Add an initial JOSS paper draft in the `paper` directory

.github/workflows/package.yml

@@ -375,6 +375,8 @@ jobs:
     steps:
     - name: Checkout code
       uses: actions/checkout@v1
+      with:
+        submodules: true
     - name: Release a Changelog
       uses: rasmus-saks/release-a-changelog-action@v1.0.1
       with:

paper/paper.bib

+@article{Prodigal:2010,
+  title = {Prodigal: Prokaryotic Gene Recognition and Translation Initiation Site Identification},
+  shorttitle = {Prodigal},
+  author = {Hyatt, Doug and Chen, Gwo-Liang and LoCascio, Philip F and Land, Miriam L and Larimer, Frank W and Hauser, Loren J},
+  year = {2010},
+  month = mar,
+  journal = {BMC Bioinformatics},
+  volume = {11},
+  pages = {119},
+  issn = {1471-2105},
+  doi = {10.1186/1471-2105-11-119},
+  pmcid = {PMC2848648},
+  pmid = {20211023},
+}
+
+@techreport{GECCO:2021,
+  title = {Accurate de Novo Identification of Biosynthetic Gene Clusters with {{GECCO}}},
+  author = {Carroll, Laura M. and Larralde, Martin and Fleck, Jonas Simon and Ponnudurai, Ruby and Milanese, Alessio and Cappio, Elisa and Zeller, Georg},
+  year = {2021},
+  month = may,
+  pages = {2021.05.03.442509},
+  institution = {{bioRxiv}},
+  doi = {10.1101/2021.05.03.442509},
+  chapter = {New Results},
+  copyright = {\textcopyright{} 2021, Posted by Cold Spring Harbor Laboratory. This pre-print is available under a Creative Commons License (Attribution 4.0 International), CC BY 4.0, as described at http://creativecommons.org/licenses/by/4.0/},
+  langid = {english},
+}
+
+@article{PhageBoost:2021,
+  title = {Rapid Discovery of Novel Prophages Using Biological Feature Engineering and Machine Learning},
+  author = {Sir{\'e}n, Kimmo and Millard, Andrew and Petersen, Bent and Gilbert, M~Thomas~P and Clokie, Martha R J and {Sicheritz-Pont{\'e}n}, Thomas},
+  year = {2021},
+  month = mar,
+  journal = {NAR Genomics and Bioinformatics},
+  volume = {3},
+  number = {1},
+  pages = {lqaa109},
+  issn = {2631-9268},
+  doi = {10.1093/nargab/lqaa109},
+}
+
+@techreport{hafeZ:2021,
+  type = {Preprint},
+  title = {{{hafeZ}}: {{Active}} Prophage Identification through Read Mapping},
+  shorttitle = {{{hafeZ}}},
+  author = {Turkington, Christopher J. R. and Abadi, Neda Nezam and Edwards, Robert A. and Grasis, Juris A.},
+  year = {2021},
+  month = jul,
+  institution = {{Bioinformatics}},
+  doi = {10.1101/2021.07.21.453177},
+  langid = {english},
+}

paper/paper.md

+---
+title: 'Pyrodigal: Python bindings and interface to Prodigal, an efficient ORF finder for prokaryotes.'
+tags:
+  - Python
+  - Cython
+  - bioinformatics
+  - gene prediction
+authors:
+  - name: Martin Larralde
+    orcid: 0000-0002-3947-4444
+    affiliation: 1
+affiliations:
+  - name: Structural and Computational Biology Unit, EMBL, Heidelberg, Germany
+date: 24 February 2022
+bibliography: paper.bib
+---
+
+# Summary
+
+Improvements in sequencing technologies have seen the amount of available
+genomic data expand considerably in the last twenty years. One of the key
+step for analysing this data is the prediction of protein-coding regions in
+the genomic sequences, known as Open Reading Frames (ORFs), which span between
+a start and a STOP codon.
+
+
+# Statement of need
+
+Prodigal [@Prodigal:2010] is an ORF finder for prokaryotes used in thousands
+of applications as the gene calling method for processing genomic sequences.
+It is implemented in ANSI C, making it extremely efficient and relatively
+easy to compile on different platforms. However, the only way to use Prodigal
+is through an executable making it less convenient for less knowledgeable
+bioinformaticians only familiar with the Python language.
+
+`pyrodigal` is a Python module implemented in Cython that binds to the
+Prodigal interinals, resulting in identical predictions and similar performance
+through a friendly object-oriented interface. It removes the need for the
+Prodigal binary to be installed on the host machine. The predicted genes are
+returned as Python objects, with properties for retrieving confidence scores
+or coordinates, and methods for translating the gene sequence.
+
+`pyrodigal` has already been used in several works in preparation [@hafeZ:2021, @GECCO:2021]
+and scientific publications [@PhageBoost:2021].
+
+
+# Performance
+
+Although using the same data structures and scoring method as Prodigal,
+`pyrodigal` improves on several aspects of the original software by
+re-implementing key parts of the original software:
+
+- Sequence data is not stored as a bitmap anymore, which comes at the cost
+  of slightly increased memory consumption in exchange of faster memory
+  access. Memory profiling has revealed that the bulk of memory consumption
+  in Prodigal is not caused by sequence data but node data, so this trade-off
+  is acceptable.
+- Memory management has been reworked to use buffers growing on insertion,
+  making the memory consumption slightly more conservative on smaller sequences
+  and addressing edges cases on sequences with a large number of start and
+  STOP codons.
+- Use of local buffers allows for thread-locality in the `OrfFinder.find_genes`
+  method. In addition, `pyrodigal` makes use of the Cython feature for
+  releasing the Python Global Interpreter Lock (GIL), which makes the ORF
+  finder class usable in multi-threaded code.
+
+In addition to these changes, `pyrodigal` implements a SIMD-accelerated
+heuristic filter for scoring connections between dynamic programming nodes.
+
+
+# Distribution
+
+`pyrodigal` is distributed on the Python Package Index (PyPI), which make it
+extremely simple to install. Pre-compiled distributions are made available
+for MacOS, Linux and Windows x86-64, as well as Linux Aarch64 machines.
+Overall, distribution through PyPI makes it easier to develop a Python
+workflow or application relying on `pyrodigal` for the gene calling, since
+end-users are not required to handle the setup of the Prodigal binaries
+themselves.
+
+
+# Acknowledgments
+
+This work was funded by the European Molecular Biology Laboratory and the
+German Research Foundation (Deutsche Forschungsgemeinschaft, DFG, grant no. 395357507 – SFB 1371).