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CHANGESH A D30-Sep-20197.2 KiB164133

LICENSEH A D30-Sep-201934.3 KiB675553

MakefileH A D30-Sep-20191.6 KiB5826

README.mdH A D30-Sep-20192.3 KiB5436

VERSIONH A D30-Sep-201922 21

bitmap.cH A D30-Sep-20191.4 KiB4213

bitmap.hH A D30-Sep-20191.1 KiB307

dprog.cH A D30-Sep-201912.5 KiB336222

dprog.hH A D30-Sep-20191.3 KiB3813

fptr.hH A D30-Sep-2019413 1817

gene.cH A D30-Sep-201920.1 KiB533429

gene.hH A D30-Sep-20192.4 KiB5730

main.cH A D30-Sep-201927.3 KiB714573

metagenomic.cH A D30-Sep-201911.7 KiB229203

metagenomic.hH A D30-Sep-20192.1 KiB5529

node.cH A D30-Sep-201959.1 KiB1,6031,252

node.hH A D30-Sep-20195.5 KiB12785

sequence.cH A D30-Sep-201927 KiB805661

sequence.hH A D30-Sep-20192.7 KiB9054

training.cH A D30-Sep-201924.6 MiB1,3551,271

training.hH A D30-Sep-20195.1 KiB10871

README.md

1# Prodigal
2
3  Fast, reliable protein-coding gene prediction for prokaryotic genomes.
4
5```bash
6prodigal -i my.genome.fna -o my.genes -a my.proteins.faa
7prodigal -i my.metagenome.fna -o my.genes -a my.proteins.faa -p meta
8prodigal -h
9```
10
11### New in 2.6.3 (February 2016)
12  * Fixed a bug in protein translation output of partial genes where TTG/GTG
13codons were being incorrectly translated to methionine.
14
15### Getting Started
16
17Prodigal consists of a single binary, which is provided for Linux, Mac OS X, and Windows with each official release.  You can also install from source (you will need Cygwin or MinGW on Windows) as follows:
18
19```bash
20$ make install
21```
22
23  For more detail, see [Installing Prodigal](https://www.github.com/hyattpd/Prodigal/wiki/installation).
24
25  To see a complete list of options:
26
27```bash
28$ prodigal -h
29```
30
31### Features
32
33  * **Predicts protein-coding genes**: Prodigal provides fast, accurate protein-coding gene predictions in GFF3, Genbank, or Sequin table format.
34  * **Handles draft genomes and metagenomes**: Prodigal runs smoothly on finished genomes, draft genomes, and metagenomes.
35  * **Runs quickly**: Prodigal analyzes the *E. coli K-12* genome in 10 seconds on a modern MacBook Pro.
36  * **Runs unsupervised**: Prodigal is an unsupervised machine learning algorithm.  It does not need to be provided with any training data, and instead automatically learns the properties of the genome from the sequence itself, including RBS motif usage, start codon usage, and coding statistics.
37  * **Handles gaps and partial genes**: The user can specify if Prodigal should build genes across runs of N's as well as how to handle genes at the edges of contigs.
38  * **Identifies translation initiation sites**: Prodigal predicts the correct translation initiation site for most genes, and can output information about every potential start site in the genome, including confidence score, RBS motif, and much more.
39
40### More Information
41
42  * [Website](http://prodigal.ornl.gov/)
43  * [Wiki Documentation](https://github.com/hyattpd/prodigal/wiki)
44  * [Options Cheat Sheet](https://github.com/hyattpd/prodigal/wiki#cheat-sheet)
45  * [Google Discussion Group](https://groups.google.com/group/prodigal-discuss)
46
47#### Contributors
48
49 * Author: [Doug Hyatt](https://github.com/hyattpd/)
50
51#### License
52
53  [GPL](LICENSE)
54