ License
This library is provided under the [GNU General Public License v3.0](https://choosealicense.com/licenses/gpl-3.0/).
The Prodigal code was written by [Doug Hyatt](https://github.com/hyattpd) and is distributed under the
terms of the GPLv3 as well. See `vendor/Prodigal/LICENSE` for more information.
*This project is in no way not affiliated, sponsored, or otherwise endorsed
by the [original Prodigal authors](https://github.com/hyattpd). It was developed
by [Martin Larralde](https://github.com/althonos/) during his PhD project
at the [European Molecular Biology Laboratory](https://www.embl.de/) in
the [Zeller team](https://github.com/zellerlab).*
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"description": "# \ud83d\udd25 Pyrodigal [](https://github.com/althonos/pyrodigal/stargazers)\n\n*Cython bindings and Python interface to [Prodigal](https://github.com/hyattpd/Prodigal/), an ORF\nfinder for genomes and metagenomes. **Now with SIMD!***\n\n[](https://github.com/althonos/pyrodigal/actions)\n[](https://codecov.io/gh/althonos/pyrodigal/)\n[](https://choosealicense.com/licenses/gpl-3.0/)\n[](https://pypi.org/project/pyrodigal)\n[](https://anaconda.org/bioconda/pyrodigal)\n[](https://aur.archlinux.org/packages/python-pyrodigal)\n[](https://pypi.org/project/pyrodigal/#files)\n[](https://pypi.org/project/pyrodigal/#files)\n[](https://pypi.org/project/pyrodigal/#files)\n[](https://github.com/althonos/pyrodigal/)\n[](https://github.com/althonos/pyrodigal/issues)\n[](https://pyrodigal.readthedocs.io)\n[](https://github.com/althonos/pyrodigal/blob/main/CHANGELOG.md)\n[](https://pepy.tech/project/pyrodigal)\n[](https://doi.org/10.21105/joss.04296)\n[](https://badge.dimensions.ai/details/id/pub.1147419140)\n\n## \ud83d\uddfa\ufe0f Overview\n\nPyrodigal is a Python module that provides bindings to Prodigal using\n[Cython](https://cython.org/). It directly interacts with the Prodigal\ninternals, which has the following advantages:\n\n- **single dependency**: Pyrodigal is distributed as a Python package, so you\n can add it as a dependency to your project, and stop worrying about the\n Prodigal binary being present on the end-user machine.\n- **no intermediate files**: Everything happens in memory, in a Python object\n you fully control, so you don't have to invoke the Prodigal CLI using a\n sub-process and temporary files. Sequences can be passed directly as\n strings or bytes, which avoids the overhead of formatting your input to\n FASTA for Prodigal.\n- **better memory usage**: Pyrodigal uses more compact data structures compared\n to the original Prodigal implementation, allowing to save memory to store \n the same information. A heuristic is used to estimate the number of nodes\n to allocate based on the sequence GC% in order to minimize reallocations.\n- **better performance**: Pyrodigal uses *SIMD* instructions to compute which\n dynamic programming nodes can be ignored when scoring connections. This can\n save from a third to half the runtime depending on the sequence. The [Benchmarks](https://pyrodigal.readthedocs.io/en/stable/benchmarks.html) page of the documentation contains comprehensive comparisons. See the [JOSS paper](https://doi.org/10.21105/joss.04296)\n for details about how this is achieved.\n- **same results**: Pyrodigal is tested to make sure it produces\n exactly the same results as Prodigal `v2.6.3+31b300a`. *This was verified\n extensively by [Julian Hahnfeld](https://github.com/jhahnfeld) and can be\n checked with his [comparison repository](https://github.com/jhahnfeld/prodigal-pyrodigal-comparison).*\n\n### \ud83d\udccb Features\n\nThe library now features everything from the original Prodigal CLI:\n\n- **run mode selection**: Choose between *single* mode, using a training\n sequence to count nucleotide hexamers, or *metagenomic* mode, using\n pre-trained data from different organisms (`prodigal -p`).\n- **region masking**: Prevent genes from being predicted across regions\n containing unknown nucleotides (`prodigal -m`).