Fast, reliable protein-coding gene prediction for prokaryotic genomes.

Features:
* Predicts protein-coding genes: Prodigal provides fast, accurate protein-coding
  gene predictions in GFF3, Genbank, or Sequin table format.
* Handles draft genomes and metagenomes: Prodigal runs smoothly on finished
  genomes, draft genomes, and metagenomes.
* Runs quickly: Prodigal analyzes the E. coli K-12 genome in 10 seconds on a
  modern MacBook Pro.
* 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.
* 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.
* 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.

WWW: https://github.com/hyattpd/Prodigal