\n- **closed ends**: Genes will be identified as running over edges if they\n are larger than a certain size, but this can be disabled (`prodigal -c`).\n- **training configuration**: During the training process, a custom\n translation table can be given (`prodigal -g`), and the Shine-Dalgarno motif\n search can be forcefully bypassed (`prodigal -n`)\n- **output files**: Output files can be written in a format mostly\n compatible with the Prodigal binary, including the protein translations\n in FASTA format (`prodigal -a`), the gene sequences in FASTA format\n (`prodigal -d`), or the potential gene scores in tabular format\n (`prodigal -s`).\n- **training data persistence**: Getting training data from a sequence and\n using it for other sequences is supported; in addition, a training data\n file can be saved and loaded transparently (`prodigal -t`).\n\nIn addition, the **new** features are available:\n\n- **custom gene size threshold**: While Prodigal uses a minimum gene size\n of 90 nucleotides (60 if on edge), Pyrodigal allows to customize this\n threshold, allowing for smaller ORFs to be identified if needed.\n- **custom metagenomic models**: Since `v3.0.0`, you can use your own \n metagenomic models to run Pyrodigal in *meta*-mode. *Check for instance\n [`pyrodigal-gv`](https://github.com/althonos/pyrodigal-gv), which \n provides additional models for giant viruses and gut phages.*\n\n### \ud83d\udc0f Memory\n\nPyrodigal makes several changes compared to the original Prodigal binary\nregarding memory management:\n\n* Sequences are stored as raw bytes instead of compressed bitmaps. This means\n that the sequence itself takes 3/8th more space, but since the memory used\n for storing the sequence is often negligible compared to the memory used to\n store dynamic programming nodes, this is an acceptable trade-off for better\n performance when extracting said nodes.\n* Node fields use smaller data types to fit into 128 bytes, compared to the \n 176 bytes of the original Prodigal data structure.\n* Node arrays are pre-allocated based on the sequence GC% to extrapolate the\n probability to find a start or stop codon.\n* Genes are stored in a more compact data structure than in Prodigal (which\n reserves a buffer to store string data), saving around 1KiB per gene.\n\n\n### \ud83e\uddf6 Thread-safety\n\n[`pyrodigal.GeneFinder`](https://pyrodigal.readthedocs.io/en/stable/api/orf_finder.html#pyrodigal.GeneFinder)\ninstances are thread-safe. In addition, the\n[`find_genes`](https://pyrodigal.readthedocs.io/en/stable/api/gene_finder.html#pyrodigal.GeneFinder.find_genes)\nmethod is re-entrant. This means you can train an\n[`GeneFinder`](https://pyrodigal.readthedocs.io/en/stable/api/gene_finder.html#pyrodigal.GeneFinder)\ninstance once, and then use a pool to process sequences in parallel:\n```python\nimport multiprocessing.pool\nimport pyrodigal\n\ngene_finder = pyrodigal.GeneFinder()\ngene_finder.train(training_sequence)\n\nwith multiprocessing.pool.ThreadPool() as pool:\n predictions = pool.map(orf_finder.find_genes, sequences)\n```\n\n## \ud83d\udd27 Installing\n\nPyrodigal can be installed directly from [PyPI](https://pypi.org/project/pyrodigal/),\nwhich hosts some pre-built wheels for the x86-64 architecture (Linux/MacOS/Windows)\nand the Aarch64 architecture (Linux/MacOS), as well as the code required to compile\nfrom source with Cython:\n```console\n$ pip install pyrodigal\n```\n\nOtherwise, Pyrodigal is also available as a [Bioconda](https://bioconda.github.io/)\npackage:\n```console\n$ conda install -c bioconda pyrodigal\n```\n\nCheck the [*install* page](https://pyrodigal.readthedocs.io/en/stable/install.html)\nof the documentation for other ways to install Pyrodigal on your machine.\n\n## \ud83d\udca1 Example\n\nLet's load a sequence from a\n[GenBank](http://www.insdc.org/files/feature_table.html) file, use an `GeneFinder`\nto find all the genes it contains, and print the proteins in two-line FASTA\nformat.\n\n### \ud83d\udd2c [Biopython](https://github.com/biopython/biopython)\n\nTo use the [`GeneFinder`](https://pyrodigal.readthedocs.io/en/stable/api/orf_finder.html#pyrodigal.GeneFinder)\nin single mode (corresponding to `prodigal -p single`, the default operation mode of Prodigal),\nyou must explicitly call the\n[`train`](https://pyrodigal.readthedocs.io/en/stable/api/orf_finder.html#pyrodigal.GeneFinder.train) method\nwith the sequence you want to use for training before trying to find genes,\nor you will get a [`RuntimeError`](https://docs.python.org/3/library/exceptions.html#RuntimeError):\n```python\nimport Bio.SeqIO\nimport pyrodigal\n\nrecord = Bio.SeqIO.read(\"sequence.gbk\", \"genbank\")\n\norf_finder = pyrodigal.GeneFinder()\norf_finder.train(bytes(record.seq))\ngenes = orf_finder.find_genes(bytes(record.seq))\n```\n\nHowever, in `meta` mode (corresponding to `prodigal -p meta`), you can find genes directly:\n```python\nimport Bio.SeqIO\nimport pyrodigal\n\nrecord = Bio.SeqIO.read(\"sequence.gbk\", \"genbank\")\n\norf_finder = pyrodigal.GeneFinder(meta=True)\nfor i, pred in enumerate(orf_finder.find_genes(bytes(record.seq))):\n print(f\">{record.id}_{i+1}\")\n print(pred.translate())\n```\n\n*On older versions of Biopython (before 1.79) you will need to use\n`record.seq.encode()` instead of `bytes(record.seq)`*.\n\n\n### \ud83e\uddea [Scikit-bio](https://github.com/biocore/scikit-bio)\n\n```python\nimport skbio.io\nimport pyrodigal\n\nseq = next(skbio.io.read(\"sequence.gbk\", \"genbank\"))\n\norf_finder = pyrodigal.GeneFinder(meta=True)\nfor i, pred in enumerate(orf_finder.find_genes(seq.values.view('B'))):\n print(f\">{record.id}_{i+1}\")\n print(pred.translate())\n```\n\n*We need to use the [`view`](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.view.html)\nmethod to get the sequence viewable by Cython as an array of `unsigned char`.*\n\n\n## \ud83d\udd16 Citation\n\nPyrodigal is scientific software, with a\n[published paper](https://doi.org/10.21105/joss.04296)\nin the [Journal of Open-Source Software](https://joss.theoj.org/). Please\ncite both [Pyrodigal](https://doi.org/10.21105/joss.04296)\nand [Prodigal](https://doi.org/10.1186/1471-2105-11-119) if you are using it in\nan academic work, for instance as:\n\n> Pyrodigal (Larralde, 2022), a Python library binding to Prodigal (Hyatt *et al.*, 2010).\n\nDetailed references are available on the [Publications page](https://pyrodigal.readthedocs.io/en/stable/publications.html) of the\n[online documentation](https://pyrodigal.readthedocs.io/).\n\n## \ud83d\udcad Feedback\n\n### \u26a0\ufe0f Issue Tracker\n\nFound a bug ? Have an enhancement request ? Head over to the [GitHub issue\ntracker](https://github.com/althonos/pyrodigal/issues) if you need to report\nor ask something. If you are filing in on a bug, please include as much\ninformation as you can about the issue, and try to recreate the same bug\nin a simple, easily reproducible situation.\n\n### \ud83c\udfd7\ufe0f Contributing\n\nContributions are more than welcome! See\n[`CONTRIBUTING.md`](https://github.com/althonos/pyrodigal/blob/main/CONTRIBUTING.md)\nfor more details.\n\n## \ud83d\udccb Changelog\n\nThis project adheres to [Semantic Versioning](http://semver.org/spec/v2.0.0.html)\nand provides a [changelog](https://github.com/althonos/pyrodigal/blob/main/CHANGELOG.md)\nin the [Keep a Changelog](http://keepachangelog.com/en/1.0.0/) format.\n\n\n## \u2696\ufe0f License\n\nThis library is provided under the [GNU General Public License v3.0](https://choosealicense.com/licenses/gpl-3.0/).\nThe Prodigal code was written by [Doug Hyatt](https://github.com/hyattpd) and is distributed under the\nterms of the GPLv3 as well. See `vendor/Prodigal/LICENSE` for more information.\n\n*This project is in no way not affiliated, sponsored, or otherwise endorsed\nby the [original Prodigal authors](https://github.com/hyattpd). It was developed\nby [Martin Larralde](https://github.com/althonos/) during his PhD project\nat the [European Molecular Biology Laboratory](https://www.embl.de/) in\nthe [Zeller team](https://github.com/zellerlab).*\n",
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